A Short Introduction to GIS

C. D. Lloyd

School of Environmental Sciences University of Liverpool

Copyright © C. D. Lloyd, 2012

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Contents

Preface ........................................................................................................... iii Acknowledgements ......................................................................................... iii Basic principles and data models

Chapter 1. Introduction to GIS ......................................................................... 1

Chapter 2. Spatial data models and data input ................................................ 4

Chapter 3. Databases in GIS ........................................................................... 8

Spatial data analysis

Chapter 4. Analysis of objects ....................................................................... 10

Chapter 5. Analysis of grids and surfaces ...................................................... 19

Data visualisation and data quality

Chapter 6. Visualisation and presentation of spatial ...................................... 27

information ..................................................................................................... 27

Chapter 7. Data quality .................................................................................. 31

Organisation and application of GIS

Chapter 8. GIS project design, management and organization ..................... 34

Chapter 9. Applications of GIS ....................................................................... 36

Future developments and summary

Chapter 10. GIS in the future ......................................................................... 42

References .................................................................................................... 44

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Preface

This booklet is intended as a very brief introduction to the principles and practice of

Geographical Information Systems and Science. The coverage of some material is

intentionally limited, but there is hopefully enough depth to help develop

understanding and guide further reading. At the end of each chapter some

recommended references are given.

This booklet is intended primarily for students attending the module

ENVS363/ENVS563 ‘Geographical Information Systems’ at the University of

Liverpool. It may be used for teaching purposes by anyone else on the condition that

is it properly cited. Citation should follow this format:

Lloyd, C. D. (2012) A Short Introduction to GIS. Liverpool: School of Environmental

Sciences, University of Liverpool.

Please contact the author with any comments or suggestions (email address:

c.d.lloyd@liverpool.ac.uk).

Acknowledgements

Conor Graham is thanked for allowing the use of case study material in Chapter 9.

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Chapter 1. Introduction to GIS

This chapter introduces the topics of spatial data and Geographical Information

Systems (GIS). One possible definition of a GIS is that it is a software environment

for collecting, storing, managing, analyzing and visualizing spatial data (as defined in

Section 1.1). Other definitions are considered in Section 1.4. Section 1.4 also

introduces the notion of Geographical Information Science.

The chapter will provide you with a summary of the basic concepts and the themes

outlined will be discussed in more detail in later chapters.

1.1 What are spatial data?

One of the reasons that GIS are different to other information systems is that they are

used to work with spatial data. Spatial data are data that have some attribute that can

be used to position them in space. This spatial information may comprise, for

example, grid co-ordinates or postcodes. Examples of aspatial (that is, non spatial)

and spatial data are shown in Figure 1.1.

Figure 1.1. Aspatial data and spatial data.

1.2 Sources of spatial data

Spatial data may be collected by, or on behalf of, the user (primary data) or data

collected by others may be input into a GIS (secondary data).

There are two ways in which (primary) spatial data may be collected:

• Ground survey: contact is generally made with the property of interest during its

measurement

• Remote sensing: no physical contact is made with the property of interest during its

measurement

Alternatively, existing data may be suitable. Information in paper maps may be input

into a computer using several procedures including digitisation and scanning.

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The topic of sources of spatial data will be addressed in more detail in Chapter 2.

1.3 Analysing spatial data

Once we have some spatial data we can analyse them (note data = plural, datum =

singular) in one of two ways. We can:

• Summarise or describe the data as though they are not spatially referenced

• Take into account the spatial location of one variable or assess spatial relationships

between variables

There may be a wide range of problems that we have to resolve using spatial data. Put

simply, we may wish to ascertain:

• Where are particular features found?

• What geographical features exist?

• What changes have occurred over a given time?

• Where do certain conditions apply?

• What will the spatial implications be if an organisation takes certain action?

(Source: Heywood et al., 2006)

The following questions detail some specific examples of problems that involve

spatial data:

• How many houses are located within two kilometres of a planned power station?

• What is the shortest route between an ambulance station and the site of an accident?

• What areas lie more than one kilometre from reservoirs, are undeveloped,

unpolluted, and on gravely soils?

• What are the characteristics of some property (e.g., amount of rainfall) at some

unsampled location?

Each of these problems concerns the spatial relations between several variables (for

example, distances between some houses and a power station). These kinds of

problems can be solved using the tools provided within a Geographical Information

System (GIS).

Applications in which GIS have been used are extensive. GIS are widely used in

maintaining, analysing and displaying many different kinds of data that are spatially

referenced, such as locations of pollution monitoring stations, topographic features or

outbreaks of disease.

It is usually the case that the data in a GIS are stored in a series of ‘layers’. Taking the

first problem stated above: the data would comprise a layer with information about

the location of houses and a layer giving the site of the proposed power station. The

analytical functionality (in other words, the analysis tools) of the GIS would then be

used to calculate the distance between the planned power station and each of the

houses in the data set. Each of the houses within the specified distance of the power

station could then be selected and highlighted using the GIS.

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1.4 Geographical Information Systems and Science

GIS have been developed to enable us to map and, more specifically, to analyse

spatial information. There are many definitions of GIS but one which summarises the

key components well views GIS as “a powerful set of tools for collecting, storing,

retrieving at will, transforming and displaying spatial data from the real world for a

particular set of purposes” (Burrough, 1986).

GIS does not stand still and the field of study that deals with developments that are

made in response to problems faced when using a GIS is called Geographical

Information Science (GIScience; see Longley et al., 2005a, for more details). The

acronym GIS is used in this resource to refer to GISystems and where GIScience is

meant this is made clear.

This guide introduces the basic principles of GIS and GIScience. The resource is

divided into four parts, defined as follows:

Basic principles and data models (this chapter and Chapters 2 and 3): discusses

how spatial features are represented in a GIS as well as data collection and input.

Spatial data analysis (Chapters 4 and 5): focuses on the key set of functions that

make a GIS different to other information systems, the ability to analyse spatial

variation.

Data visualisation and data quality (Chapters 6 and 7): reviews the main

approaches to visualising spatial data in a GIS.

Organisation and application of GIS (Chapters 8 and 9): a key component of an

GIS is the way in which it is set up and operated, this part of the guide reviews the

organisation of GIS and looks at some case studies.

The final chapter (9) is concerned with recent and possible future developments in

GIS.

1.5 Further reading

Each chapter has at its ends a list of recommended texts that should be used to

develop your theoretical knowledge of GIS. Good general introductions to spatial data

and GIS are the books by Burrough and McDonnell (1998), Heywood et al. (2006)

and Longley et al. (2005a).

The next chapter deals with the representation of spatial features in a GIS, sources of

spatial data and the input into GIS of information from existing paper-based sources.

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Chapter 2. Spatial data models and data input

This chapter deals with two issues: (i) how reality is represented in a GIS and (ii)

sources of spatial data. The chapter will describe the two main data models used in

GIS and the key means of acquiring spatial data will be outlined.

2.1 Spatial data models

Spatial information may be represented in a computer in variety of ways. In other

words, reality may be modelled, abstracted, or represented using different approaches.

The two main data models in GIS are called vectors and rasters.

Vector data comprise points, line and polygons (enclosed areas). The vector model is

illustrated in Figure 2.1. Rasters are images or grids comprising cells called pixels.

The raster model is illustrated in Figure 2.2. The vector and raster models are

described in more detail below.

