Prospective crime mapping in operational context Final...

The views expressed in this report are those of the authors, not necessarily those of the Home Office(nor do they reflect Government policy).

Prospective crime mapping inoperational context

Final report

Home Office Online Report 19/07

Shane D JohnsonDaniel J BirksLindsay McLaughlinKate J BowersKen Pease

Prospective crime mapping in operational context Final report

Shane D Johnson Daniel J Birks Lindsay McLaughlin Kate J Bowers Ken Pease

UCL, Jill Dando Institute of Crime Science Second Floor, Brooke House 2-16 Torrington Place, London WC1E 7HN Tel. (020) 7679 0809 Fax. (020) 7679 0828 Email: [email protected]

Online Report 19/07

1. UCL JILL DANDO INSTITUTE OF CRIME SCIENCE

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Acknowledgements The authors are grateful to a number of people for their help and advice throughout this project. These include but are not limited to Assistant Chief Constable John Wright, DCI Rick Gooch and other members of the Command Team, Derbyshire ‘A’ Division. Intelligence Analysts Deborah Rimell and Bill Wallage, PC Hayley Vincent for filling out the tactical options log; Sergeant Kevin Pellatt and David Lynam (partnership analyst) from Safer Derbyshire Research and Information Team who provided the data analysed; Inspector Matt Thompson ‘A’ Division Community Safety; All ‘A’ Division front-line officers and Sergeants; Sgt Alan Beeson for driving Lindsay around to all the sections on the day of the surveys; Steve Brookes and Phil Taylor from Government Office for the East Midlands; and, Michael Wilkinson, Lindsey Poole, Mark Bangs, Steve Wilkes and Niall Hamilton-Smith from the Home Office. We would also like to thank two anonymous reviewers for their insightful comments.

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Contents Acknowledgements i

Executive summary v

1. Introduction 1 Background and past research 1 Aims and objectives 3 2. Testing the generalisability of prospective mapping 4 Is the risk of burglary communicable in the East Midlands? 6 Predicting the future 18 3. Tactical options and selecting a pilot site 28 Potential pilot sites 28 A tactical options matrix for reducing burglary 30 4. System development and evolution 38 Time of day consistency? 39 Conclusion 41 5. Process evaluation 43 Process evaluation methodology 43 ‘A’ Division, management and day-to-day running 44 IT and dissemination 45 Tactical delivery 52 Summary 57 6. Changes in patterns of burglary 59 Change in the time of day burglaries were committed 62

7. Conclusions 67 References 70 Appendices

1 The information technology nexus 74 2 Prospective Mapping Survey 79 3 Detailed evaluation methodology 83 4 Promap graphical user interface and an illustration (step by step) of how the

system is used 95

List of tables 2.1 Knox contingency table example 4

2.2 Knox ratios for Mansfield 7

2.3 Monte-Carlo results for Mansfield 7

2.4 Weekly Knox ratios for Mansfield 8

2.5 Knox ratios for Wellingborough 8

2.6 Monte-Carlo results for Wellingborough 9

2.7 Weekly Knox ratios for Wellingborough 9

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2.8 Knox ratios for Ashfield 10

2.9 Monte-Carlo results for Ashfield 10

2.10 Weekly Knox ratios for Ashfield 11

2.11 Knox ratios for Corby 11

2.12 Monte-Carlo results for Corby 12

2.13 Knox ratios for ‘A’ Division 12

2.14 Monte-Carlo results for ‘A’ Division 13

2.15 Weekly Knox ratios for ‘A’ Division 13

2.16 Summary of the analyses concerned with the communicability of risk 14

2.17 Weekly Knox analysis for area 1 15





2.22 Average number of crimes correctly identified per forecast for cumulative methods

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2.23 Average percentage of crimes correctly identified per forecast 23

2.24 Average number of crimes correctly identified per forecast for single point methods

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2.25 Average percentage of crimes correctly identified per forecast 24

2.26 Predictive accuracy for analyses for which the same number of cells were identified by each method 25

3.1 Comparison of three potential pilot sites 29

3.2 Tactical options matrix 32

4.1 Accuracy of the prospective model including the opportunity surface 38

5.1 Number of times prospective maps were used in ‘A’ Division’s daily briefing 52

5.2 Sample characteristics 53

5.3 Number of respondents who had heard of prospective mapping, by section 54

5.4 Number of times maps were used for targeted police activity 54 5.5 Number of respondents who were either involved in or responsible for employing operational tactics, by section 55 5.6 The interpretation and usefulness of prospective maps 56

6.1 Change in the volume of burglary and odds-ratio statistics 62

A 3.1 Change in the volume of burglary and odds-ratio statistics 85

List of figures 2.1 The five policing areas in ‘A’ Division 15

2.2 Two-dimensional and three-dimensional hotspot lattices 19

2.3 Opportunity surface for ‘A’ Division 21 2.4 Differences in cells identified as being at the highest future risk by retrospective and prospective methods 25

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2.5 Illustration of a simple nearest neighbour analysis for two data sets 26

2.6 Nearest neighbour index: retrospective and prospective methods 27

4.1 Similarity in time of day for near-repeats and unrelated burglaries 40

4.2 An example image of the final GUI 41

5.1 Promap dissemination process across ‘A’ Division 48

5.2 A series of predictions for one area 49

5.3 Timeline for prospective mapping pilot in ‘A’ Division 51

6.1 Time-series graph of the count of burglary before and during pilot 61

6.2 Changes in the proportion of burglaries committed during the evening over time (for events for which the interval between the earliest and latest reporting times was less than eight hours) 63 6.3 Changes in the proportion of burglaries committed during the morning over time (for events for which the interval between the earliest and latest reporting times was less than eight hours) 64 6.4 Changes in the proportion of burglaries committed during the daytime over time (for events for which the interval between the earliest and latest reporting times was less than eight hours) 65 6.5 Changes in the proportion of burglaries committed during the evening over time (for events for which the interval between the earliest and latest reporting times was less than eight hours) 66 A1.1 Internal application utilising existing GIS for visualisation 75 A1.2 Stand-alone application utilising existing GIS 76 A1.3 Stand-alone application with integrated GIS 77 A3.1 Changes in the spatial distribution of risk following the introduction of the pilot 88 A3.2 Lorenz curves showing the distribution of burglary risk 90 A3.3 An illustration of a triple (bottom) and quad chain (top) 91 A3.4 The proportion of events belonging to different k-event series before and during the pilot 93 A5.2 An enlargement of the shift analysis options 96 A5.3 An example of fictitious prospective map 96 A5.4 Map navigational options 97 A5.5 Prospective map magnified to neighbourhood level 97 A5.6 Prospective map magnified to street level 98 A5.7 Prospective map magnified to household level 98

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Executive summary The systematic identification and management of risk is one element in the Home Office’s reform plan published on 19 July 2006, with a stated emphasis on proactivity in risk management. The research reported here provides an innovative means of doing precisely that in respect of one offence, describing and applying a technique whose extension to all offence types would make a significant contribution to realisation of the Home Office’s reform agenda. Knowing when and where to deploy resources is pivotal to the crime reduction enterprise. The attempt to identify and police ‘hotspots’ of crime has for some time been a part of crime reduction strategy. The positive effects of such measures have been acknowledged, with a range of interventions based on this approach having significant impacts on levels of crime. The aim of the project reported here is to understand the regularities in patterns of burglary across a range of geographical areas and to develop and test an emerging forecasting technique, prospective mapping, thereby helping the police and their crime reduction partners to prevent and detect more crime. The risk of burglary is not evenly distributed – some areas experience more burglary than others. Within areas, some homes are victimised more than the rest. However, little research has focused on the accurate prediction of which areas and locations are most likely to experience burglary next. Research concerned with crime mapping has focused almost exclusively on the description of what happened last week, last month or over the last year, with the implicit assumption that the future will be much like the past. Insofar as crime moves (and it does), such an approach is at best sub-optimal. The current study sought to develop an accurate way of forecasting where burglary is most likely to next occur, and to decide whether the resulting system had potential for use in operational policing. In addition to addressing the technical issues of how to forecast future patterns of burglary, the authors attempted to identify issues of implementation that might impede police adoption of predictive mapping systems and how such mapping might be integrated with other approaches to crime reduction Spatial and temporal patterns of crime are fluid. Research by the authors and others had demonstrated that the risk of burglary appears not only to move, but to cluster in space and time in much the same way as a communicable disease. When a burglary occurs at one home, another is likely to occur swiftly nearby. As time elapses, this risk decays so that after four to eight weeks, homes located near to a previously victimised home experience only a level of risk normal for the area in which they are located. If such patterns are ubiquitous (a notion supported by the work reported), the risk of burglary moves. The consequence is that the location of future events might be better predicted by means more sophisticated than the simple extrapolation of past patterns. The central aims of the project were as follows.

• To determine whether patterns of burglary were communicable across diverse areas of the East Midlands, and if so, whether the pattern varied between areas.

• To develop a predictive mapping system usable in an operational policing context. • To test the accuracy of the system and compare it with contending alternatives. • To determine how the system could be used operationally, identify obstacles to

implementation, establish how it was received by those who might use it, and identify necessary refinements.

• To provide an idea of the likely efficacy of the system during a field trial by evaluating its impact on crime and influence on crime reduction strategies in the area.

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Project outcomes Patterns of burglary Using a modification of an approach developed in the field of epidemiology, analyses were conducted to see if burglary does cluster in space and time and if the patterns vary across location. Briefly, to do this, for each area each burglary event is compared to every other and the space-time distance between them recorded. The pattern of results observed is then compared with what would be expected on the basis of chance, determined using what is known as a Monte-Carlo simulation. Burglary was considered to be communicable if more events occurred near to each other in both space and time than would be expected on the basis of chance. Without exception in the East Midlands areas studied, the risk of burglary is communicable up to a distance of around 400m for at least one month. This finding was used as a general rule in the construction of basic prospective maps. Sensitivity analyses were conducted to examine the duration of the elevation in risk. These suggested that risk (albeit diminishing) extended beyond one month and in most cases up to around eight weeks. The patterns were to some extent specific to the time of day considered. For example, if a burglary occurred at one location during the afternoon, a further burglary was more likely nearby soon after and at a similar time of day. In supplementary work not reported here, theft from (but not theft of) vehicles was also found to be communicable.

Developing the predictive system and measuring its accuracy On the basis of the above findings, a method of prospective mapping (hereafter, Promap), based on the authors’ previous work, was developed. Across the different areas studied in the initial phase of the research, police analysts were already using descriptive hotspot mapping. Promap was tested against an optimised version of the ‘retrospective’ hotspotting method in current use. The optimised versions were substantially more predictive than maps which resemble those typically used in crime analysis. Promap outperformed the optimised ‘retrospective’ hotspot mapping system. It did this in three ways.

• Promap correctly predicted more burglaries than other methods. For example, the final version of Promap could identify the locations of 78 per cent burglaries that occurred within the next seven days of a forecast, whereas for the same interval the retrospective model could identify only 51 per cent.

• Relative to the retrospective maps, it yielded hotspots which formed more solid, coalescent, hence readily patrollable, areas.

• The final version of Promap accurately predicted more crime while identifying a smaller area than other methods. For example, the same fraction of burglaries occurring within two to seven days of a prediction could be forecasted by identifying patrolling areas half the size of those generated by retrospective maps

Implications for operational policing Research conducted by the authors and others suggests that police officers’ perceptions of where burglary hotspots form are often imperfect. This is particularly true for recent rather than enduring problems. The fluidity of burglary risk provides an explanation for this. Crime moves and patterns evolve. Why should police officers be able to anticipate such changes? They should not, but computerised support systems that can assist in the crime reduction enterprise should. The advantages of Promap are that it could act to facilitate more targeted crime reduction interventions, increasing the likelihood that resources are deployed to the right places at the right time, rather than where they were needed last week or last month (as with conventional hotspot methods). Risky areas can be better defined so that patrols could move through priority areas more efficiently, spending less time in places with lower risk. The maps can also be produced for specific shifts to ensure their continued relevance over the course of the day.

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Developing predictive mapping in an operational policing context Support systems are only valuable if they can be used and understood by those operating them. Promap software was developed for use in one police Basic Command Unit (BCU) in the East Midlands in consultation with local police and community safety practitioners. Using the software, maps could easily be produced at regular intervals by crime analysts, discussed during shift briefings and provided to beat officers. The maps clearly defined the areas with the highest predicted risks, against a background of the housing distribution and significant geographical points of reference, which could then be used as guides to patrolling. In response to feedback, different maps were generated for each of the three police shifts of the day. The system was modified in response to practitioner requests. Considerable effort was expended to optimise the algorithms used to ensure that the system could generate maps rapidly. The final version could generate maps for the entire participating BCU in around 20 seconds. A different system which generates descriptive (i.e. not predictive) maps, took ten minutes to complete an analysis of the same area, with extra time required to display the resulting output.

Issues of implementation encountered in situ Following consultation with the staff in the pilot BCU and their crime reduction partners, the system was modified and used in an operational context over a period of six months. Promaps were generated for each of five sections which comprised the BCU by crime analysts located at police headquarters, and disseminated using the force IT system. A process evaluation was conducted over the implementation period to see how the system was used. This involved observation of briefing meetings, a log of the tactical options used in response to the maps, and a survey of front-line police officers.

Issues with system implementation • Despite the fact that the system itself was considered simple to use, changes of key

personnel (including the BCU commander) and force IT requirements (which initially rendered the system unnecessarily complex) made for a slow start. Those surveyed came to regard it as useful to the point of enquiring about the possibility of its extension to cover other crime types.

• Timely dissemination of relevant maps to front-line officers was a source of initial difficulty, resolved to general satisfaction distressingly late in the pilot period. At first there were issues with the physical transfer of maps from the crime analysts, located at police headquarters, to the staff throughout the BCU. The maps were initially generated daily but beat officers felt that there were only minor changes in the maps when generated with this frequency. Eventually, these issues were partially resolved by providing local staff access to the mapping software, and by producing the maps twice a week. Implementation issues of this kind currently represent one of the most important limiting factors in prospective mapping utility, but their resolution is mostly a matter of ensuring that basic IT infrastructure is adequately configured, and the system is not hobbled by adherence to IT custom and practice.

• Part of this project involved a review of possible tactical options that could be used in conjunction with the new maps. These ranged from well-established anti-burglary initiatives, such as target hardening and police patrols to novel techniques suggested in the light of the burglary patterns found. The review of each technique included documented success or failure, financial costs, and the speed with which implementation was plausible, swiftness of implementation being important in the current project. It appeared that the most favoured methods were those that combined the maps with other local intelligence (such as data on known offenders) to direct police patrols, and

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those that involved collaboration with other crime prevention partners. The point of central importance is that the availability of accurate prospective maps requires reconsideration of tactical options in order for their potential to be realised.

• Interviews with front-line officers were undertaken towards the end of the project. Across the five sections of the BCU, there were differences in the degree to which officers could define what the system actually did. In one area, only 27 per cent of officers interviewed could provide an accurate description, although in the remaining four areas a better understanding was apparent, with 75-92 per cent providing good definitions. There were also differences across the five areas with respect to how frequently the maps were reported to have been used to inform police tactics. Typically, the maps were reported to have been used more often in those areas in which officers had a better understanding of Promap. This suggests that further effort should be expended to ensure that officers have a full appreciation of the approach before implementation begins.

• There was a marked reduction of domestic burglary during the pilot study. This was a mixed blessing. On the plus side, burglary declined more in the pilot area than in the comparison area, and declined most during the shift for which the police had the greatest opportunity to use, and reported most frequently using, Promap. On the negative side, this meant that priorities other than domestic burglary came to the fore, with diminished use of the maps as a consequence. Further, the decline in burglary was evident when implementation was at a rudimentary stage, and hence it is difficult to attribute the change to Promap. A fair test of the potential of prospective mapping may only be realised when the method is tested across a range of areas and ideally when it is extended to cover a range of crime types. This was beyond the scope of the current project, for which the aim was to determine the potential utility of the system. This research design is consistent with clinical trials of new pharmaceuticals, whereby different phases of the trial are used to evaluate the efficacy of the drug. The initial phase, analogous to the approach adopted here, is essentially designed to uncover any problems with the drug and potential effectiveness rather than to demonstrate a systematic effect.

An important element of the research was to gain feedback from those involved in the piloting process to assess the potential of this type of system. A key message from this process was that the development of Promap is seen as a promising route towards intelligence-driven police patrolling and the informed allocation of responsibilities within crime and disorder reduction partnerships. This is evidenced by the fact that (as noted above) officers enquired about the potential (immediate) development of the system for other types of crime, including theft from a vehicle.

Recommendations The potential applications of Promap are manifold. It would have been too much to expect that this potential could be fully exploited within a six-month trial period, where all parties were starting from scratch. Listed below are a number of recommendations for implementation that could help realise improvements in operational practice, and a consequent reduction in crime.

• Police officers located in diverse areas could be consulted in the development of the maps so that their operational usefulness can be tailored to different contexts. Maps could distinguish between areas for which risks are increasing and those for which the level of risk is stable or declining. Which type of map is most useful may depend upon resources available and problem profiles. Bespoke mapping systems, locally optimised and responsive to tasking and co-ordination wishes are feasible.

• All those tasked with acting upon the maps might be provided with regular information, which could become an integral part of the daily routine. If possible, new maps should be provided two to three times per week and display shift-specific risks.

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• To maintain momentum the system could either become part of the long-term routines of operational policing or used for time-limited highly resourced operations. A middle ground, where attention is only paid when burglary is a priority, would limit the utility and understanding of the system.

• Risks identified by Promap could usefully be combined with other forms of intelligence to optimise operations. For example, high risk areas could be scanned for trends in data on modus operandi or for information on known offenders which could help focus police tactics. This type of analysis could, to some extent, be automated, thereby freeing analyst time to allow a more thorough interrogation of patterns, and for them to consider the range of crime reductive responses possible.

• It would be desirable to develop procedures that enabled evaluation of the system without any demand on police time or resources. For example, an index of changes in the patterns of crime clustering over time could be automatically produced and recorded. Evaluation would also be facilitated by systematic documentation of the activities and arrests made by beat officers, although any paperwork burden would need to be minimised, perhaps through the use of a simple computerised data collection tool.

• In any mapping system, the risks identified are relative to the level of crime in a particular area, and to the time frame selected for analysis. Hence it is possible to produce maps showing areas of relatively high risk even when there is a lull in the underlying problem. Thus, for the crime type(s) predicted some indication of the anticipated scale of the problem would be a useful additional feature of the system.

• The authors believe that Promap should be extended to other crime types, such as vehicle crime and violence. Predictions may be produced for all crime (if subsequent empirical research suggests this to be sensible) and weighted by seriousness of each crime type. Because different types of crime might require different solutions, an indication of the anticipated volume of each type could be provided as an indication of what to prioritise and when.

As noted above, the use of forecasting systems such as Promap should encourage the continual consideration and reconsideration of operational tactics. Favoured approaches may require rethinking when it is possible to more accurately predict where resources are most needed. The future? It is believed that the development of the techniques described will offer a step change in the power and applicability of crime mapping as a tool of crime reduction. In perhaps fifteen years, using techniques such as those discussed here and those as yet unrealised, predictive mapping could be available for all crime types; real time information on risk would thereby become available to police patrols, where the seriousness of different crime types is weighted automatically so that an optimal patrolling pattern is provided to each police vehicle to maximise the total seriousness of crimes to be preventively patrolled. Using Promap type systems in concert with Lab-on-a-chip forensic testing, where DNA and other tests would be possible in police vehicles, would facilitate swift forensic identification of perpetrators of crimes not prevented, and patrolling informed by Promap would mean faster response times to arrive before crime scenes are compromised for forensic purposes. In parallel with optimised patrolling, Promap would deliver information about longer-term patterns and stabilities in crime and disorder to Crime and Disorder Reduction Partnerships, enabling them to put in place design and maintenance changes. Nothing in such a future is unfeasible even with today’s technology. It does however require an effort of imagination to discern the centrality of prospective mapping to such a future. While the East Midlands study reported here was in most respects successful, the big prizes of intelligent crime reductive practice will be won only through an integrated developed programme rather than a succession of ad hoc piecemeal projects.

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1. Introduction

This report represents a summary of a project concerned with short-term burglary prediction. The work was completed in three stages, a short initial research phase to develop and establish the accuracy of the method, a short development phase during which the system was refined for use in one police Basic Command Unit (BCU) with tactical implications identified and discussed, and finally a six-month field trial to test whether the system could be used in an operational context and how the police engaged with it. The structure of this report largely follows the chronology of the research, thereby providing the reader with an impression of substance and sequence. The first section is a review of the literature that informed the project, and the second the empirical research, initial development and testing of the system. The third section contains a discussion of the types of tactical option that were originally identified as having the potential to be used with the system, or a tailored variant of it, and the wider ideology of the approach. The fourth section discusses final refinements to the system used in the field trial, and then reviews how the system was eventually used and police officers’ perceptions of its utility. Lessons learned regarding implementation are also highlighted and discussed. In the penultimate section, changes in patterns of crime coincident with the work are explored, and in the final section future directions and recommendations are discussed. Background and past research Criminological research has demonstrated that crime is concentrated. For all crime types analysed, a small number of victims are repeatedly victimised and hence experience a large proportion of crime (for reviews, see Pease, 1998; Farrell, 2005); a large proportion of crime occurs in a small number of areas; and a small number of offenders commit a large proportion of crime (e.g. Spelman, 1994). In relation to the geographical distribution of crime, this manifests itself as spatial clustering, with ‘hotspots’ of crime such as burglary being a typical characteristic of deprived areas (e.g. Johnson et al., 1997). These findings conform to what is more generally known as the 80:20 rule. This pattern is not confined to crime but is a more general phenomenon. For instance, a small proportion of the earth’s surface holds the majority of life on the planet, and a small proportion of earthquakes account for the majority of damage caused by them (Clarke and Eck, 2003). For burglary, the focus of the current research, the relationship between different types of concentration has also been studied. Specifically, are incidents of repeat burglary victimisation the work of a common offender, or do different offenders simply exploit the same opportunities for crime? These explanations have been referred to within the literature as the boost and flag hypotheses, respectively (Pease, 1998). A number of approaches to investigating these hypotheses exist, but perhaps the most direct is to examine data for detected offences. In their analysis of a sample of data for offenders detected for burglary offences, Everson and Pease (2001) demonstrate that 86 per cent of the incidents of repeat victimisation were committed by the same offenders (see also Everson, 2003). Further corroborative evidence comes from studies in which offenders have been interviewed regarding their offending behaviour. Typical findings illustrate that around one in three burglars admit to returning to the same property to commit a further offence (Gill and Mathews, 1994; Ashton et al. 1998) and their reasons for so doing include the following:

“the house was associated with low risk …., they were familiar with the features of the house …., to get things left behind or replaced goods.”

Ericsson, 1995 Perhaps the most succinct account was given by a Scottish burglar to Mandy Shaw. Upon asked why he returned, he replied “Big house, small van”. Thus, whilst recognising that some repeat offences may be committed by unrelated offenders, a consensus of opinion is emerging that repeat victimisation is largely the work of the same offenders. A further finding that supports this conclusion and which has immediate crime prevention implications is the

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time course of repeat victimisation (RV). Research consistently demonstrates that when RV occurs it does so swiftly offering a limited but precise window of opportunity for intervention (e.g. Polvi et al., 1991). Risk is unstable. Thus, repeat victimisation may be said to be a special case of space-time clustering, events tending to occur swiftly at the same locations. Inspired by the precepts of optimal foraging theory, the authors have recently examined whether RV is part of a more general foraging pattern (Johnson and Bowers, 2004a). The theory, borrowed from behavioural ecology, is that when searching for resources, offenders will aim to limit the time spent searching for suitable targets, whilst simultaneously seeking to maximise the rewards acquired thereby minimising the associated risks. RV is arguably an example of optimal foraging. A conjecture from Farrell et al. (1995) illustrates this. Farrell et al. suggest that:

“a burglar walking down a street where he has never burgled before sees two kinds of house – those presumed suitable and those presumed unsuitable. (The latter identified by dint of an alarm, by occupancy, the presence of a barking dog, and so on). He burgles one of the houses he presumes suitable, and he is successful. Next time he walks down the street, he sees three kinds of house – the presumed unsuitable, the presumed suitable, and the known suitable. It would involve the least effort to burgle the house known to be suitable.”

Farrell et al. (1995) Thus, offenders target those properties with which they are most familiar, and which combine good rewards and acceptable risks. A natural extension of this strategy would be to target not only those previously burgled and known to be suitable but also those houses that are most similar to them, in terms of the likely risks and rewards and the effort involved in burgling them. The first law of geography states that things which are closest to each other in space are the most similar. It follows that homes nearest to burgled houses may represent the next-best targets. For this reason, using data for the county of Merseyside and methods developed in the field of epidemiology (Knox, 1964), the authors conducted a series of studies to examine whether the risk of burglary clusters in space and time more generally. That is, does the risk of burglary appear to be communicated from one property to another in much the same way as the behaviour of a disease?1 A series of confirmatory findings followed. In particular, for the area studied, the research demonstrated that the risk of burglary was communicated over a distance of about 400m and this elevated risk endured for around one month (Johnson and Bowers, 2004a), after which it appeared to move to other nearby areas (Johnson and Bowers, 2004b). Additionally, a disproportionate increase in risk for those on the same side of the street as the burgled home was evident. The communicability of risk varied by area, with risk appearing to be most communicable in the most affluent of areas (Bowers and Johnson, 2005a), though some degree of communicability was well-nigh universal. The practical implications of this programme of research are clear: crime reductive action should be directed towards the burgled home, and also to those nearby. However, one concern raised was the practicability of implementing such a strategy on a large scale. Consider that the implementation of a strategy for which every burgled household and neighbours within 400m received crime reduction attention would require substantial resources if implemented across an area such as a police Basic Command Unit. For obvious reasons, such a strategy is unlikely to generate much enthusiasm. What is required is a more precise method of generating reliably accurate predictions of where crime will most likely next occur. Such a method should enable the efficient deployment of resources. The rough location of a high concentration of crime could easily be predicted by simply identifying a large urban area, but this would be of little operational value. The challenge, then, is to identify where a high concentration of crime will occur for a relatively small area. Considering the findings in relation to crime concentration, for an

1 The authors do not suggest that burglary exudes a bacillus but that the clustering of events in space and time might suggest that it does so.

