Spatio-Statistical Analysis of Urban Crime; A Case Study...
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American Journal of Environmental Policy and Management
2018; 4(1): 9-20
http://www.aascit.org/journal/ajepm
ISSN: 2472-971X (Print); ISSN: 2472-9728 (Online)
Keywords Spatio-Statistical,
Urban Crime Management,
GSCMS,
GIS
Received: July 30, 2017
Accepted: November 23, 2017
Published: January 11, 2018
Spatio-Statistical Analysis of Urban Crime; A Case Study of Developing Country, Kaduna Metropolis, Nigeria
Nzelibe Tobenna Nnaemeka*, Bello Ashiru
Department of Urban and Regional Planning, Ahmadu Bello University, Zaria, Nigeria
Email address [email protected] (N. T. Nnaemeka) *Corresponding author
Citation Nzelibe Tobenna Nnaemeka, Bello Ashiru. Spatio-Statistical Analysis of Urban Crime; A Case
Study of Developing Country, Kaduna Metropolis, Nigeria. American Journal of Environmental
Policy and Management. Vol. 4, No. 1, 2018, pp. 9-20.
Abstract Geographical Information Systems (GIS) and statistical methods are implored in
developing a Geospatial Crime Management System (GSCMS) to facilitate for an in-
depth crime pattern analysis. Exploring the spatial and statistical dimensions of crime is
optimized by the GIS capabilities to relate spatial and attribute data effectively. While the
statistical analysis techniques facilitates for handling of the computation and processing
aspects of such attribute data for a cogent database. However, the effectiveness of these
quantitative approaches is been proliferated. This paper examines the spatial pattern of
urban crime in Kaduna Metropolis (KdM), Nigeria; using computer based spatio-
statistical techniques of crime analysis with a view to offering possible options, for
effective urban crime management. The analysis highlights harnessing innovative
capabilities for an effective urban crime management system in cities of the developing
countries.
1. Introduction
The rate of crime and its implications on urban areas have led to the development of
the emerging urban crime management as a major issue in urban studies. The realization
that crime could be explained and understood in more depth surfaced in the 1970s.
Recently emerged, are the capabilities to identify patterns and concentrations of crimes;
the relationships between crime and environmental characteristics as well as assessing
the effectiveness of law enforcement agencies and crime reduction programs on urban
crime, for a more informed and proactive urban crime management and decision making
on urban crime policing, policy formulation, evaluation, and reform.
The study of crime has, not surprisingly, been dominated by research in criminology,
sociology and law, but spatial and ecological perspectives on crime by mostly
criminologists, did preceded the ‘recent past’ after which professional geographers
entered the crime research arena. The results of the questions are lightning how the
environmental factors provide the opportunities for crime. There are three crime theories
in environmental criminology that have interest in easy and appealing opportunities
which are driving people to crime [2]: Rational choice theory, Crime pattern theory,
Routine activities theory.
This feature of crime events distribution was described as an ‘inherent geographical
quality’ by [4] and was explained by theories such as the ecology of crime [3] or routine
activities [5], amongst others. A more eloquent identification of patterns and
concentrations of crime emphasize the spatial dimentions of crime.
10 Nzelibe Tobenna Nnaemeka and Bello Ashiru: Spatio-Statistical Analysis of Urban Crime; A Case Study of
Developing Country, Kaduna Metropolis, Nigeria
Complete, consistent, and reliable sets of crime data, up-
to-date information, skilled staff, appropriate geographical
information systems (GIS) background and related statistical
software are requirements to utilize the high technology
advances in crime. Data is always one of the most important
parts of the analysis. Sufficient, clear and utilizable data is
the result of severely, carefully collected and manipulated
data. Geographical information system (GIS) is a computer
based technology that requires being applied by professional
to obtain satisfactory results [8].
Crime mapping has long been a subset of the process today
known as crime analysis. Before the development of
computerized crime mapping, incidents were represented by
traditional maps with pins stuck in it. Since the traditional pin
maps have serious limitations, crime maps are now supported
with computer technology. Continual development in
computer technology innovated geographical information
systems (GIS) for the studies where the geography should be
concerned. Many industries and organizations are the users of
GIS. Crime maps started to be created with GIS to archive,
manipulate and query the crime data; to update crime
patterns; to make spatial analysis and to develop crime
pattern prediction and prevention models.
Crime mapping plays an important role in proactive
policing and crime prevention in stages of data collection,
data evaluation and data analysis. The application areas of
crime mapping are recording and mapping crime activities,
predicting crime, identifying crime hot spots and patterns,
monitoring the impact of crime reduction measures and
communicating with stakeholders [4]. Although a wide
variety of statistical and analytic techniques exist to examine
crime problems, analysts are increasingly using geographical
information systems (GIS) and mapping software to identify
areas of crime concentration. Spatial crime analysis softwares
are capable of data entry, data manipulation, pattern
identification, clustering, data mining, and geographic
profiling. Analyzing crime with related softwares is complex
but an advanced and reliable method to reach satisfactory
results [2].