Figure 2.1. The vector model. Figure 2.2. The raster model.

In Figure 2.1, the point feature could be the location of a well hole; the line could

represent a road and the polygon (an area) would be a suitable means of representing

a lake. Vector lines and polygons are composed of nodes (the dots in Figure 2.1 with

co-ordinates next to them), arcs, the lines that connect nodes to one another and

vertices. Vertices represent changes in direction of arcs. Vector data comprise (i)

spatial information (that is, their location defined by co-ordinates and topological

information for lines and polygons) and (ii) attribute information (such as a line which

carries the attribute ‘road’).

In Figure 2.2, the cells are shaded according to the numbers assigned to them. Cells

(or pixels) with the value 1 (coloured white) could represent, for example, a low level

of pollution whilst cells with a value of 4 (coloured black) could represent a high level

of pollution. Unlike vector data, the cells in a raster grid do not have information

stored concerning their location. Rather, the location of a cell is known within a grid

(that is, a cell may be in fourth position along the x axis and seventh position along

the y axis). In other words, a cell is not considered a separate object. The area covered

on the ground by a cell is referred to as its spatial resolution. So, a grid with a spatial

resolution of 5 metres has cells that cover an area of 5 by 5 metres in the real world.

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The vector model is commonly used to represent discrete feature such as ‘road’,

‘park’ or ‘county boundary’. In contrast, raster grids are primarily used to represent

continuous variables such as ‘elevation’, ‘precipitation’ or ‘soil acidity’. Exceptions

are when vectors are used to represent topography (for example, using contour lines)

and digital remotely sensed imagery (raster grids) which may include a range of

different objects such as houses, trees or water.

One of the most important decisions that must be made when a spatial database is

established concerns whether to use the vector or raster data model. The choice

between the two is not always straightforward and it will depend on many factors, not

least of which is the availability of digital data. Where the data are input by the user

then clearly there is greater choice.

Once spatial data are available in a digital format it is usually fairly straightforward to

convert between data formats, for example vector to raster conversion. In addition,

there is often a desire to convert data to different co-ordinate systems, this is a

standard function of most GIS.

2.2 Spatial data collection and input

Data may be divided into two classes: primary data and secondary data. Primary data

are acquired by the user for a specific purpose. Secondary data are data collected by

another group or individual. A common limitation of GIS analyses is that the data

used may have been collected with a purpose in mind which is different to that for

which the data are required. Thus, they may not be ideally suited to task in hand.

2.3 Primary data

Spatial data may be collected on the ground or through remote sensing. There are

several widely used means of obtaining information on the spatial position of features

through ground survey. These include:

• Traditional survey techniques: tape and offset surveying and levelling.

• Theodolites (for measuring angles).

• Electronic Distance Measurers (EDMs).

• Total stations: high precision theodolite and an EDM.

• Global Positioning Systems (GPS).

As well as conventional aerial photography remotely sensed data may be obtained

through the use of different kinds airborne and spaceborne sensors which detect

radiation from different parts of the electromagnetic spectrum. Sensors may be

divided into two groups:

• Passive sensors: sense naturally available energy.

• Active sensors: supply their own source of energy to illuminate selected features.

Examples are radar and LiDAR (Light Detection and Ranging). Radar emits pulses of

macrowave energy and LiDAR emits pulses of laser light.

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2.4 Secondary data

Data may be converted from an analogue format (for example, a paper map) into a

digital format using several different approaches. These include keyboard entry,

digitisation and scanning. Each of these approaches are summarised below.

Keyboard entry: Spatial attributes (for example, co-ordinates or postcodes) and non-

spatial information (for example, descriptions of the features being mapped) are input

manually.

Digitisation: features are traced using either a mouse (for on-screen digitising) or a

puck (for digitising features on a paper map).

Scanning: the source document (for example, a map) is sampled using transmitted or

reflected light.

The basic principles of digitising can be summarised as follows:

• The digitiser works on the basis that it is possible to ascertain the location of a puck

(rather like a mouse) as it passed over a table that is inlaid with a fine mesh of wires.

• Digitising results in vector representation of the features traced.

• Digitising can also be conducted on-screen by using the mouse cursor to trace

features (for example, you could trace roads on a scanned aerial photograph). On-

screen digitising is also called heads-up digitising.

• Digitisers can be set only to record points each time the puck button is pressed

(point mode). Alternatively, stream mode can be used. In stream mode, the digitiser

can be set to record points when the cursor moves for a set distance or period of time.

Figure 2.3 shows a digitiser in use. The operator is tracing the features on a map using

a puck which has a clear window with a cross-hair superimposed with which the map

features are traced.

Figure 2.3. Digitising in action.

The key steps in the digitising process are as follows:

1. The paper map is taped to the digitising tablet.

2. Control points are selected on the map.

3. The control points are entered one at a time to register the map to the tablet.

4. Features are traced using the cursor.

5. After completion of step 4 the digitised features are checked for errors.

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6. Attribute data are added.

The basic principles of scanning can be summarised as follows:

• The scanner works by scanning line by line across an analogue source (e.g., a paper

map) and recording the amount of light reflected from the surface of the data source.

• The map is scanned in using a drum scanner (a flat bed scanner may also be used if

accuracy is less of a concern).

• Scanning results in a new raster (cell-based) representation of the map. Features on

the map must be identified as objects as a separate step.

2.5 Further reading

There are many good introductions to the principles of spatial data models and data

input. The books by Burrough and McDonnell (1998), Heywood et al. (2006) and

Longley et al. (2005a) all provide information on these issues. In addition, Part 2,

Section C of the book edited by Longley et al (2005b) provides detailed accounts of

specific issues in spatial data collection.

The next chapter looks at databases in GIS, the database is the core of a GIS and a

well designed database is central in allowing efficient and rapid access to the data

stored within the system.

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Chapter 3. Databases in GIS

GIS was defined in Chapter 1. One definition of GIS is that it is a spatial database.

That is, GIS store data like any other database. The difference with other databases is

that in a GIS the data are spatially referenced — they have a spatial location.

As was noted in Chapter 2, vector data are composed of spatial information and

attribute information. Spatial data may be input into the GIS by manual input,

scanning or digitising (as detailed in Chapter 2) but in these cases attribute data are

keyed in separately. Each of the spatial features, such as a length of road, is assigned

unique identifiers. If the attribute data stored in a spreadsheet or database have the

same identifiers then the spatial and attribute information can be linked. This, of

course, enables the user to select and draw features with specific attributes. Before

looking at this concept in a little more detail the chapter will define databases.

3.1 Defining a database

A database is a set of structured data — “An organised filing cabinet is a database, as

is a dictionary, telephone directory, or address book. Thus, databases can be

computer-based or manual” (Heywood et al., 2006, p. 111). Once information is

stored in a computer-based database it can be sorted, summarised and combined in

various ways. For a database to be useful it must contain appropriate data that can be

accessed effectively and efficiently.

3.2 Database Management Systems

Data stored in databases are managed and accessed using a database management

system (DBMS). If, for example, you wished to extract all records in a database that

fulfilled certain conditions (for example all shops with more than 25 staff) then you

would use DMBS computer software to achieve this. Structured Query Language

(SQL) is a language that has been developed to enable users to access information in a

database in a fairly flexible way.

3.3 The relational database

Various different approaches to storing data in computers have been developed. The

most widely used form of database at the present time is called a relational database.