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optimally calibrated system, the goal might be to be able to identify for each day or police shift the 20 per cent of locations in which 80 per cent of crime was most likely to occur. The extent to which this can be achieved will, of course, be affected by the degree to which crime concentrates in spatial and temporal terms, and the degree to which spatio-temporal sequences can be captured as maps. The key conceptual point in the enterprise is that past events are not themselves mapped in the conventional way, but contribute risks to locations over time and space to diminishing extents. This approach has come to be known as prospective mapping (see Bowers et al., 2004; Johnson et al., 2005). The system uses recent historic crime data to generate forecasts which can be displayed using a Geographical Information System (GIS) and overlain on an Ordinance Survey (OS) map of the relevant policing area, allowing crime reductive resources to be deployed to those areas when and where they will be most needed. The model is calibrated according to the dimensions of the communicability of risk for the area considered. Early evaluation of the system has shown that for the county of Merseyside the locations of a large percentage of burglaries (64%-80%) (occurring up to three days after the generation of the forecasts) can be identified within a small fraction of the total area considered (20%). This level of accuracy outperformed existing methods of hotspot mapping (Bowers et al., 2004), which themselves outperformed predictions based on police officers perceptions of high risk areas (McLaughlin et al., forthcoming), and the efficiency of a prediction slowly diminished after three days (Johnson et al., 2005) as was anticipated. Aims and objectives The results of the above studies show considerable promise for crime reduction. However, the external validity of the findings, that is to say how generalisable they are to other areas and different contexts, remained unknown. The main aims of the current project were six-fold.

1. To see if the patterns of communicability discussed above are observed in another area of the UK, namely the East Midlands.

2. To see how this pattern varies between areas within the East Midlands.

3. To investigate how the police and their crime reduction partners currently deploy

crime reduction resources, what they do and how they identify where to implement strategies, as a baseline against which to consider the action implications of predictive mapping.

4. Given that crime does cluster in space and time as observed on Merseyside, to

evaluate the predictive accuracy of prospective mapping in these areas by comparing it with the systems currently used there, and against what would be expected if patrols were directed to areas randomly

5. To develop and field-test the system in one police BCU to explore its feasibility in an

operational setting on a routine basis, and to identify obstacles to implementation.

6. To evaluate the impact on crime of crime reduction strategies informed by the system in the area.

In the chapters that follow, the authors will discuss the results of the research and illustrate how the theory of prospective mapping (hereafter, Promap) evolved into a tactical entity that was used in an operational context.

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2. Testing the generalisabil ity of prospective mapping

As discussed above, research concerned with geographical hotspots of crime demonstrates that over a given period of time crime is spatially concentrated (see, for example, Eck and Weisburd 1995). Other research (e.g. Farrell and Pease, 1994) demonstrates that within a specific geographical region, crime also exhibits temporal clustering, with incidence rates increasing with the onset of the winter, and diminishing around the spring. However, unless the risk of victimisation is communicable, one would expect these spatial and temporal clusters to be independent of each other, with the incidence of new series of crime events failing to exhibit localised spatial and temporal increases. Simply put, the communicability of burglary risk may be demonstrated only by showing that burglary clusters in both space and time. Conformity to this pattern would be evident if houses near the burgled home were victimised shortly afterwards more than would be expected on the basis of chance. Those readers not concerned with technical details on the demonstration of clustering should skip to the beginning of the next chapter at this point. Empirical research concerned with the space-time clustering of events was first conducted by Knox (1964) to study epidemics of leukaemia. The rationale underlying the Knox test is to determine whether there are more events that occur close in space and time than would be expected on the basis of a random distribution. To do this, each event is compared with every other and the distance and time between them recorded. For n cases, this generates ½n (n-1) pairings. A contingency table, such as shown in Table 2.1, with i columns and j rows is then populated. The spatial and temporal increments (or bandwidths) used in the rows and columns can be arbitrary, although they should be so defined as to allow specific hypotheses informed by the underlying theory to be tested. For instance, in the case of crime the question concerns the distance over which crime has an impact and for how long? The bandwidths selected should have relevance to operational policing. Table 2.1: Knox contingency table example

1 month

2 months

3 months

…………..

100m

n11

n21

n31

200m

n12 n22

N32

300m

n13

n23

N33

400m

……..

……..

n14

n24

n34

Note: The mathematical notation nij refers to the observed frequency for the cell occurring in column i and row j. Thus, n11 refers to the cell in column 1, row 1. The contingency table generated can be compared with a chance distribution. One complication is that the assumption of independence of observations, a criterion for most inferential statistical methods, is violated. However, Knox suggested that in the absence of a space-time interaction, the statistical distribution of the expected values for the cells of the contingency table would conform to a Poisson distribution and could be computed in the same way as a Chi-Square test, using the marginal totals of the table. Thus,

5

eij= n.j x ni.

n

Where, eij is the expected value for each cell, nij is the observed, and ni and nj are the row and column totals respectively The results of the analysis can be interpreted by inspecting adjusted residuals, computed for each cell using the following formula (see, Agresti, 1996, pp31-32): rij = nij – eij [eij(1-row proportion of ni+)(1-column proportion of n+j)]1/2

Thus, the adjusted residuals are a measure of the difference between the observed and expected values for each cell. The adjusted residuals have a mean value of zero and a standard deviation of one, hence adjusted residual scores exceeding values of two (or even more stringently three) are considered statistically significant. The null hypothesis is that events are not clustered in space and time. Negative values indicated that there are fewer events occurring within a particular space-time interval. The majority of research concerned with crime (Johnson and Bowers, 2004; Townsley et al., 2003) has used the Knox approximation to examine space-time clustering. However, an alternative approach, for which the independence of observations is not a requirement, may also be computed (Johnson et al., 2006). This approach uses Monte-Carlo simulation to generate an expected distribution, rather than using the marginal totals (Besag and Diggle, 1977). To do this, the data are permuted, in effect mixing up the dates and locations across the events. Thus, the dates are randomly shuffled using a pseudo-random number generator, whilst the spatial locations remain fixed. The hypothesis is that if there is statistically significant space-time clustering in the data then there should be more events observed to occur close in space and time than for 95 per cent of the random permutations generated. The process of generating permutations is repeated (iterated) a number of times, typically around 999, a new contingency table generated each time and compared with the contingency table for the observed distribution. The probability that the observed value for each cell occurred on the basis of chance may be calculated using the following formula (see North, 2002):

11

++−=

nranknp

Where n is the number of simulations, and rank is the position of the observed value in a rank ordered array for that cell One possible reservation about the p-values generated using the Monte-Carlo approach (and the Knox residuals) is that they are not as readily interpretable as one might like. Interpretation of the results can be aided by deriving a simple measure of effect size.2 In this case, a Knox ratio, which contrasts the observed value for each cell with the average ‘simulated’ value for that cell (or the expected value derived using the marginal totals of the table), was computed. Thus, a Knox ratio of 2.0 so derived would indicate that twice as many burglaries occurred within a particular distance and time of each other than would on average be expected on the basis of a chance distribution. A value of one would indicate that the result conformed to what would be expected on the basis of chance, and values of less than one that fewer burglaries occurred within a particular distance and time of each other than one would expect. Which measure of central tendency is best used to compute the odds ratio depends upon the distribution of the simulated data. If the data are skewed, or there are

2 The Knox residuals can be interpreted in the same way to some extent but are not as simple to understand. In simple terms, the larger the Knox residual the larger the effect.

6

extreme values, the mean can misrepresent the distribution. For this reason, the authors use both the mean and median values for each cell. On the whole, for the data analysed the two typically converge and hence for the results that follow, the authors will report only results derived using the median values. This approach is to be preferred over the Knox approximation, particularly as no assumptions regarding the statistical distribution are required. However, limitations in computing power have meant that where a large number of events are to be analysed, the process can take some time. This is particularly true where the dimensions of the contingency table or the number of events analysed are large. Fortunately, computing power is now sufficient for Monte-Carlo simulation of this kind to be completed for small data sets (e.g. n=1000) and a 10 x 10 contingency table fairly rapidly, and for larger data sets (e.g. n=2000) over an hour or so. Larger contingency tables or data sets can still take a matter of days, but are now easily computed by those prepared to be as patient as the present writers! Is the risk of burglary communicable in the East Midlands? In the sections that follow, the results for each policing area that was analysed in the East Midlands, namely Mansfield and Ashfield Sectors in Nottinghamshire, Alfreton (‘A’) Division in Derbyshire, and Corby and Wellingborough Sectors in Northamptonshire, will be presented. To allow easy direct comparisons the results will be presented here in tabular form. A number of analyses were conducted for each data set using both the Knox and Monte-Carlo methods described in the previous section. For instance, analyses have been conducted using months as the temporal bandwidth whereas others have used weeks. For each area monthly analyses will be presented for both Knox and Monte-Carlo approaches. As will become apparent these varied little. Thus, analyses for the Knox approximation only will be presented looking at the communication of risk over weekly intervals. In all analyses, for the sake of simplicity, repeat victimisation is excluded. Thus, the results consider the distance over which the risk of a household’s burglary is communicated to different households and for how long this endures. Because of the exclusion of repeats against the same household, the benefits of patrolling and resourcing patterns based upon propinquity in time and space to a burgled home will be considerably understated in what follows. To anticipate the results, it would appear that as a general rule the risk of burglary is communicable up to a distance of around 400m for at least one month. There was no area for which this was not the case, hence universal application of the 400m one-month rule is in no case inappropriate, it merely excludes a proportion of other high-risk homes. The sensitivity analyses conducted to examine the duration of the elevation in risk suggested that this extended beyond one month in most cases, up to around eight weeks. This will subsequently be referred to as the classic profile. Those not wishing to understand the detail of area differences should skip to the next section, in which the value of prospective mapping in operational context is explored.

Mansfield Police Sector For Mansfield, the data analysed cover the period January-December 2004 and comprised 1,156 burglary events. The results of the monthly Knox and Monte-Carlo analyses are shown as Tables 2.2 and 3.2 respectively. The results are consistent in showing that the risk of burglary communicates up to a distance of 200m and endures for one month. Additionally, the Monte-Carlo results suggest that for houses within 100m of burgled homes the risk endures for up to two months. The same (two-month) result is apparent for the Knox residual analysis, although it marginally fails to be statistically reliable.

7

Table 2.2: Knox ratios for Mansfield (values in bold are statistically significant according to the residual scores (not shown), N=1,566)

MONTHS 1 2 3 4 5 6

100 1.26 1.10 0.97 0.85 1.02 1.03 200 1.08 0.99 0.93 1.04 0.92 1.01 300 1.00 1.07 1.01 1.01 1.01 1.13 400 1.03 0.96 1.07 1.04 1.01 1.06 500 1.01 1.06 0.97 0.98 1.11 1.05 600 1.03 1.02 0.98 1.08 0.98 1.03 700 1.03 1.04 0.96 1.00 1.01 1.02 800 1.04 1.01 0.97 0.98 1.00 1.00 900 1.03 1.06 0.97 0.96 0.98 1.03

1000 1.06 1.01 0.96 1.02 0.99 1.02

A number of other cells are statistically significant in each table, namely those for 1,000m and one-month and 500m and five months. Due to the results for a large number of cells being analysed, some cells (60*0.05=3) will have significant values by chance. For this reason they will be discussed no further. In contrast, the analyses for the cells in the top left of the tables are based on an a priori hypothesis and are of clear relevance. Visual inspection of the Knox ratios in Table 2.3, shows that around 25 per cent more burglaries occur within 100m and one month of burgled homes than would be expected on the basis of chance, eight per cent more between 101-200m. 12 per cent more up to 100m and between 30-60 days. Otherwise, the number of burglaries approximate what would be expected on the basis of chance. (Note that the Knox residuals cannot be interpreted as percentage deviations from chance as they are actually z-scores, see preceding section). Table 2.3: Monte-Carlo results for Mansfield (values in bold are statistically significant, N=1566)

MONTHS 1 2 3 4 5 6

100 1.26 1.12 0.92 0.90 0.99 1.08 200 1.08 0.99 0.93 1.02 0.95 0.99 300 1.00 1.06 1.00 1.01 1.00 1.17 400 1.03 0.98 1.08 1.03 1.00 1.03 500 1.01 1.05 0.96 1.00 1.10 1.06 600 1.03 1.02 0.97 1.09 0.97 1.03 700 1.03 1.04 0.94 1.02 1.00 1.01 800 1.04 1.01 0.99 0.96 1.01 0.98 900 1.03 1.05 0.96 0.98 0.98 1.00

1000 1.06 1.00 0.97 1.01 1.00 1.03

To increase the sensitivity of the results, an additional Knox analysis was conducted to examine the temporal pattern in more detail. Thus, weekly rather than monthly patterns were analysed. As noted above, these were computed using the Knox approximation rather than Monte-Carlo simulation.

8

Table 2.4: Weekly Knox ratios for Mansfield (values in bold are statistically significant according to the residual scores (not shown), N=1566)

WEEKS 1 2 3 4 5 6 7 8 9 10 11 12

100 1.43 1.29 1.31 1.23 1.21 1.20 1.17 1.14 1.13 1.06 1.03 0.98 200 1.38 1.20 1.14 1.09 0.99 0.94 0.94 0.95 0.98 1.01 0.99 0.96 300 1.09 1.04 1.00 1.00 1.01 1.04 1.06 1.09 1.05 1.03 1.05 1.00 400 1.07 1.06 1.02 1.04 1.00 0.99 0.98 0.94 0.97 1.01 1.06 1.05 500 0.97 0.98 1.00 1.01 1.01 1.05 1.05 1.08 1.06 1.03 1.00 0.95 600 1.03 1.10 1.06 1.04 1.03 0.99 0.99 1.00 1.03 1.01 1.02 1.01 700 0.99 1.01 0.99 1.02 1.03 1.04 1.04 1.03 1.05 1.04 1.03 1.00 800 1.04 1.02 1.03 1.05 1.04 1.03 1.03 1.00 1.02 1.01 1.00 0.98 900 1.02 1.02 1.06 1.04 1.04 1.05 1.02 1.04 1.05 1.03 1.03 1.00

1000 1.10 1.09 1.07 1.07 1.04 1.00 1.00 0.98 1.02 1.00 0.99 0.99

The results, shown as Table 2.4, suggest that the risks for houses within 100m of a burgled home endure for up to nine weeks, and for those within 200m four weeks. Thus, the results of the monthly and weekly analysis reveal essentially the same pattern. Wellingborough Police Sector For Wellingborough, the data cover the period for which the most recent data were available at the time of analysis, in this case January-December 2003. There were 1,350 burglaries over this period. Tables 2.5 and 2.6 show the Knox and Monte-Carlo analyses. A similar pattern emerges. The results suggest an elevated risk to those proximate to the burgled home for around one month. Table 2.5: Knox ratios for Wellingborough (values in bold are statistically significant according to the residual scores (not shown), N=1350)

MONTHS 1 2 3 4 5 6

100 1.63 1.04 0.70 0.82 0.96 0.64 200 1.37 1.00 0.85 0.78 0.89 0.85 300 1.27 0.96 0.90 0.82 0.89 0.93 400 1.20 0.98 0.96 0.94 0.93 0.89 500 1.17 1.03 0.94 1.01 0.92 0.93 600 1.10 1.04 1.00 0.92 0.93 0.87 700 1.01 1.05 0.99 0.96 0.94 0.93 800 1.08 0.98 1.03 1.02 0.98 0.91 900 1.01 0.97 0.95 0.95 1.02 0.95

1000 0.99 1.00 1.04 0.98 0.93 1.05

The results of the Monte Carlo analysis show that 57 per cent more burglaries occurred within 100m and one month of each other than would be expected on the basis of chance. A large number of burglaries also occur up to 400m and one month of each other, thereafter the Knox ratios diminish although they remain statistically significant up to 700m. To examine the pattern in more detail, a weekly analysis was conducted. The results, shown as Table 2.7, suggest a similar pattern but that the elevated risks endure for more than one month.

9

Table 2.6: Monte-Carlo results for Wellingborough (values in bold are statistically significant, N=1350)

MONTHS 1 2 3 4 5 6

100 1.57 0.97 0.77 0.82 1.00 0.72 200 1.35 1.01 0.85 0.77 0.91 0.86 300 1.26 0.96 0.90 0.81 0.90 0.93 400 1.21 0.99 0.96 0.91 0.91 0.92 500 1.16 1.03 0.93 1.00 0.94 0.95 600 1.10 1.03 1.00 0.93 0.92 0.86 700 1.01 1.05 0.98 0.98 0.95 0.91 800 1.08 0.98 1.03 1.02 0.99 0.89 900 1.01 0.98 0.95 0.96 0.99 0.94

1000 0.98 1.01 1.04 0.98 0.92 1.05

These findings illustrate a problem with analyses where a certain level of aggregation is employed. This is known as Simpson’s paradox (Simpson, 1969). This occurs where data aggregated up to a fairly large unit of analysis such as one month, mask subtle trends apparent where data disaggregated to a finer level of resolution (such as one week) are used. In this case, it would appear from the analysis shown in Table 2.7 that the risk to houses up to 600m from victimised homes endures for around six to seven weeks rather than four. In any event, it is apparent that the risks are consistently greater within the first month. Table 2.7: Weekly Knox ratios for Wellingborough (values in bold are statistically significant according to the residual scores (not shown), N=1350)

WEEKS

1 2 3 4 5 6 7 8 9 10 11 12

100 1.82 1.78 1.76 1.67 1.53 1.39 1.22 1.06 0.97 0.87 0.78 0.76

200 1.64 1.56 1.46 1.39 1.27 1.18 1.09 1.03 1.00 0.96 0.89 0.86

300 1.52 1.43 1.37 1.29 1.20 1.12 1.04 1.00 0.94 0.91 0.88 0.88

400 1.39 1.28 1.23 1.21 1.15 1.09 1.04 0.99 0.95 0.94 0.96 0.95

500 1.27 1.23 1.19 1.18 1.13 1.11 1.06 1.04 1.01 0.98 0.97 0.95

600 1.11 1.08 1.09 1.11 1.08 1.08 1.06 1.05 1.03 1.03 1.02 1.01

700 1.02 1.01 1.02 1.00 1.02 1.04 1.06 1.06 1.04 1.01 1.00 1.00

800 1.16 1.08 1.10 1.07 1.05 1.04 1.02 1.00 0.99 1.00 1.00 1.03

900 1.04 1.03 1.01 1.01 1.01 0.99 0.98 0.97 0.98 0.98 0.98 0.98

1000 1.00 1.01 1.01 0.99 0.97 0.97 0.96 1.00 1.01 1.01 1.04 1.04

Ashfield Police Sector For Ashfield, the data cover the period January-December 2004 and a total of 1,012 burglary events. The results of the monthly Knox and Monte-Carlo analyses are shown as Tables 2.8 and 2.9 respectively.

10

Table 2.8: Knox ratios for Ashfield (values in bold are statistically significant according to the residual scores (not shown), N=1012)

MONTHS 1 2 3 4 5 6

100 1.19 0.96 1.04 1.04 0.96 0.79 200 1.17 0.99 0.94 0.97 1.00 0.91 300 1.18 0.96 0.99 0.99 0.95 1.00 400 1.11 0.96 0.96 1.11 0.94 1.01 500 1.02 1.03 0.99 1.05 0.96 1.00 600 1.09 1.04 1.01 1.07 0.97 0.98 700 1.12 0.96 1.07 1.05 0.91 0.89 800 0.99 1.01 1.02 0.98 0.96 1.06 900 0.96 0.96 1.08 0.96 1.05 0.98

1000 0.97 1.02 1.02 1.01 1.06 0.92

The results are again very consistent. Both analyses suggest that the risk of burglary communicates over a distance of up to 700m and endures for one month. Interestingly, the risk communicated to houses within 401-500m appears to be non-significant. This is difficult to explain. It could be an expression of the spatial distribution of targets across the area, or perhaps suggests the existence of a natural boundary that generates this pattern by discouraging some offenders from travelling from one area to a second nearby. More research would be required to understand this effect. Nevertheless, the results clearly demonstrate that the risk of burglary is communicable.

Table 2.9: Monte-Carlo results for Ashfield (values in bold are statistically significant, N=1012)

MONTHS 1 2 3 4 5 6

100 1.18 0.96 1.03 1.05 0.96 0.80 200 1.17 0.98 0.95 0.97 1.00 0.92 300 1.17 0.97 0.98 0.99 0.94 1.01 400 1.11 0.95 0.97 1.11 0.95 1.00 500 1.02 1.03 0.98 1.05 0.96 0.98 600 1.08 1.04 1.01 1.07 0.98 0.97 700 1.12 0.96 1.07 1.06 0.90 0.90 800 0.99 1.00 1.03 0.97 0.96 1.06 900 0.96 0.96 1.08 0.94 1.07 0.98

1000 0.97 1.01 1.03 1.02 1.07 0.92

As before, an additional Knox analysis was conducted to examine the temporal trend in more detail. The results, shown as Table 2.10, suggest a similar pattern of risk, but additionally suggest that houses between 401-500m of burgled homes are at an elevated (albeit marginally non-significant) risk of victimisation for up to two weeks after an initial burglary. Thus, this suggests that the risk of victimisation is, in general, communicable up to 700m, but for slightly different time periods.

11

Table 2.10: Weekly Knox ratios for Ashfield (values in bold are statistically significant according to the residual scores (not shown), N=1012)

WEEKS 1 2 3 4 5 6 7 8 9 10 11 12

100 1.61 1.44 1.28 1.22 1.06 1.02 0.96 0.96 0.96 0.97 0.97 0.97 200 1.46 1.29 1.25 1.18 1.08 1.00 0.99 0.99 0.98 1.01 0.94 0.93

300 1.43 1.39 1.23 1.17 1.08 1.05 0.99 0.98 0.94 0.97 0.95 1.00 400 1.20 1.16 1.15 1.12 1.08 1.06 1.00 0.96 0.96 0.95 0.95 0.98

500 1.13 1.10 1.05 1.03 1.00 0.99 0.97 1.01 1.02 0.96 0.97 0.96 600 1.20 1.16 1.13 1.09 1.07 1.05 1.01 1.04 1.04 1.04 1.04 1.05

700 1.35 1.20 1.16 1.13 1.06 1.02 1.01 0.96 0.96 0.99 1.01 1.04 800 1.06 1.03 1.04 1.00 0.98 0.96 0.97 0.97 0.98 1.01 1.02 1.05

900 0.96 0.98 0.97 0.97 0.96 0.91 0.90 0.94 0.96 1.00 1.08 1.12 1000 0.93 0.96 0.97 0.96 0.99 0.99 1.00 1.03 1.03 1.06 1.03 1.06

Corby Police Sector For Corby, the most recent data available at the time of analysis covered the period January-December 2003. There were only 429 burglaries for this period in Corby. The results, shown as Tables 2.11 and 2.12, are again very consistent. Unlike the above analyses, while more burglaries occur within 100m and one month of each other than would be expected on the basis of chance, the difference is not statistically reliable. However, this may be due to the low sample size (e.g. the expected cell count for that cell was five times lower than the same cell for Mansfield), particularly for that cell, rather than reflecting an irregularity in the pattern of the communicability of risk. As with the analyses for Mansfield the analyses suggest that the risk of burglary communicates over a distance of up to 700m and endures for one month, although houses within 401-500m appear not to be at significantly heightened risk. Table 2.11: Knox ratios for Corby (values in bold are statistically significant according to the residual scores (not shown), N=429)

MONTHS 1 2 3 4 5 6

100 1.17 1.07 1.11 1.02 1.06 0.87 200 1.20 0.99 1.22 1.02 0.89 0.96 300 1.13 0.94 0.90 1.12 0.96 0.92 400 1.22 1.01 1.02 1.05 1.10 0.96 500 1.04 1.04 1.02 1.09 1.09 1.03 600 1.11 1.08 1.11 1.02 1.02 1.02 700 1.12 1.01 1.11 0.91 0.98 1.03 800 1.02 1.06 1.07 0.98 1.13 1.06 900 1.02 1.04 1.05 1.03 0.93 1.11

1000 1.10 0.91 1.09 1.03 1.06 1.06

As with the results for Mansfield, the Monte-Carlo analysis suggests that around 20 per cent more burglaries occurred within 101-200m and one month of a burgled home. Also consistent with the findings for Mansfield, the risk to houses within 401-500m of burgled properties appears to be elevated yet non-significant. For completeness, an additional Knox analysis was conducted to examine the temporal trend in more detail. However, as this analysis generated a larger number of cells than the monthly analysis and there was such a low volume of crime for this area, the results were considered unreliable and will not be discussed further. Because of the essential similarity of the data

12

across areas it is thought likely that the same patterns underlie the Corby data, and application of predictive mapping in low crime areas is by no means regarded as unprofitable. Table 2.12: Monte-Carlo results for Corby (values in bold are statistically significant, N=429)

MONTHS 1 2 3 4 5 6

100 1.18 1.02 1.12 1.02 1.09 0.87 200 1.20 0.96 1.23 1.03 0.88 0.96 300 1.13 0.93 0.86 1.16 0.91 0.96 400 1.22 1.03 1.04 1.07 1.09 0.96 500 1.04 1.04 1.04 1.07 1.05 1.10 600 1.11 1.12 1.08 1.01 1.04 1.00 700 1.12 1.03 1.07 0.93 0.99 1.03 800 1.02 1.07 1.06 1.02 1.14 1.07 900 1.03 1.07 1.08 1.02 0.97 1.10

1000 1.09 0.92 1.09 1.04 1.06 1.06

Alfreton ‘A’ Division For ‘A’ Division in Derbyshire, the period for which the most recent data were available at the time of analysis was April 2003-March 2004. There were 1,703 burglaries during this period. More recent data are now available but were not used for the analyses in this section of the report. Tables 2.13 and 2.14 show the Knox and Monte-Carlo analyses. A similar pattern emerges to those presented above, although the risk appears to communicate over a greater distance than for the other areas. Thus, the results suggest an elevated risk to those up to 900m from burgled homes for around one month. Table 2.13: Knox ratios for ‘A’ Division (values in bold are statistically significant according to the residual scores (not shown), N=1703)

MONTHS 1 2 3 4 5 6

100 1.37 0.93 0.91 0.93 0.85 0.76 200 1.29 0.93 0.91 0.93 0.93 0.96 300 1.19 1.00 0.94 0.94 0.83 0.90 400 1.17 0.97 0.99 0.94 0.85 0.91 500 1.08 0.95 0.91 0.98 0.85 1.02 600 1.09 1.02 0.95 0.95 0.89 0.95 700 1.07 1.00 0.95 0.95 0.88 1.07 800 1.06 1.02 0.94 0.98 0.98 0.95 900 1.16 1.03 0.97 0.91 0.93 0.89

1000 1.04 1.03 0.99 1.01 0.86 0.94

As with the other analyses, the Knox ratios shown in Table 13.2 demonstrate a pattern of distance decay. That is, the risk of burglary is greatest nearest to burgled homes, after which it remains elevated but clearly diminishes. Expressed in a slightly different way, just under 40 per cent more burglaries occurred within 100m and one month of each other than would be expected on the basis of chance. This is the highest concentration observed for that cell across the data sets analysed. In contrast, around eight per cent more burglaries occurred between 400-800m and one month of each other than would be expected.