Spatial patterns are identified to detect hot spots that are
explained by [4], as a geographical area of higher than
average crime. It is an area where crime incidents densely
populated compared to the average. Researchers and crime
analysts are concerned with identifying a crime hot spot
reliably and objectively. Spatial statistical techniques with
geographic information systems are combined to detect real
crime hot spots. Hot spot analysis using crime mapping is
classified into five general techniques: [14]. Which are Visual
interpretation, Crime areas as a hot spot, Choropleth
mapping, Grid-cell analysis, point pattern or cluster analysis,
spatial autocorrelation
Although, factors that explain crime have been widely
studied, its dynamics and implications on the management
of cities remained cumbersome to determine and quite
subjective. This is because of issues surrounding readily
available useful database in formats pre-developed for
crime analysis. The traditional and age-old system of
intelligence and criminal record maintenance has failed to
live up to the requirements of the existing crime scenario.
The solution to this ever-increasing problem lies in the
effective use of Information Technology. GIS can be used to
plan effectively for emergency response, determine
mitigation priorities, analyse historical events, and predict
future events. Response capabilities of GIS often rely on a
variety of data from multiple agencies and sources. The
ability to access and process information quickly while
displaying it in a spatial and visual medium allows agencies
to allocate resources quickly and more effectively. GIS
software helps coordinate vast amounts of location-based
data from multiple sources. It enables the user to create
layers for the data and view the data most critical to the
particular issue or mission [9].
The recognition of the lack of GSCMS in Kdm instigated
developing a standard model that enables rapid appraisal of
crime levels over multiple spatial scales, for developing
countries to cater for subjectivity surrounding locational
attributes and policy adequacy, using Kdm as a case in this
research. The selected precinct being centrally placed in
north western Nigeria, serves as a confluence point, while its
industrial nature has attracted a massive influx of population
interested in economic strive, estimated to be about
1,769,032 in 2014. Evidently the level of reported crime in
KdM, from the annual crime records in locations such as
Rigasa, ungwan sanusi, and narayi amongst others is
alarming [12].
Despite efforts for a more effective urban crime
management in developing countries there is still a lack in
develop stringent modalities to curb the dynamic crime
situation which is due to lack of a GeoSpatial Crime
Management System (GSCMS) with the potentials to process
and build the usual voluminous data associated with crime
into a systematic database with spatial attributes, that can be
adopted for measuring the direct implication of urban crime
and its management effectively. The GSCMS development as
a robust system that enables rapid appraisal of urban crime
occurrences over multiple spatial scales, will facilitate for a
better understanding of urban crime pattern and their spatial
tendencies. It will provide readily available useful database
with high precision and accuracy in formats pre-developed
for crime analysis, to enable envisioning and modeling urban
crime and management scenario, in a manner that is easily
visualized and understood for an informed decision making
on urban crime management policies. Most cities with
efficient crime management system such as Cape Town have
designed an integrated tool of crime analysis for combating
crime more proactively by creating a better understanding of
the patterns of crime. It was established that a large
proportion of the men of the Nigerian Police Force can
hardly ascertain the areas under the jurisdiction of their
stations or define the shortest route from their station) to
specific crime areas. He concluded that the police stations in
Ikeja LGA are far from being distributed according to
American Journal of Environmental Policy and Management 2018; 4(1): 9-20 11
geographical spread, population characteristics or crime
incidence. Currently, GIS is not being used for crime control
and management in Nigeria. This is probably due to the lack
awareness of the benefits offered by GIS in crime control and
management in the country.
Applications of GIS to crime mapping and management
have been successful in many developed countries.
Information associated with crime in Lima and Columbus
(Ohio) for instance was acquired and integrated in a GIS
environment [11]. Analysis in Lima has spanned crime from
1999 to the present. As a result, the work informed policy
and decision making in Lima Police Department activities.
Also [15] has advised throughout crime analysis that
patterns are identified and relationships of crime and law
enforcement information with different sorts of info are
studied. Such information embodies Socio-demographic
and Spatial (location). [13] has proposed effective crime
analysis employs data mining, crime mapping, statistics,
fresh methods, charting and a solid understanding of
criminal behavior. In this sense, a crime analyst serves as a
combination of an information systems specialist, a
statistician, a researcher and a planner for law enforcement
agency. [7] has proposed that the use the Location
Quotients of crime (LQC) is a method of mapping the
prevalent types of crime across urban neighborhood and
adopting crime density to investigate the relationships
between neighborhood characteristics and crime. Their
research used three aspects (1) the rationale of the
employment of official crime rates for neighborhood. (2)
An enhanced understanding of the effectiveness and
statistical properties and (3) Models of crime measured by
density, crime rates and LQCS.