The relational data model was introduced by Codd (1970). In a relational database,

data are organised in a set of two-dimensional tables. Each table contains information

for one entity (for example, the table ‘Cinemas’, may contain information about the

addresses of different cinemas and the number of screens they have). Different tables

are linked by identifiers called keys.

Figure 3.1 illustrates how, in a relational database, spatial features are linked to

attribute data tables that may be linked to further attribute data tables.

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Figure 3.1. Storage of spatial and attribute information in a relational database.

In Figure 3.1, the field ID is the common key that links the two tables. The table

containing co-ordinate information is linked to the arcs (line features) by unique

numbers (that is, there is only one arc and table entry with the value 1).

3.4 Further reading

The books by Burrough and McDonnell (1998), Worboys and Duckman (2004),

Heywood et al. (2006) and Longley et al. (2005a) all provide discussions about

databases and DBMS. More in-depth accounts on specific issues are provided in Part

2, Section B of the book edited by Longley et al (2005b).

The next chapter deals with the analysis of objects in a GIS.

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Chapter 4. Analysis of objects

4.1 Introduction

At the heart of GIS is the capacity to analyse data – to explore patterns or answer

specific questions and to derive information from these data. It is easy to think of

many problems that entail analysing the spatial distribution of objects or events. Some

questions we may be interested in asking include:

1. Is there a link between cases of childhood leukaemia and the location of a power

station?

2. Does a particular mineral cluster in a rock?

3. How many houses are located with 10 km of a road?

4. What areas comprise high quality soil and low levels of industrial contamination?

5. Does the relationship between altitude and precipitation vary across Ireland?

6. What is the value of precipitation at a location where no measurements have been

made?

7. Which areas have a slope of greater than 25 degrees?

8. What is the shortest road route between one place and another given the need to

visit three particular locations on route?

This chapter and the next introduce a range of methods that can be used to help

answer some of the kinds of questions given above. GIS analysis tools may be divided

into several groups as follows (with the chapter that outlines the methods given in

parenthesis):

1. Point pattern analysis (quadrat analysis etc) (this chapter).

2. Grids and modelling (Chapter 5).

3. Single layer operations (univariate statistics, buffers) (this chapter).

4. Multiple layer operations (proximity analysis, overlay etc) (this chapter).

5. Surface analysis (Chapters 5).

6. Network analysis (network connectivity, shortest path etc.) (this chapter).

7. Spatial modelling (identification “of explanatory variables that are significant to

the distribution of the phenomenon…” (Chou, 1997, 265)).

(Modified from Chou, 1997)

Spatial modelling (no. 7 above) is outside the remit of this resource. A variety of text

books (e.g., Burrough and McDonnell, 1998) provide discussions about spatial

modelling.

This chapter deals with objects (often, although exclusively, represented using

vectors) while the following chapter is concerned with continuous variables (e.g.,

elevation or precipitation amount, often represented using raster grids).

The following section is concerned with the simplest form of object – the point

location.

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4.2 Point pattern analysis

A point pattern is simply a set of events or points within a specific study area. There is

often a desire to assess how clustered or dispersed such data are. Alternatively, we

may be concerned with assessing how similar two point patterns are. Clustered and

dispersed point patterns are shown in Figure 4.1.

Figure 4.1. Left: clustered point pattern. Right: dispersed point pattern.

Examination of point patterns in the form of scatter plots (that is, a simple plot of the

location of the points) provides a sensible first step in the analysis of a point pattern.

This enables a first impression of the tendency towards clustering or regularity and

also local variation in the point pattern. However, to gain an idea of how clustered a

point pattern is it is necessary to estimate statistics that summarise in various ways

properties of the point pattern.

Simple summaries of spatial point patterns include the mean centre and the standard

distance. The mean centre is the mean x and mean y co-ordinates and the standard

distance measures the dispersion around the mean (that is, it tells us something about

the range of point locations) (see Lloyd, 2010, for more detail).

Quadrat analysis is one widely used method for analysing point patterns. In quadrat

analysis, a set of areas are superimposed on the region containing the points we are

analysing. Quadrat analysis entails using either (i) a set of quadrats covering the

whole of the area of interest or (ii) placing quadrats randomly across the area of

interest (this latter approach is often used in fieldwork). In either case, we count the

number of points within each quadrat and we can map the counts to get a sense of

whether or not there are clusters — areas where the density of points is high. There

may be a need to know if spatial variation in a particular property is structured — that

is, do values of a particular property tend to more similar when the observations are

close together than when they are far apart? Spatial structure is expressed by the term

spatial autocorrelation. One approach is to carry out a quadrat analysis and then

estimate the degree of spatial autocorrelation by comparing the counts of points in

adjacent quadrats. If, for example, the results of an analysis of spatial autocorrelation

indicate that quadrats containing large numbers of points occur close to other quadrats

with large numbers of points while quadrats with small numbers of points tend to

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occur next to other quadrats with small numbers of points then the results indicate

clustering.

There are many other methods for analysing spatial point patterns. Kernel estimation

(KE) is a method for generating maps that show the estimated intensity of points

across the area of interest. That is, KE enables estimation of the intensity of points at

all locations in the area of interest irrespective of whether or not there is a point at any

particular location. Another class of methods use the distances between points to

analyse point patterns. For example, the mean nearest neighbour distance is the

average of the distances between each point and its nearest neighbour. For clustered

point patterns, the mean nearest neighbour distance will be smaller than for dispersed

point patterns.

A widely used method for analysing distances between points is the K function (see

Lloyd, 2010, for a detailed introduction). The K function summarises the number of

points separated from each point by specific distances. The K function can be

obtained by (i) finding all points within radius r of an event (that is, a point) and (ii)

counting these and calculating the mean count for all events after which the mean

count is divided by the overall study area event density which gives the K function for

a particular distance, (iii) the radius is then increased by some fixed step and (iv)

repeating steps (i), (ii) and (iii) to the maximum desired distance. The output from the

last stage for each distance can be plotted against the distance. The K function

provides information on how clustered a point pattern is at different scales. Knowing

about how many of a point’s neighbours are close, and how many are far away, tells

us something about the spatial structure of the point pattern we are analysing.

The measures discussed in previous sections provide various ways of assessing the

degree of clustering or dispersion of a point pattern. To consider how clustered or

how dispersed a point pattern is there is a need to use a statistical test of some kind.

This is often achieved my considering if an observed process is likely to be an

outcome of some hypothesized process (for example, in effect, are the locations of the

points due to some random process or are the points a function of some process that

causes them to cluster?). Models of complete spatial randomness (CSR) are used

widely. For a CSR process the intensity is constant (that is, each location has an equal

chance of containing an event) and events are independently distributed. There are

various tests that can be applied to assess if a point pattern corresponds to a CSR

process. In other words, such tests enable us to assess if the point pattern appears to be

random or if there is some structure in it.

Most GIS have some functions that enable the user to analyse point patterns, but these

are often limited.

4.3 Single layer operations

This chapter introduces some methods for analysis using single variables or objects.

Univariate statistics (e.g., the mean average or standard deviation) may be estimated

from single properties in a GIS, but the focus here is on spatial methods.

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4.3.1 Computing distances

One of the most commonly-applied tasks in GIS is computing distances from objects.

It is a trivial task in a GIS to calculate the distance between objects or along linear

features (such as a road represented by a vector line).