13

Table 2.14: Monte-Carlo results for ‘A’ Division (values in bold are statistically significant, N=1703)

MONTHS 1 2 3 4 5 6

100 1.36 0.93 0.93 0.94 0.85 0.79 200 1.27 0.95 0.91 0.93 0.93 0.93 300 1.20 0.99 0.95 0.95 0.82 0.90 400 1.18 0.96 0.98 0.93 0.85 0.95 500 1.08 0.96 0.93 0.95 0.86 1.02 600 1.08 1.01 0.97 0.95 0.88 0.99 700 1.08 0.97 0.96 0.94 0.88 1.04 800 1.06 1.01 0.95 0.98 0.98 0.96 900 1.16 1.03 0.97 0.92 0.92 0.89

1000 1.04 1.05 0.96 1.02 0.86 0.92

To examine the pattern in more detail, a weekly analysis was conducted. The results, shown as Table 2.15, suggest a similar pattern but that again the elevated risks endure for more than one month in some cases (e.g. 101-200m), less in others (e.g. 601-700m). Table 2.15: Weekly Knox ratios for ‘A’ Division (values in bold are statistically significant according to the residual scores (not shown), N=1703)

WEEKS 1 2 3 4 5 6 7 8 9 10 11 12

100 2.38 1.77 1.54 1.40 1.08 0.97 0.96 0.96 0.96 0.90 0.92 0.85

200 1.63 1.41 1.38 1.30 1.15 1.10 0.99 0.98 0.96 0.96 0.94 0.92

300 1.38 1.35 1.24 1.21 1.11 1.05 1.02 0.99 0.97 0.97 0.99 0.96 400 1.26 1.22 1.21 1.18 1.16 1.11 1.06 0.97 0.94 0.91 0.93 0.95

500 1.05 1.10 1.08 1.07 1.04 1.03 1.00 0.97 0.96 0.94 0.93 0.91 600 1.14 1.13 1.09 1.08 1.04 1.02 1.03 1.01 1.03 1.02 0.97 0.98

700 1.18 1.09 1.06 1.06 1.04 1.06 1.05 1.00 0.98 0.94 0.93 0.95

800 1.15 1.11 1.08 1.06 1.02 1.02 1.03 1.04 1.03 0.97 0.93 0.94 900 1.21 1.20 1.17 1.16 1.12 1.10 1.10 1.09 1.05 1.00 0.99 0.95

1000 1.20 1.05 1.04 1.03 1.02 1.04 1.04 1.05 1.04 1.03 1.03 0.99

Summary of the patterns so far The above results demonstrate that for all areas analysed, there was clear evidence that the risk of burglary is communicable, although the distances over which this occurred varied by area. In relation to the time over which this elevated risk endured, this tended to be at least one month, but in some cases extended over longer intervals. One way of summarising the patterns observed is presented as Table 2.16. Each cell of the table is shaded to indicate for how many areas the particular cell had an over-representation of burglary pairs. This allows a quick comparison to see for how many areas the risk of burglary communicated up to say 100m and one month, 201-300m and two months, and so on. It is clear that the most common pattern was for the risk of burglary to extend up to 400m and for one month, although for some areas the risk communicated over greater distances, even up to 1,000m. Considering the unexplained results that were not anticipated by the theory (e.g. 200m and three months for Corby), it is clear that there was no regularity in these results, suggesting that they probably reflect spurious correlations or statistical artefacts. Each cell also contains the average Knox ratio for that cell, calculated across the five data sets. Not surprisingly the results correlate with the colour coding, with those cells shaded in

14

the darkest colours having the highest values. It is also apparent that in general there is a pattern of distance decay, such that the highest Knox ratios are for the shortest spatial intervals. Table 2.16: Summary or the analyses concerned with the communicability of risk (cell values are Knox ratios generated using Monte-Carlo simulation)

MONTHS 1 2 3 4 5 6

100 1.26 1.00 1.01 0.99 0.97 0.87 200 1.20 0.97 0.99 0.98 0.95 0.94 300 1.17 0.98 0.95 1.02 0.92 1.01 400 1.18 0.97 1.01 1.05 0.97 0.99 500 1.04 1.02 0.98 1.02 0.99 1.03 600 1.08 1.05 1.01 1.04 0.97 0.99 700 1.08 0.99 1.02 1.00 0.93 0.98 800 1.04 1.02 1.01 0.98 1.01 1.03 900 1.03 1.01 1.03 0.96 1.00 0.99

1000 1.04 1.00 1.02 1.02 1.01 0.97

Key 4-5 2-3 0-1

Thus, it would appear that as a general rule the risk of burglary is communicable up to a distance of around 400m for at least one month. There was no area for which this was not the case, hence universal application of a 400 metres one month rule is in no case inappropriate, merely that it excludes a proportion of other high-risk homes. The sensitivity analyses conducted to examine the duration of the elevation in risk suggested that this extended beyond one month in most cases, up to around eight weeks. This will subsequently be referred to this as the classic profile. Spatial variation? Compared to the other areas considered, ‘A’ Division covers a large geographic area, this being approximately 150 square miles. For this reason, additional analyses were conducted for areas nested within the BCU. In so doing, the analyses control for another example of Simpson’s paradox (Simpson, 1969), commonly referred to as the Modifiable Areal Unit Problem within the research literature (e.g. Openshaw, 1995). In this case, the problem is that it is possible, even likely, that patterns evident at the aggregate level (i.e. across all five areas) differ from those that would be apparent at the local level. Thus, further analyses were conducted to examine the patterns at the local level for five smaller areas within ‘A’ Division. To do this, analyses were conducted for each of the five policing sections within ‘A’ Division. However, one problem with conducting such analysis is that the geographical policing boundaries used are usually artificial and hence if the data are analysed for one area at a time using the existing boundaries, this can create what is known as an edge effect. The problem that arises is that patterns which occur across and around the boundary can remain undetected. Where boundaries are defined by natural features such as rivers or other topological barriers this of course is not a problem. To reduce the likelihood of encountering this problem, a 1km buffer was generated around each area and the data within the larger area analysed. In this way, any patterns of offending at the edges of the policing boundaries should be detected. The five different policing areas and the buffer zones used are shown in Figure 2.1.

15

Figure 2.1: The five policing areas in ‘A’ Division (right panel shows the buffer zone used in the analyses)

The results of the analysis for each area are shown in Tables 2.17-2.21. For area 1, it would appear that the risk of burglary communicates up to around 400m and particularly for one week after an initial event. This is clearly different to the ‘A’ Division wide analyses presented above. It is also apparent that many more burglaries occur within 100m and one week of each other than would be expected if there were no communication of burglary risk. Table 2.17: Weekly Knox analysis for area 1 (values in bold are statistically significant according to the residual scores (not shown), N=409)

WEEKS 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00 11.00 12.00

100 2.71 0.86 1.14 0.86 0.68 0.86 1.07 1.23 0.59 0.89 1.12 1.08 200 1.65 1.22 0.91 0.89 1.00 1.23 1.16 1.03 1.05 0.94 1.31 1.00

300 1.44 1.17 1.14 1.22 1.09 0.84 1.00 1.01 1.02 0.77 1.13 1.10 400 1.32 1.16 1.04 1.15 1.21 1.00 0.89 1.05 0.89 0.79 1.09 0.94

500 0.98 1.17 1.17 0.91 1.10 1.07 0.92 0.97 1.28 1.14 0.85 0.93 600 1.22 1.00 0.92 0.99 1.02 1.18 0.79 1.04 1.00 1.02 1.05 1.08

700 1.09 0.97 0.97 0.97 1.10 1.13 1.04 0.93 0.96 0.98 0.98 1.08 800 1.17 1.10 0.99 1.08 1.06 1.07 1.24 1.07 0.82 0.83 0.90 0.96

900 1.00 0.96 1.04 1.06 1.07 1.14 0.99 0.93 1.24 0.96 0.80 0.80 1000 0.94 1.01 1.00 1.17 0.92 0.91 1.09 0.91 1.15 1.07 0.90 0.90

The results for area 2, shown in Table 2.18, reveal a similar pattern of results, although the pattern of distance decay is less dramatic than for area 1.

16

Table 2.18: Weekly Knox analysis for area 2 (values in bold are statistically significant according to the residual scores (not shown), N=270)

WEEKS

1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00 11.00 12.00

100 1.90 1.29 0.70 0.75 1.16 0.89 1.00 1.26 0.83 1.00 0.72 1.18

200 1.49 1.26 1.15 0.91 0.77 0.66 1.14 1.05 0.95 0.95 0.71 0.88

300 1.38 1.11 0.86 0.84 0.85 0.82 1.24 1.15 1.00 1.18 1.19 0.81

400 1.22 1.18 1.09 0.88 1.06 1.06 1.04 0.96 0.92 0.80 0.79 1.16

500 1.02 0.99 0.88 0.93 1.03 1.05 0.90 1.01 0.93 0.91 0.99 0.93

600 1.31 0.96 1.00 1.09 0.89 0.90 0.88 0.92 0.79 0.99 0.79 1.08

700 1.12 0.99 0.87 0.74 1.02 1.05 1.06 0.82 0.90 0.98 1.01 0.94

800 1.12 1.15 0.81 1.04 1.02 1.02 0.99 1.15 1.01 0.95 0.94 1.09

900 1.03 1.17 0.98 1.10 0.98 0.93 0.99 1.12 0.95 0.87 1.06 0.91

1000 1.11 0.96 1.01 0.88 1.15 1.11 0.99 1.07 0.86 1.10 0.99 0.98

For area 3, the results are somewhat different, suggesting that in this area the risk of burglary communicates over longer distances and endures a little longer. Table 2.19: Weekly Knox analysis for area 3 (values in bold are statistically significant according to the residual scores (not shown), N=357)

WEEKS 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00 11.00 12.00

100 2.20 1.26 1.45 1.14 1.03 1.09 1.03 1.03 1.03 1.06 1.26 0.88 200 1.61 1.33 1.39 1.07 1.04 0.99 0.83 0.97 0.75 0.97 0.86 0.72 300 1.42 1.51 1.04 1.01 1.17 1.00 0.96 1.03 0.75 0.85 1.02 0.81 400 1.33 1.13 1.17 1.03 1.07 1.02 1.07 1.04 1.01 0.86 0.94 0.91 500 1.04 1.22 1.02 1.11 0.94 0.89 0.79 0.91 0.92 0.88 0.89 0.93 600 1.16 1.17 1.09 1.01 0.88 1.02 0.99 0.94 0.96 1.01 0.82 1.09 700 1.13 1.06 0.81 1.05 1.04 0.97 1.00 0.90 1.04 1.01 0.87 0.92 800 1.14 1.09 0.97 0.92 0.91 1.09 1.01 0.90 1.01 0.72 0.99 0.89 900 1.19 1.06 1.13 1.07 0.95 1.10 0.93 0.98 0.99 1.00 1.00 1.02

1000 1.17 0.92 1.08 0.91 1.01 0.88 1.03 1.06 1.00 0.93 0.96 0.96

For area 4, there appears to be a more classic effect, with the communication of risk being up to around 400m and for up to three weeks. Again, the risk to those closest to burgled homes is striking. Table 2.20; Weekly Knox analysis for area 4 (values in bold are statistically significant according to the residual scores (not shown), N=407)

WEEKS 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00 11.00 12.00

100 2.12 1.61 1.29 1.00 0.81 1.08 1.38 1.00 1.16 0.84 1.17 1.00 200 1.36 1.10 1.36 1.13 1.31 1.08 1.03 0.95 1.04 1.00 0.85 0.87 300 1.30 1.18 1.03 1.15 1.14 1.10 1.04 1.13 1.05 1.12 1.05 1.08 400 1.22 1.04 1.23 1.13 1.12 1.23 1.15 0.85 0.90 1.05 1.21 1.16 500 1.03 1.03 1.03 1.10 0.98 1.12 1.16 1.10 0.98 0.92 0.91 0.87 600 1.01 1.01 1.17 1.00 1.05 1.02 1.10 1.14 1.04 0.98 0.84 1.05 700 1.07 1.15 1.19 1.08 1.17 1.05 0.98 0.97 1.13 1.04 0.91 0.91 800 1.16 1.07 1.06 1.05 0.97 1.07 0.96 1.04 1.06 0.97 0.96 0.98 900 1.14 1.21 1.05 1.10 1.28 1.10 1.01 0.86 0.91 0.94 1.12 0.99

1000 1.11 0.95 0.95 1.01 1.14 1.08 1.03 0.93 1.09 0.97 1.03 0.86

17

The results for area 5 again demonstrate that the apparent risk to houses near to the burgled home is particularly dramatic. However, in common with area 3 the risk of burglary appears to communicate over a greater distance in this area. Table 2.21: Weekly Knox analysis for area 5 (values in bold are statistically significant according to the residual scores (not shown), N=311)

WEEKS 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00 11.00 12.00

100 5.29 1.14 0.86 0.71 0.71 0.67 0.83 0.29 0.71 1.17 0.50 0.33 200 2.31 1.77 1.14 0.77 0.69 1.08 0.92 0.85 0.46 1.38 1.00 0.45 300 2.20 1.21 0.80 0.86 1.07 0.62 0.36 1.14 0.64 1.29 1.67 0.92 400 2.33 1.07 0.87 0.71 1.21 0.29 0.71 0.71 0.86 1.00 1.15 0.83 500 1.64 1.50 1.43 0.69 0.86 0.54 1.38 1.15 0.77 1.08 0.58 1.08 600 1.11 1.06 0.72 0.76 0.94 1.00 1.56 0.88 1.29 1.12 0.53 0.73 700 1.53 0.72 0.63 0.89 0.72 0.88 1.24 0.72 1.00 1.12 0.38 1.06 800 1.47 0.68 0.79 0.83 1.17 0.94 0.82 1.06 1.06 0.56 1.44 0.94 900 1.32 1.11 1.32 0.89 1.00 1.06 1.59 1.06 0.78 0.89 0.81 0.63

1000 0.72 1.06 1.22 1.41 0.89 1.18 1.41 1.47 0.71 1.12 1.40 1.07

Thus, although caution is required when interpreting the above results due to the sample sizes involved, the results suggest different profiles across the different areas. That said, for every area, the largest Knox ratio (and residual) was for the shortest space-time interval, suggesting the ubiquity of this finding at all levels of geographic resolution.

Temporal variation? For a number of reasons, it is possible that the profile of the communication of risk for each area may change over time. For instance, different offenders may adopt distinct foraging strategies. Some may prefer to commit crimes very close to previously burgled homes immediately afterwards, whereas others may prefer to commit them nearby after a short interval has elapsed. This may particularly affect the time-space patterning of burglary if new offenders move into an area, were arrested or move elsewhere. Also, it is possible that the development or demolition of housing would change the availability of targets and hence the patterns of crime. For this reason, Knox profiles were generated for Mansfield and ‘A’ Division for sequential time periods to determine whether or not the profiles do change, and if so in what way. To do this, for each area a Knox profile was generated using twelve months worth of data. Next, a new profile was generated using twelve months of data but which, compared to the previous analysis, included the next two weeks of data, and excluded the oldest two weeks of data. This was repeated 22 times for each area. Thus the analysis was completed using a moving window of data, which included new data on each iteration. The results of the analyses were then animated and compared. The general pattern of results suggested that while the Knox profiles did vary a little from one profile to the next they tended to remain stable over time. This may suggest that, in general, offenders adopt similar foraging patterns or that a sufficiently small proportion of active burglars were ever in custody to modify the overall picture. There is ubiquity of the finding that burglary clusters in space and time, as evidenced by recent research that shows that these patterns are apparent in all other countries for which data have so far been analysed such as the UK, US, Netherlands, New Zealand and Australia (Johnson et al., 2006). One explanation for the differences in the profiles observed for each area could relate to differences in target density between the different areas. Thus, the risk of victimisation may communicate over greater distances in areas in which houses are more dispersed, whereas risks may communicate over shorter distances where houses are spatially concentrated and

18

hence available nearby targets plentiful. Considerations of land use provide the most plausible accounts of such area variation in pattern. Other possibilities exist.

Predicting the future Having demonstrated that the risk of burglary is indeed communicable in the East Midlands, the next task was to determine whether it is sufficiently predictable to suggest that the production of predictive mapping software would aid operational policing. Thus, research was conducted to compare the effectiveness of:

1. Promap;

2. what the police are currently doing;

3. a simple variant of retrospective hotspotting; and

4. what might be expected if areas were prioritised for crime reductive attention randomly.

At the time of the research, none of the areas currently used retrospective mapping techniques per se. Instead, they generated pin maps on a fortnightly basis using two weeks’ data. This simply involved generating a map which shows the locations of all burglaries that occurred within the previous two weeks. On the basis of these maps, operational resources may be deployed. This is similar to retrospective hotspot mapping in that both methods summarise historic data, and hence in what follows the authors will use retrospective hotspotting as a proxy for what the analysts currently do. However, it should be noted that problems with pin mapping are well documented (e.g. Chainey & Ratcliffe, 2005) and thus the retrospective approach used here as an analogue is significantly superior to what the analysts were doing. Item 2 in the list above has thus been eliminated, being a sub-optimal type of retrospective hotspotting. As is intuitively clear, random allocation of resources will be less efficient than either prospective or retrospective hotspotting, so the choice of method comes down to prospective or retrospective. While necessarily technical, the remainder of this section should be read at least in a cursory way by the interested practitioner, since it identifies the computational differences between the two primary contending approaches. The technique most commonly used to identify hotspots involves the generation of a two-dimensional lattice to represent the area of interest. As shown in Figure 2.2, a two-dimensional lattice (Fig. 2.2(2)) is overlain upon a study area (Fig. 2.2(1)). This comprises a series of (x*y) cells, each with identical proportions. The challenge of delineating a hotspot lies in the derivation of a set of values, one per cell, that reflects the intensity of crime risk at each location. Thus, a methodology and mathematical algorithm is required that can generate risk intensity values for every cell. One technique commonly used to do this is called the ‘moving window’. Here, a circle with a predetermined radius (referred to as the bandwidth) is drawn from the midpoint of each cell (Fig. 2.2(3)), and each of the events that falls within the circle is used to generate the risk intensity value for that cell. The risk intensity value for each cell is determined by the number of crime events (in Figure 2.2(1), four for the cell considered) that occurred within the circle and how far away they are located from the midpoint of the cell. Those closest to the midpoint are typically assigned a greater weighting than those further away. To illustrate the method, three of the cells in Figure 2.2(3) have been shaded to indicate the intensity of risk at those locations. Those shaded darkest exhibit the highest risks. An example of a hotspot (quartic) function (Bailey & Gatrell, 1995) is described by equation (1):

2

2

2

2 13)( ⎟⎟⎠

⎞⎜⎜⎝

⎛−= ∑

≤ τπτλ

ττ

i

di

ds (1)

Where, )(sτλ = risk intensity value for cell s τ = bandwidth

di = distance of each point (i) within the bandwidth from the centroid of the cell

19

The bandwidth used to generate the hotspot, and the mathematical equation used to generate the risk intensity values vary, but the basic rationale is the same. Unfortunately, these are typically not informed by theory or by an in-depth understanding of the crime problem, but often because they produce elegant maps. In other cases, the default settings of the software used are adopted. Figure 2.2: Two-dimensional and three-dimensional hotspot lattices (1. study area, 2. study plus lattice, 3. lattice and retrospective moving window, 4. lattice and prospective moving window) (Figure taken from Johnson et al., 2005)

Crime events Bandwidths Street networks

▼ occurred 1 week ago d1 – Spatial bandwidth ● occurred 2-4 weeks ago d2 – Temporal bandwidth Prospective maps are generated using a variant of the moving window technique. The novel feature is that the amount of time elapsed between events is considered as well as the distance between the crime events and cells. When defining the model used, consideration of the size of the cells used in the two-dimensional lattice is required. Whilst the optimal size is difficult to determine, it is clear that if a cell is too large much effort would be wasted in policing low risk locations. Overly large cells would also suffer from the Modifiable Areal Unit Problem (Openshaw, 1995) discussed above. For instance, if one calculates a risk intensity value for one large area which encapsulates two smaller areas with very different risks, the true risks for neither of the smaller areas will be accurately reflected by the risk intensity value for the aggregated area. Thus, it is wise to use a cell size that enables differences in relative risk across (and within) cells within the lattice to be revealed accurately. However, it is also wise to avoid cell sizes that are simply too small. For instance, it is unlikely that a method which used one million cells, each 1m x 1m, would reveal more useful intelligence than a method in which larger cells were used.

d2

d1

Spatial distance

Spat i

al d

ista n

cetim

e

(4)

(1) (2)

d1

Spatial distance

Spat

ial d

istan

ce

(3)

20

A further point of note concerns the trade-off between having cells of small proportions, which increases the precision of the maps generated, and the time taken to complete the calculations. For instance, the generation of a forecast for a grid which has 100m x 100m cells will take one-quarter of the time that it would take to complete the same task for a grid which contains cells that are 50m x 50m. Where analyses are required for an area the size of ‘A’ Division, this can be of considerable importance. One issue then concerns the timely production of the forecasts, a consideration whose importance will be amplified if the analyst has to produce forecasts within a limited window of time. The next issue concerns the bandwidth used. As is illustrated in Figure 2.2(4), Promap uses two bandwidths. The first, the spatial bandwidth, may be calibrated in a variety of ways but should relate to the distance over which the risk of victimisation is communicable. The second type of bandwidth concerns the time elapsed since a crime occurred and the production of the forecast. This bandwidth is conditional upon the first, as a burglary event should contribute to the risk intensity value of a cell only if it occurred within a given distance of that cell. As with the spatial parameter, for the temporal bandwidth a variety of settings could be used but again this should reflect the period of time over which the communicability of risk can reasonably be expected to endure, or slightly longer. A further methodological difference between the authors’ approach and that used in retrospective hotspotting lies in avoiding use of the distance from the midpoint of the cell and the relevant burglary events in the derivation of the risk intensity values. Consider that for retrospective hotspotting a risk intensity value is calculated for the midpoint of the cell and this value is then allocated to all other points within that cell. If risk intensity values were computed for all points within a cell it is unlikely that they would be identical. Thus, using the value for the centroid of the cell gives the impression that the risk of victimisation is uniform across the cell. This is unlikely to be true. This problem will be amplified as the cell size increases. Instead of using the exact Euclidian distance between all events and the cell midpoint, for Promap the number of cells, the actual unit of analysis considered, between the event and the cell is instead used. Thus, if a crime occurred within the cell under consideration, the distance would be zero (actually, for computational reasons 1), if it occurred within an adjacent cell, two, and so on. By adopting this approach the risk intensity values for each cell are likely to reflect more accurately the risks across the entire cell, rather than at one single point. Formula (2) was used to derive the risk intensity values for the prospective map, as follows:

ieici i ecs 111)( ∗⎟⎟

⎠

⎞⎜⎜⎝

⎛+= ∑

≤∩≤ υττλ (2)

Where, )(sτλ = risk intensity value for cell s τ = spatial bandwidth υ = temporal bandwidth ci = number of cells between each point (i) within the bandwidth and the cell ei = time elapsed for each point (i) within the temporal bandwidth Evaluating the accuracy of the different methods As discussed in earlier work (e.g. Bowers et al., 2004), there has been little attention given to the measurement of the accuracy of mapping techniques used to inform the deployment of operational resources. This is clearly essential. For this reason, in earlier papers the authors proposed a number of different standard metrics. These include the hit rate, which is simply the number of future crimes correctly identified. A second important factor is the area of the ‘at risk’ locations to be policed. Where operational resources are limited, to compare two techniques one may need to equate the latter to allow a like-for-like comparison. One way of controlling for this is to select the same geographic area for each method and then compare the hit rate for each. Where possible this approach has been adopted in what follows. A related question concerns the area that should be selected within which crime could occur. In

21

a densely populated urban area this can often be done by simply generating a rectangular grid and selecting a percentage of the cells. However, in a more rural area such as Derbyshire and the other areas considered here, this approach would be less useful. A large proportion of the grid would not contain any houses, and hence opportunities for burglary. Thus, to make the analyses realistic, for each area an opportunity surface was approximated using the data available. Specifically, every burglary was mapped using a GIS and buffer of 1km for each point generated. Next, those cells defined by this process were selected and the rest discarded. Figure 2.3 shows an example for ‘A’ Division. The left panel shows a rectangular grid which encapsulates all of the crime events. The panel on the right shows the opportunity surface as defined using the above method. This map (non-white areas) contains approximately 50 per cent of the rectangle and thus demonstrates the importance of this method. For instance, consider that if ten per cent of the surface area of each map was selected to allow a standard comparison between two mapping techniques, for the map on the left one would actually select around twice as much area as one would for the map on the right. Whilst this is not a perfect solution it represents a considerable advantage over the alternative by excluding places where burglary cannot happen. Figure 2.3: Opportunity surface for ‘A’ Division

In the current research two further standards for comparison were constructed. The first simulated what would be expected on the basis of chance. That is to say, how much better would it be to use the methods than to simply direct resources to locations on the basis of chance? To do this, Monte-Carlo simulation was used to generate a chance distribution. To elaborate, for each area ten per cent of the opportunity surface for that study area was selected at random using a pseudo-random number generator. The accuracy of these random forecasts was then compared to the actual distribution of crimes in the same way as above. For each area, this was repeated a number of times (in this case, 50) and the average of the simulations computed. The size of the cells selected using this approach were varied (100m, 400m, and 1km) to test the sensitivity of so doing. This is a simple approximation to choosing a specific location, a street or a neighbourhood. However, the results produced using each of these different levels of resolution were remarkably consistent and hence we will report only those generated for 100m cells. A second issue is discussed in some detail below but uses existing statistical methodology to derive a measure of the extent to which hotspots generated by different methods vary in terms of coalescence. That is, are there a large number of hot cells that are generally some distance from each other, or are there a smaller number of coherent hotspots. The latter may

22

be preferred from the perspective of the deployment of operational resources, in that between hot-spot travel time is minimised for patrolling officers. Thus, by using the above metrics it is possible to compare the different approaches to each other in a variety of ways, and also to test how they differ to what would be expected on the basis of chance. The latter facilitates an easy interpretation of the effectiveness of each technique. To facilitate a robust evaluation of the different techniques it was necessary to generate and test a number of predictions for each method in every area. Thus, new computer software was developed to automate the procedure of generating and subsequently testing the forecasts. This software was written in Visual Basic. This was necessarily complex and computational exhaustive. As a consequence, the software took some time to develop, to test each model and to generate the results (typically three to four hours for each model for each area).

Results In what follows the results for every area will be presented alongside each other to allow the direct comparison of area variation in the effectiveness of the techniques. A total of 22 forecasts were generated for Mansfield, Corby, Wellingborough and ‘A’ Division. Each forecast was generated for intervals beginning on the 15th of every month. The reason for doing this was to ensure that any seasonal effects would be controlled for (both days of the week and months of the year). To generate the predictions, between eight and twelve weeks’ data were used, depending upon the model tested. A number of models were generated but only the main results will be presented here. The first results relate to a prospective cumulative risk model used in previous research. Here, every crime within the bandwidth contributes to the risk intensity for each cell. The theory underlying this method is that every prior crime confers some degree of risk, and that these risks accumulate. This would be particularly relevant were there a series of offenders committing crime in an area. For the second set of results for the prospective method, for each cell, the crime that confers the most risk is identified and the cell is assigned the risk intensity value generated by that one point. For this method the theory is that the same burglar or burglars are responsible for all the burglaries in a local spate, but that their behaviour is shaped only by the most recent (a Markov process), i.e. that having burgled No 10 Acacia Avenue, then No 20 Acacia Avenue, only the latter is relevant to the offender’s decision where to burgle next. Colloquially, it assumes that burglars have the memory of goldfish in target selection (and/or that local burglaries were attributable to one, or very few, burglars).