The conventional tools for the mapping and analysis of
urban crime are based on manual methods that are slow,
tedious and expensive. The process is also dependent on
qualitative descriptions and subjective analysis.
Advancement in technology such as the computer
technology; spatial data visualization in geographic
information systems and an enhanced spatial statistical
analysis techniques, has largely been adopted in this research
as a modern techniques in crime analysis in the field of
environmental criminology.
In reacting to what the magnitude and typology of crime in
Kaduna metropolis is, as well what is their spatial pattern
take up in recent times and implications? For the purpose of
this paper, the stipulated intent is limited to examining the
existing strategies for urban crime management; Process
retrospective crime data on Kaduna metropolis, using
computer based spatio-statistical technics; and as well
analyse and identify the spatial pattern and implications of
identified crime pattern in Kaduna metropolis. This is as to
recommend adequate measures for managing urban crime in
the frame of urban management.
2. Study Area
Kaduna’s urban centre is one of the largest cities in
northern Nigeria, created by the colonial government. It is
located on latitude 10° 30’N and longitude 7° 28’E with
height of about 600-650m above mean sea level. Spatially, it
spans to about 7.7 hectares, from Katabu in the north to the
oil refinery in the south. Figure 1 below shows kaduna
metropolis in context.
The Google earth imagery streaming, maintaining 1 metre
altitude provided for a birdeye view of the study area, using
the Google earth pro 4.2 package. This served as a means to
enhancing the basemap. Figure 2 is the Google earth image
as downloaded from the Google earth software.
Basemap of Kaduna Metropolis was collected from the
Kaduna State Urban Planning and Development Authority
(KASUPDA). It provided a platform, on which crime data
and other related datasets were overlaid. It equally provided
insight on administrative boundaries for delineating the study
area (Kaduna Metropolis) as well as the 34 administrative
wards as defined at the time of the 2006 Census, and road
layer amongst others. The basemap was collected in a hard
copy format, thus, it was scanned, georeferenced and
vectorised in the ArcGIS environment. Ground truthing was
also carried out in order to enhance the information provided
by the basemap.
The demographic data from the National Population
Commission projection from 2006 census puts the population
of Kaduna Metropolis at 2014 to about 1,769,032. The
population were derived at ward level to serve as derivative
in computing ‘population Density’, and equally facilitated
computing for crime intensities in Kaduna metropolis.
12 Nzelibe Tobenna Nnaemeka and Bello Ashiru: Spatio-Statistical Analysis of Urban Crime; A Case Study of
Developing Country, Kaduna Metropolis, Nigeria
Figure 1. Kaduna metropolis in context of Nigeria [10].
American Journal of Environmental Policy and Management 2018; 4(1): 9-20 13
Figure 2. Google Image of Kaduna Metropolis [6].
14 Nzelibe Tobenna Nnaemeka and Bello Ashiru: Spatio-Statistical Analysis of Urban Crime; A Case Study of
Developing Country, Kaduna Metropolis, Nigeria
3. Materials and Methods
3.1. Method of Data Acquisition
Crime data used in this research were obtained from
Kaduna Police Command. Crime data was collected free of
charge from Kaduna Police Command, through the Kaduna
Police commissioner by submitting an open record request.
Acquiring the crime data through an open record request
results in a more complete and accurate dataset. The crime
dataset includes all reported crimes classified according to
the Nigerian Police (NP) Uniform Crime Reporting (UCR)
program. This research examines seven of category A Crimes
which include murder and non-negligent manslaughter,
forcible rape, robbery, aggravated assault, burglary, larceny-
theft and auto theft. Only Category A Crimes are included in
this research because these crimes are taken as more serious
than others in crime analysis and the data sources are more
reliable. The police are usually on the scene or visit the scene
to record these types of crimes in most cases, as obtained
from Nigerian Police, (2012) Report. Table 1 shows the UCR
codes for the seven Category A Crimes adopted in
classification of offenses in this research.
Table 1. UCR classification offenses codes and Impact Factor for Category A Crimes.
Category A Crimes (Category A crime, included in the Crime Index.)