One of the most commonly used GIS tools is the buffer function. This draws a

polygon a fixed distance around a feature. For example, the buffer (outer line) on

Figure 4.2 could enclose all areas less than 300 metres away from a polluted stretch of

river (the inner line). If we wished to locate how many houses were located within

two kilometres of a planned power station (an example given above) then we could

use a buffer to locate areas with two kilometres of the power station. Then we could

overlay (as described below) the buffer layer with the layer containing information on

the locations of houses. Then we could select all houses that fall within the buffer

area.

Figure 4.2. Polygon buffer around a sewer.

Another commonly used method is Thiessen polygons. Thiessen polygons are

generated from point data and they comprise a set of polygons that delineate the

‘catchment area’ of each point. In other words, the area inside a polygon is closer to

the point around which it centres than any other point. This may be useful if, for

example, we are concerned with identifying the potential trading territory of a set of

retail outlets (Chou, 1997). Chapter 5 has more information on this method.

4.3.2 Modifying boundaries

There are various ways that we can modify boundaries. One such approach entails

appending (or joining) adjacent maps. That is, we may have four maps that cover four

quarters of a region and these can be joined to form a single larger map. One

commonly used tool dissolves boundaries between polygons where adjacent polygons

contain the same attribute values. For example, if two neighbouring polygons have the

same soil type they may be merged to form one larger polygon by dissolving their

common boundary. There are a variety of other ways of manipulating boundaries

(Chou, 1997, gives some examples).

4.4 Multiple layer operations

This section introduces methods for analysing multiple variables or objects. A

frequent requirement is to ascertain how the spatial locations of one set of features

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relate to the spatial locations of some other set of features. We are thus concerned

with overlaying different data layers.

4.4.1 Overlay of spatial data

One of the strengths of GIS is its ability to analyse spatial relationships between

different variables. For example, we might ask the question ‘how many sites of

special scientific interest (SSSI) occur in areas with soil suitable for agricultural use?’.

This kind of question is well depicted using Boolean logic. If SSSIs are termed ‘A’

and soil suitable for agricultural use is termed ‘B’ then the top-left diagram of Figure

4.3 would correspond to our question - all areas fulfilling both criteria are selected.

Boolean logic may be put into practice in a GIS through overlay functions. In Figure

4.4, we have a vector data layer representing the location of a SSSI and a layer

showing good urban soil and good agricultural soil. Both are treated as polygons

(enclosed areas). If we overlay them and keep all information in both layers then we

perform a UNION overlay. As can be seen in Figure 4.4, where polygons overlap new

polygons are formed. The layer at the bottom of Figure 4.4, the result of overlaying

the original two layers, comprises four polygons. The polygon labelled 3, for

example, shows the area with good agricultural soil that lies in a SSSI.

Figure 4.3. Boolean logic.

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1 = Good urban soil, not SSSI 3 = Good agricultural soil, SSSI

2 = Good urban soil, SSSI 4 = Good agricultural soil, not SSSI

Figure 4.4. Polygon overlay: union operator, described below.

Overlays are very useful spatial analysis operations. They include polygon overlay,

line-in-polygon overlay and point-in-polygon overlay. Polygon overlay is a spatial

operation that overlays one polygon layer on another to create a new polygon layer.

The spatial locations of each set of polygons and their polygon attributes are joined to

derive new data relationships in the output data layer. Joining polygons enables you to

perform operations requiring new polygon combinations. Line-in-polygon allows the

line features to inherit the attributes of the polygon in which they lie. Point-in-polygon

overlay transfer the attributes of the polygon in which the point lies to the point.

Three commands perform polygon overlay: union, intersect, and identity. These

operations are similar, differing only in the spatial features that remain in the output

layer. The illustrations below show the results of the commands.

Union overlay polygons and keep all areas from both layers, so it makes no difference

which is the input layer and which is the union layer (Figure 4.5).

Input layer Union layer Output layer

Figure 4.5. Union operator.

Intersect overlay points, lines or polygons on polygons but keep only those portions of

the input layer features falling within the overlay (intersect) layer features (Figure

4.6).

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Input layer Intersect layer Output layer

Figure 4.6. Intersect operator.

Identity overlay points, lines or polygons on polygons and keep all input layer

features (Figure 4.7).

Input layer Identity layer Output layer

Figure 4.7. Identity operator.

The union, intersect and identity operators combine information from two (or more)

layers. There are operators that entail overlay but do transfer attributes in this way.

The erase and clip operators are such tools.

Erase creates a new layer by overlaying two sets of features. The polygons of the

erase layer define the erasing region. Input layer features that are within the erasing

region are removed (Figure 4.8).

Input layer Erase layer Output layer

Figure 4.8. Polygon erase.

Clip is similar to Erase except that the features that are within the clip region are

preserved (Figure 4.9).

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Input layer Clip layer Output layer

Figure 4.9. Polygon clip

Raster grids may also overlaid. In the example in Figure 4.10, all of the cells in the

first grid are multiplied by all of the cells in the second grid.

Figure 4.10. Multiplication of raster grids.

Application of algebraic operations in this way using raster data only makes sense in

particular situations. For example, it would not make sense to add a raster map

representing elevations above sea level to one representing the amount of some

pollutant. But, it might make sense to add two raster maps which represent pollution

scores of some kind (e.g., low, medium, or high level of threat).

4.5 Network analysis

For questions such as ‘what is the shortest route by road between point a and point b’

network analysis is appropriate. If we have a network of vector lines representing road

and we have information on how different roads are connected then network analysis

may be used. Several measures or summaries of network structure and connectivity

may be computed. These include:

• Network structure: The structure of a network (relative complexity and connectivity)

may be measured in a range of ways. The fundamental properties of a network are

measured by the (gamma) and (alpha) indices.

index: evaluates the relative complexity of a network. Values close to 0 indicate a

complex network and values close to 1 a simple network.

index: the ratio of the number of circuits in a network to the maximum possible

number of circuits in the network – a measure of connectedness.

• Network diameter: the maximum number of steps required to move from any node

to any other node through the shortest route.

• Network structure valued graph: in which every link is labelled with a value as a

measure of the link (e.g., length or cost of travel).

18

More detail on these measures is provided by Chou (1997) and Lloyd (2010).

Particular problems associated with network analysis include:

• Shortest path problem: ascertain the shortest route from one place to another for a

given transport network.

• Travelling salesperson problem: ascertain optimal route for a trip making multiple

stops.

• Shipment problem: optimising transport of goods and people from multiple origins

to multiple destinations.

Software which addresses issues such as these obviously has much value in various

contexts for both public and private bodies. Organisations such as the AA

(Automobile Association) and the RAC (Royal Automobile Club) make direct use of

transport data, as do companies of all kinds who must transport goods (supermarket

deliveries are an obvious case).

4.6 Further reading Most introductions to GIS (including the books by Burrough and McDonnell, 1998;

Heywood et al., 2006; Longley et al., 2005a and Chang, 2008) include discussion of

methods for the analysis of objects. Lloyd (2010) discusses all of the above issues in

greater detail than was the case in this chapter. A summary of key methods for point

pattern analysis is provided by O’Sullivan and Unwin (2002). Network analysis is a

large subject and there are many text books which look at the subject in detail. Chou

(1997) and Lloyd (2010) provide summaries.