Cumulative models Results for the cumulative model used in earlier research (Bowers et al., 2004; hereafter the classic prospective model) are shown in Tables 22.2 and 23.2. Table 2.22 shows the average number of crimes predicted for the sample of forecasts generated. Thus, for ‘A’ Division, the classic prospective method identified almost 20 crimes for each seven-day forecast, almost, twice as many as the retrospective method. Table 2.23 shows the same data in a slightly different way, indicating the percentage of crimes whose location was correctly identified. The percentages were derived by simply dividing the total number of crimes correctly predicted across all forecasts for each area by the total number of crimes occurring in that area over the same interval. Thus, for ‘A’ Division the classic prospective model correctly identified the locations of 61 per cent (433) of all burglaries that occurred over the 22 one-week periods (a total of 708 crimes). Both prospective models outperform the retrospective model. Inferential statistical tests (paired-samples t-tests) confirmed that for all pair-wise comparisons for Wellingborough and ‘A’ Division the prospective models significantly outperformed the retrospective model. The classic prospective model also generated significantly better predictions than the

23

retrospective method for seven-day forecasts for Mansfield and Corby, although the actual differences were (reliable but) small. Table 2.22: Average number of crimes correctly identified per forecast for cumulative methods (N=22)

Retrospective

Promap (specific)

Promap (classic)

2 days 7 days 2 days 7 days 2 days 7 days

Wellingborough

3.26

10.96

4.32*

15.68*

4.17*

14.7* Mansfield 3.32 10.18 3.59 11.05* 3.46 10.73*

Corby 0.68 3.32 0.90 3.59 0.68 3.55+

‘A’ Division 3.32 11.36 4.77* 15.77* 5.55* 19.68* * Significantly better than retrospective method (p<.05, one-tailed) + Better than retrospective method (p=.09, one-tailed) For ‘A’ Division, levels of chance performance were on average 11 per cent and 10.5 per cent for the seven- and two-day forecasts respectively. Thus, the retrospective model was just over three times better than chance. Better still, the prospective methods were around five to six times more accurate than would be expected had areas been selected on the basis of chance. Thus, Promap was significantly more accurate than a random targeting strategy. Table 2.23: Average percentage of crimes correctly identified per forecast (N=22)

Retrospective

Promap (specific)

Promap (classic)


Wellingborough

67% 64%

83%*

83%*

86%*

86%*

Mansfield 51% 45% 59% 50%* 54% 48%*

Corby 38% 40% 48% 44% 38% 44%+

‘A’ Division 37% 35% 49%* 53%* 53%* 61%*

* Significantly better than retrospective method (p<.05, one-tailed) + Better than retrospective method (p=.09, one-tailed)

Maximum risk conferred by a single point model As noted above, a second model tested considered the risk conferred by the single event which conferred the greatest risk. The results shown in Tables 2.24 and 2.25 show that this approach generated similar results, but that overall the cumulative model offered slightly better predictions.

24

Table 2.24: Average number of crimes correctly identified per forecast for single point methods (N=22)

Retrospective

Promap (specific) Promap (classic)


Wellingborough

3.26

10.96

3.46*

13.00*

3.73*

13.54* Mansfield 3.32 10.18 2.68 8.23 3.32 9.50 Corby 0.68 3.27 0.55 2.81 0.91+ 3.46 ‘A’ Division 3.32 11.36 4.72* 15.68* 5.32* 19.00* * Significantly better than retrospective method (p<.05) + Better than retrospective method (p=.06) Table 2.25: Average percentage of crimes correctly identified per forecast (N=22)

Retrospective

Promap (specific)

Promap (classic)


Wellingborough

67%

64%

78%*

84%*

84%*

87%* Mansfield 51% 45% 45% 34% 51% 41% Corby 38% 40% 30% 35% 50%+ 42% ‘A’ Division 37% 35% 49%* 53%* 59%* 59%*

* Significantly better than retrospective method (p<.05) + Better than retrospective method (p=.06) One problem with the results so far discussed is that for some areas the retrospective hotspot method failed to generate risk intensity values above zero for ten per cent or more of the cells. This was a problem for ‘A’ Division and Wellingborough, for which the retrospective method identified only around five per cent of the cells as having risk intensity values above zero. One explanation for this is that in contrast to the area of Merseyside considered in previous research (e.g. Bowers et al., 2004), for which around 26 and 70 crimes occurred in the two-day and seven-day periods respectively, clearly fewer crimes occur in the East Midlands areas over the same intervals and per square metre. A second reason for this is that the retrospective technique was essentially developed to summarise historic patterns rather than to predict future ones, and hence takes no account of the fact that crime will move. Thus, the retrospective method essentially predicts that crime will most likely occur precisely where it did in the past. To illustrate, consider Figure 2.4. This shows a retrospective hotspot in the panel on the left, a prospective map on the right. It is clear that the prospective map identifies more cells as being at risk. Interestingly, the cells tend also to coalesce with greater consistency.

25

Figure 2.4: Differences in cells identified as being at the highest future risk by retrospective and prospective methods (cells shaded darkest have the highest risk intensity values)

On the one hand this is not a problem as it demonstrates that the prospective method is more accurate than the retrospective approach probably because it accurately identifies more locations that are at risk of burglary, particularly those that are yet to be victimised. Thus, the above results provide a good test of and support for the theory proposed. On the other hand, in terms of practical policing, as the prospective maps identify more areas this means that the results are not strictly comparable. One way of facilitating a direct comparison would be to select a smaller percentage of the cells as identified at risk by the prospective method. However, this means that the authors would unfairly disadvantage the prospective method by constraining how it works in a way which is precluded for the retrospective method. An alternative approach is to increase the number of cells the retrospective method identifies as having a higher risk intensity value. To do this, the above analyses were repeated for ‘A’ Division using additional historic data in the generation of each forecast. Instead of eight weeks of data, twelve were used. The same volume of data was also used for the prospective methods to make the test comparable. Due to the time involved in the analysis, this was not completed for the other areas. Table 2.26: Predictive accuracy for analyses for which the same number of cells were identified by each method (N=22)

Retrospective

Promap specific

Promap (classic)


‘A’ Division

58% 61%

66%+

66%*

64%+

64%*

* Significantly better than retrospective method (p<.05) + Better than retrospective method (p=.06) The results, shown as Table 2.26, demonstrate that the prospective algorithm outperforms the retrospective method under these conditions.

26

Patrolling efficiency As discussed elsewhere (Bowers et al., 2004), even though a map may predict a large volume of crime, it may be of little utility in an operational context if it is made of a large number of dispersed hotspots. The best map would perhaps be one with a relatively small number of clearly defined hotspots. One way of measuring this is to count the number of hotspots generated. Another way, which is slightly more sophisticated is to conduct a nearest neighbour analysis. Nearest neighbour analysis is a test of spatial randomness. The nearest neighbour index (nni) in particular measures how clustered points or cells are relative to what would be expected on the basis of chance. Here, the authors consider the application of this to the analysis of hot cells - the ten per cent of cells with the highest risk intensity values. How close together are they? In this context, a value of one would indicate that the hot cells were randomly distributed across the area. The lower the value of the nni, the more the hot cells coalesce to form coherent (and hence policeable) hotspots. The index can be computed for the nearest high-risk neighbour for each cell, which will often be the adjacent cell. It can also be computed for the next nearest neighbour, the next, and so on, up to k-orders. The value of k is specified by the researcher. Thus, the k-order parameter describes which neighbour is being analysed, the nearest (1st order), the next nearest (2nd order) and so on (up to the kth order). To illustrate, consider Figure 2.5. The nni for the two examples would be the same for the first order nearest neighbours. However, for the data on the left the second order nni would be lower than that for the data on the right, thereby indicating that the hot cells in the former are more spatially clustered than the latter. The most efficient hotspots would perhaps be those with a low nni for the nearest neighbour, for the next, but particularly for the higher orders. This is because the greater the number of orders for which the nni remains low is an indication of a lower number of hotspots that are more coalescent. A series of dispersed hotspots would have a low distance for the first neighbour, but the distance would increase for each successive order. A patrolling police officer would then have to spend more time moving through low risk areas. Figure 2.5: Illustration of a simple nearest neighbour analysis for two data sets ● ● ● ● ● ● ● ● Readers may be aware that this type of analysis is traditionally used to examine the degree to which crime is clustered in space, but as should be evident from the above rationale it is of clear analytic value here, albeit a novel application of the test. To recapitulate, this type of analysis can be used as one index of patrolling efficiency. The lower the nni for higher k-orders, the more efficient the map in this respect. Figure 2.6 shows an example analysis of this kind for ‘A’ Division for both a retrospective hotspot and for a prospective map. The results are clear. The prospective map has a low nni across all orders, whereas for the retrospective map whilst the nni is initially low, at around order ten it starts to increase. Analyses for other maps generated for different days revealed the same pattern of results. It is difficult to overstate the importance of this result for the applicability of Promap to patrol deployment.

27

Figure 2.6: Nearest neighbour index: retrospective and prospective methods

0

0.1

0.2

0.3

0.4

0.5

0.6

1 9 17 25 33 41 49 57 65 73 81 89 97

k-order

near

est n

eigh

bour

inde

x

retrospectiveprospective

The results of the nearest neighbour analysis suggest that the prospective method generates hotspots that are perhaps of more practical use than their retrospective equivalents, in that they yield more burglary risk per distance moved for patrolling officers. Shift by shift analysis A still further development of the system would be to generate predictions that were specific to particular police shifts. Recorded crime data are imperfect for this purpose, since they retain a degree of uncertainty about the precise time of an event, but within the limits of the data to hand, knowing how crime patterns vary by shift offers a clear operational benefit. Otherwise, a forecast for the day would represent the average pattern over the three shifts, but fail to represent the actual geographical patterns for any individual shift. Simpson’s paradox emerges again! To illustrate, consider two areas, A and B. Area A has a high crime risk during the morning and evening, whereas area B has its highest risk during the afternoon. If one takes the average risks across the two areas, area A would be identified as having the greater overall risk. However, during the afternoon it would be area B to which operational resources would most profitably be deployed. The importance of this effect depends upon a number of factors, one of which is the particular operational tactics to be deployed. Where high visibility policing is used, or temporary measures are implemented with celerity, it is crucial to know how crime problems move over the course of the day. If longer term crime prevention interventions are deployed, the highest implementation dosage would be directed to the areas that have the overall highest risks. The point can be made the other way round. Between-shift differences in presenting crime problems should be one factor in determining the balance between short-term and long-term interventions. To summarise the story so far:

1. the effects identified in Promap are robust, occurring with slight variation across all the areas studied;

2. exploring the relationships closely offers a route forward to refining and optimising Promap;

3. the basic instrument could be developed to meet the tailored needs of a willing police area.

28

3. Tactical options and selecting a pilot site In this section there will be a discussion of process by which a pilot area was chosen. Additionally, consideration is given to the types of tactical option that might be usefully employed alongside the system developed. The reason for this was that the success of any crime reduction strategy is contingent upon the identification of the right tactical options that will trigger the relevant crime reduction mechanisms (e.g. Tilley, 1993). In the current project, which places its emphasis on the application of a predictive mapping system updated routinely, consideration of a number of factors is required. At the time of the research, the following section was produced to provide a number of tactical options that could be used operationally to realise the advantages which may accrue from use of the Promap technique. Since the Promap exercise currently concentrates on the crime of domestic burglary (although it has wider potential applicability) so too do the tactical options. It is important to say that despite this exercise the authors deferred to the policing craft of the operational officers who used Promap. The attempt here is simply to make available their current thinking on tactical implications based on the literature on effective crime reduction. To provide a context for the review that follows, similarities and differences among the three police BCUs that were selected as potential pilot sites for the study will be outlined. Following from this, a matrix of a number of tactical options, constructed from evidence of what works, will be discussed. The matrix also provides some guidance on the agencies that are in all likelihood most suitably placed to implement the different options at the local level. This will be followed by a discussion of a number of novel possible tactics. Finally, the area and timescales for the delivery of these tactics will be mentioned emphasising the necessary mechanisms needed to reduce burglary. Potential pilot sites To identify a suitable pilot site researchers visited three areas during the first quarter of 2005 to gain an in-depth knowledge of how each dealt from day to day with its problem of residential burglary from both analytical and operational perspectives. Visits took place over ten weeks and involved meetings with key officers as well as attendance at tasking and co-ordination meetings for each of the areas. The three sites visited were Corby and Wellingborough in the Northamptonshire force area and, ‘A’ Division in Derbyshire Constabulary. Table 3.1 provides a comparison of the areas and summarises some of the main points for each. All three areas were National Intelligence Model (NIM) compliant and tasking and co-ordination is thus organised in a similar way. ‘A’ Division is much larger than either Corby or Wellingborough Sectors, comprising an entire BCU, and had a much higher volume of domestic burglary. Intelligence is disseminated in a similar fashion across the three areas and all analysts were capable of producing crime point maps using a GIS, and did so fortnightly. None of the analysts in any of the areas routinely (if at all) generated retrospective hotspot maps of the kind discussed in the previous chapter. All three areas have gazetteers that can geocode data. This is done only when a map is produced. The geocoding process itself appeared to be quick in all areas. However if the datum was not assigned a postcode (as is the case in about 10% of crimes), geocoding was manual and hence time-consuming. While each area had a different crime recording system, ‘A’ Division incorporated the most recent data as its system was updated every twenty-four hours.

29

Table 3.1: Comparison of three potential pilot sites

Area Key Questions

Corby Wellingborough ‘A’ Division How many burglaries occur per year on average in recent years?

Approx 900* Approx 650+ Approx 1800

What is the annual burglary rate per 1,000 households

6.7** 9.9** 7.5***

What is the annual detection rate? Approx 12%* Approx 10%+ Approx 13%

Burglary a priority? Moderate Moderate High Is there a targeted burglary initiative in place?

Operation Busted (Jan – Mar 2005)

Operation Yarn (Feb 2005 onwards)

How quickly does data get onto the crime recording system?

24 hrs-2 days 24 hrs-3 days 24 hrs

How many analysts are there? 2 2 2

GIS software used? MapInfo MapInfo Blue8 What kind of maps do analysts produce and how often are they produced?

- retrospective point maps fortnightly - choropleth maps every 6 months

- retrospective point maps fortnightly - choropleth maps every 6 months

- retrospective point maps fortnightly - contour hotspot maps every 6 months

How often do key officers meet to discuss intelligence and tactics?

- daily tasking - daily ‘gold’ meeting - 2-week tactical tasking and co-ordination - 6-month strategic assessment

- daily tasking - 2-week tactical tasking and co-ordination - monthly operational performance group - 6-month strategic assessment

- daily tasking - daily command - 2-week tactical tasking and co-ordination - 6-month strategic assessment

Are there any ways intelligence is disseminated to officers outside of scheduled meetings?

‘front page’ internal website

No No

How eager is the area to participate? Moderately Moderately Extremely

* This figure is for the whole of the Northern Area (2 Sectors), thus figures for Corby Sector itself would be lower + This figure is for the whole of the Eastern Area (2 Sectors), thus figures for Wellingborough Sector itself would be lower ** Denominator derived from the 2001 Census *** This figure is based on figures from the 2003/2004 business plan

30

Selecting a pilot site The decision as to which area in which to implement the pilot was taken by a steering group which included members of the Home Office, Government Office for the East Midlands and researchers from the UCL Jill Dando Institute of Crime Science. The main reasons for selecting the site chosen, ‘A’ Division, were that the Command Team expressed a strong desire to participate in the pilot, they were especially concerned with the residential burglary problem in their Division, and they had been experiencing a stable volume of burglary prior to the pilot.

A tactical options matrix for reducing burglary Innovations require new ways of thinking. Early software often sought to mimic electronically what had previously been done manually. Only with time was the potential to do things in new ways realised. One poignant instance of how innovation requires rethinking comes with the introduction of the battle tank. Its qualified success upon introduction at the Battle of Cambrai derived from the failure to modify infantry movements to capitalise on the advantages created by the tank, with long-term unhappy consequences for military tactics (see Dixon 1976). The worst fate for Promap (short of neglect) would be implementation without rethinking policing tactics. As noted above, prior to the start of the pilot a literature review was undertaken to summarise the available literature on tactical options used in the reduction of burglary, so that those that might be used in response to the maps could be identified. However, by outlining the apparent efficacy of extant burglary reduction tactics, in a sense one falls into the mode of thought which Promap is intended to make obsolete. Improving one’s capacity to predict thereby changes the terrain. It may mean that hitherto untried or apparently unsuccessful approaches become potentially effective. For example, the potential of patrolling as a crime reductive measure has generally been considered low, but when directed by better prediction, this may change. At the risk of appearing to oversell Promap, it may change the ground rules for crime reduction. For this reason, it was strongly emphasised that the learning process involved in field testing may involve rethinking some of what is set out below. There is no single menu of burglary reductive options that is guaranteed to work in all circumstances, rather measures need to be tailored according to the specific opportunities and situations apparent in an area. One of the main reasons why replications of interventions often fail is because the knowledge and understanding of the specific contexts and mechanisms for an intervention are lacking (Tilley, 1993). Sometimes sheer lack of effort makes for implementation failure. Something that has been successful in one area or situation will not automatically be successful in others. As such, it must be recognised that whichever tactical options are selected to be used with the predictive mapping system, they may need to be specifically tailored for the purpose of reducing residential burglary in the chosen pilot area. Moreover, the most effective burglary prevention strategies involve a combination of complementary responses (Lamm Weisel, 2002), and hence consideration of how a variety of different interventions might interact, and/or complement the system should be considered. To inform this element of the project, a review of the available literature was conducted and is presented here as a Tactical Option Matrix in Table 3.2. The matrix can be broken down into five main component parts: the type of intervention, a summary of the evidence of its effectiveness, cost (financial and latency of implementation), the geographical coverage of the measures, and the potential partners who may be involved in the implementation of that intervention. The following sections elaborate upon and clarify the content of these five elements of the matrix.

Types of intervention and evidence of effectiveness The list is not exhaustive by any means, however, the most commonly used crime prevention measures for reducing residential burglary are discussed. Over the last two decades there has been a plethora of research on the effectiveness of various types of crime prevention measures and some of the key studies are mentioned here. While the authors are cognisant

31

that each study varies in its research design and analytical techniques and thus cannot therefore be directly compared, the Tactical Options Matrix merely provides a summary of information and is therefore not inclusive of every opinion on each measure. In relation to the symbols used, +, --- and – symbols are used to illustrate whether the intervention has worked in the past, had no impact, or did not work respectively. In some cases, reviews are mixed and both a + and – sign are given to the intervention. As mentioned previously, ultimately the tactical options selected for this project could have included a combination of the interventions presented here, and would be affected by the partners involved and their ability to implement the tactics in a timely manner, and the resources available.

Cost In the matrix, the cost of implementing successful crime reduction tactics is classified in two ways: firstly in terms of how much the measure will cost to implement in a financial sense; and secondly, how quickly it can be mobilised. Both costs are expressed as either low, medium or high. While a certain measure might be expensive initially, it is essential to look at its cost in terms of swiftness of effect. If an intervention can be implemented quickly and in those areas identified as being most at future risk, the potential exists not only to reduce burglary but also to prevent low crime areas at a tipping point from evolving into high crime areas. Thus, it is crucial to compare both types of cost when deciding what will most likely work best for the selected pilot site and in combination with the proposed approach. Perhaps the optimal solution is offered by the intervention that has a medium financial cost but swiftness because it can be mobilised quickly. Geographic coverage of the measures Situational methods of crime prevention work in a variety of ways: increasing effort; increasing risks; reducing rewards; reducing provocation; and removing excuses. Once the nature of the crime opportunities is identified, then a selection of measures can be made either to block or reduce these opportunities (Clarke, 1997), and these can be applied on at least two geographic levels: the specific household or the area. Accordingly, each tactical option outlined in Table 3.2 is classified as representing either an area-level or household-specific crime prevention measure. Measures that prevent crime at an area level will obviously do so by influencing risk at the household level, but they may cost more, take longer to implement and will devote resources unnecessarily to many households that are individually at low risk. Conversely, measures that are extremely effective at the specific household level may have a limited impact across the larger neighbourhood and thus fail to reduce crime on a larger scale if they are focused on only a subset of those individually at risk. The ultimate option perhaps is to have a combination of both specific and area-level situational measures that are both successful in reducing crime, providing benefits that address specific problems at particularly vulnerable households, lower the risk of burglary more generally and produce a realisable deterrent effect.

Partners involved and local responsibilities The involvement of police, local partners and crime reduction agencies are (in current fashion) important to the success of any crime prevention intervention. Multi-agency crime reduction has been a political priority for some years and, since the Crime and Disorder Act 1998 the locally responsible agency is the Crime and Disorder Reduction Partnership (CDRP) rather than the police alone. Table 3.2 shows potential partners such as the police, local authority and other organisations who could have participated in the implementation of the listed interventions. Putting together the best tactical options menu can be a difficult process, as the decision depends on the available resources as well as the eagerness of police and local partners to get involved. Thus, the aim of the exercise was to inform the discussions with the different crime reduction agencies that followed.

32

Table 3.2: Tactical options matrix

Evidence C

ost C

ost Situational crim

e prevention

Partners Involved Type of intervention

Study D

oes it w

ork? +/ --- / -

Details

Financial low

/med/high

Celerity

slow/m

ed/ sw

ift

Area or specific location

Police Local

Authority

Multi-

agency group

Target-hardening (victim

-centred)

Tilley & Webb

(1994) +

Reduces the risk of

individual revictimisation

but may not on its ow

n affect area rates.

Low-high

Swift

Specific �

� �

CC

TV

Gill et al (2005);

Welsh &

Farringdon (2002); C

larke (1997)

+/-

Only likely to im

pact upon com

mercial burglaries

and those houses within

or very near the areas of coverage. C

an increase detectability, act as a deterrent and provide reassurance.

High

Med

Area �

� �

Redeployable

CC

TV (RC

CTV)

Gill et al. (2005);

Gill & Spriggs

(2005) -

Limited research suggests

that while R

CC

TV allows

flexibility, the system is

also difficult to use and very sensitive to m

isuse. N

o long-term reduction in

crime levels.

High

Swift/m

ed Area

� �

�

High visibility

police Blake & C

oupe (2001)

+

Two-officer patrols have

few advantages over

single-officer patrols and use m

uch more

resources.

Med

Swift

Area �

�

�

Street closures

Wagner (1997);

White (1990);

Beavon, Brantingham

& Brantingham

(1994)

+

These studies have show

n that there is a relationship betw

een street access and crim

e rates.

Low

Slow

Area �

� �

33

Evidence C

ost C


e prevention

Partners involved Type of intervention

Study D

oes it w

ork? +/ --- / -

Details

Financial low

/med/high

Celerity

slow/m

ed/ sw

ift


Police Local

Authority

Multi-

agency group

Secure by D

esign, Protecting Property and target-hardening by area

Ekblom (2002);

Clarke, (1997);

Tilley & Webb

(1994)

+

Designing out crim

e is a w

ell established concept that has had profound effects on reducing crim

e. The concept of securely designed housing and environm

ents is often not difficult and can be achieved, at the pre-build stage, for very little extra cost.

Low-m

ed M

ed Specific &

area �

� �

Alley-gating Bow

ers et al. (2003)

+

There is good evidence on the potential benefits from

gating rear alleys as a m

eans of reducing burglary.

High

Med-slow

Area

�

�

�

Clarke (1997);

Laycock (1985) +/---

This measure reduces the

anticipated rewards of

crime by m

aking property harder to dispose of. H

owever, any im

pact is likely to be due to associated publicity not the intervention itself.

Property m

arking

Nelson et

al.(2002); Sutton (1998)

-

These studies emphasise

that in reality, property m

arking does little to deter offenders from

breaking into a house or stealing m

arked property from

within it.

Med

Swift-M

ed Specific

�

�

�

34

Evidence C

ost C


e prevention

Partners involved Type of intervention

Study D

oes it w

ork? +/ --- / -

Details

Financial low

/med/high

Celerity

slow/m

ed/ sw

ift


Police Local

Authority

Multi-

agency group

Use of intelligence & targeting know

n offenders

Farrell et al. (1998); Stockdale & G

resham (1995)

+

These studies show that

the greater use of intelligence helped focus resources.

High

Swift-m

ed Area & specific

�

�

�

Publicity cam

paigns

Stockdale & G

resham

(1995); Burrows

& Heal (1980); R

iley (1980); Johnson & Bow

ers (2003), see also Sm

ith et al. (2002)

+/---

Cam

paigns on their own

rarely change behaviours, although they m

ay achieve a ‘drip feed’ effect if continued over tim

e. Publicity associated w

ith crim

e prevention tactics can have a crim

e reductive effect of its ow

n.

Med

Swift -m

ed Area

�

�

�

Repeat

Victimisation

Strategies

Pease (1998); Forrester et al. (1988); Farrell (2005)

+ O

nce a property has been burgled its chances of subsequent victim

isation increases; a graded response m

odel (bronze, silver, gold) has proved effective.

M

ed

Med

Specific

�

�

�

Ringm

aster

Lister et al. (2004)

+/-

A system that alerts local

residents and voluntary groups of up-to-date crim

e information. W

hile m

any organisations w

orried about raising fear of crim

e among their

clients, they circulated the info only to front-line staff. M

ixed reviews about the

success of the dissem

ination of inform

ation.

Low

Med

Area �

�

�

35

Evidence C

ost C

ost Situational C

rime

Prevention Partners Involved

Type of Intervention

Study D

oes it w

ork? +/ --- / -

Details

Financial low

/med/high

Celerity

slow/m

ed/ sw

ift

Area or Specific location

Police Local

Authority

Multi-

Agency G

roup N

eighbourhood W

atch Schem

es & “C

ocoon W

atches”

Laycock & Tilley (1995); Forrester et al. (1990)

+/--- NW

has a greater impact

when residents are hom

e during the day. The virtual cocoon that is form

ed by alert neighbours around the burgled hom

e can increase the likelihood that an offender w

ill be caught if he/she returns to the property. Target cocoons rather than general schem

es perhaps work

best.

Low

Med

Area �

�

�

Forensic Traps (i.e. chem

ically treated m

ats to pick up intruders foot prints) & silent alarm

s

Research to

date is limited –

Anderson et al. (1995)

--- In relation to burglary of retail prem

ises both Clarke

(2002) and Tilley & Hopkins (1998) em

phasise that these high-tech devices can pose num

erous practical problem

s. Evidence is good on the effectiveness of silent alarm

s (Anderson et al.).