Code Violent Crimes Percentage Impact
0 Murder And Non-negligent Manslaughter 25
2 Forcible Rape 14
3 Robbery 21
4 Aggravated Assault (Class I) 7
Non-Violent Crimes
5 Burglary 4
6 Larceny – Theft (Includes Burglary of Motor Vehicles) 11
7 Auto Theft 18
Total 100
[12]
In addition to Offenses type by classification the dataset
includes the offense date and time, police beat, and address,
where the offense took place. A complete set of crime data
for the selected seven Category A Crimes were obtained for
Three fiscal years from records of the Kaduna Metropolis
Police jurisdictional division (PJD) for the study period
(July, 2011 - June, 2014) were used in this research, The
crime data were compiled in a Microsoft Excel format and
limited to those crime Incidence recorded by the police,
ranging from: July 2011 – June 2012, July 2012 – June
2013, July 2013 – June 2011. The impacts of the various
category A crime types on the other hand were factored into
determine crime weight so as to facilitate conducting an
unbiased analysis
3.2. Method of Data Processing
In the collection of data for this research, data mining
methodology was adopted. This enabled crime analysis in
multidimensional space and allows the integration of
methods from other disciplines by considering the
semantic ties among data objects. This will enable the
searching of interesting patterns among multiple
combinations of dimensions at varying levels of
abstraction. The data from the crime incidence in this
article was being entered into an Oracle 10g relational
database and then analyzed using statistical techniques,
and ArchGIS. The data from the case study will be
managed using Oracle 10g, which allows for unified
integration into MATLAB and ArchGIS. The Oracle 10g
platform allows for efficient data management and
analysis through its unique architecture system. This
allows integration of the spatial data into a GIS type
environment contained completely within the database
software. The software will be used to store spatial data in
tabular form to allow output queries based on a
determined field to other software for graphic display.
Geocoding was performed with function to geocode to
the next level of precision, The ability to assign confidence
codes that describe the accuracy and precision of
geographic coordinates that have been determined for each
crime record was a function included in the geocoding
process. Each incidence with a valid known address will be
assigned a latitude/longitude coordinate on the WGS84
datum coded using the Administrative blocks and street
database. The geocoding process will assigns an “x and y”
value for each spatial point representing an incident. This
“x and y” value will then be placed on a map using a
predetermined coordinate in a specified unit. These features
will then be displayed graphically on a base map. Once, the
data is geocoded, it is migrated back to the Oracle 10g
database and joined with associated address records. Once
brought into ArchGIS environment, the files will then be
displayed in a variety of formats. The base map will then be
used to overlay layers that contained crime information.
This will allow for the presentation of the data making it
possible to recognize the location where an incident of
crime took place. This processes facilitated performing
American Journal of Environmental Policy and Management 2018; 4(1): 9-20 15
spatial queries on the crime data. The queries will be
performed using a standard database language called
Structured Query Language (SQL) that builds logical
expressions to select data of interest. The types of queries
that will be performed in this research include Selecting by
attribute information, grouping by attribute information and
Selecting by geographic area
3.3. Method of Application of the GSCMS in
Crime Analysis
GSCMS will automate crime data into a unified platform
that permits for manipulating spatial data in various scales. It
will equally facilitate to describe and visualize spatial
distributions; identify typical locations or spatial outliers;
discover patterns of spatial associations, clusters, or hot
spots; and suggest spatial regimes or other forms of spatial
heterogeneity. GSCMS will be used in the analysis of crime
patterns in spatial variance in KdM. Through the use of
GSCMS, distribution of crime incidents across KdM will
yield vital information regarding concentrations of crime
spatially.
GSCMS is a robust crime management and analysis tool
that served as a unified medium, with capability to
automate crime data collected on varying spatial scales,
quantify crime and combine the various factors of crime,
into spatial crime pattern analysis. Its ability to manipulate
spatial data in one medium is the second logic in the tool
design. The demand for a unified tool is based on the
limitations of individual existing techniques being limited
to the treatment of one or at most two aspects of a spatial
phenomenon at a particular instance. The use of statistical
techniques for data processing and computational analysis
for instance computes for crime pattern, but always falls
short in visually presenting crime pattern analysis spatially.
This is because it falls to combine other layers as to
facilitate visualizing crime pattern in context. This implies
that it cannot be used in absolute isolation to achieve the
level of the analysis presented in this paper, for decision
makers to easily visualize and project crime implication.
The shortcomings of the techniques of GIS and Remote
Sensing lie with the lack of versed computation capacity to
determine crime indicators such as population in the case of
this research. The GSCMS enables cost effective
acquisition of data to enable crime mapping, pattern
analysis and computation of crime indicators. In such an
environment it is easy to undertake crime pattern analysis
and computation of correlation for the different units of
analysis. The crime Pattern model adapted in this research
is from a component of the model generated [1].
4. Spatio-Statistical Analysis of
Crime in Kaduna Metropolis
This section presents the results and discussion from a
descriptive spatial-statistical stand point, by aggregating the
crime incidence from administrative wards comprising the
identified 14 Police Jurisdictional Division (PJD) within
Kaduna metropolis area command Jurisdictional Division
(KDMACJD).