The next chapter deals with the analysis of grids and surfaces.

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Chapter 5. Analysis of grids and surfaces

5.1 Introduction

This chapter introduces some approaches for the analysis of properties represented

using raster grids. A second concern is with properties that can be represented as

surfaces (topography is the most obvious example, although precipitation amount or

some airborne pollutant, for example, could also be represented as a surface). There

are vector-based representations of surfaces (e.g., triangulated irregular networks,

contours or isolines), but the focus here is on grids.

The analysis of grid data could form a course in its own right so we will only briefly

discuss this subject. Many GIS have extensive functionality for analysing and

processing raster data. The first topic to be addressed is how values can be assigned to

pixels.

5.2 Data value assignment

A pixel (cell) may cover an area in the real world that contains several spatial

features. For example, a pixel representing an area of 10 by 10 m may cover an area

that contains several trees and part of a building. A problem in such cases is to assign

a single value to the pixel despite the fact that several values could be assigned.

Approaches to assigning pixel values include:

• The centroid method

• The predominant type method

• The most important type method

• The hierarchical method

The first three methods are illustrated in Figures 5.1, 5.2 and 5.3 respectively. With

the centroid method (Figure 5.1) the value (property) in the real world at the centre of

the area covered by a pixel determines the value of the pixel.

Figure 5.1. Value assignment using the centroid method. Based on an illustration in

Chou (1997).

With the predominant type method (Figure 5.2) the value (property) in the real world

which covers the greatest part of the area covered by a pixel determines the value of

the pixel.

20

Figure 5.2. Value assignment using the predominant method. Based on an illustration

in Chou (1997).

With the most important type method (Figure 5.3) a particular value (property) in the

real world is labelled as most important. The value of the pixel is determined by the

most important value/property located with the area covered by a pixel.

Figure 5.3. Value assignment using the most important type method. Based on an

illustration in Chou (1997).

With the hierarchical method complex rules can be used to determine the value

assigned to a pixel.

5.3 Grid operations

Grid operations may be divided into four groups

• Local functions: work on every single cell (a cell is treated as an individual object).

• Focal functions: derive a new value based on the neighbourhood of a pixel.

• Zonal functions: work on each group of cells of identical values.

• Global functions: work on a cell based on the data in the entire grid.

(Source: Chou 1997, p. 361)

Adding two grids together (that is, adding together the values of the pixels in the two

images that share the same location) constitutes a local function.

Focal functions are used widely in GIS and a spatial filter is a particular form of focal

function. To understand how a filter works it is necessary to understand the concept of

21

a moving window (illustrated in Figure 5.4). With reference to Figure 5.5, the top left

pixel is selected first and in the output grid it is with the mean average of all the pixels

to which it is directly connected (including itself). Then select the second pixel is

selected and in the output grid is replaced with the mean of itself and its immediate

neighbours and so on until all pixels in the input have been visited and subjected to

the same process. This is called a smoothing filter since the effect of extreme values is

diminished (the mean must be no larger than the smallest input value and no smaller

than the maximum input value). This kind of function may be useful if the raster grid

is a remotely-sensed image and there is ‘noise’ (measurement error) in the image, as

this noise could be reduced with a smoothing filter. Alternatively, each pixel could be

replaced by the variance, minimum, maximum or any other function of its neighbours.

Figure 5.4. A moving window. The grey cell is the centre of a three by three pixel

window.

Figure 5.5. Central pixel as the mean of its neighbours (smoothing filter).

Figure 5.6 show a simple example of a zonal function. The function illustrated is a

zonal sum. On the left of the figure are cells with one of two values. These comprise

two zones. In the centre, cells in another grid are shown to have a value of 1 or zero.

In this case the ones in each zone are summed and the output grid, shown on the right,

has for each cell a value that represents the sum of values from grid 2 that fall with the

zones defined in grid 1.

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Figure 5.6. Zonal function example.

This can be expanded to include any number of zones. A zonal function can

summarise the data by zone in various ways. Obvious examples are zonal maximum

and zonal mean.

A common example of a global function is the computing of Euclidean (straight line)

distance from one or more source locations to all cells in a grid (see Figure 5.7).

Figure 5.7. Computing the Euclidean distance from the source cell.

Left: source grid, right = distances of each cell from the source cell.

5.4 Spatial interpolation

Surface modelling and analysis deal with variables which can be represented as the

third dimension of spatial data. This may refer to a physical surface (that is,

topography) or some other continuously varying property such as rainfall or airborne

pollution.

5.5 Representing surfaces

Surfaces are usually represented using one of two models: a Digital Elevation Model

(DEM; Figure 5.8) or a Triangulated Irregular Network (TIN; Figure 5.9). A DEM

(also called an altitude matrix) is a grid with cell values that represent the height

above some arbitrary datum such as mean sea level. A TIN is constructed by joining

known point values into a series of triangular facets based on Delaunay triangulation.

In Chapter 6 a 3D perspective of coloured TIN is shown. That is, colours are used to

represent the elevations of the triangular facets.

23

Figure 5.8. Altitude matrix.

Figure 5.9. TIN.

In Figure 5.8, low elevations are white and high elevations are black, as is indicated

by the key. Any continuously-varying property may be represented as a TIN or a grid.

5.6 Sources of elevation data

TINs or DEMs may be derived from existing data (e.g., by digitising contours and

spot heights and using interpolation, discussed below, to derive a grid) or data may be

collected through ground survey or remote sensing, as discussed in Chapter 2.

5.7 Interpolation

In many instances data are available only at discrete locations. For example, airborne

pollutants are measured at monitoring stations. If we wish to obtain information at

locations for which we have no data then we have to make an estimate based on the

surrounding data, this is called interpolation.

With interpolation, we used the observations in a certain neighbourhood to estimate

the value of a property at a location for which we have no data. Spatial interpolation

works on the principal of spatial dependence: values close together in space tend to be

more similar than those far apart. With reference to Figure 5.10, the prediction will be

more similar to observations close to it than it will be to those further away. There are

many techniques than can be used for interpolation, indeed entire books have been

written on the subject. There are several ways of distinguishing methods.

24

Figure 5.10. x0 is the point to predict, x1 to x6 are the observations

(known values) used for interpolation.

Interpolation methods may be divided into two broad groups:

• Global methods: all of the available data are used to make each prediction.

• Local methods: a subset of the data is used to make each prediction.

Many methods can be adapted to make use of all data or only subsets. Key advantages

of using data subsets is that computing time needed is less and the predictions may

better represent small scale variation in the property being mapped. Another division

of interpolation methods is:

• Point interpolation: based on prediction from points.

• Areal interpolation: concerned with interpolating between different areal units (e.g.,

in a UK context, transferring population counts from those recorded within 1991

census boundaries to 2001 census boundaries, which differ to those used in 1991).

5.7.1 Point interpolation

There is a wide variety of point interpolation methods. These include:

• Nearest neighbours (Thiessen polygons): Cells or polygons given value of nearest

sample

• Inverse distance weighting (IDW): Close by samples given large weights, samples at

a greater distance given smaller weights. IDW is one of the most widely used

techniques for generating grids from point data.