High

Med

Specific �

�

�

36

Other potential tactical options At the time of completing this phase of the research, the published literature on the effectiveness of various burglary reduction options/tactics was reviewed. To stimulate alternative ways of thinking, also discussed were tentative suggestions for further tactics which may complement the ‘near repeat’ burglary victimisation pattern. Due to the communicability of risk in time as well as space, meaning that risks surround burgled homes for a fairly limited time period, many of the suggested measures take existing technologies and add a time dimension to them. Note that the redeployable CCTV option outlined in the matrix above, an example of this type of approach, is not without its problems. These approaches would therefore only work in a setting where measures could be relocated swiftly. The proposed measures use the following research findings:

• that risks are concentrated within 400m and one month; • that some areas have particularly high levels of near repeats; and • that near repeats often share MO characteristics (see Bowers and Johnson,

2005b)

Use of ANPR/surveillance ‘rings’ One possible detection-focused scheme could ‘net’ high risk areas. This could involve a mobile ring of covert cameras and/or Automatic Number Plate Recognition (ANPR) devices surrounding the area. Here a burgled house in the middle of areas where burglary clusters in space and time would be identified and cameras placed around it at a 400m radius. This provides a way of monitoring all entrances and exits to the area for a limited period of time. This is now set up to act a little like a sting operation as the likelihood is that there will be further activity in the area. If a further burglary does happen it should be possible to narrow down when it occurred, to a reasonable time band at least, by asking the victim and looking at the police record. It would then be possible to look at people and vehicles exiting/entering the area for a feasible time band during which they could have entered the area and conducted the burglary. With ANPR it would be possible to work with the Driver & Vehicle Licensing Agency (DVLA) to rule out cars that were registered within the area, which would leave a subset of cars to investigate.

Moveable publicity This would involve the use of offender-orientated publicity. To increase the perceived risks of committing burglary in an area when risk is highest (for perhaps two weeks after an initial burglary), signs could be erected around the boundary of the area with a message indicating that people are entering a high-priority burglary clampdown area. The use of surplus police vehicles parked in strategic places (as is sometimes done to deter filling station drive-offs) would be one available form of publicity.

Repeat Victimisation neighbours scheme Similar to a cocoon-watch approach, this would involve always target-hardening houses a certain number of doors away from a repeatedly burgled home, as well as enhancing the security of the property itself. It may be that a general publicity campaign describing the near-repeats phenomenon would sensitise otherwise complacent neighbours to take crime reduction measures.

Targeted, MO-specific crime prevention advice The same vulnerability is often exploited in near-repeat incidents. This means that it is possible to produce intelligence on the likely MOs of future incidents of burglary. This could be used as a basis for prioritising different elements of risk assessments. Crime prevention advice and assistance would be tailored to MO type and provided to as many near neighbours of a burgled house as possible. It would (of course) also direct patrolling officers to the part of a home through which entry is most likely to be gained.

37

Mobile phone cell broadcast Cell broadcast is a very rapid way of transmitting information to all of the mobile phone handsets within a particular area of radius. Here, a broadcast would go out to everyone in a 400m radius of a burgled home as soon as an event occurred. The message would tell people to be vigilant as there was a potential risk of burglary. If an offender within the area owned (or had stolen!) a mobile phone, he/she would also receive the message with a potential deterrent effect.

Full forensic examination of transient hotspots Near-repeats are taken to be more often the work of the same offenders than more isolated events, and hence more likely to be prolific and persistent. Thoroughness of forensic examination should be informed by location within a prospectively identified hotspot. There are strengths, weaknesses and controversies associated with each of the various options suggested above. Furthermore, there is no published evidence on the effectiveness of these techniques (although this does not distinguish them from much of what is currently attempted). The central point is that Promap requires reconsideration of policing tactics, not superimposition on them.

Emerging versus enduring risks? Finally, one element of Promap as originally conceived, yet to be discussed, is the facility to distinguish between areas that are currently at a heightened risk of burglary that have been for some time (areas with enduring risks), and those that are currently at an elevated risk but that have not been in the recent past (areas with emerging or transient risk). In those areas which represent enduring hotspots, situational crime prevention measures aimed at reducing opportunities for burglary may be the best core option (e.g. alley-gating). On the other hand, in areas that are currently emerging as risky, those interventions that can be implemented most swiftly would be favoured. This may include targeted police patrols, the use of publicity or deployable CCTV, to name but a few. However, at this stage of the research the Command Team and their partners felt that this facility would complicate the pilot and requested that this element of the system be developed at a later stage after the pilot, short in duration as it was, had run its course.

38

4. System development and evolution Between the initial stage of the research and implementation the system was developed in a number of ways. First, an opportunity surface, a rendering of the spatial distribution and concentration of houses, was generated and used to weight the predictions generated. As Table 4.1 reveals, this improved the predictive accuracy of the system considerably. A further analysis showed that for a smaller proportion of the study area (5 per cent) the final version accurately predicted the location of around 60 per cent of burglaries, the retrospective approach just over 40 per cent. Expressed another way, the final version was able to identify the same amount of crime as the retrospective method, but for a patrolling area of half the size. Table 4.1 Accuracy of the prospective model including the opportunity surface (N=22)

Retrospective

Promap

(specific)

Promap (opportunity

surface)


‘A’ Division

58% 61%

66%+

66%*

70%*

78%*

* Significantly better than retrospective method (p<.05) + Better than retrospective method (p=.06)

Second, topographic information was added to the maps to provide a meaningful point of reference for those using them. Importantly, time was also spent optimising the efficiency of the software. The reason for this was that for such a large area the predictions originally took a long time to generate. The final version could generate maps for the entire participating BCU in around 20 seconds. A different system which generates descriptive (i.e. not predictive) maps, took over ten minutes to complete an analysis of the same area, with extra time required to import and display the resulting output.

Finally, the Command Team and analysts were consulted about how they felt that the system should look and any additional features that they desired. A number of issues were identified.

• There was a consensus of opinion that Graphical User Interface (GUI) should be simple. Thus, the system was developed so that only those functions that were required to generate the predictions were included in the GUI.

• There was a request that as well as generating the predictive maps that it should be

possible to display the locations of the crimes which occurred within the last fourteen days. This feature was added.

• A desire was expressed to have different maps for each police shift that would reflect

any differences in the spatial distribution of crime at different times of the day.

• Rather than generating a single map for the whole Division, different maps were requested for each section in the BCU, of which there were five.

• There was a request that the system should be able to identify smaller areas for

intervention. Each of the requests was met prior to the inception of the pilot. The following section discusses the further research conducted to see if there was any regularity by the time of day that events that occurred close together in space and time (over days).

39

Time of day consistency? As noted above, a further question raised was whether burglaries that occur very close to each other in space and time (within a few days) are committed at the same time of day usually within the same police shift as each other? The fact that hotspots change according to shift (e.g. see Ratcliffe 2002) inclines one to that view, but a more direct test was sought. An affirmative set of results would suggest that a burglary not only confers risk for a particular geography and duration, but also for a specific time of day (morning, afternoon or evening). To illustrate, consider that an offender might have a preference for certain activities during the day (see Rengert and Wasilchick, 2000). Consequently, he/she may visit certain locations during daytime hours. Whilst there, he/she may commit offences if the opportunity presents itself. This rhythm of activity, if regular enough, means that an offender will be at certain places at certain times. If he/she chooses to commit crimes, and in particular near-repeats at these locations, then one would expect to see some consistency in the time of day at which burglaries are committed in those areas. Burglars would appear to work shifts. To examine this hypothesis, one year’s data (January-December 2004) were analysed for the police force area (N=8,968). Each crime was compared to every other and the number of crimes that occurred at different spatial and temporal intervals identified. In line with the authors’ earlier work, the spatial intervals here used were multiples of 100m, and the temporal intervals one week periods. Next, all events were compared to see if they occurred during the same police shift (7am to 3pm, 3pm to 10pm, or 10pm to 7am) and a contingency table populated. Comparisons between events that occurred on the same day as each other were excluded from the analyses presented as their inclusion has the potential to inflate the consistency observed3 - as a crime series committed during one evening would naturally be consistent in terms of the police shift during which the events occurred as well as where they occurred. To determine during which shift an event occurred, data concerned with the day and time of each burglary were analysed. Typically, most burglaries occur when a victim is away from the property. Accordingly, rather than recording a single time at which a crime may have taken place, the police ask for a likely time window, expressed as the earliest time to the latest time the burglary could have occurred. This was mentioned earlier as a qualification on the analysis of crimes by shift. In the analysis that follows, the midpoint of these two times was used as an indicator of the time of the event. Crimes were excluded from the analysis if the time window (for the earliest and latest day and times) exceeded 15 hours. Follow-up analyses used shorter intervals of four and eight hours, and revealed the same pattern of results. To determine whether the emergent patterns differed from what would be expected on the basis of chance, if the time of day that near-repeat burglaries were committed were unrelated, Monte-Carlo simulation was used to generate a chance distribution. To do this, using a pseudo-random number generator, each crime was randomly assigned the police shift for a different burglary (each shift was reassigned only once), and a new contingency table derived. This was completed 999 times. If the observed results represent a statistically significant pattern, then one would expect that for any space-time combination (e.g. events that occurred within 100m and seven days of each other) the number of burglaries for which the shifts are concordant would be greater than the Monte-Carlo results for at least 95 per cent of the simulations. This equates to a threshold of statistical significance at the five per cent level. In this analysis, as so many comparisons were made, the more conservative one per cent level of statistical significance was adopted. A subset of the results, shown as Figure 4.1, are presented as the ratio of the observed number of burglary pairs for which the events occurred during the same time of day (shift), divided by the mean of the Monte-Carlo simulations. Thus, a value of one would indicate that the observed value was equal to that expected. Statistically significant results are highlighted with exaggerated markers on the graph. The dotted line shows that for events which occurred between 1,000m of each other, the observed number of burglary pairs that occurred during

3 Analyses which included such comparisons produced an identical pattern of results.

40

the same time of day as each other was roughly equivalent to what would be expected on the basis of chance. This likelihood remains stable irrespective of the number of days between events. In contrast, the time of day at which near repeats (those within 100m for illustration) and repeats proper are committed appears to synchronise with antecedent events with an elevated consistency. This pattern was also evident for events that occurred slightly further away (up to around 400m) for one week after an initial event (for clarity of presentation these results are not shown). Generally, the distance over which, and the extent to which, consistency was evident for burglary pairs resembled the pattern observed for the communication of risk in space and time alone. Thus, there was a pattern of distance decay - events that occurred closest to each other in space and time tended to be committed during the same time of day with higher likelihood. This would, therefore, suggest that the time of day that an initial event is committed offers some additional predictive value beyond the dimensions (days elapsed and distance) from an earlier offence. Figure 4.1: Similarity in time of day for near-repeats and unrelated burglaries

0.000.100.200.300.400.500.600.700.800.901.001.101.201.301.401.501.601.701.801.902.00

7 14 21 28 35 42 49 56 63 70 77 84 91

Days between events

Obs

erve

d/Ex

pect

ed R

atio

RV100m1000m

p<0.01 In line with the hypothesis, as the number of weeks between events increases the time of day that near-repeats and repeats proper are committed becomes more and more asynchronous with the reference events. However, for repeat victimisation as narrowly conceived, there is a spike between weeks five and six. As a tentative explanation, the timing of these spikes is reminiscent of the characteristic two-month hump in the time course of repeat victimisation - often explained by the (boost) hypothesis that offenders return to the same property after this amount of time has elapsed to take things replaced after the initial event (see Pease, 1998). This explanation chimes with burglar accounts.

41

The implication of the results is that the generation of three daily forecasts, one for each police shift, should improve the efficiency of resources deployed. Thus, as a final change to the system, the predictive model was developed so that it produced different maps for the different shifts, weighting more heavily those events that occurred during the same shift as that for which the prediction was made.

Conclusion The final Promap system was considerably more accurate than extant methods. The system was improved ‘on the hoof’ in a number of ways, partly in response to requests from the police Command Team. Risking tedious repetition, it must be stressed that the role of predictive mapping in crime reduction is emergent. It provides a tool which may modify police and Crime and Disorder Reduction Partnerships’ (CDRPs’) practice. There may be false starts and initiatives that turn out to be ill-founded. A parallel may be made with cervical screening and PSA (Prostate Specific Antigen) testing for cervical and prostate cancer respectively. Neither of these approaches affords perfect prediction, but both allow a reconsideration of risk assessment and treatment prioritisation for the conditions concerned. The argument is that crime reduction is enabled by a more precise specification of risk, but any benefits are contingent upon the shaping of the craft of policing to take maximum advantage of improved prediction. To give the reader an idea of what the GUI looked like, an example screen image is shown as Figure 4.2. As can be seen, the GUI was quite simple but provided some flexibility so that the analysts could generate different kinds of maps. Appendix 4 provides further detail of the software interface and illustrates how simple it is to use. Figure 4.2: An example image of the final GUI

© Crown Copyright. All rights reserved. Derbyshire Constabulary 100021015. In the Chapters that follow, an account is given of the field trial of the system. Chapter five discusses what the police did and problems encountered. To anticipate the findings,

42

unfortunately, implementation or use of the system was less than desired. As will become apparent, this was not because the utility of the system was perceived to be limited (in fact the reverse was true), problems associated with using the system or even a lack of enthusiasm, but instead largely because policing priorities changed over the evaluation period. This was unavoidable and simply due to the fact that the rate of vehicle crime increased whilst burglary declined.

43

5. Process evaluation The aim of this element of the evaluation was to understand and assess exactly what happened and to identify any factors that particularly facilitated or impeded implementation. For instance, who generated the maps, how often, how were they interpreted, who looked at them, were tactics shaped by the results, and if so, how? The reasons for this emphasis are (at least) two-fold. First, this type of analysis is essential if the mechanisms through which an intervention worked (or failed to work) are to be identified and understood. Given that this project is the first in the UK to see if and how police officers can use crime forecasting methods in an operational context, this was of particular importance. Consequently, there is much to be learned not only in terms of whether these types of systems could help reduce crime but also in terms of how they are received by those who would use them, technical issues that may arise, and how the information can be usefully disseminated and interrogated. A particularly poor outcome of the project would have been to find that the system was not used but with no account of why that was the case. Another undesirable outcome would have been to find that the system had not been used for reasons that could have been corrected during the implementation phase. The work reported in this section sought to preclude such outcomes. The second reason for conducting the process evaluation was to document how implementation occurred, who was involved and when, so that intelligent replication elsewhere would be plausible, or so that mistakes made could be avoided in subsequent projects. Central questions addressed whether or not the police and their crime reduction partners used the maps in the deployment of operational resources and, if so, how? Of course, if the answer to the first of these questions was ‘no’ then it would not be possible to attribute any crime reductive effects to the approach. Thus, the process evaluation was integral to, and informed the evaluation of change in levels of crime that followed. In subsequent sections the approach taken for this element of the evaluation is considered and results discussed. Process evaluation methodology A variety of measures were used to identify and document the processes through which the pilot was implemented. These included: semi-structured interviews, a survey of front-line police officers, the completion of a tactical options log by the Command Team, and direct observation by the research team. In this section, to provide an overview of the approach taken, each of the different methods used will first be discussed before discussing the collective results. The maps were routinely generated by the two crime Intelligence Analysts who worked on the Division. To gain an understanding of how they perceived the usefulness of the system, both in terms of user friendliness of the software and the maps generated, three semi-structured interviews were conducted with both analysts at the beginning, middle and end of the seven-month implementation period. The questions asked were divided into two general categories: 1) how useful did they feel the maps were; and, 2) how much did the production of the maps impact on their daily work. Semi-structured interviews were also carried out with 12 Section Sergeants in Alfreton, Belper, Long Eaton and Ripley towards the end of the pilot period. These were intended to provide an in-depth narrative of how useful, if at all, the maps were to those who used them, and to seek understanding of how they were used on a day-to-day basis. At the end of the implementation period, a survey was conducted with 57 front-line police officers in ‘A’ Division to help gain an understanding of how useful the officers felt the maps were as well as whether

44

any tactical options were employed as a result of the maps. The questionnaires consisted mainly of closed questions, however, there were three open-ended questions which were intended to help provide a more complete picture of officers’ feelings towards the maps. Officers were provided with a paper copy of the questionnaire and reassured that their responses would remain anonymous and not be provided to the Command Team. To ensure an objective account of how the maps were used on a day-to-day basis, the Command Team were asked to fill out a log sheet each time they used the maps. This meant that the tactical options selected by the Command Team as a result of the maps were logged and evidenced on a regular basis. In addition to asking crime analysts and police officers about the project, researchers observed daily briefing meetings and tactical assessment and co-ordination meetings on three occasions at the beginning, middle and end of the implementation period. It was originally hoped that these visits could be done on a surprise basis to minimise the extent to which demand characteristics came into play. This proved impossible due to the co-ordination and compliance of key members of the Command Team and Intelligence Analysts who required warning of each visit due to their busy schedules. Rather than present the results collected using each methodology separately, to avoid repetition, a synthetic approach is taken to help provide a complete picture of how the pilot was implemented, used and received in ‘A’ Division. ‘A’ Division, management and day-to-day running Derbyshire Constabulary is divided into four Divisions: Alfreton ‘A’, Buxton ‘B’, Chesterfield ‘C’ and Derby ‘D’. With a population of 240,000, ‘A’ Division covers an area of approximately 150 square miles and comprises a number of large towns as well as more remote rural communities. The Command Team comprises a small number of key officers including the Chief Superintendent and Superintendent for the Division, who are in charge of all Division-level decisions. In addition, the Field Intelligence Unit and Volume Crime Team also help develop intelligence and carry out essential police operations and make tactical decisions at a Division level. ‘A’ Division is divided into five sections, which comprise a total of 32 beats (with between five and seven in each section). Each section has its own Criminal Investigation Department (CID), Local Intelligence Officer (LIO), Uniformed Officers, and Community Support Officers (CSOs). Pivotal to these smaller areas are the Section Inspectors and Sergeants who have complete ownership over their section in terms of tasking and co-ordinating their section’s beat team. Each beat has a number of Beat Officers who are responsible for front-line police activity and providing reassurance to members of the community. As already discussed, ‘A’ Division is National Intelligence Model compliant. By following standards set out by NIM, ‘A’ Division manages intelligence using a structured system of meetings and briefing documents to ensure the dissemination of knowledge reaches all of the key people and is done in an efficient way: • Daily Briefings (daily): short meetings are held at ‘A’ Division Headquarters, daily at 9:15a.m. and

are chaired by a member of the Command Team and held via videoconferencing across all five sections. The meetings are used to discuss current problems and target resources for the day across the whole Division.

• Section Tasking Meetings (daily, shift by shift): occur on a shift by shift basis (7am, 3pm and

11pm) and are chaired by the Section Sergeant in charge of that shift. These meetings are intended to be a quick and efficient way to inform and task police officers using an intelligence document prepared by the section’s Local Intelligence Officer.

45

• Tactical Assessment and Co-ordination (fortnightly): tasking and co-ordination is an important

time when the intelligence produced by analysts is disseminated among key officers. These meetings occur on a fortnightly basis and are attended by the Tasking and Co-ordination Group (including key officers such as the Chief Inspector, Section Inspectors, Community Safety Team members, and Intelligence Analysts). Chaired by a member of the Command Team, all departments and partnership agencies are invited. These meetings are primarily informed by the Tactical Assessment document prepared by analysts. This document contains details of significant crime trends, observations and current issues (intelligence and prevention) on a number of crimes as set out in the Control Strategy (e.g. violent crime, dwelling burglary, and vehicle crime). The main purpose of these meetings is for key members of the Division to come together to discuss pertinent issues that have occurred since the previous meeting and target available resources accordingly.

• Strategic Assessment (every 6 months): A comprehensive Strategic Assessment document is

produced by analysts every six months. This includes analyses of any crime series, trends and hotspots that have been identified on ‘A’ Division and is discussed among senior rank officers.

IT and dissemination To minimise disruption to what the police have to do on a day-to-day basis it was important to ensure that the maps could be generated for, and discussed during, the existing structure of briefing meetings. During the first months of implementation, as with any pilot, a number of problems arose. These along with the solutions to them are discussed below.

How long did it take the analysts to produce the maps each day? It was decided by the Command Team at an early stage that since the two Intelligence Analysts were usually responsible for the production of Divisional intelligence products (including maps), they were best placed to produce prospective maps. Due to data protection issues and other security concerns, the force IT department felt that the Promap software could not be installed directly onto the networked force computers. Instead the software was installed onto a stand-alone laptop which would be used by the analysts to produce the maps. This was extremely unfortunate as it increased the work involved in generating the maps. It meant that the data required to produce the maps would not be readily available on the computers used. Thus, each time a new map was produced, the analysts had to do the following:

1. extract the data required using a networked machine; 2. write this to a CD4; 3. copy the data onto the laptop; 4. produce the maps; 5. copy the maps to a CD; 6. open the files on the networked computers, and store them in the folders allocated to each

section.

This, of course, increased the amount of time required to produce the maps. Had the software been installed on the networked machines, steps 2, 3 and 5 would have been redundant. This method of operating should be avoided at all costs in any replication as being immensely wasteful of a skilled human resource (for a more detailed discussion of these issues, see Appendix 1). At the start of the pilot, the maps took the analysts up to an hour each to produce. As they became more familiar with the process involved this decreased significantly, ranging from between 15 and 40 minutes towards 4 USB drives were disabled on all force machines for security reasons.

46

the end of the pilot. In addition to producing the maps, an extra 30 minutes of the analysts’ time was required to enable them to attend the daily meetings held at 9:15a.m. Their attendance at these meetings was required to ensure that the maps were understood when being discussed by the senior officers, and so that maps could be interrogated in some detail if required.

Dissemination of the maps Some initial concerns arose pertaining to the practicalities of producing and disseminating the maps. These had to be overcome quickly. The main concern was about how to provide the information in a practical format to those who would use it, as the Section Sergeants, who would ultimately have to inspect the maps, were based at different locations. An initial thought was for the Intelligence Analysts to email copies of the maps to the relevant senior officer in charge of tactical delivery. This proved problematic as not all officers had routine access to email. Furthermore, the maps produced required that sufficient memory was available for their storage, making it difficult to send them as attachments. Other ideas included the production of hard copies of the maps which could be attached to regular briefing documents. This would have been costly and would have limited the number of different maps that could be generated and the time involved in their production. Ultimately, the compromise adopted was for the Intelligence Analysts to produce the maps and then copy them as jpeg files into each section’s briefing folder, located on the Divisional IT system. The folders also contained daily briefing documents prepared by the LIOs that all Section Sergeants were responsible for, so this ensured that the maps would not be missed. Maps were transferred to the briefing folders in this way from the insemination of the pilot, in August 2005. Once the maps were available, they were accessed in two main ways (see Figure 5.1): (1) If burglary was a particular concern for the Division then a member of the Command Team would request that the maps be shown during the daily briefing meeting at 9:15a.m. where they could direct police action and tactics to Section Inspectors and/or Sergeants. (2) Section Sergeants who wanted to look at the maps specific to their section could also access them directly. During the first few weeks of the pilot, ‘A’ Division invested in a videoconferencing system as well as plasma screens for each of the five sections. The videoconferencing system was to be used during the daily briefing meetings to connect all sections, thus eliminating the need for officers to travel from their section to Divisional Headquarters. The plasma screens were installed in each section and this had the advantage that Section Sergeants could also show the maps in their shift-specific briefing meetings. This enabled all front-line officers to see the maps and the areas they should be targeting. During the pilot period, the Intelligence Analysts and Command Team discovered a problem with the maps produced for the Section Sergeants. The issue was that when producing the maps, only a limited number could easily be generated for each section due to the time involved, and thus the analysts had to make a selection based upon their own judgement about what was interesting and what was not. Unfortunately, as the software was not installed on the force IT system, the Section Sergeants could not themselves interrogate the maps if they wanted to take a closer look at the pattern of risk within a particular area, or if they wanted to ‘zoom’ into a particular location. Nor could time be devoted to taking a closer look at each map for every section during the daily meetings. The resulting difficulty was that when a particular Section Sergeant felt that burglary was a problem and wanted to look at the maps in some detail, only a partial picture of the problem might be available. The solution to this difficulty was to provide a version of the Promap software to each section. Thus, a new version of the software was developed that could be used by the LIOs in each section. Rather than have each section produce their own maps, which would have required them to download new data each day and then generate the maps, the analysts continued to process the data with their software. However, instead of just providing the sections with jpeg images of the maps, they were now able to

47

export the maps themselves, which could then be interrogated in detail by the Section Sergeants using the software provided. Consequently, the Intelligence Analysts trained each LIO to use the software once they had acquired the relevant equipment, in this case laptops. Unfortunately, due to logistical issues involved in acquiring the laptops, this did not take place until the end of December in two sections and the end of January in the other three. Thus, this solution was not fully realised until very late in the pilot period. Figure 5.1 illustrates this method of dissemination. A fourth method of dissemination was through the fortnightly tactical assessment and co-ordination meetings. The maps were used during these sessions if, and only if, the Command Team felt that burglary had been a significant concern over the last two weeks. These meetings allowed for a focused discussion of the maps and potential tactical options that could be used in the identified areas. One such response that evolved in January 2006 was that of a collaboration between the Community Safety Team who worked with the Division to organise and implement a promap-specific targeted operation. The operation was intended to provide high visibility policing and crime reduction measures to the areas identified through Promap, with the intention of:

• increasing public awareness around home security and property marking; and • increasing neighbourhood watch schemes in priority areas.

This involved the co-ordination of the Divisional Community Safety Unit and all sections. To elaborate, on days that the maps were produced (i.e. Mondays and Thursdays) one Section Beat Officer and one Community Safety Constable would use the Mobile Police Station (i.e. a marked police van) with the intention of targeting identified hotspots in two ways: firstly by providing a high visibility presence to deter potential offenders; and secondly to increase public awareness visiting residential premises in the identified areas giving out crime reduction advice, making home security assessments where needed, referrals to other agencies and considering whether a neighbourhood watch scheme would be appropriate for the identified area(s). It was felt by ‘A’ Division as well as the research team that this collaboration was a success in its own right as it helped improve the already good relationship between the Community Safety Unit, community partners and front-line Beat Officers.

48

Figure 5.1: Promap dissem

ination process across ‘A’ D

ivision (squares shaded grey were present throughout the pilot w

hile squares in white w

ere only added to the dissem

ination process during the final two m

onths of the pilot)

Tasking and Co-ordination

If burglary

is of

considerable concern,

maps

are discussed

with

key officers

and partners

and operational

tactics are

decidedupon

Com

munity Safety Team

H

ave the

potential to

use the

information discussed around the

maps to deliver tactics, e.g. giving

crime

prevention advice

to residents

in Prom

ap identified

hotspots

Section Inspectors and Sergeants

Responsible

for im

plementing

the tactical

options w

ith front

line officers

Beat O

fficers R

esponsible for

carrying out

any tactical options in response to

them

aps

Intelligence Analysts Produce

maps and put them

in

network

DivisionalSection

folders

Daily M

eetings (‘daily prayers’) The

Com

mand

Team

decide w

hether or not the maps should be

shown during the

meeting. If they

are, any tactics that evolve from the

maps

arediscussed

andplanned

Section Sergeants (daily shift m

eetings) Section

Sergeants responsible

for accessing

the m

aps and

deciding whether or not to use

themfortargeted

policeactivity

Section LIOs

Responsible

for navigating

the m

aps with the Section laptops.