The presentation of findings for this spatial analysis is as
crime density maps indicating the spatial distributions (SD)
of crime per 10,000 population (C/10,000 pop.). This
facilitated exploring as to identifying for the spatial pattern of
crime in Kaduna Metropolis (KDM). Crime risk prone areas
through the study period were then discussed, as to
evaluating for risk prone locations in space.
All Figures presented in this section were produced from
police records within KDMACJD with elaborate details on
each crime types, author’s fieldwork and related data drawn
from archives of independent agencies within KDM. While
the spatial dimensions was to PJD resolution as delineated by
aggregating Administrative Wards (AW) as defined by The
Nigerian Police (NP).
4.1. Crime Rate in Kaduna Metropolis by
Administrative Words Constituting PJD
As indicated in the map in figure 3 Rigasa has the highest
recorded Crime incidence with 391 (10.46%) report cases,
among the 34 administrative wards constituting Kaduna
metropolis having Crime incidence of 3738 recorded. This is
followed by Ungwan_Sanusi 360 (9.63%), Ungwan_Rimi
263 (7.04%), Kabala 250 (6.69%), Tudun_Nupawa 237
(6.34%), Hayin_Banki 231 (6.18%), Badarawa 179 (4.79%),
Sardauna 176 (4.71%), Barnawa 136 (3.64%), Shaba 132
(3.53%), Ungwan_Shanu 124 (3.32%) and Badiko 114
(3.05%), ranking 2nd to 12th place respectively which are all
located above the Crime mean of 109.94 (2.94%),
constituting 2593 (69.37%). The 22 other wards with the
least Crime incidence below the stated mean constituted
about 1145 (30.63%).
16 Nzelibe Tobenna Nnaemeka and Bello Ashiru: Spatio-Statistical Analysis of Urban Crime; A Case Study of
Developing Country, Kaduna Metropolis, Nigeria
Figure 3. Crime Intensity in Kaduna Metropolis for the Study Period (July, 2011 – June, 2014) [10].
American Journal of Environmental Policy and Management 2018; 4(1): 9-20 17
4.2. Crime Intensity in Kaduna Metropolis by
Administrative Wards Constituting PJD
Per 10,000 Pop
This has led to drawn a beneficial deduction such as the
spatial pattern of recorded crime incidence in Kdm varied
from that of its intensity per 10,000 population by location
(ward). As deduced from the findings of this analysis
presented in the map in figure 3 and Table 2 shows Kaduna
metropolis crime intensity for the study period and details on
crime numbers, rates per 10.000 population by wards
constituting PJD’s; Details on recorded crime in Kaduna
metropolis for the study period, indicating that Rigasa has the
highest recorded Crime incidence with (10.46%) report
cases, which is followed by Ungwan_Sanusi (9.63%),
Ungwan_Rimi (7.04%), Kabala (6.69%), Tudun_Nupawa
(6.34%), Hayin_Banki (6.18%), Badarawa (4.79%),
Sardauna (4.71%), Barnawa (3.64%), Shaba (3.53%),
Ungwan_Shanu (3.32%) and Badiko (3.05%), ranking 2nd to
12th place respectively are all located above the local
statistically calculated mean crime in Kdm of (2.94%), and
constituting (69.37%) of the total reported crime in Kdm.
While Sardauna (18.60%), Shaba (15.69%), Ungwan_Sanusi
(8.71%), Kabala (7.93%), Ungwan_Rimi (6.58%),
Ungwan_Sarki (4.76%), Tudun_Nupawa (4.38%), Maiburji
(3.75%), Hayin_Banki (3.41%), are 9 wards identified to be
all located above the local statistically calculated mean crime
in Kdm of (2.94%), constituting (73.81%) of the total
reported crime in Kdm per 10,000 population. This is to say
in relation to population size, crime intensity varies widely
within each ward.
Table 2. Crime Intensity in Kaduna Metropolis per 10,000 pop.