• Thin plate splines: these can be viewed as surfaces fitted to a local data subset. Often

a parallel is drawn with stretching a sheet of rubber over points representing elevation

etc. There is a variant of thin plate splines where tension of splines may be adjusted to

deal with, for example, data representing flood plains (low tension to represent

smooth topography) or mountains (high tension to represent sharp breaks of slope)

• Kriging: notionally similar to inverse distance weighting except that the weights

assigned to samples (their influence in making a prediction) are obtained through

modelling spatial variation with a function called a variogram (see Burrough and

McDonnell 1998, pp. 132–161 and Lloyd 2010, pp. 140–150).

25

Interpolation is commonly used to generate complete grids from sparse data. The grid

in Figure 5.8 was derived using IDW. With reference to Figure 5.11, interpolation

could be seen as comprising two steps: (i) superimposing a grid of the required cell

size over the data and (ii) using the data to predict values for every cell. The IDW

estimate is a weighted average. That is, rather than simply taking the average of, say,

the 8 nearest observations the observations closest to the estimate location have the

greatest influence.

Figure 5.11. The prediction (grey cell) is a made using the neighbouring data.

5.7.2 Areal interpolation

Conversion of data from one set of areal units to another requires some form of

interpolation where the boundaries cross one another (that is, there are not common

boundaries). In other words, areal interpolation entails the determination of values

from the source zones to the target zones. Most applications of areal interpolation are

in human geography (see Martin, 1996 for more on this subject). Overlay provides the

simplest approach to converting between one set of zones and another. That is, one set

of zones may be split into smaller zones using a second set of zones which are

overlaid on top. However, using a standard overlay procedure such as the union

operator, if a value attached to the original is 1200 and the zone is then split into five

sections each of these new smaller zones will also have values of 1200. Clearly this is

nonsensical if the variable was, for example, population. So, a common solution is to

assume that the value of the variable can be divided based on the areas of each of the

new zones. So, if one of the new zones covers two fifths of the area of the original

zone then the value attached to it would become 400. This is termed the areal

weighting method. Some more sophisticated approaches have been developed and

some involve generating a surface from areal data and then using this surface to

assign the data to new areal units.

5.8 Analysis of surfaces

This chapter introduces some approaches to analysing the form of surfaces. One of the

most widely used varieties of raster grids is the digital elevation (or terrain) Model

(DEM), as illustrated above (as example was given in Figure 5.8). The values

assigned to the cells in a DEM represent elevation above some arbitrary datum (for

26

example, mean see level). If we have a DEM it is possible to derive the gradient of the

terrain over each pixel and the aspect (the direction in which the gradient is facing).

Gradient and other derivatives of altitude are usually approximated by computing

differences between values in a square filter or by fitting surfaces to the data in the

filter. Typically a three by three cell moving window is used to obtain derivatives.

The gradient or other derivative is then assigned to the central pixel. Alternatively, a

TIN model may be used and slopes derived from the triangular facets.

Gradient and aspect are the two first-order derivatives of altitude. The gradient has

been used directly in modelling water runoff, soil erosion and for cost surface

analysis, as well as other applications. Profile convexity and plan convexity are the

two second-order derivatives of altitude. Slope comprises gradient (the maximum rate

of change in altitude) and aspect (the direction of the maximum rate of change; also

called the azimuth). The terms slope and gradient are sometimes used interchangeably

(Burrough and McDonnell, 1998).

Other products derived from DEMs include drainage networks. GIS also enables

visibility analysis: it is possible to estimate the area visible from one location given

the form of the topography in the area of interest.

5.9 Further reading

The books by Burrough and McDonnell (1998) and Chang (2008) have more in-depth

accounts of many of the issues discussed in this chapter. Chou (1997) is a good

starting point in learning more about grid operations. Lloyd (2010) provides more

details on all of the analytical methods summarised in this chapter with examples of

the approaches illustrated.

27

Chapter 6. Visualisation and presentation of spatial

information

6.1 Introduction

GIS packages usually include a range of facilities for generating conventional paper

maps from digital data. However, data stored in a GIS are not static. Unlike a paper

map, a GIS can be used to update and display the data in innovative ways. For

example, animation can be used to create ‘fly-throughs’ of digital topographic

surfaces or too illustrate change through time.

6.2 Permanent and ephemeral output

There are two kinds of visual output from GIS, permanent and ephemeral. Permanent

output includes paper maps. Ephemeral output refers to visualisation on-screen, which

is widely seen as part of the decision making process that may lead to permanent

output.

Figure 6.1 shows a simple map which has the usual features of a conventional map

including a key, scale bar and north arrow.

Figure 6.1. A simple map generated using a GIS.

6.3 Class intervals for display

A key decision when displaying attributes that have a range of different values is

selecting an appropriate set of class intervals. There are several key ways of dividing

attributes into classes for display. These include:

• Exogenous: Based on meaningful threshold values.

• Arbitrary: No rationale.

• Idiographic: Derived from the data, includes percentile classes.

28

• Serial: A consistent numerical sequence. For example, equal subdivisions of the data

range.

Figure 6.2 is a map of the population of states in the (mainland of the) USA in 1999.

Population is split into ten classes using an serial class interval. The classes are,

therefore, of the same size.

Figure 6.2. A map of population in mainland USA, 1999.

6.4 Cartograms

As well as generating maps (on paper or on screen) that aim to represent accurately

the position of, and relations between, spatial features it is possible to use cartograms

whereby spatial arrangements of the objects depicted are modified according to the

characteristics of the variable in concern. Figure 6.3 shows an area cartogram,

whereby the area of the features (left) are distorted according to a particular attribute

attached to the features (right).

Figure 6.3. An area cartogram.

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6.5 ‘3D’ visualisation

Most GIS include facilities for visualising surfaces in three-dimensions. Fishnet maps

and wire frame diagrams (Figure 6.4) are common ways of visualising surfaces. In

addition, it is possible to drape other data layers (e.g., an aerial photograph) over a

surface. Figures 6.4 and 6.5 show 3D perspectives of surfaces. In Figure 6.6 a shaded

3D perspective of a simple TIN is shown, where the shades (colours) represent the

elevations of the triangular facets.

Figure 6.4. Wire frame visualisation of a Figure 6.5. ‘3D’ visualisation of a

surface. surface.

30

Figure 6.6. Shaded ‘3D’ visualisation of a TIN.

In working with 3D maps users can adjust:

• Angle of view

• Viewing azimuth

• Viewing distance

Modern GIS allow the user to alter viewing direction and angle in real time. In this

way, it is possible to generate fly-throughs.

6.6 Non-map output

One of the principal advantages of GIS over conventional cartography is the

associated analytical functions. Tables and charts are common outputs from GIS.

Tables and charts may be used in addition to, or instead of, cartographic output. They

may clarify information presented in maps or serve as a more useful alternative to

maps.

In some cases the output of an analysis in a GIS may be tabular or textural

information which is not viewed by the user. The information may be transferred

directly to another computer package. A common case would be transfer of

information between a GIS and a statistical package.

6.7 Further reading Good discussions about visualisation of spatial data are provided by Longley et al.

(2005a) and Heywood et al. (2006).

The focus in this chapter has been the visualisation of spatial data; the following

chapter stresses that impressive outputs may not reflect high quality data.

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Chapter 7. Data quality

7.1 Introduction

GIS are not immune from errors. The phrase ‘Garbage in, garbage out’ reflects the

truth that the quality of the output from a GIS can only be as good as the lowest

quality input. In this chapter the main factors that may affect the quality of spatial data

will be outlined. Also, some key definitions will be given which you will need to

understand to be able to describe the quality of spatial data.