This is

dependent on

Section specific problem

areas

1 2

3 4

49

How often were the maps produced? From the beginning of the pilot (15 August 2005) and up until 4 September 2005, the maps were produced five times a week (Monday to Friday). However, producing the maps five days a week had a number of disadvantages. Most important, the areas identified as being most at risk did not always change significantly from day to day. In reality, the pattern from one day to the next would not be totally stable, but would be serially correlated. Thus, some areas identified as being at a high risk on one day would most likely be at high risk the next day, and possibly the day after that. The reason for this is that the data used to generate the predictions would be similar from one day to the next, reflecting the activity of offenders. The exception would be during periods of time when a large volume of offences takes place each day. In this case, the predictions would vary considerably from one day to the next. However, even when the daily volume of crime was low, as a few days pass the predictions would change, keeping pace with the flux of crime. Nevertheless, it is possible that viewing the maps one at a time, on sequential days, may have created an illusion of stationarity. After all, to detect differences in the maps each day officers would have to remember the exact locations identified from one day to the next, and human memory and perceptual systems are known to be susceptible to distortion. To illustrate, a series of predictions are shown in Figure 5.2. These predictions were generated for one area every Monday for a period of four sequential weeks. As can be seen, in some areas the risks are relatively stable, but elsewhere more fluid. Detecting the changes, instead of being deceived by an illusion of stationarity, requires one to look quite carefully. For example, the reader is invited to look at the pattern from week to week in the centre of the map. This area always has some degree of risk associated with it, but the areas shaded in the darkest shade (blue for those with intact colour perception and a colour version of the report) clearly move. In fact the blue areas always move at the level of resolution at which policing tactics would be deployed, but a glance at the map may suggest stability. Thought should be given to modes of depiction of maps which highlight change. Figure 5.2: A series of predictions for one area (blue areas are those most at risk)

Further concerns about generating the maps each day arose because it was felt that they would not be taken seriously if officers were forced to look at them five days a week, on top of all the other intelligence that they had to assimilate. Over exposure to the maps might mean that officers would lose interest. For these reasons, it was decided that from 5 September 2005 the maps would be produced three times a week (Monday, Wednesday and Friday). During the next six weeks the project received a lot of positive reception from the officers and seemed to be used regularly. However, from November onwards the residential burglary numbers dropped further and, consequently, officers perceived that the three maps produced each week were beginning to look similar to each other. Consequently, the Command Team decided that at this point the maps would be produced twice a week (Mondays and Thursdays). If, however, the analysts felt that there was a significant concern regarding residential burglary and that producing the maps more than twice a week would be helpful, they were free to do so, and did on a number of occasions. Maps were produced twice a week from 14 November 2005 Timing issues Unfortunately, the pilot got off to a false start because of a number of fundamental timing issues. First, the Divisional Commander, who had been extremely supportive and helpful during the planning stages of the pilot and who was instrumental in securing ‘A’ Division as the pilot site, received a promotion and left the Division in August. As is the way, there was no advance warning that this would happen and thus appropriate measures could not be taken to minimise the impact this had on the pilot. Due to

50

the busy schedule of the new Divisional Commander, the research team was unable to meet with and brief him on the project until the end of October, nearly two and a half months into the pilot. Furthermore, key members of the Command Team had arranged annual leave and were unavailable during the early days of the project period. During the first meeting that could be arranged post-August, it was revealed that instead of using the predictive capability of the maps, when the maps were used, attention was instead focused on the locations of burglaries that had occurred within the last two weeks, the approach that had previously been used on the Division (and the approach that Promap outperformed and was designed to replace). The reason for this was that it was felt that the areas identified as being at risk were too large. Consequently, the software was further refined to generate predictions for smaller areas. As a result of these factors, summarised in Figure 5.3, it took a few months for the system to be fine-tuned and for the Command Team members to unite to give Promap a high profile across the Division. Because the pilot was only seven months in duration and almost three months had elapsed before the maps were being produced in the way intended, it was not implemented over a sufficient period to fulfil its potential. As will become apparent, the pilot expired just when it was becoming accepted and understood across the Division. User-friendliness and impact on workload Generally, the Intelligence Analysts found the maps easy to produce, however, they voiced the opinion several times throughout the pilot that having the system on the Divisional network could have significantly improved the ease of producing the maps. More automation of the process would have also reduced the time and effort involved. For example, the analysts regularly spent time manually removing distraction burglaries from the dataset (as they felt that these burglaries would conform to different patterns and trends than other residential burglaries, although this is an empirical question worth addressing). Only approximately 80 per cent of the data were automatically geo-coded by the force IT system, and hence the analysts had to spend time manually geo-coding the remainder. Of course, these issues are germane to the force IT system rather than the Promap software. However, they are worth noting as it is likely that similar issues may arise in other police force areas. Despite these issues, the analysts did not feel that the maps impacted greatly on their day-to-day work. In some cases, where they were specifically responsible for summarising a burglary problem (e.g. producing a problem profile or a specific tasking document), they reported that the system helped to illustrate patterns and trends they felt would not otherwise have been considered. The only significant effect that the production of the maps had on the Intelligence Analysts was their starting time at work. Because the maps had to be available to discuss in the daily briefing meeting at 9:15a.m., the analysts had to ensure they were at work by 8a.m. By the end of the pilot, each analyst was only producing the maps once a week, and so the time involved was not a considerable inconvenience.

51

Figure 5.3: Timeline for Prospective M

apping Pilot in ‘A’ D

ivision (August 2005 – M

arch 2006)

Aug-05

Sep-05 O

ct-05 N

ov-05 D

ec-05 Jan-06

Feb-06 M

ar-06

1, 2 Mar

• Evaluation visit #3 • Analyst interview

#3 (Bill Wallage)

• Promap survey (57 officers)

• Com

munity Safety Team

(100 packs)

20 Mar

• Com

munity Safety Team

(45 packs)

8, 9 Mar

• Analyst interview #3 (D

eborah Rim

ell) • C

omm

unity safety team (90 packs)

15 Aug • O

fficial start date

Mid-End Aug

• Several of the Com

mand Team

m

embers on leave in A

ugust

26 Oct

• Evaluation visit #1 • M

eeting to inform new

Divisional C

omm

ander of project

5 Oct

• Maps stopped being produced 5 tim

es/wk (M

-F) and were

produced 3 times/w

k (M, W

& F) 1 Nov

• Analyst interviews #1

4 Nov

� Consensus by the steering group that

maps w

ill now be produced 2 tim

es/wk

(Mon and Thurs)

18 Jan • Evaluation visit #2 • Analyst interview

s #2

19 and 23 Jan • C

omm

unity Safety Team proactive patrol,

handing out door-to-door info packs (70 and 35 packs handed out)

28 Feb • O

fficial end date of pilot

20 Feb – 3 Mar

• A Researcher from

the Home O

ffice interview

ed four Section Sergeants (all but Ilkeston)

17 Aug • R

etrospective points show

n on m

aps

28 Oct

• Retrospective

points no longer show

n on m

aps

14 Nov

• Maps produced 2

times/w

k Mon & Thurs

End Jan • Laptops

collected by Long E

aton, R

ipley & Ilkeston

23 Dec

• Laptops collected by Alfreton and Belper (LIO

s show

n how to use

Promap)

End Aug • D

ivisional C

omm

ander John W

right left ‘A’ D

ivision

52

Tactical delivery Command Team daily briefing (9:15am) As described above, prospective maps were shown and discussed at the daily briefing meetings if the Command Team felt they would be a valuable addition to the meeting. If burglary numbers had been sufficiently low for the past few days, then the maps were usually not shown. Table 5.1 shows the number of times that prospective maps were used in the daily briefing meetings from initial implementation to the end of the evaluation period. Numbers decreased dramatically towards the latter months because burglary numbers were falling so considerably, and Divisional priority had been shifted to dealing with auto-crime which rose over the same period. Table 5.1: Number of times prospective maps were used in ‘A’ Division’s daily briefing

According to the tactical logs completed by the Command Team, of the 30 times prospective maps were used in daily briefings, tactics were employed as a result of the maps 27 times across the Division. The most common tactics employed in response to the maps were foot patrols, drive-through patrols in hotspot areas and the targeting of known offenders believed to operate in or nearby the areas identified. Section front line response On 2 March 2006 a survey was conducted with 57 officers across all sections to assess their level of knowledge and understanding of Promap as well as to identify any factors that may have either facilitated or impeded implementation. A copy of the questionnaire used is provided as Appendix 2. Table 5.2 provides a breakdown of the sample of officers surveyed across the Division.

Number of times prospective

maps were used in daily briefings

AUG 05

6

SEP 05 8

OCT 05 8

NOV 05 4

DEC 05 1

JAN 06 2

FEB 06 1

Total

30

53

Table 5.2: Sample characteristics

Number

%

Male

42

73.7 Sex

Female

15

26.3

Police Constable 49 86.0

Sergeant 6 10.5 Inspector 1 1.8 Rank Special Constable

1

1.8

Less than 1 year 6 10.5

1-5 years 30 52.6 More than 5 years 20 35.1 Time in Rank

No answer

1

1.8

Alfreton 9 15.8 Belper 13 22.8 Ilkeston 15 26.3 Long Eaton 12 21.1

Section

Ripley

8

14.0

Table 5.3 shows the number of respondents who had heard of prospective mapping, by section. In all sections, aside from Ilkeston, the majority of respondents had heard of Promap. In Ilkeston, only four of the fifteen respondents in that section, had heard of the pilot.5 Those who had heard of Promap were asked how they would define Promap in their own words. Two researchers, one who did not work on the project and one who did, examined the responses to ensure that there was no bias of interpretation. The inter-rater reliability for the two researchers was high (Cronbach’s Alpha=0.99) and thus the coding appropriate. 52 per cent (21) correctly identified the definition of Promap, i.e. the maps ‘predict where burglaries are likely to occur’. Those that did not provide an accurate definition showed a basic understanding of the system (e.g. ‘shows burglary hotspots’ and ‘shows crime hotspots’). A few respondents, 21 per cent (8) did not fully understand the maps as they thought that they illustrated both burglary and auto crime hotspots.

5 It was suggested by a member of the Command Team that one possibility why the number of respondents is so low is because of the terminology used in the questionnaires. It seems that the Division was using the term ‘JDI maps’ when referring to the pilot, whereas the questionnaire specifically asked if respondents had heard of ‘Prospective Mapping’. One would assume that if this were the case, however, that the same trends would have been evident in the other four Sections.

54

Table 5.3: Number of respondents who had heard of prospective mapping, by section

Section Yes No

No, b/c new

starter Total

Alfreton 88% (8) 12% (1) 0% (0) 16% (9) Belper 92% (12) 8% (1) 0% (0) 23% (13)

Ilkeston 27% (4) 73% (11) 0% (0) 26% (15) Long Eaton 83% (10) 17% (2) 0% (0) 21% (12)

Ripley 75% (6) 12% (1) 12% (1) 14% (8)

Total

70.2%

(40)

28.1%

(16)

1.8%

(1)

100.0%

(57)

When asked how frequently the maps were routinely used for targeted police activity, officers reported using them with different frequencies. Table 5.4 shows that around 30 per cent reported using them more than twice a week, and a similar proportion reported that their supervisors had used them at this rate. Follow-up interviews with the Section Sergeants, the LIOs and the analysts suggested that this had been the case when burglary was a priority, but that, understandably, the maps were used significantly less when it was not. The general perception, which is corroborated by the records from the tactical options log, was that the maps were used more during the first few months of the pilot when burglary was more of a priority6, although the maps were still produced and disseminated throughout the pilot period twice a week thereafter. Table 5.4: Number of times maps were used for targeted police activity

Number of times officers used prospective maps(s) for

targeted police activity

Number of times officers’

supervisors used prospective map(s) for targeted police activity

More than twice a week

21% 21%

Twice a week 9% 11%

Once a week 11% 11%

Once every two weeks

4% 4%

Once every three weeks

0% 2%

Less than once a month

5% 2%

Don't know 0% 2%

No answer 27% 47%

6 Unfortunately, this was when the maps produced reflected retrospective point maps rather than predictive surfaces.

55

Table 5.5 shows the number of tactics reported to have been used in response to the prospective maps when they were used. Those most commonly used were drive-through patrols in identified areas, foot patrols and the targeting of offenders known to operate in or around the areas highlighted in the maps. Table 5.5: Number of respondents who were either involved in or responsible for employing operational tactics, by section

Tactic either employed or involved in

Alfreton

Belper

Long Eaton

Ripley

Ilkeston

Total

Drive-through patrols in hotspots 6 6 6 4 2 24

Foot patrols around hotspots 5 5 1 3 1 15

Targeting known offenders 1 3 4 3 0 11

Repeat victimisation strategies 0 1 3 1 0 5

Target-hardening 1 1 1 1 0 4

Publicity campaigns 2 0 0 0 1 3

Redeployable CCTV 0 0 1 1 0 2

Crime prevention advice given 0 0 1 0 0 1

As noted, officers were sometimes tasked to target known offenders, and although this is something they have always done, it was suggested by some that the prospective maps had helped them combine the intelligence gathered on offenders by Intelligence Analysts and LIOs with the identified areas, providing an overall enhanced tactical option for catching offenders. In one case, one officer mentioned that “I can’t give names or give numbers but it’s [offenders who have been caught have been linked back to certain areas that were identified by the maps] certainly happened a few times”. Repeat victimisation strategies are apparently commonly used on ‘A’ Division, regardless of Promap; however, one Section Sergeant in Long Eaton mentioned that he tasked officers to revisit burgled properties to give out crime prevention advice and this would often be done in areas identified by the maps. Target hardening also is something that is normally done on the ‘A’ Divisions; it is part of the Derbyshire Constabulary business plan. This involves a Crime Prevention Officer visiting local residents and offering crime prevention advice and advice on having security measures fitted. If the householder wishes to have security measures installed, the crime prevention officer can arrange for this. Although respondents from each of the sections mentioned that they used target-hardening in response to the maps, when speaking to the Sergeants in each of the sections, they mentioned that target-hardening was a tactic that has always been used on the Division, regardless of Promap and thought that perhaps the respondents had recalled being involved in target-hardening prior to filling out the survey and thus had misconstrued using the tactic as a result of the pilot. This, of course, serves to illustrate the importance of triangulating evidence from different sources when completing a process evaluation. Although it was decided by the Command Team in October 2006 that it did not wish to launch any publicity campaigns in response to the pilot for fear of frightening local residents, some sections chose to put short press releases in their local paper(s) alerting residents to ‘take care’ and make sure their property is safe and offering crime prevention advice. However, this form of publicity was not specific to the project, even if it was evoked by it. A final aspect of police working that was not highlighted in the survey, but came up a few times when interviewing Section Sergeants and LIOs, concerned the planning of ‘night-time patrols’. According to Sergeants in Alfreton, Long Eaton and Ripley, when resources

56

permitted, up to four plain clothed officers were deployed at night and patrolled those areas identified by the prospective maps. This tactic was usually done at night because the night shift is often slow moving. Consequently, there are fewer calls for Beat Officers to respond to compared to shifts during other times of the day, which frees up available resources for proactive patrols. Although it was not possible to enumerate the frequency with which night-time patrols were informed by the predictions generated, this does illustrate that when resources were available and burglary was a priority, police officers used Promap for crime prevention and detection purposes. As previously mentioned, during the final few months of the pilot, and during March (one month after the end of the pilot), the Community Safety Unit deployed a mobile police vehicle in the hotspot areas designated by the maps and distributed residential burglary crime prevention packs door-to-door in the areas identified as being at the most risk. The dates on which, and the number of packs distributed were as follows: • 19 January: 70 packs; • 23 January: 35 packs; • 2 March: 100 packs; • 9 March: 90 packs; • 20 March: 45 packs. Usefulness of maps As can be seen from Table 5.6, 87.5 per cent of the sample who had heard of the pilot found that prospective maps were either easy or fairly easy to interpret, whereas only 12.5 per cent found the maps to be either fairly difficult or difficult to interpret. In terms of how useful officers found the maps to be, although 67.5 per cent of the sample found the maps to be either very or at least somewhat useful, 32.5 per cent (10) found them not to be very useful. Similarly, when asked whether or not the maps identified risky areas that respondents would have not otherwise have considered risky, 47.5 per cent (19) of the sample felt that the maps ‘never’ identified unknown risky areas, whereas 42.5 per cent (17) felt that the maps ‘sometimes’ identified risky areas that they would not have otherwise considered as such. In relation to this point, research conducted by the authors of this report (McLaughlin et al., 2006) demonstrates that whilst police officers often have a good impression of where burglary generally occurs, they are less accurate at identifying where it recently took place. The implication is that they are unlikely to be able to anticipate where it will next occur. Moreover, interviews with LIOs and the analysts, suggested that although the maps often identified priority areas in known risky neighbourhoods, the precise location or timing of elevations in risk were not always expected.

Table 5.6: The interpretation and usefulness of prospective maps

Number

%

Easy

10

25.0

Fairly easy 25 62.5

Fairly difficult 4 10.0 Interpretation of maps

Difficult 1 2.5

Very useful

4

10.0

Somewhat useful 23 57.5

Not very useful 10 25.0 Usefulness of maps

Not useful 3 7.5

57

In addition to the questions discussed above, survey respondents were asked two open-ended questions to canvass any thoughts not captured by the closed questions asked. Firstly, they were asked to outline any extra comments that they might have about the usefulness of the maps and how they might be improved. Secondly, respondents were asked to outline extra comments about their knowledge and understanding of prospective maps and any tactical options that they had employed. To be frank, on the basis of experience the authors had expected negative feedback in response to these questions. Instead, few chose to respond. Of those that did, 27.5 per cent (11) commented on how the maps could be improved, for example three suggested that:

“the colour of the hotspots should be red not blue”; one suggested that:

“the maps are too simplistic and should show MO, point of entry, class etc.” Perhaps surprisingly only two opined that:

“the maps don’t tell me anything that I did not already know”. In terms of extra comments about police officers’ knowledge and understanding of Promap, only four made comments, with one asking

“how accurate are the maps?” and another suggesting that

“the maps are good for supporting information for patrol strategies”. Although the official end-date of the pilot was the end of February 2006, the maps are still being produced on a biweekly basis to inform tactical delivery. Moreover, the Command Team have decided to use the system more frequently if the rate of burglary increases and the priority returns to this type of crime.

Summary As already discussed, during the fist two to three months of the pilot, rather than using the predictive capability of the system developed, pin maps showing the locations of recent crime patterns were used. This was unfortunate as it was in conflict with what Promap was designed to achieve - accurate predictions of the future locations of burglaries for those as yet unvictimised (as well as repeat victims). Despite this and some initial negativity towards the approach, there now seems to be a moderate to high level of acceptance of the maps and the Division has embraced the system as a useful tool. Unfortunately, during the pilot period there was a substantial increase in theft from vehicles across the Division (and elsewhere in Derbyshire) in October which was coincident with a reduction in burglary. Consequently, there was a shift in policing priority at this time towards vehicle crime across the Division. The problem with this in terms of the evaluation was that it meant that less time was focused on burglary, and the application of the Promap system. In short, this precludes a fair evaluation of the impact on crime of Promap. Nevertheless, given that this is the first attempt to implement such a system in the UK, it is a non-trivial finding to be able to say that feedback from those using the system has been on the whole very positive. In fact, most of those interviewed, formally or otherwise, have suggested that should burglary increase in the future they would use the system to tackle the problem, which in itself illustrates the utility of the system as perceived by those who were exposed to it. In line with this observation, despite the fact that the pilot has come to an end and that burglary continues to remain low within the Division, at present the maps are still generated every two weeks to inform the tasking and co-ordination meetings. A further

58

observation made by those who used the system was that when used it helped to focus their attention on the problem and how they might reduce it. As a further testament to police officer acceptance of the usefulness of the system, a number asked if it could be used to predict incidents of vehicle crime. Since completing the pilot, it has been shown (Johnson et al., 2006) that patterns of theft from motor vehicle (TFMV) conform to the same spatial and temporal patterns as burglary and hence the answer to this question is likely to be yes. Thus, an anticipated future development of the system would be to facilitate predictions of TFMV also. Thus, from the perspective of the evaluation, the pilot served to demonstrate police officer acceptance of, and confidence in, the system, and to show that it could be used in an operational setting without disrupting other activity. Important lessons were learned in relation to the dissemination of the maps. Initially, it was thought that the analysts could generate and distribute the maps, with Section Sergeants having only to look at the output. However, it was soon felt that the sections would benefit from being able to interrogate and navigate the maps themselves, thereby allowing them to gain a more detailed picture of the problem. The solution to this problem was straightforward, but for a variety of logistical reasons implementation of it occurred too late in the evaluation period to have any potential impact on crime.

59

6. Changes in patterns of burglary Key to robust evaluation is the notion of effect signatures. This concerns the pattern of results which reflect mechanism – just as signatures bespeak identity – and equally important, which fail to reflect cherished but erroneous ideas about mechanism. With respect to the current project, a number of signatures would be anticipated if reductions in crime were achieved as a consequence of Promap- influenced intervention. For example, reductions in crime would be expected to coincide with the timing and intensity of implementation. However, changes in implementation intensity can be measured in a number of ways. In a project such as this, where the deployment of police resources varies by time of day as well as day of the year, analyses should consider variation in crime for different intervals of the day as well as by week or month of year. With respect to changes in patterns of crime in space, particular a-priori expectations may also be expressed. For example, one would expect distinct changes in the spatial concentration of burglary following intervention. In the extreme, if an intervention had the effect of preventing all burglaries subject to prediction (those that conform to an identified regularity), the spatial distribution of crime would appear random following intervention. Of course, this scenario is unlikely but distinct changes in the spatial concentration of crime should be observed where an intervention has an effect. Finally, in addition to expecting changes in the temporal and spatial distribution of crime, distortions in the space-time clustering of crime would be expected. To illustrate, consider that spatial hotspots of crime are defined by a series of crimes that occur during some interval. The precise timing of the events is unimportant, with a spatial hotspot being defined only by virtue of a clustering of events in the spatial dimension. An alternative signature is a series of crimes that cluster in both space and time; a localised spate. Where police intervention is designed to anticipate such activity, as was the case here, one indication of success would be a truncation in the duration of patterns that might suggest such activity. Consequently, a series of novel analytic techniques were developed to detect signatures of the type discussed. However, as is illustrated in earlier sections, implementation of the pilot was insufficient to facilitate an appropriate test of the potential crime reductive impact of the predictive approach. As such, it is suggested that presentation of a sophisticated statistical analysis here would be unwise. Instead, in the sections that follow, simple analyses are presented to examine the changes in the patterns of burglary observed before and during the pilot project. The reader interested in the more detailed analytic approaches used is referred to Appendix 3 for an illustration of the analyses and a more detailed discussion of their rationale. The basic approach adopted in most evaluations is to compare the change in the volume of crime before and after intervention in both an action and comparison area. If a reduction is observed in the action but not comparator, or the reduction in the former exceeds that in the latter then a positive inference may be drawn. Figure 6.1 shows the change in the volume of burglary in the action area, and a comparison area, Derbyshire ‘C’ Division. The latter was selected partly because the trends in the two areas followed similar patterns prior to intervention but also because discussions with the Command Team suggested that both Divisions employed similar approaches to operational policing, not least because they were located within the same police force, Derbyshire. It is evident from the time-series graph, which shows the patterns for two years prior to the pilot and thereafter, that there was a reduction in burglary in both areas over time. Prior to intervention, which of the two areas had the higher volume of burglary each month varied. For this period, the mean monthly count of crime was 112 (SD=31.6, N=24) in the pilot area, 116 (SD=38.2, N=24) in the comparator.

60

A simple time-series analysis (see Appendix 3) confirmed that the two areas followed a similar trend and experienced a similar volume of burglary for the two years before the pilot. At the start of the pilot (August-September) the volume of burglary rose in both areas, after which it remained somewhat stable in the comparison area but fell in the pilot area. For the months of January and February 2006 the volume of burglary in the pilot area was the lowest it had been for at least the last five years, being less than half the volume for the equivalent period of time in the previous year.7 For reference, also shown in Figure 6.1 are the times at which the major implementation outputs of the pilot began. At this point, it is perhaps useful to provide the reader with a little more contextual information regarding other policing initiatives implemented in ‘A’ Division before or around the time of the pilot. Interviews with the Command Team, LIOs and the analysts for the Division, suggested that the only intervention implemented across the Division was the prioritisation of prolific and priority offenders (PPOs). This began around February 2005 and is ongoing. The aim of the intervention is to target prolific offenders, those who commit the bulk of offences, across the entire BCU with the aim of detecting and consequently reducing crime. Given the focus of this intervention, it is plausible that this could have impacted upon the incidence of burglary before and during the pilot period. To see if any changes in burglary offences observed in ‘A’ Division were likely to be attributed to this strategy, the number of detections recorded for the seven-month periods before and during the pilot were considered and compared to those in ‘C’ Division (which also focused on PPOs). This analysis revealed that in ‘A’ Division the number of detections per 1,000 burglaries increased slightly over time, but less so than it did in ‘C’ Division for the same period of time. Moreover, in the pilot area the rate of detections was a little lower for both periods than it was for the same period of time one year earlier, whereas for the comparison area the reverse was true. This pattern of results would suggest that changes in the pilot area over time are unlikely to be attributable to the targeting of prolific offenders. Complicated analyses could be conducted to attempt to determine whether the reduction in the incidence of burglary observed was statistically significant. Readers interested in what such analyses might show are directed to Appendix 3 of this report. However, as already discussed it is proposed that the interpretation of such analyses would be unclear as implementation of the pilot on the ground was so limited. Thus, in this section a simple measure is presented as a guide to the changes observed. The metric computed, an odds ratio, merely contrasts the change in the intervention and comparison areas before and after intervention. An odds ratio of one indicates that the changes in the two areas were commensurate, suggesting no change in the pilot area. An odds ratio of greater (less) than one suggests a reduction (increase) in the intervention area relative to the change observed in the comparison area. The statistical significance of the odds ratio can also be computed (see Lipsey and Wilson, 2001) by estimating the standard error of the value derived. This technique, which is readily interpretable, has been frequently used in research concerned with what works in reducing crime (for examples, see Welsh and Farrington, 2006; Gill and Spriggs, 2005), but is not without it critics, particularly for analyses conducted at the small area level (for which fluctuations over time may occur even in the absence of intervention: Marchant, 2005). However, the problems articulated about this approach are likely to be less problematic for analyses conducted at the BCU level, for which the variation over time is less of an issue than for smaller areas (see Farrington and Welsh, 2006). Thus, the approach is used here because it provides a simple assessment of how things changed in the pilot area relative to the comparator.