Wards
Aggravated Assault in
Kdm (per 10000 pop)
Auto Theft in Kdm (per
10000 pop)
Burglary in Kdm (per
10000 pop)
Forcible Rape in Kdm (per
10000 pop)
intensity % intensity % intensity % intensity %
Sardauna 240.5154 22.01% 309.2341 24.80% 97.35147 15.60% 180.3865 17.71%
Shaba 149.4586 13.68% 192.161 15.41% 97.60561 15.64% 149.4586 14.68%
Ungwan_Sanusi 48.72107 4.46% 93.96207 7.54% 65.42544 10.48% 77.95371 7.65%
Kabala 58.86779 5.39% 47.80242 3.83% 54.88426 8.79% 99.14575 9.74%
Ungwan_Rimi 94.58769 8.66% 46.01563 3.69% 40.90278 6.55% 97.14411 9.54%
Ungwan_Sarki 72.30782 6.62% 15.49453 1.24% 34.4323 5.52% 48.20522 4.73%
Tudun_Nupawa 50.81733 4.65% 76.6015 6.14% 27.03582 4.33% 56.0743 5.51%
Maiburji 42.39855 3.88% 36.34161 2.92% 40.37957 6.47% 28.2657 2.78%
Hayin_Banki 25.43361 2.33% 72.28501 5.80% 21.41778 3.43% 16.06334 1.58%
Badarawa 29.41235 2.69% 18.00756 1.44% 14.40605 2.31% 25.21059 2.48%
Barnawa 10.49251 0.96% 61.67024 4.95% 15.41756 2.47% 20.98501 2.06%
Sabon_Gari_North 34.67635 3.17% 24.76882 1.99% 7.705856 1.23% 23.11757 2.27%
Dadi_Riba 32.6408 2.99% 9.325942 0.75% 12.43459 1.99% 29.01404 2.85%
Ungwan_Shanu 33.36738 3.05% 40.85802 3.28% 9.07956 1.45% 19.06708 1.87%
Sabon_Gari_South 19.4955 1.78% 14.32323 1.15% 12.73176 2.04% 16.71043 1.64%
Badiko 12.56665 1.15% 22.61997 1.81% 10.77141 1.73% 20.10664 1.97%
Sabon_Gari_West 21.35248 1.95% 12.6707 1.02% 7.508564 1.20% 9.854991 0.97%
Sabon_Gari 18.13505 1.66% 16.65464 1.34% 5.181443 0.83% 10.36289 1.02%
Ungwan_Dosa 7.48543 0.68% 9.624124 0.77% 4.277389 0.69% 14.97086 1.47%
Rigasa 12.31193 1.13% 13.07664 1.05% 6.576558 1.05% 11.77663 1.16%
Tudun_Wada_South 3.664921 0.34% 14.13613 1.13% 4.188482 0.67% 0 0.00%
Kawo 4.615435 0.42% 11.86826 0.95% 1.318696 0.21% 11.53859 1.13%
Mekera 5.77882 0.53% 8.915893 0.72% 6.604365 1.06% 13.86917 1.36%
Narayi 2.535528 0.23% 23.9064 1.92% 3.380703 0.54% 11.83246 1.16%
Kakuri_Hausa 2.282733 0.21% 5.869884 0.47% 6.522094 1.05% 0 0.00%
Tudun_Wada_North 19.05873 1.74% 0 0.00% 0 0.00% 0 0.00%
Nasarawa 10.45044 0.96% 0 0.00% 5.11858 0.82% 0 0.00%
Kakuri_Gwari 2.591777 0.24% 9.996853 0.80% 2.221523 0.36% 2.591777 0.25%
Kakau 3.020008 0.28% 9.707167 0.78% 2.157148 0.35% 3.020008 0.30%
Kujama 7.249979 0.66% 11.18568 0.90% 2.485707 0.40% 8.699975 0.85%
Yelwa 6.188301 0.57% 5.304258 0.43% 0.589362 0.09% 4.125534 0.41%
Sabon_Tasha 3.812784 0.35% 5.602459 0.45% 2.801229 0.45% 0 0.00%
Rido 4.723878 0.43% 6.748397 0.54% 1.199715 0.19% 5.248753 0.52%
Television 1.811735 0.17% 0 0.00% 0 0.00% 3.62347 0.36%
Total 1092.829 100.00% 1246.739 100.00% 624.1134 100.00% 1018.424 100.00%
Average 32.14204 2.94% 36.6688 2.94% 18.35628 2.94% 29.95364 2.94%
18 Nzelibe Tobenna Nnaemeka and Bello Ashiru: Spatio-Statistical Analysis of Urban Crime; A Case Study of
Developing Country, Kaduna Metropolis, Nigeria
Table 2. Continued.