7.2 Factors affecting the quality of spatial data

The factors that affect the quality of spatial data can be divided into seven groups:

1. Currency: are data up to date?

2. Completeness: is coverage of area complete?

3. Consistency: are mapping conventions the same for different maps?

4. Accessibility: are data of appropriate format and cost? Are there issues of

copyright?

5. Accuracy and precision: discussed below.

6. Sources of errors in data: discussed below.

7. Sources of errors in derived data and the results of modelling and analysis:

discussed below.

7.3 Defining data quality

Data quality refers to how good data are. For a data set to be useful it should be

complete, compatible, consistent and applicable (Heywood et al., 2006):

• Completeness: requirement for complete spatial coverage and complete information

on necessary attributes.

• Compatibility: need to ensure that two or more data sets can be used in conjunction.

For example, the scale of the original maps on which the data sets were based

should be similar. In other words, we must ensure that differences between

maps are real and not a function of differences in scale of measurement etc.

• Consistency: refers both to consistency within one data set and between data sets.

Data may have been added by different people using different techniques and

based on different sets of decisions.

• Applicability: refers to the appropriateness of suitability of a data set for a particular

task.

Data should also have errors only within acceptable limits. Error is defined as the

difference between the real world and our representation.

Accuracy is the degree to which a measurement approaches its true value. Accuracy is

made up of bias and precision. This is often illustrated using the analogy of a target

(Figure 7.1). For a measurement to be accurate it must be both unbiased (there should

be no systematic over or under estimation) and precise (our measurements should be

repeatable).

32

Figure 7.1. A target, illustrating the concepts of bias and precision.

7.4 Key sources of error

Sources of error in data include errors made during data collection, data input errors

and inappropriate data model choice. Modelling and analysis of spatial data may

introduce further errors.

Errors in source data may derive from mistakes by surveyors or through a piece of

equipment malfunctioning during fieldwork. Also, it may be the case that there have

been changes since the original survey: any dataset is only a snapshot. There may ne

insufficient observations to enable accurate description of the spatial distribution of

some property. An additional problem is where data contain boundaries between

features that are not distinct (for example, soil types). In such a case different people

might determine the boundaries very differently.

Data input may also introduce errors. Digitising (outlined in Chapter 2) can be very

time consuming and tedious so errors are often introduced at this stage. Some

common digitising errors are illustrated in Figure 2.

Figure 7.2. Common digitising errors.

A wide range of different kinds of errors may be introduced during data processing

and input. Most GIS software packages include functions for converting between

vector and raster data and this can lead to problems. For example, if a vector map is

33

converted to a raster map and the resolution of the new raster map is coarser than the

details in the original vector map then the output may have an unrealistically blocky

appearance. This is illustrated in Figure 7.3.

Figure 7.3. Vector to raster conversion error.

Another potential problem is that data could be classified inappropriately, and thus

their value to future users will be diminished.

7.5 Managing and identifying errors

One way of ensuring the value of your data to future uses is to keep metadata (data

about data). Metadata usually record information about the source of the data, method

of data capture, data model used, stages of transformation, editing and manipulation

as well as the hardware and software used.

There are various ways of identifying errors in (spatial) data. These include:

• Visual inspection: in some cases errors may be obvious.

• Double digitising: if resources are available selected features could be digitised

twice and the results compared.

• Examination of error signatures: various studies have shown that different operators

of digitising equipment have different error signatures. That is, a particular person

may have a tendency to produce over-shoots when digitising. This sort of information

could be used to make informed judgements about the kinds of errors that may be

present.

• Statistical analyses: statistics can be employed in error-checking in various ways.

For example, there may be outliers in a data set that are unlikely to be representative

of reality.

It is worth reiterating that is you collect or modify data it is important to keep up-to-

data metadata, particularly if anyone else is likely to use the data in the future.

7.6 Further reading

Detailed accounts of data quality and error modelling are provided by Burrough and

McDonnell (1998), Longley et al (2005a) and Heywood et al. (2008). In-depth

accounts on particular issues are provided in Part 1, Section B of the book edited by

Longley et al (2005b).

34

Chapter 8. GIS project design, management and organization

8.1 Introduction

A GIS is only a worthwhile investment if it is set and used efficiently and effectively.

There are many stories of organisation investing time and money in technological

resources which are, ultimately, little used. This chapter outlines some factors that

should be considered during the set up and use of a GIS.

8.2 Setting up and implementing a GIS

Organisations establishing a GIS must consider several factors including:

• GIS applications: available software and hardware

• The needs of potential users

• Can investment in GIS be justified?

• Which system is appropriate and should it be implemented?

• What changes will GIS cause in an organisation?

(Source: Heywood et al., 2006)

One obvious way of assessing the more obvious potential problems and benefit is to

set up a pilot system. The design of a GIS requires various inputs. The design of the

software necessitates technical knowledge whereas the design of the system as a

whole (interaction of people and computers in an organisation). GIS system design

can be split into two parts: (i) technical design (internal) issues such as system design

and database; (ii) institutional design (external) issues including funding and technical

support (DeMers, 1999).

The way in which a GIS is set and its level of sophisticated will be largely a function

of the applications it is intended the GIS will be put. Applications of GIS can be

divided into three groups:

• Pioneering: high risk development of new applications. Either specialist

organisations or organisations with substantial financial resources.

• Opportunistic: taking advantage of the pioneer's work.

• Routine: use of a tried and tested product. Low risk strategy.

(Source: Heywood et al., 2006)

In most cases, the way in which GIS is used in an organisation will evolve. That is, as

users become aware of the functions available of the software their use of that

software is likely to expand with GIS becoming ever more central to the operation of

the organization concerned (Heywood et al., 2006).

8.3 Costs and benefits of a GIS

In setting up a GIS there will be costs relating to:

• Hardware and software

• Data

• Staff (including restructuring)

35

• Implementation study

Some of the key costs and benefits encountered in setting up a GIS are summarised in

Figure 8.1.

Figure 8.1. Summary of costs and benefits (after Obermeyer 2005, p. 609).

8.4 GIS project design

A key factor in the success of a GIS project is the design of the project. There are

various conceptual models and cartographic models than can be used to help design

an efficient, effective and flexible project plan. It is useful to develop a clear

schematic view of the stages which will be worked through during the project.

One approach to project design, System Life Cycle (SLC), consists of:

1. Feasibility study.

2. System investigation and system analysis.

3. System design.

4. Implementation, review and maintenance.

An alternative approach, prototyping, was developed in part as a response to the

limitations of SLC which is often considered overly structured and technocentric. The

prototyping approach has a greater emphasis on users and a prototype system is

introduced that can be adapted to meet the needs of users (Heywood et al., 2006).

8.5 Further reading

The book by Heywood et al (2006) and the chapter by Obermeyer (2005) informed

much of the account given in this chapter and they are good starting points for further

reading.

The next chapter outlines some of the applications within which GIS has been used.

36

Chapter 9. Applications of GIS

9.1 Introduction

There is a vast range of disciplines and organisations for whom GIS plays an

important role. Some examples include:

Agriculture Government

Archaeology Law enforcement

Banking and insurance Mining

Conservation Navigation

Defence Real Estate

Education Retail and commercial

Emergency services Site evaluation and planning

Engineering Telecommunications

Environmental management Transportation

Forestry Utilities

There are well-developed literatures concerned with all of these areas. This chapter

reviews applications in two different contexts through case studies. The first case

study is concerned with surveying an archaeological earthwork and generating maps

of its surface. The second case study deals with development of a coastal zone

management digital resource.