7 Perhaps ironically, it was at this point in time, during which the burglary rate had remained stable for the last year, that ‘A’ Division was selected as the pilot location.

61

Figure 6.1: Time-series graph of the count of burglary before and during pilot

0 50

100

150

200

250Aug-03

Sep-03

Oct-03

Nov-03

Dec-03

Jan-04

Feb-04

Mar-04

Apr-04

May-04

Jun-04

Jul-04

Aug-04

Sep-04

Oct-04

Nov-04

Dec-04

Jan-05

Feb-05

Mar-05

Apr-05

May-05

Jun-05

Jul-05

Aug-05

Sep-05

Oct-05

Nov-05

Dec-05

Jan-06

Feb-06

Month

Burglary count

PilotControl

Targeted patrols

Burglary prevention

pack distribution

62

Two approaches were used to compute the standard errors (since these are critical in determining the significance of the effect-size derived), one used by Farrington and colleagues (see Welsh and Farrington, 2006), the other by Gill and Spriggs (2005),8 Both approaches converged on similar estimates and hence only the former are reported here. To calculate the odds ratios (OR), the count of crime for the same period of time before and during the pilot were contrasted.9 The standard errors were computed in the usual way (see Lipsey and Wilson, 2001) as well as using monthly variation as suggested by Gill and Spriggs (2005). Table 6.1 shows the count of burglary for the periods before and during the pilot phase along with the OR, and associated confidence intervals and z-score. The confidence intervals shown, calculated using the traditional approach indicates the upper and lower estimates of the odds ratios. These suggest that the true odds ratio lies somewhere between 0.99 and 1.34. The z-score provides an indication of the likely statistical significance of the OR. For a two-tailed test, the z-score is required to exceed 1.96. On the basis of these results, the analysis would suggest that the reduction in burglary observed in ‘A’ Division exceeded that in the comparison area, but that the trend was marginally non-significant.

Table 6.1: Change in the volume of burglary and odds-ratio statistics

Before

After

Gross change

Odds ratio

Confidence interval

z-score

Pilot

663

554

109

1.15

0.99-1.34

1.80

Comparison area

684 659 25

Given the duration of the pilot and the implementation issues experienced the only surprising feature of this result is that it came so close to statistical reliability. The above analysis considers only general changes in burglary over time and thus in the next section changes in the time of day when burglaries occurred will be examined.

Change in the time of day burglaries were committed As noted in the implementation section of the main report, there was a consensus of opinion that the predictive maps were more frequently used during the evenings, when reactive policing demands on patrolling officer time were typically less acute. Thus, one expectation would be that if the predictive maps were used more frequently for resource allocation during the evenings, there should be observed a greater reduction in the number of burglaries that occurred during this time of day following the inception of the pilot. To explore this, rather than analysing changes in the rate of burglary for each hour of the day, the approach adopted in an earlier section of the report was used. That is, the shift during which every burglary occurred was identified (by computing the mid-point of the earliest and latest times the event could have occurred) and the monthly patterns summarised. Only burglaries for which the window of reporting was less than eight hours were included in the analysis. Consequently, there was some attrition in the volume of data analysed (50% of events occurred within an interval of eight hours). A further analysis (not shown), conducted using a window of fifteen hours to increase the sample size (64% of events occurred within a fifteen-hour reporting window), revealed the same pattern of results. Figure 6.2 shows the change over time in the proportion of all burglaries committed within ‘A’ Division that occurred during the evening shift, before and after the start of the pilot. A trend line is included to illustrate the pattern prior to the start of the scheme. The figure illustrates that there was some

8 Gill and Spriggs (2005) use a slightly different approach to calculate the standard by considering the monthly fluctuation in the volume of crime to reduce a problem known as over-dispersion. 9 The pilot started in the middle of August but in the analyses that follow, for simplicity 1 August is taken as the start date. The effect of so doing is to make the analyses more conservative.

63

variation in the proportion of burglaries that were committed during the evening (mean = 0.20, SD=0.07, N=52) before the start of the pilot, and that over time the overall trend was ever so slightly upwards. Following the start of the pilot, and particularly from September onwards, the proportion of burglaries committed during the evening dropped (mean = .11, SD=0.04, N=7); the monthly variation observed also decreased. Thus, it would appear that following the start of the pilot the proportion of burglaries committed during the evening decreased. Figure 6.2: Changes in the proportion of burglaries committed during the evening over time (for events for which the interval between the earliest and latest reporting times was less than 8 hours)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

Apr-01

Aug-01

Dec-01

Apr-02

Aug-02

Dec-02

Apr-03

Aug-03

Dec-03

Apr-04

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Apr-05

Aug-05

Dec-05Month

Pro

porti

on o

f all

burg

larie

s

BeforeDuringLinear (Before)

Further analyses explored the change in the patterns for the other two shifts. Naturally, changes would be expected in at least one of these as the unit of analysis was a proportional measure. Figures 6.3 and 6.4 show the changes observed during the morning and daytime, respectively. There was little change in the mean proportion of events committed during the morning before (Mean=0.39, SD=0.09, N=52) and during the pilot phase (Mean=0.36, SD=0.09, N=7).

64

Figure 6.3: Changes in the proportion of burglaries committed during the morning over time (for events for which the interval between the earliest and latest reporting times was less than 8 hours)

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

Apr-01

Aug-01

Dec-01

Apr-02

Aug-02

Dec-02

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Dec-03

Apr-04

Aug-04

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porti

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s


During the daytime, more events were committed during the pilot phase (Mean=0.53, SD=0.09, N=52) than before (Mean=0.40, SD=0.10, N=7), although (and unlike the trend observed for events committed during the evening) the pattern observed during the pilot phase was not completely unlike that observed in the recent past.

65

Figure 6.4: Changes in the proportion of burglaries committed during the daytime over time (for events for which the interval between the earliest and latest reporting times was less than 8 hours)

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

Apr-01

Aug-01

Dec-01

Apr-02

Aug-02

Dec-02

Apr-03

Aug-03

Dec-03

Apr-04

Aug-04

Dec-04

Apr-05

Aug-05

Dec-05Month

Pro

porti

on o

f all

burg

larie

s


Considering the changes in the comparison area, as shown in Figure 6.5 there was also observed a reduction in the proportion of burglaries committed during the evening, although the change over time was not as distinct (means = 0.24 and 0.17, SDs = 0.11 and 0.05, N=52 and 7, respectively) as for the pilot area and in the comparison area the trend was more similar to the historic trend, particularly if one takes account of the outlying observation in July. As a further analysis, and to take account of seasonality, the proportion of burglaries committed during the evening for the pilot interval (August 2004 to February 2005) and for the same period for the year before (August 2003 to February 2004) were compared for both pilot and comparison areas. For the pilot area the proportion was lower during implementation (mean=0.11, SD=0.04, N=7) than for the same period the year before (mean=0.17, SD=0.07, N=7), a difference which achieved statistical significance (z=2.03, p<0.05). In the comparison area, the proportions did not differ significantly (z=0.34, p=0.74) before (mean=0.17, SD=0.09, N=7) and during the pilot (mean=0.17, SD=0.05, N=7), nor did they differ from the proportion in the pilot area for the same interval before the pilot (Z=0.32, p=0.81).

66

Figure 6.5: Changes in the proportion of burglaries committed during the evening over time (for events for which the interval between the earliest and latest reporting times was less than 8 hours)

0.00

0.10

0.20

0.30

0.40

0.50

0.60

Apr-01

Aug-01

Dec-01

Apr-02

Aug-02

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ortio

n of

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larie

s


The above results, which show a selective pattern, are certainly in line with what would be expected if the system had been used most frequently during the evening and was helpful in reducing burglary. However, to provide a more conclusive result, data for a longer period of time during which implementation occurred would be required to demonstrate continuation of the observed trend. In particular, this would further help to rule out any seasonal effect. Thus, it is suggested that the result shown is enticing but inconclusive. As discussed at the start of this section, detailed analyses were conducted to explore changes over time in the spatial and spatio-temporal distribution of burglary. For reasons already discussed, these analyses are presented in Appendix 3.

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7. Conclusions In this section, the main findings of the research will be discussed and recommendations inspired by them presented. The central aims of the current project were as follows:

• to determine whether patterns of burglary are communicable across a range of areas; • to test the accuracy of a predictive mapping system across the same areas and compare it

with contending alternatives; • to tailor the system for use in an operational context; • to see if the system could be used operationally and how it was received by those who might

use it; and • to test the efficacy of the system during a field trial in one police BCU.

The first four aims were achieved. The results demonstrated that across all areas the risk of burglary was communicable. Following a burglary at one home the risk to those nearby was elevated for a period of time afterwards. This pattern conformed to a pattern of spatial and temporal decay. Those nearest were at the greatest risk, and the change in risk decreased as time elapsed. Further research demonstrated that when events occurred close in space and time they tended to do so at a similar time of day. For repeat victimisation proper, this consistency also emerged around five to six weeks later, a finding compatible with explanations of the timing in the elevation in risk often observed for the time course of repeat victimisation more generally (i.e. offenders revisiting homes to steal replaced goods). Exploratory analyses described later in the report considered the length of space-time clusters of crime for one area. This approach will be developed in ongoing research underway by the authors. Recommendation 1 Given the evident ubiquity of the space-time clustering of burglary across the areas studied, it would be wise for some of the analyses discussed in the report to be conducted by police analysts to provide a better understanding of crime in their area. Whilst simple analytic software is currently unavailable, an application that performs the same kinds of analyses described here is currently being developed as part of a National Institute for Justice-funded project (Ratcliffe, 2006). This will be released as freeware in 2007 and will be compatible with a variety of off-the-shelf software tools.

Recommendation 2 Where it is found that crime (burglary and other types) clusters in space and time acutely, strategies aimed at the prevention of further crimes in a local spate could be developed to prevent or detect crimes. As a short-term strategy, this could be partially achieved (for example) using a GIS and the prioritisation of police resources by police analysts to homes nearby, and similar to those recently burgled. A number of police forces, including Cleveland, Dorset and the Police Service for Northern Ireland have developed such strategies. Relative to Promap, this will be a sub-optimal approach but may be a useful first step. As for the accuracy of Promap, this method was shown to be superior to those extant, even when the latter were optimised. In addition to producing maps that more accurately predicted where future burglaries occurred, the maps identified more coalescent areas for targeted patrolling. Further development of the system involved the inclusion of an opportunity surface that was used to weight the predictions made. This enhanced the accuracy of the system still further. Prior to implementation, a final feature of the mapping system developed was the facility to produce predictions on a shift-by-shift basis, which understandably appealed to those who used the system. Despite initial scepticism by some officers, by the end of the pilot the system was well received and generally perceived as a useful tool for targeted crime reduction. As testament to this, a number of officers asked if the technique could be applied to other types of crime such as theft from a motor vehicle. Recent work by the authors suggests this to be the case (Johnson et al., 2006), and further projected work will seek to develop this use. An additional feature of the future system, welcomed by those interviewed as part of the project, would be the facility to anticipate changes in which crime type should be prioritised for reduction over the next few days or weeks.

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With respect to implementation realised during the pilot, despite widespread belief in the usefulness of the system, a number of factors limited the extent to which operational tactics were deployed in response to the predictions generated. These have been described in the body of the report. The analyses of the potential impact on burglary, many of which were novel, were carried out and are reported in fulfilment of contract rather than in expectation of success given tardy implementation. It was not realistic to anticipate crime reductions during the currency of the project as delayed, and the encouraging trends at its end must be considered as an unexpected bonus. Had they not emerged, the writers would have been no less excited and energised by the potential of prospective mapping for crime reduction. The reasons for that excitement will be elaborated below. Recommendation 3 Analytic methods used to identify mechanisms of change in the evaluation of crime reduction interventions are often limited. The techniques described in Appendix 3 of this report illustrate a number of ways in which particular ‘signatures’ that might bespeak mechanism for interventions aimed at dispersing hotspots may be sought. It is recommended that such approaches are used more widely to evaluate intervention. The Promap system trialled in Derby allows the prediction of burglary events far better than previous mapping systems. Predictability of crime location is a major aid to its prevention, by disruption and detection. Sting operations are uniquely effective because the time and location of crime is known. Prospective mapping in the shape of Promap takes us much closer to achieving predictability. The quantum leap in performance it achieves over previous systems comes by the incorporation of the time dimension. The soccer cliché is that a striker has to be in the right place at the right time to score a goal. The former striker, Gary Lineker, makes the point that this is meaningless in that if a player is in the right place at the wrong time, that makes it the wrong place. If the player is in the wrong place at the right time, that makes it the wrong time! Only by thinking about time and place together as time=place does the cliché make sense (however banal). Similarly, a police officer must be in the right time-place to disrupt or detect crime. Historically, and with a few recent exceptions, crime mapping for the police service has neglected the time dimension. The Derby trial of Promap highlighted how ‘slippery’ and shift-specific hotspots are, overlain on a degree of location stability. Perhaps it is the mismatch between a conventional map showing a nightclub to be a hotspot with its quietness every Monday morning as experienced by a patrolling officer which leads to a schizoid view of the relevance of mapping for operational policing. Promap, by the centrality of time in its construction, negates that problem. Some technologies are recognisable as having massive potential future applicability while early in development. (What use is a new born baby)? Nanotechnology is one obvious current example. Stem cell use for organ repair is another. Whilst on a different scale, the writers believe Promap is another example. However, there must be a development process which does not seek dramatic early crime reductions (although some should occur) and be addressed to resolving two issues, set out in the following paragraphs. Should effort be given to resolving them? The writers’ emphatic view is that they should. The game is very much worth the candle. The first problem standing between Promap and routine use is that the police have to attend incidents of crime and disorder generally, while Promap as yet covers a limited number of crime types. Development must extend to all categories of crime and disorder. The relevant science completed, the relative importance of different events in driving patrolling patterns must be incorporated in the Promap algorithm. This is a matter of policing policy. Unpublished work from the Department of Operational Science at Lancaster University in the late 1970s demonstrated that such policy choices must be tested and that police preferences changed according to the mix of offences detected. Apart from this immediate complication, police preferences may change from time to time with changing priorities. There must, in short, be some weighting to direct a patrol to a location which will host three assaults and two thefts rather than a location which will host three thefts and two assaults. The second problem to be resolved before Promap can realise its potential would remain even after the first is resolved. Promap output must be delivered in real time to police officers in the form of presumptive patrolling patterns. This is not difficult even with current technology but comes at a cost. The most prominent obstacle is bedding Promap into policing craft. Officers must always be able to override a presumptive patrolling pattern on the basis of personal knowledge, but must come to trust that the presumptive pattern of patrol is soundly based.

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Recommendation 4 Further development of Promap or a variant should, in the authors view, be a priority. The research presented here demonstrates the superiority of the approach over existing contenders and shows that it is welcomed by the police. To achieve what is clearly possible will require further development of the system and a series of field trials across a range of different contexts. Recommendation 5 The utility of the approach should be explored for a range of crime types. Recommendation 6 It would be useful to distinguish between areas for which risks are increasing and those for which they are stable or declining. Operational tactics would vary for these two types of area. If the writers might be allowed to end on a flight of fancy, they envisage a situation in perhaps fifteen years when predictive mapping is available for all crime types, real time information on risk is available to police patrols, where the seriousness of different crime types is weighted automatically so that an optimal patrolling pattern is provided to each police vehicle to maximise the total seriousness of crimes to be preventively patrolled. Used in concert with Lab-on-a-chip forensic testing, where DNA and other tests would be possible in police vehicles, would facilitate swift forensic identification of perpetrators of crimes not prevented, and patrolling informed by Promap would mean faster response times to arrive before crime scenes are compromised for forensic purposes. In parallel with optimised patrolling, Promap would deliver information about longer-term patterns and stabilities in crime and disorder to Crime and Disorder Reduction Partnerships, enabling them to put in place design and maintenance changes. Nothing in such a future is unfeasible even with today’s technology. It does, however require an effort of imagination to discern the centrality of prospective mapping to such a future. The authors’ nightmare scenario is that Promap suffers death by a thousand trials. When assaultive crime is shown to be Promap predictable, minor changes in such crime will likely be achieved. However, since the police have to put Promap for assault alongside non-Promap-based decision making for other crime types, they will have to decide at any moment whether they are in Promap or non-Promap mode. Only when Promap is the default basis for routine policing will its benefits become visible. This is a brazen plea that any further funding of Promap focuses on the eventual realisation of an integrated system, rather than short-term and crime-specific operational trials.

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Appendix 1. The information technology nexus Much of this report concentrates on the principles of prospective mapping and police officers’ reactions to it. If systems such as Promap are to become commonplace in an operational policing context, of equal importance is the practicality of producing the maps on a routine basis.

The integration of novel information technology based analysis tools into an existing IT framework can produce several challenges. In this section, possible methods of delivering them are discussed, some key issues and problems associated with the development and integration of them identified, and a number of solutions (both short- and long-term) to these suggested. In order to produce prospective maps, as a minimum crime data relating to the timing and location of previous events are required and must be extracted from existing crime recording systems. This means that the ability to retrieve, process and analyse these data in a timely fashion is of key importance to the approach. In the main, as any new software must in effect be grafted onto force IT systems, there are two main approaches to implementing new analytic software products.

1. New software may be integrated into existing systems. This solution would mean that data could be directly accessed by the new software from an internal structure/data warehouse, process it and produce output within existing analysis systems. This option is presented schematically as Figure A1.1. It would require the least intervention on the part of the analyst and is the preferred option. However, there is typically a reluctance from IT staff regarding this approach. The reason for this is simple. Where software is not properly tested, it is possible that the computer program code could have a detrimental affect on existing systems, slowing them down or much worse. Consequently, this option requires considerably more effort in terms of system development and testing. A programme of formal specification would be required. At least three stages are desirable. Program validation would ensure that the software provided sufficient functionality to serve its purpose. Program verification on the other hand would be required to check that the software is implemented correctly. Additionally, an extensive testing regime would be needed to demonstrate that the system would not have any unwanted side effects on other elements of the IT infrastructure. For even a relatively inexperienced programmer the latter is unlikely but testing is an important step. In addition, it is worth noting that any direct integration of a new application into an existing information technology infrastructure is understandably a complex process. From a software implementation perspective the majority of police forces currently implement unique data infrastructures. This means that all attempts at integration must be tailored to interface with specific systems, Subsequently successful products are not immediately transferable between systems. This issue is currently being addressed to some extent by the IMPACT programme, which aims: “To deliver an effective integrated national, regional and local information-sharing and intelligence capability, which will improve the ability of the police and partner agencies to proactively use information for intelligence purposes to prevent crime, bring offenders to justice, safeguard children and vulnerable people and further professionalise the investigation process”.

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Figure A1.1: Internal application utilising existing GIS for visualisation

2. A stand-alone application may be developed that can process crime data exported from

existing IT systems. The major disadvantage of this approach is that data have to first be extracted, stored on some form of portable media and then imported to the external application. This takes time, particularly where the maximum rate of data transfer possible for the portable media used is slow. The use of Universal Serial Bus hard disk dives (USB HDD) or Firewire connections can increase the celerity of data transfer, but such connections are typically disabled on police IT systems for reasons of security.

Once the data have been processed they can be relayed to the user in one of two ways.

a) As shown in Figure A1.2, data may be imported into an existing Geographical

Information System and used to produce maps. A disadvantage with this approach is that further time is required to import and manipulate the data, and to add any required images of the urban backcloth.

b) Alternatively, a GIS visualisation tool may be implemented within the stand-alone

application to allow maps to be generated and interrogated within it (see Figure A1.3). This has the distinct advantage that the process of visualising the data can be automated and images may be produced to a standard (but modifiable) template.

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Figure A1.2: Stand-alone application utilising existing GIS

For reasons already discussed, option 2b was used in the current project. That is, a stand-alone application was developed which accepts data from a predefined export procedure installed on a personal computer with access to a live crime recording system. The application runs on a laptop, providing all the processing and visualisation elements. As the visualisation component of the system was specifically designed for this purpose, the relevant images of the urban backcloth are automatically selected by the application at the correct level of resolution for the map displayed.

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Figure A1.3: Standalone application with integrated GIS

Whilst this was the optimal solution for the current project, option 1 discussed above is the desired approach. However, whatever option is used in future projects, a number of recommendations with respect to police IT systems and the recording of data that may be realised in the short and longer term are presented below.

Recommendations that may be realised in the short term • Crime data should be recorded in an accurate and timely manner. Georgaphical grid co-

ordinates should be accurate to a resolution of one metre and available for analysis within 24 hours or less.

• All force IT systems should have standard applications that enable data to be exported in a standardised format, such as the comma separated (.csv) format which is compatible with most commercial software, and programming languages.

• Physical access to data should be possible, either using a CD burner or (for faster data transfer) USB HDD. Security measures would, of course, be required to restrict access to the data. Different approaches to encrypting data should be explored.

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Recommendations that may be realised in the longer term • Force IT systems should be developed with standardised interaction architectures, which

allow the incremental addition of analysis elements to both crime recording (such as data cleaning) and analysis systems (such as Promap).

• Standardise recording and IT architectures across different forces, regions and organisations. This would facilitate the use of the same applications across police forces and thus eliminate the need for bespoke applications. This would also facilitate analysis of cross border offending.

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Appendix 2. Prospective Mapping Survey Please read carefully: The UCL Jill Dando Institute of Crime Science has been commissioned by the Home Office and the Government Office for the East Midlands (GOEM) to undertake a research project that has been piloted in Derbyshire ‘A’ Division from August 2005 to February 2006. It is essential with a project of this kind that an appropriate evaluation is carried out to understand and assess exactly what happened and to identify any factors that particularly facilitated or impeded implementation. This survey has been designed by the research team to gain an understanding of how much you know about the pilot and how useful you feel the maps were in ‘A’ Division, and more specifically, your section. Please note that this is an anonymous survey. Your individual answers will not be seen by anyone other than our research team and we will not be able to identify you by the answers that you give, nor would we wish to do so.

Thank you for taking the time to fill out this questionnaire.

Please turn over to begin

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Section 1: knowledge and understanding of prospective mapping 1a) Have you heard of the prospective mapping pilot that is taking place in ‘A’ Division?

(please tick)

Yes .......................................................................................... (if yes, please proceed to 1b) No............................................................................................ (if no, please proceed to Section 3)

1b) In your own words, please briefly say what you understand prospective

mapping to be?

1c) Have you seen any prospective maps over the last six months?

Yes .......................................................................................... (if yes, please proceed to 1d) No............................................................................................ (if no, please proceed to Section 3)

1d) Were these maps used by yourself or your supervising officer for targeted police

activity (e.g. directed patrols, targeting offenders)? Yes ......................................................................................... (if yes, please proceed to 1e) No............................................................................................ (if no, please proceed to 1g) Don’t know .............................................................................. (if don’t know, please proceed to 1g)

1e) How often do you (or your supervising officer) use prospective map(s) for targeted

police activity? You supervising officer More than twice a week Twice a week Once a week

Once every two weeks Once every three weeks Once every month Less than once a month

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1f) What tactics have you employed, or been involved in, in response to the maps? Employed Involved in

Foot patrol around hotspots Drive through patrols in hotspots Target-hardening (e.g. locks, alarm systems) Repeat victimisation strategies Targeting known offenders Redeployable CCTV Publicity campaigns Street closures Alleygating

Other, please specify: ……………………………………………………………… ………………………………………………………………………………………… ……………………………………………………………………….........................

1g) How easy do you feel the maps are to interpret?

Easy ........................................................................................ Fairly easy............................................................................... Fairly difficult ........................................................................... Difficult .................................................................................... I don’t understand the maps ...................................................

Extra Comments (please outline any extra comments you have about your knowledge and understanding of prospective maps and the tactical options that you have employed)

Section 2: usefulness of the maps 2a) How useful do you feel the maps are in your day-to-day work?

Very useful .............................................................................. Somewhat useful ................................................................... Not very useful ........................................................................ Not useful ...............................................................................

2b) Have the maps ever identified risky areas that you would not otherwise have

considered as being risky?

Often ....................................................................................... Sometimes .............................................................................. Never....................................................................................... Yes, but I trusted my own judgement rather than the maps ..

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Extra Comments (please outline any extra comments you may have about the usefulness of prospective maps, how they might be improved etc.)

Section 3: personal details (Please remember that this survey is both confidential and anonymous and the information you give will only be used by the research team to help put your answers into context.) 4a) What is your sex?

Male ........................................................................................ Female ....................................................................................

4b) What is your current rank/position?

Police Constable..................................................................... Sergeant ................................................................................. Inspector ................................................................................. Other (please state) ................................................................

4c) How long have been in your current rank?

Less than 1 year .................................................................... 1-5 years ................................................................................. More than 5 years ...................................................................

4d) What Section do you work in?

Alfreton.................................................................................... Belper...................................................................................... Ilkeston.................................................................................... Long Eaton.............................................................................. Ripley ......................................................................................

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Appendix 3. Detailed evaluation methodology The purpose of this appendix is to describe the evaluation techniques used that were not discussed in the main body of the report. Some of these are novel and have a potentially wider application than this project and are thus discussed so that others might use or improve upon them. This section of the report is intended to be self-contained and so there is some duplication with the text presented in the main report, although this repetition is minimal. The methods discussed consider changes observed over time, in space and in space and time, in that order.

Analyses of change over time Which statistical method is most appropriate for establishing the statistical significance of changes in levels of crime in a single area over time is the matter of some debate. A number of approaches exist. First are those that consider the overall difference in the volume of crime before and after intervention. The basic approach is to compare the change in the volume of crime for a particular unit of time (say 12 months) before and after intervention in both an action and comparison area. If a reduction is observed in the action but not comparator, or the reduction in the former exceeds that in the latter then a positive inference may be drawn. To determine whether the difference in the change between the two areas is significant, a measure of effect size and the associated standard error is derived. There are a number of approaches to computing effect sizes for single-case designs (Allison and Gorman, 1993; Lipsey and Wilson, 2001). Here attention will be given to two techniques: one used within the criminological literature and elsewhere, the other developed within the field of psychology for the analysis of behavioural change, but (variants) also used more widely within other fields of investigation, such as economics. The simplest approach is to compute an odds ratio, which simply compares the change in the intervention and comparison areas before and after intervention. An odds ratio of one indicates that the changes in the two areas were commensurate, suggesting no impact of the scheme. An odds ratio of greater (less) than one suggests a reduction (increase) in the intervention area relative to the change observed in the comparison area. The statistical significance of the odds ratio can also be computed (see Lipsey and Wilson, 2001) by estimating the standard error of the value derived. This technique, which is readily interpretable, has been frequently used in research concerned with what works in reducing crime (for examples, see Welsh and Farrington, 2006; Gill and Spriggs, 2005), but is not without its critics for analyses conducted at the small area level (for which fluctuations over time may occur even in the absence of intervention: Marchant, 2005). However, the problems articulated about this approach are likely to be less problematic for analyses conducted at the BCU level, for which the variation over time is likely to be relatively stable. Thus, the approach is used here not least because it provides a simple assessment of how things changed in the pilot area compared to the comparator. Two approaches are here used to compute the standard errors (since these are critical in determining the significance of the effect-size derived), one used by Farrington and colleagues (see Welsh and Farrington, 2006), the other by Gill and Spriggs (2005).10 However, both approaches converged on similar estimates and hence only the former are presented. An alternative to using data which has been aggregated for two periods of time (before and after intervention) is the analysis of time-series data. For this approach, data for a number of intervals are instead analysed. This allows more complex patterns in the data to be identified and considered in the analysis. For example, time-series analysis can help control for what is known as serial dependence in the data: that is, to control for the fact that the residual error for an observation at one time point is likely to be highly related to that for an adjacent time point. If such dependence exists within the data then failing to correct for it can increase the likelihood of Type I statistical error – the likelihood of incorrectly rejecting the null hypothesis. 10 Gill and Spriggs (2005) use a slightly different approach to calculate the standard by considering monthly fluctuation in the volume of crime to reduce a problem known as over-dispersion.