Wards
Larceny-Theft in Kdm
(per 10000 pop)
Murder in Kdm (per 10000
pop)
Robbery in Kdm (per
10000 pop)
Crime in Kdm (per 10000
pop)
intensity % intensity % intensity % intensity %
Sardauna 1354.331 17.56% 465.2827 21.53% 450.9664 16.08% 3098.067 18.60%
Shaba 939.454 12.18% 381.2719 17.65% 704.5905 25.13% 2614 15.69%
Ungwan_Sanusi 849.8347 11.02% 139.2031 6.44% 175.3959 6.25% 1450.496 8.71%
Kabala 681.6271 8.84% 154.9152 7.17% 223.0779 7.96% 1320.32 7.93%
Ungwan_Rimi 570.4477 7.40% 109.561 5.07% 138.0469 4.92% 1096.706 6.58%
Ungwan_Sarki 407.1619 5.28% 107.6009 4.98% 108.4617 3.87% 793.6645 4.76%
Tudun_Nupawa 305.655 3.96% 118.9076 5.50% 94.62538 3.37% 729.7169 4.38%
Maiburji 299.8183 3.89% 50.47446 2.34% 127.1956 4.54% 624.8738 3.75%
Hayin_Banki 227.1815 2.95% 52.5883 2.43% 152.6017 5.44% 567.5712 3.41%
Badarawa 224.4943 2.91% 40.01681 1.85% 67.22824 2.40% 418.7759 2.51%
Barnawa 150.7495 1.95% 48.17987 2.23% 67.45182 2.41% 374.9465 2.25%
Sabon_Gari_North 160.4469 2.08% 55.04183 2.55% 52.01453 1.85% 357.7719 2.15%
Dadi_Riba 148.1789 1.92% 51.81079 2.40% 43.52106 1.55% 326.9261 1.96%
Ungwan_Shanu 167.2909 2.17% 22.6989 1.05% 28.60061 1.02% 320.9624 1.93%
Sabon_Gari_South 131.2963 1.70% 39.78674 1.84% 50.1313 1.79% 284.4752 1.71%
Badiko 108.6117 1.41% 49.36897 2.28% 30.15996 1.08% 254.2053 1.53%
Sabon_Gari_West 108.4049 1.41% 41.06246 1.90% 29.56497 1.05% 230.4191 1.38%
Sabon_Gari 105.8495 1.37% 32.38402 1.50% 15.54433 0.55% 204.1118 1.23%
Ungwan_Dosa 82.33973 1.07% 40.10052 1.86% 22.45629 0.80% 181.2543 1.09%
Rigasa 90.00707 1.17% 14.33843 0.66% 25.69446 0.92% 173.7817 1.04%
Tudun_Wada_South 77.74869 1.01% 19.63351 0.91% 32.98429 1.18% 152.356 0.91%
Kawo 68.90186 0.89% 12.36277 0.57% 38.07734 1.36% 148.683 0.89%
Mekera 74.46422 0.97% 16.51091 0.76% 17.33646 0.62% 143.4798 0.86%
Narayi 51.79721 0.67% 15.09243 0.70% 20.28422 0.72% 128.8289 0.77%
Kakuri_Hausa 78.91733 1.02% 8.152617 0.38% 6.848198 0.24% 108.5929 0.65%
Tudun_Wada_North 55.62038 0.72% 9.723843 0.45% 16.33606 0.58% 100.739 0.60%
Nasarawa 51.61235 0.67% 21.32742 0.99% 8.957516 0.32% 97.4663 0.59%
Kakuri_Gwari 30.54594 0.40% 9.256345 0.43% 7.77533 0.28% 64.97954 0.39%
Kakau 24.91506 0.32% 2.696435 0.12% 18.12005 0.65% 63.63587 0.38%
Kujama 27.34278 0.35% 5.178557 0.24% 0 0.00% 62.14268 0.37%
Yelwa 14.58671 0.19% 7.367025 0.34% 12.3766 0.44% 50.53779 0.30%
Sabon_Tasha 14.55083 0.19% 3.890596 0.18% 13.0724 0.47% 43.7303 0.26%
Rido 14.02167 0.18% 1.874555 0.09% 4.723878 0.17% 38.54085 0.23%
Television 14.23506 0.18% 12.94096 0.60% 0 0.00% 32.61123 0.20%
Total 7712.441 100.00% 2160.603 100.00% 2804.222 100.00% 16659.37 100.00%
Average 226.8365 2.94% 63.54713 2.94% 82.47712 2.94% 489.9815 2.94%
Note: Rate is expressed in per 10,000 population
Total Population of Kaduna metropolis = 1,769,032
4.3. Implications of Crime Pattern in Kaduna
Metropolis
The findings contained in this study demonstrate the
importance of incorporating spatial and demographic effects
into empirical models of crime. A related implication is that
global theories of crime may need to be further modified or
expanded in order to take spatial patterns and spatial dynamics
more explicitly into account. Indicated results as in Table 2.
Given recent developments in GIS technology and spatial
analysis applications, there is now available a rich array of
tools that can be applied to the study of crime in its spatial
context. This opens the door for new ways to explore,
visualize, and understand hot spots and clusters of crime,
spatial diffusion processes, and differences based on spatial
scale or location which is what was accomplished using
ITCA to modelling crime pattern in Kdm.
The crime rate by police jurisdictional division for the
Study Period (July, 2011 – June, 2014), in figure 4 reflects
clearly that Kawo, Rigasa, Tudun Wada and Unguwan Sanusi
PJD’s have a crime report rate above local average and
accounts for about 37.1% of the total of all Category A
Crimes with 9.4%, 10.1%, 8.4% and 9.2% respectively.