9.2 Case study 1: Generating a surface from point data

In this first case study a medieval earthwork (a rath) in Ballyhenry, Northern Ireland,

was surveyed using GPS. A surface was then generated from the point measurements

using interpolation. Figure 9.1 shows the locations of the positional measurements

made using GPS. Figure 9.2 shows a TIN derived from these point data while in

Figure 9.3 a 3D perspective of the same TIN is shown.

37

Figure 9.1. Locations of GPS positional measurements.

Figure 9.2. Shaded TIN derived from the point data in Figure 1.

38

Figure 9.3. '3D' view of shaded TIN derived from the point data in Figure 1.

When the archaeological site was surveyed it was due to be destroyed as it is within a

development area. As such, the digital representation is the most detailed record of an

archaeological feature which no longer exists.

Note that an introduction to GIS in archaeology is provided by Conolly and Lake

(2006).

9.3 Case study 2: Developing a multimedia coastal zone management resource

This case study outlines the contents and functions of the Down District Council

Coastal Zone Management Information System (DDCCZ MIS). The resource

contained a range of vector and raster data, the latter including photographs taken on

the ground that could be seen when certain sites were selected in the GIS.

39

Figure 9.4. The DDCCZ MIS.

The resource was intended to unify a range of data sources to aid in their

management. Figure 9.5 shows an example of the attribute data linked to vector

features. In Figure 9.6 a georeferenced aerial image is shown (that is, the image is

referenced using the same co-ordinate system as the vector data).

40

Figure 9.5. The linkage between vector spatial and attribute data.

Figure 9.6. A georeferenced remotely sensed image.

41

By unifying a diverse range of data sources such a resource can become a powerful

tool for a wide range of users, whether they wish to simply find out more about a

particular place or conduct detailed analyses of the spatial relations between different

spatial features.

9.4 Further reading

The best way of finding out about applications of GIS is to use an internet search

engine or look through recent volumes of academic journals like the International

Journal of Geographical Information Science (previously called International Journal

of Geographical Information Systems). Part 4 of the book edited by Longley et al.

(2005b) contains outlines of several applications areas and it is strongly

recommended.

A web search for GIS + any applications area is likely to result in many relevant hits.

In some contexts, terms such as ‘digital mapping’ may be preferred over GIS and this

should be considered when searching for material.

In the next, and final, chapter the concern is with recent developments in GIS and

how it may develop in the near future.

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Chapter 10. GIS in the future

10.1 Introduction

This brief chapter focuses on some recent developments in GIS, but the main concern

is with some of the challenges ahead.

10.2 Problems and developments in GIS

The 1990s has seen extensive growth in the development, awareness and use of GIS.

In that decade, a major focus was on data input and huge GIS databases have been

developed from paper-based resources. GIS has become a key tool in the operations

of a wide range of organisations. In addition, the number of training courses has

proliferated.

Extensive digital datasets representing elements of both the physical and

socioeconomic environments are now available, and in many cases at no cost. With

the development of GIS software (and allied technologies such as Google Earth)

which can be freely downloaded, the capacity to visualise and manipulate digital

maps is limited only by access to computers and the internet. Free GIS environments

like GRASS GIS (http://grass.itc.it/) have extensive functionality and can be used to

implement the whole range of approaches detailed in this text.

Limitations in data structures (e.g., relational databases) and functionality have been

recognised and research continues to develop more intuitive models and more

powerful tools.

10.3 Where next for GIS?

There are several key areas in GIS which are likely to develop in the next decade.

Possible focuses concern:

• GIS type applications becoming ever more widely used (e.g., in spreadsheets, car

navigation etc).

• Virtual Reality enhancements.

• Enhancement of the role of GIS from decision support and problem solving to

generation of ideas and improvement of participation in decision support by all

players.

While the nature of specific developments are open to question it seems certain that

GIS is destined to grow further and to become an increasingly important component

(albeit hidden for many) in the lives of a large proportion of the world’s population.

10.4 Further reading

GIS is a rapidly changing field. Textbooks, such as those by Longley et al. (2005a)

and Heywood et al. (2006) can provide perspectives on developments up to the date

of publication, but these perspectives may become dated quickly.

Recent viewpoints are provided in the final chapters of the book edited by Wilson and

Fotheringham (2008). The internet and recent issues of academic journals, like the

43

International Journal of Geographical Information Science, are sensible starting

points in assessing the very latest developments.

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References

Burrough, P. A. (1986) Principles of Geographical Information Systems for Land

Resources Assessment. Oxford: Oxford University Press.

Burrough, P. A. and McDonnell, R. A. (1998) Principles of Geographical

Information Systems. Oxford: Oxford University Press.

Chang, K. (2008) Introduction to Geographic Information Systems. Fourth Edition.

Boston: McGraw-Hill.

Chou, Y.-H. (1997) Exploring Spatial Analysis in Geographic Information Systems.

Albany: OnWord Press.

Codd, E. F. (1970) A relational model of data for large shared data banks.

Communications of the Association for Computing Machinery, 13, 377–387.

Conolly, J. and Lake, M. (2006) Geographical Information Systems for

Archaeologists. Cambridge: Cambridge University Press.

DeMers, M. N. (1999) Fundamentals of Geographic Information Systems. Second

Edition. New York, Wiley.

Heywood, I., Cornelius, S. and Carver, S. (2006) An Introduction to Geographical

Information Systems. Third Edition. Harlow: Pearson Education.

Lloyd, C. D. (2010) Spatial Data Analysis: An Introduction for GIS Users. Oxford:

Oxford University Press.

Longley, P. A., Goodchild, M. F., Maguire, D. J. and Rhind, D. W. (2005a)

Geographic Information Systems and Science. Second Edition. Chichester: Wiley.

Longley, P. A., Goodchild, M. F., Maguire, D. J. and Rhind, D. W. (2005b) (Eds.)

Geographical Information Systems: Principles, Techniques, Management and

Applications. Second Edition, abridged. Hoboken, NJ: Wiley.

Martin, D. (1996) Geographic Information Systems: Socioeconomic Applications.

Second Edition. London: Routledge.

Obermeyer, N. J. (2005) Measuring the benefits and costs of GIS. In Longley, P. A.,

Goodchild, M. F., Maguire, D. J. and Rhind, D. W. (Eds.) Geographical

Information Systems: Principles, Techniques, Management and Applications.

Second Edition, abridged. Hoboken, NJ: Wiley, pp. 601-610.

O’Sullivan, D. and Unwin, D. J. (2002) Geographic Information Analysis. Hoboken,

NJ: John Wiley and Sons.

Wilson, J. P and Fotheringham, A. S. (2008) (Eds.) The Handbook of Geographic

Information Science. Maldon, MA: Blackwell Publishing.

Worboys, M., and Duckman, M. (2004) GIS: A Computing Perspective. Second

Edition. Boca Raton, FL: CRC Press.

General resources

Academic journals

IEEE Transactions on Geoscience and Remote Sensing

International Journal of Geographical Information Science

Transactions in GIS

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Web resources

Guide to Geographic Information Systems: http://www.esri.com/what-is-gis