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In relation to the evaluation of interventions, the method used is known as an interrupted time-series design (Shadish, Campbell and Cook, 2002). The rationale for the approach in the current context would be that if an intervention has an impact on the crime rate of an area then following implementation the trend in the crime rate should change. This can occur in at least two ways. First, the mean level of the series may change, or there may be a change in the slope of the time-series following the inception of a scheme. Thus, following intervention the average monthly crime rate may fall by an average amount (say 20 crimes per month), or there may be a downwards trend with the crime rate falling by an incremental amount each month. The basic method used is to first analyse the data for the pre-intervention series. The purpose of so doing is to derive a set of parameters that describe monthly (or some other interval of time) changes in the crime rate before implementation. These parameters include any trend in the series (linear or otherwise), the intercept of the series (the value at time zero), and the extent of serial-correlation in the data. Other parameters may also be modelled but are not discussed here for parsimony. Once these parameters have been estimated they can be used to see how well they describe the post-intervention time-series. Moreover, and importantly, the aim of the analysis is to see if the timing of intervention, modelled as a binary variable (or a continuous variable if data on the intensity of implementation are available) explains a significant amount of the variation in the time-series that is not already explained by the parameters estimated in the earlier steps of the analysis. In many fields of investigation, data are often available for long time-series (say 30-50 months) before and after intervention and this allows reliable analyses to be conducted. In the field of crime reduction, the length of the series is typically shorter. In the current evaluation, although a long series was available for the pre-intervention series, the post-intervention was fairly short, at only seven months. Even where a time-series is short Shadish et al. (2002) recommend the use of time-series analysis, even if the analysis undertaken is simply visual inspection of the trend. However, time-series approaches have also been adopted in other fields of investigation for which the length of the series is frequently much shorter. For example, in reviewing the methods available for the calculation of effect sizes for single-case designs where a control group is unavailable, Allison & Gorman (1993) propose a method for the analysis of shorter time-series. The rationale behind the approach is that in the absence of intervention, the time-series before intervention can be used to predict that afterwards. Their approach uses ordinary least squares (OLS) regression to diagnose the general trend before intervention, and the resulting regression equation is applied to the data post-intervention. Their formulation allows both changes in intercept and slope to be identified. Formally:

Y = b0 + b1X + b2X(t-na) + e

Where, Y is the residual error from the initial OLS model

b0 is the estimate of the intercept b1 is the estimate of the change in intercept associated with the intervention

b2 is the estimate of any change in slope associated with the intervention t is the time point

e is the error term

Where data concerned with changes in a control group are unavailable, this approach would help rule a number of threats to internal validity; that is, factors other than an intervention that might explain the pattern observed. Such threats include (for example) regression to the mean and history. Regression to the mean may occur where an area is selected for intervention on the basis of an extreme pre-intervention crime rate, and where the rate is extreme not only compared to other areas but relative to itself at other times. The problem is that the observed elevation in the crime rate may be explained by temporary phenomena. Thus, even in the absence of intervention the crime rate would soon regress back to the level typical for that area. With a suitably long time-series of data, such effects can be identified and modelled. Similarly, history occurs where there is a downwards trend in the crime rate

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prior to intervention, or specific events unrelated to the intervention occur that might impact upon the incidence of crime. However, one limitation of the type of analysis discussed above is that it is insensitive to more general changes that might occur. Consider that there may be changes in policy that would have a generic effect on the rate of burglary. For example, a county-wide policy introduced on a particular date would be expected to impact upon burglary across the entire area. Unless such a policy were identified it could not be modelled using the type of analytic design discussed above. Changes in recording practices, such as those introduced as part of the National Crime Recording Standard, may also affect the time-series. Thus, a variant of the above approach for which changes in a comparison area are used to de-trend the time-series in the first step of the analysis are here used to increase the validity of the analysis. By doing so, this means that any changes in the action area that may be explained by factors such as those already discussed can be estimated and removed from the time-series. For the current evaluation a fairly long pre-intervention time series facilitated a reliable diagnostic analysis of the pre-intervention time-series. However, many researchers recommend that where possible it is wise to employ a range of statistical approaches to the analysis of data. The advantage of so doing is that where the results of the analysis converge, one can be more confident that conclusions drawn are not based on the specific biases of a particular statistical procedure. For this reason, in this report analyses are conducted using both the odds ratio approach discussed above and a variant of the approach to the analysis of short time-series proposed by Allison & Gorman (1993).

Odds ratio analysis To calculate the odds ratios, the count of crime for the seven months before and during the pilot were contrasted. The standard errors were computed in the usual way (see Lipsey and Wilson, 2001) as well as using monthly variation as suggested by Gill and Spriggs (2005). Table A3.1 shows the count of burglary before and during the pilot along with the OR, and associated confidence intervals and z-score. The confidence intervals shown, calculated using the traditional approach indicate the upper and lower estimates of the odds ratios. These suggest that the true odds ratio lies somewhere between 0.99 and 1.34. The z-score provides an indication of the likely statistical significance of the OR. For a two-tailed test, the z-score is required to exceed 1.96. On the basis of these results, the analysis would suggest that the reduction in burglary observed in ‘A’ Division exceeded that in the comparison area, but that the trend was marginally non-significant. Table A3.1: Change in the volume of burglary and odds-ratio statistics

Before After

Gross change

Odds ratio

Confidence intervals

z-score

Pilot

663

554

109

1.15

0.99-1.34

1.80

Comparison area

684 659 25

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Time-series analysis For the purposes of illustration, Figure 6.1 (in the main body of the report) shows a time-series graph of the change in the count of burglary in the action and control areas for a period of two years before intervention up until the end of the evaluation period. As a first step, an OLS analysis, with the monthly count of burglary in the action area for the period prior to implementation was regressed against that in the comparator. Also included in the model was an (independent) variable which indicated the time point (see above). This analysis indicated that changes in the comparison area, ‘C’ Division, were significantly associated with those in the action area (ß=0.33, ß(s.e)=0.09, t(51)=3.90, p<0.001). Thus, changes in the action area followed a similar trend to that in the control area. Having partitioned the variance in changes in the action area that were explained by those in the control area, there was no additional temporal trend (upwards or downwards) in the changes observed (ß=-0.11, ß(s.e)=0.27, t(51)=-0.43, p=0.43). The residuals (the difference between the predicted values generated by the model and the actual values) from this analysis were then further analysed to determine whether there was evidence of serial correlation, or other patterns. A correlelogram confirmed that the data conformed to a pattern of first-order serial correlation, and diagnostic statistics confirmed that the residuals were normally distributed. Thus, in the analyses that follow a first order Autoregressive model was used (AR1). Having diagnosed the pre-intervention series, an OLS regression was used to estimate the predicted monthly count of burglary in the action area following intervention. Residual values were derived by simply computing the difference between the predicted and observed values. Having done so, residual values were available for the entire series and thus a final analysis was conducted. The null hypothesis was that if there was no reliable change in the count of crime following the start of the pilot, then a binary variable, which indicated when the pilot was active, should fail to explain a significant amount of the variance observed in the residual scores (the de-trended data), after the variance explained by serial correlation had been accounted for. The results of the analysis confirmed that a significant amount of the variance was explained by serial autocorrelation (ß=0.33, ß(s.e)=0.13, t(59)=2.57, p<0.02). In addition, the binary variable explained a significant amount of the remaining variation (ß=-31.2, ß(s.e)=12.96, t(51)=2.46, p<0.02), suggesting that when the trend observed in the comparison area, and serial correlation had been accounted for, there was a significant change in the monthly count of burglary following the introduction of the pilot. For this analysis the time-series was not stationary (demonstrating variability in the variance over the series), a condition of time-series analysis. Taking the square root of the values made the series more stationary and generated the same results. However, differencing the data, another technique for making a series stationary, produced different results with the binary variable no longer explaining a significant amount of variance in the data. One problem with this analysis is that only seven observations were available for analysis in the post-intervention period, which affects the reliability of the analysis. There are two issues to consider. First, it is difficult to know whether the trend observed will be sustained should implementation continue. The only way to resolve this issue would be to conduct a much longer pilot exercise, perhaps over two years. Using a more extended period would allow a more reliable analysis of any impact realised, and represent a better test of the intervention. The second issue relates to sample size. Ideally, data analysed would be available for a much longer period, increasing the number of data points (and hence degrees of freedom) considered in the analysis. Using the available data, the only approach that could be employed to increase the sample size would be to analyse changes for smaller intervals of time, such as weekly periods.11 Thus, the analysis discussed above was repeated using changes in the weekly count of burglary. In line with the analysis of monthly changes, the analysis indicated that changes in the comparison area, ‘C’ Division, were significantly associated with those in the action area (ß=0.21, ß(s.e)=0.05, t(216)=4.23, p<0.001). Thus, changes in the action area followed a similar trend to that in the control area at the weekly level. Having partitioned the variance in changes in the action area that were

11 The authors would like to thank Machi Tseloni for this suggestion and for comments on the analysis more generally.

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explained by those in the control area, there was no additional temporal trend (upwards or downwards) in the changes observed (ß=-0.01, ß(s.e)=0.01, t(51)=-1.14, p=0.26). Again, a corrolellogram confirmed that the data were serially correlated, conforming to an AR1 model, and thus this model was used to see if the reduction observed was coincident with the timing of the pilot. The results of the analysis confirmed that a significant amount of the variance in the weekly burglary count in the Division was explained by serial autocorrelation (ß=0.74, ß(s.e)=0.04, t(245)=17.22, p<0.00001), and, that the binary variable also explained a significant amount of the remaining variation (ß=-3.13, ß(s.e)=1.13, t(245)=-2.78, p<0.01), demonstrating that when all other factors (considered) had been accounted for, there was a significant change in the monthly count of burglary following the introduction of the pilot. However, the issue in relation to stationarity arose as before and the two approaches to achieving produced the same results. Taking the square root of the residuals suggested a significant reduction in the volume of burglary which was associated with the timing of the pilot, whereas differencing the data did not. Thus, the results of changes in the weekly and monthly counts of burglary generated the same pattern of results. However, whether the reduction observed was significant or not is unclear for two reasons. First, different methods of transforming the time-series to make it stationary produced different results. And, secondly, and perhaps more importantly, data were not available for a sufficient period of time over which to make an adequate assessment of change. Of course, it is also important to bear in mind that this type of analysis simply provides a measure of the correlation between two factors, in this case the change in the burglary rate and the timing of intervention, and thus does not demonstrate causality. With a one-sample design (with a control area) such as evaluated here, the attribution of causality is difficult even where the results are unambiguous. Moreover, in the current situation for which implementation was only marginal, any inferences drawn on the basis of such analyses may be unreliable.

Changes in patterns in space Having examined the change in the timing of victimisation, an obvious question relates to the spatial distribution of burglary. Did this change and, if so, how? Perhaps the simplest approach is to display the data using a GIS and compare the changes by eye. However, as already discussed, detecting differences in this way is not as straightforward as it may sound. Thus, an analysis of change was performed to help identify any change in the overall placement of burglary following the introduction of the pilot. To do this, kernel density maps were generated for a grid with equally sized cells for the seven-month periods before and during the pilot. Next, to enable an easy analysis of change, the risk intensity value for each cell for one period was subtracted from the value for the same cell for the other. The results are shown as Figure A3.1. The areas shaded in darkest red are those for which there was the greatest increase; those shaded darkest blue are those for which there was the greatest decrease.

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Figure A3.1: Changes in the spatial distribution of risk following the introduction of the pilot

It is important to bear in mind that this is a purely visual and descriptive analysis. Moreover, the patterns are relative and do not necessarily indicate where the risk of burglary was greatest, but just where it changed the most. Nevertheless, what Figure A3.1 does illustrate is that risk moves even when considered over fairly lengthy intervals of time, such as seven months. This mobility of risk is one of the underpinning justifications of Promap. With respect to statistical significance, an analytic approach worthy of discussion has been developed by Ratcliffe (2005). The aim of this is to detect changes in the spatial placement of crime over time within a particular area, such as a BCU. Using a Monte-Carlo simulation, the spatial patterns observed can be compared against the null hypothesis that any changes observed could have occurred on the basis of chance. Consequently, the test developed provides an inferential statistical test of the visual analysis presented in Figure A3.1.

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A still further approach to exploring the stability in burglary risk is to compute the correlation between the count of burglary across a series of areas encapsulated by the BCU before and during the pilot. A large correlation would suggest that the distribution of risk was stable, a small coefficient that it was fluid. To do this, areas at different levels of geographical resolution could be used. For example, one could examine the correlation at the level of a town or for a grid of smaller regular sized cells. Obviously, the larger the area considered, the more stable the patterns would be expected to be. Thus, analyses were conducted at two levels of geographic resolution, first at the census output area level, and second for a series of 1kmX1km cells. There are a total of 29 census output areas in ‘A’ Division and at this level of resolution the rank-order correlation of .89 (p<0.0001, N=29) was high suggesting that patterns of burglary were stable for these areas. Output areas with high concentrations of burglary before the pilot had high concentrations afterwards. For the 1kmX1km cells, the rank-order correlation was computed only for those areas in which crime actually occurred, either before or after the pilot. In this case the correlation of 0.55 was much lower (p<0.0001, N=358). This confirms the perception generated by Figure 6.6, that when considered at the more local area level, over time the risk of burglary moved considerably. Albeit useful, this type of analysis considers only the change in the ranking of areas in terms of the volume of crime experienced. A different approach would be to explore whether there was actually a change in how concentrated crime was over time at the area level. A simple but underused approach to doing this is to derive what are known as Lorenz curves (for a detailed discussion, see Tseloni & Pease, 2004), which can be used to illustrate the degree of inequality in the distribution of crime risk. Used more commonly in economics the distribution of a variable of interest, be it wealth or in this case crime, is explored by plotting the cumulative percentage of crime against the cumulative percentage of the population. If crime is evenly distributed then the resulting graph will consist of a 45 degree line, indicating that X per cent of the population account for the same percentage of crime. If crime is, as one knows it to be, unevenly distributed, then the emergent trend will be a curve situated underneath the 45 degree line of equality. That is, the graph will show that a large proportion of victims experience only a small proportion of the total volume of crime, whereas a small percentage account for the majority. The question of particular interest here is whether the pattern of inequality changes post intervention. Under different circumstances in which the pilot had been implemented fully, one prediction would be that as a consequence of sustained targeted activity, burglary risk would be redistributed reducing the inequality observed. For the purposes of illustration, Lorenz curves were generated for the seven month periods before and during intervention. One challenge involved the identification of a meaningful unit of spatial analysis. A number of possibilities exist and were indeed used, but each converged on the same outcome and thus only one is presented here. The approach taken was to define a grid of 1km cells encapsulated or intersected by the BCU boundary. Of these, only those that contained households were selected for analysis. Figure A3.2 shows the Lorenz curves for the periods before and during the pilot.

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Figure A3.2: Lorenz curves showing the distribution of burglary risk

The general pattern for both periods of time confirms what the authors already knew, that burglary is highly concentrated in space. It also shows that there was little change in the spatial concentration of burglary following the start of the pilot. This is slightly disappointing. With fuller implementation, it would become worthwhile to compare Lorenz curves for pilot and comparison areas, since there may be a general tendency for crimes as they become rarer also to have a very different distribution. Changes in patterns in space and time The burglary pattern which gives prospective mapping its superiority over retrospective mapping is its spatio-temporal clustering. The traditional definition of a geographical hotspot is an area for which crime is particularly concentrated in space for a period of time such as one year or six months. Thus, hotspots are essentially the accumulation of pairs of crimes which occur close to each other within the time period of interest. In consequence, the analysis of hotspots does not consider the timing12 of events, other than requiring them to have occurred within the interval of interest, say over the last six months. The analyses so far presented have looked at distributions in time and space separately. An alternative and more sensitive approach to the examination of spatio-temporal patterns of crime would be to identify clusters or series of event pairs that occur not only close in space but also in time. Put too simply, one would expect prospective mapping to reduce the length of series of burglaries close in time and space. The identification of such clusters and their length would have useful implications for crime reduction in at least two ways. First, this would provide the police with an idea of the tempo of localised increases in burglary risk. For example, if the longest clusters that could be identified consisted of only two events, this would suggest that the elevated risk in an area had a fairly slow tempo, whereas if clusters of ten events were routinely identified this would suggest a higher frequency of space-time clustering. This form of analysis chimes with the Promap approach. It is also novel. The reader is invited to think of what follows in this light, namely as an exploratory approach which no doubt needs refinement but is more suited to the technique it aspires to understand and evaluate. This type of analysis is useful for evaluation research by enabling the detection of qualitative changes in spatio-temporal patterns of crime. It is perhaps easier to illustrate this point by starting with a special case of

12 By timing, the authors mean the time between events rather than the regularity of the time of day that crimes occur. The latter has been usefully explored by Ratcliffe (2002).

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space-time clustering: repeat victimisation. Considering evaluations of area-based interventions, it is possible to not only measure changes in burglary incidence (the rate per 1,000 households) following an intervention but also how concentrated on individual properties crime is. For example, for an intervention aimed at reducing repeat victimisation, simply examining the change in the incidence of burglary may be insufficient to detect the full impact of a scheme, or the potential crime reduction mechanisms through which any change was realised. Instead, a more sensitive analysis (see Forrester et al., 1990) would be to examine the rate of repeat victimisation. A reduction in this type of victimisation would suggest an impact of the scheme, although a reduction in incidence would be required to demonstrate that target-switch displacement did not occur. On the other hand, a reduction in incidence within an area that was unaccompanied by a reduction in repeat victimisation would provide less convincing evidence that any reduction could reasonably be attributed to the scheme. In the same way, where strategies are employed to suppress emerging or enduring hotspots of crime, as was the case in the current pilot, one desired outcome of the scheme would be to truncate the length of space-time clusters of burglary. That is, if resources are directed to the right places at the right times in a way that anticipates where the next event in a cluster is most likely to occur, then it would be hoped that emerging spatio-temporal clusters of crime would be targeted before they have a chance to propagate. To examine this issue, software was developed to identify series of events that occurred close in both space and time, ranging from two events (pairs) onwards. The approach allowed the frequency of series of different (k-event) lengths (e.g. pairs, triples, quads and so on) to be summarised and compared for the periods before and during the pilot. The identification and summary of series of events can be done in a variety of ways. Here, for every burglary (the reference) event, any antecedent burglary that occurred within a critical distance and time of it was identified and added to the series. Using this approach only burglaries that occurred within the critical time and distance of the reference event could be identified. This precludes the identification of other burglaries that might be linked to the others in a cluster. This problem is illustrated conceptually in Figure A3.3. In the top and bottom of Figure 6.8, there are four burglaries that occurred within a few days of each other. In the first chain, all burglaries occur within the critical spatial distance of the reference event (the leftmost event) and thus one identifies a chain of four burglaries. For the second series, for the same reference event one would identify a chain of only three burglaries as the final event occurred further away from the reference event than the critical distance. However, it occurred within the critical distance of other events in the chain and hence should be included in the series. Figure A3.3: An illustration of a triple (bottom) and quad chain (top)

An alternative approach would be to include in a cluster all burglaries that are within the critical distance and time of one or more events already identified as part of that series. Using this approach, both series shown in Figure A3.3 would be classified as being a four event series. In what follows the critical distance and time used were 400m and one-week, respectively. Other definitions could be used, and other analyses (not shown) using different definitions revealed a similar pattern of results.

Critical distance

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To recapitulate and elaborate, the approach taken may be summarised as shown below:

1. The burglary data were sorted in date order. 2. For each period of time (before and during the pilot), the previous week’s events were used as

a historic buffer period to allow all events that occurred within seven days of another to be identified.

3. Each burglary event was then considered in sequential order. a. For the first event considered, only the historic data were searched to see if earlier

burglaries occurred within the critical thresholds of the first event; where they did they were added to the series for that reference event.

b. For the second event considered, all of the historic data (including the first reference event) were searched, and events that belonged to a series identified as per step a.

c. Once all crimes had been considered, for every event it was possible to calculate the maximum length of the chain for which that event was the terminal event and how many chains of every length were identified.

Using this approach, it was possible to answer the question, ‘when a burglary occurs how many crimes previously occurred nearby in space and time?’ It is, of course, possible that any cluster so identified could be part of a longer cluster which included events that subsequently occurred. Expressed a slightly different way, clusters identified in this way may overlap with one another with one cluster including some events of another – although for any particular series length no two clusters would include exactly the same events – there would be at least one different crime in the longer chain (the shorter being a subset of the other). Having summarised the data in this way, it was possible to calculate what proportion of burglaries were the terminal event for different lengths of space-time series. The results, shown as Figure A3.4, illustrate that around one-third of events occurred within 400m and one week of at least one antecedent both before and during the pilot. Around 15 per cent of events occurred nearby and within one-week of at least two others. For the two intervals of time considered, the longest series identified consisted of 12 burglaries that occurred close to each other in both space and time. Before discussing the differences for the two periods of time, it is important to note the implication of this finding, which is that it clearly illustrates the flux of crime. If burglaries occurred in the same places all the time, either considerably longer series would have been identified, or a higher proportion of events would belong to longer series. This emphasises the need for a dynamic predictive capability for the deployment of crime reductive resources. Simply targeting the same areas over time is unlikely to direct resources to the right places at the right times.

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Figure A3.4: The proportion of events belonging to different k-event series before and during the pilot

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Considering the patterns for the periods before and during the pilot, there are some differences. First, the longest series (12 events, of which there were three examples) identified occurred in the period before intervention. More generally, longer series were identified in the period before the pilot, than during it. Conversely, during the pilot there was a slightly higher proportion of shorter series (two or three events). On the face of it, this finding is in line with what would be expected if the pilot had been successful. However, the reader should be mindful of two things.

a. For the period during the pilot, a smaller number of burglaries were committed, which may have affected the pattern of results. For what it is worth, sampling only the same number of events for the period before (the first 554 events) as that after generated exactly the same pattern of results. Consequently, differences in sample sizes would not appear to explain the results observed.

b. Perhaps more importantly, the effect observed was very subtle. This is not

unexpected given that implementation, or rather the use of the system, was at best only minimally realised. With such a small effect size, and under the circumstances of partial implementation, it is inappropriate to conduct inferential statistical tests.13 Thus, this finding is interpreted as being inconclusive, but the analytic approach derived suggested a useful method for future research concerned with the stability of clusters of crime.

13 Such a test would involve comparing the observed distribution of k-event series with what would be expected if the timing and location of events were random. Thus, a variant of the Monte-Carlo approach used elsewhere in this report would seem appropriate.

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Concluding comments on method The techniques described illustrate a number of ways in which particular ‘signatures’ that might bespeak mechanism may be sought. Only by conducting such analyses is it possible to differentiate between the likely contributions of different plausible mechanisms of crime reduction. Unfortunately, methods of this kind are in their infancy and hence there is a need to develop and disseminate those that might have wider application.

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Appendix 4. Promap graphical user interface and an illustration (step by step) of how the system is used This appendix provides further detail concerning the Promap interface, how it is used, and the output generated. The maps produced show anticipated risks at various levels of geographic resolution, the finest being at the household level. However, for the purposes of anonymity, all of the maps shown here are at a fairly coarse level of resolution and the predictions shown are fictitious rather than reflecting the actual distribution of crime risk. For the purposes of elaboration, the notes that follow describe in a step-by-step fashion how the software is used. These are a version of the instructions provided to the crime analysts.

Step 1: Load the Promap application.

Figure A5.1: The Promap application

Step 2: If you wish to produce a prospective map for the current date, select the ‘today’ radio button (see Figure A5.1). If you wish to change the test date select ‘other’ and use the date selection box. If you are producing a map for a specific shift – enable the shift analysis mode with the tick box and then use the radio buttons to select which shift the map should be tailored for (see Figure A5.2).

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Figure A5.2: An enlargement of the shift analysis options

Step 3: Once you are happy with your selection, click the 'Generate PROMAP' button, this will perform the analysis and generate the prospective maps required. While these calculations take place a ‘processing data’ message will remain in the foreground of the map window.

Step 4: When analysis is complete the processing message will disappear and the prospective map of your area will become visible (as below). The key in the bottom left of the map indicates the fraction of the area identified. This ranges from the five per cent (shaded yellow), two per cent (shaded orange) and one per cent (shaded blue) of the total area predicted to be most at risk of victimisation. Thus, according to the forecast, the blue area is that which should be prioritised first. Figure A5.3: An example of fictitious prospective map

© Crown Copyright. All rights reserved. Derbyshire Constabulary 100021015.

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Step 5: Once the prospective map has been generated, navigational controls in the bottom right of the window (shown in Figure A5.4) allow further user interrogation of the map. Brief descriptions of the functions are shown below. Figure A5.4: Map navigational options

• The control allows the user to navigate the map at the current level of magnification, moving the map in combinations of East/West and North/South.

• The button zooms into the map window providing more detail for a specific area. This

can be done by clicking at a particular point on the map or dragging a box around the area to view. Promap will dynamically switch between the relevant Ordnance Survey map layers for the level of zoom selected.

• The magnifying glass zooms out of the map window.

Figure A5.5: Prospective map magnified to neighbourhood level.

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Figure A5.6: Prospective map magnified to street level

© Crown Copyright. All rights reserved. Derbyshire Constabulary 100021015. Figure A5.7: Prospective map magnified to household level

. © Crown Copyright. All rights reserved. Derbyshire Constabulary 100021015.

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Using the navigational tools, it is possible to interrogate the map at the level of resolution desired. Such analysis may be combined with other intelligence to help further prioritise resource allocation within the areas identified and to refine tactical options. The 'export view as image' button does exactly what it says – it exports the current map window view as a gif image for inclusion in documents or briefings. By default all captured images can be found in ‘C:\promap\exported_images\’ and are given a name based upon the area in question, and the type and date of analysis performed.

Produced by the Research Development and Statistics Directorate, HomeOffice

This document is available only in Adobe Portable Document Format (PDF)through the RDS website

http://www.homeoffice.gov.uk/rds

Email: [email protected]

ISBN: 978 1 84726 532 6

Crown copyright 2007

Prospective crime mapping in operational context Final...

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