American Journal of Environmental Policy and Management 2018; 4(1): 9-20 19
Source: Derived from Nigerian Police on Authors Field Work, (2015)
Figure 4. Spatial Distribution of Crime by Police Jurisdictional Divisions in Kaduna Metropolis for selected (seven Category A) Crime Type for the Study
Period (July, 2011 – June, 2014).
Apart from Gabasawa and Sabon Tasha PJD with the
lowest reported crime rate conversely takes up about 10.6%
of the total of all Category A Crimes with 5.2% and 5.4%
respectively other PJD’s are near average crime rate of all
Category A Crimes.
This may be explained by the fact that Gabasawa PJD have
a very high security levels Sabon Tasha on the other hand
seemingly low security awareness while Kawo, Rigasa,
Tudun Wada and Unguwan Sanusi PJD’s have very high
population.
The results of this study thus have both theoretical and
methodological implications and point to several directions
for future research. First, the results indicate that different
processes may be operating in Kaduna metropolis. In fact, it
could be said that one of the least understood topics in the
field of criminology is that of spatial and spatio-temporal
crime pattern. Thus, there is a need for further research on
spatio-temporal crime pattern which takes location and
geographic context seriously. Future more these findings
should address the spatial dynamics of crime as a product of
social, economic, and demographic factors.
Secondly, these findings also point to the need for
spatially-informed theory construction in the field of
Criminology. Ecological approaches to the study of crime
may provide fruitful theoretical directions for studying the
spatial dynamics of crime. Testing the relative merits of the
stratification and social control perspectives from a more
spatially informed model building approach should therefore
prove to be a promising direction for future research as well.
Based on the implications of the present study, it may well be
that a spatially informed stratification model of crime would
be more appropriately applied in urban context.
Finally, the present research shows the value of applying
Geographic Information System (GIS) technologies and
spatial analytic procedures to the study of aggregate crime
patterns. The main advantage of using GIS and related
technologies is that it enables the researcher to look more
rigorously at the spatial patterns and ecological contexts of
crime. Furthermore, the analytical applications of GIS can be
used in either an exploratory or confirmatory capacity. As an
exploratory data analysis tool, GIS can be used to examine
data visually as a way of generating new hypotheses from the
data or as a way of identifying unexpected spatial patterns.
As a confirmatory data analysis tool, GIS has been given
increased analytical power with the introduction and
development of Programmable spatial statistical packages
such as MATLAB. Thus, future studies could benefit
substantially by systematically investigating factors
associated with crime from a spatial perspective utilizing the
contributions that GIS and geographic information analysis
can provide. By employing spatial analytic procedures within
a GIS environment, contextual and ecological factors
identified as theoretically relevant in studies of crime.
Overall, the findings of the present study show how
important spatial and contextual analysis can be in the study of
urban crime across various levels of geography. By combining
graphical, analytic and statistical tools in a GIS environment,
researchers can explore spatio-temporal patterns, which may
warrant further empirical examination as well as formally test
spatially informed theoretical models for their applicability at
different spatial and temporal scales and locations.
5. Conclusions
The pattern of urban crime in Kaduna metropolis was
examined using GSCMS, with a view to offering possible
options for effective urban crime management. However this
was successful in accordance to the set objective of this
paper, the following conclusions where then ascertained.
From extensive review carried out for this research it was
established that crime is an increasingly serious problem in
cities all over the world, especially in developing countries or
countries in transition. However, the study of crime is still
scanty. Advancement in technology such as the computer
20 Nzelibe Tobenna Nnaemeka and Bello Ashiru: Spatio-Statistical Analysis of Urban Crime; A Case Study of
Developing Country, Kaduna Metropolis, Nigeria
technology, data visualization and geographic information
systems (GIS), has enhanced spatial statistical analysis
techniques. The field of environmental criminology has
largely adopted these techniques in crime analysis. Indeed,
city officials and policy makers have recognized that crime
patterns could be of greater importance when their spatial
dimensions are taken into account. However, in practice the
lack of geo-spatial awareness and thinking has resulted in an
un-urgent sense of developing GSCMS demonstrated as a
model in Kaduna Metropolis.
In spite of the current crime prevention strategies in
Kaduna Metropolis there has not been one which reference
analysis with a spatio-statistical inclination which implies
policy makers and police departments cannot fight or take
precautions against the symptoms of crime actively, for a
more proactively policing. The finding of this research imply
that crime treatment and prevention strategies should be re-
examined carefully to incorporate a spatio-statistical
dimensions of crime so as to reach a result that can serve as a
useful reference for policy makers and police departments for
crime prevention and related decision making process.
Finally it cannot be overemphasized that the long-term
solution to the crime problem will rely greatly on good
governance, with policies that will foster inclusiveness equity
and social justice as geared towards reduction of poverty.
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