UNIVERSITY OF NAIROBI

USE OF GIS AND GPS TECHNOLOGIES ON CRIME HOTSPOT ANALYSIS AND CCTV SITE

RATIONALIZATION

A CASE STUDY OF NAIROBI CBD AND ITS

ENVIRONS

By

KOECH CHERUIYOT PETER

F19/2566/2008

A project report submitted to the Department of Geospatial and Space Technology in partial fulfillment of the requirements for the award of the degree of:

Bachelor of Science in Geospatial Engineering

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APRIL 2013

Abstract

Crime is increasingly becoming one of the greatest challenges to Kenya as a nation with many wondering what mitigation measures can be implemented. It has a serious impact on development both socially and economically. Most of these have been attributed to the fact that Kenya’s population is increasing drastically with overwhelming rates consequently calling for more reactive policies. It is therefore a high time that the governmental agencies especially internal security embraces proactive law enforcement policies that tend to be more technology based in order to supplement on other resources. For example the International Standards recommend one policeman to serve one hundred and fifty people but Kenya has not yet attained that standard.

Although the crime incidences appear to be random and their patterns varied there is a spatio-temporal aspect to them. Geographic Information System can be a very useful tool to substitute on the manual handling and analysis of the criminal activities. It is unfortunate that most organizations handling crime have not yet given priority to this approach and still appear totally unaware of the advantages of GIS databases as opposed to traditional ways of record keeping.

In this area of study, the spatial distribution of crime hotspots has been derived from the crime and incidence reports files kept at the Nairobi Central Police station between August 2010 and September 2011.Thematic maps were used to show the high crime areas and dot maps were used to show the particular hotspots. Time series maps were then displayed to show the variation or the pattern of those hotspots over time. Statistical analysis in terms of type of most committed crime, time of attack and the frequency of occurrence were also considered and displayed using graphs.

It would be incomplete as a geospatial engineer to determine the hotspots without trying to propose a solution on how to control and monitor those crimes. The use of closed circuit televisions (CCTV) would assist the security personnel especially the police in on-screen crime detection and with effective communication would direct their agents to those particular spots. The CCTV sites were strategically located to ensure maximum coverage during surveillance.

The study concludes that GIS is one of the most effective ways of identifying the hotspots, analyzing them and visualizing the crime patterns over time and space. Adoption of this technology would lead to better management and allocation of the appropriate resources especially in law enforcement.

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Dedication

To all my family members who have always done everything to give their best in any way for the betterment and success in whatever academic aspiration I would like to achieve.

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Acknowledgements

First and foremost I would like to thank Almighty God for having given me good health not only during the time of this project but also for the entire time of my studies.

Secondly I would like to thank the following people for their invaluable contribution towards the accomplishment of this study.

Much gratitude goes to my project supervisor, Mrs. Tabitha Njoroge for her guidance and encouragements that saw me through to the completion of this project.

I am also much indebted to my parents Mr. and Mrs. Chirchir who were much more than willing to provide everything they could right from the time of data collection to the completion of the project.

The laboratory technicians Mr. Mwandongo, Mr. Oyugi and also Mrs. Mary Gwena who advised and assisted me to get the necessary materials for the project.

The OCPD of Nairobi Central Division, Mr. Robinson Mboloi , Director of Operations Mr. Sang From Nairobi Police Provincial Headquarters who were ever willing to assist me in acquiring the necessary crime and incidence reports that were basically the pillars of this project.

Finally to my friends Gideon Sigei, Joshua Kibet, Joseph Ndana and Beatrice Atieno for their numerous contributions towards the success of the project.

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Table of Contents

Page Title Page………………………………………………………….…………………….i Abstract………………………………………………………........................................iiDedication……………………………………………………………………………....iii

Acknowledgement……………………………………………………………………...iv

Table of Contents………………………………………………………………….........v

List of Tables……………………………………………………………………….......viii

List of Figures……………………………………………..…………………………….ix

List of abbreviations……………………………………………………………………..x

CHAPTER ONE: INTRODUCTION

1.1 Background……………………………………………………………… ...1

1.2 Problem Statement……………………………………………………….....1

1.3 Objectives of Study…………………………… ………………………....2

1.4 Scope of Study……………………………………………………………...3

1.5 Organization of the Report……………………………………………….....3

CHAPTER TWO: LITERATURE REVIEW

2.1 Definition of crime..........................................................................................4

2.1.1 Classification of Criminal Activities……………………………...4

2.2 Crime in Kenya……………………………………………………………...5

2.3 Crime in Nairobi………………………………………………………….....7

2.4 Management of Information on Criminal Activities………………………..9

2.5 Use of Geographic Information Systems in Monitoring Crime…………....10

2.6 Components of GIS Technology…………………………………………..10

2.7 GIS Techniques and Technology…………………………………………..12

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2.8 Identifying Crime Hotspots…………………………………………………13

2.8.1 Hotspot Categories……………………………………………….13

2.9 Crime Analysis……………………………………………………………..14

2.10 Geostatistical Crime Analysis…………………………………………….14

2.10.1 Use of Morans I in Crimestat…………………………………...15

2.10.2 G-Statistic for Measuring High or Low Clustering……………..15

2.11 GPS Intervention on Crime Mapping……………………………………..16

2.12 Application of GPS to Crime Activities…………………………………..17

2.13 CCTV Surveillance Systems……………………………………………....18

2.14 Application of CCTVs in Kenya…………………………………………..20

2.15 CCTV Rationalization…………………………………………………….21

CHAPTER THREE: METHODOLOGY

3.1 Study Area…………………………………………………………………..22

3.2 Data Collection……………………………………………………………...23

3.2.1 Data Identification Process………………………………………..24

3.2.2 Attribute Data Entry……………………………………………....24

3.2.3 Geographic Data Acquisition……………………………………..25

3.2.4 GPS Receiver Operation……………………………………….....26

3.3 Geocoding the Map…………………………………………………………28

3.4 Database Creation…………………………………………………………..28

3.5 Georeferencing……………………………………………………………...30

3.6 Map Digitization Process…………………………………………………...32

3.7 GIS Database Query………………………………………………………..33

3.8 Cartographic display and visualization…………………………….……….34

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3.8.1 Thematic Mapping…………………………………………….....34

3.8.2 Graduated Maps………………………………………………….34

3.9 CCTV Site Selections……………………………………………………...35

CHAPTER FOUR: DATA ANALYSIS

4.0 Results and Analysis……………………………………………………….36

4.1. Generation of Crime Distribution Maps…………………………………..37

4.2 Crime Statistical Analysis by Other Attributes………………………….....44

CHAPTER FIVE: CONCLUSION AND RECOMMENDATIONS

5.1Conclusions………………………………………………………………...51

5.2 Recommendations………………………………………………………….53

References……………………………………………………………………………...54

Appendices……………………………………………………………………………..58

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List of Tables

Table1 Datasets and their Sources………………………………………………….….24

Table 2 Tabulated Crime Data…………………………………………………………25

Table 3 Geographic Data Entry………………………………………………………...26

Table 4 Coordinates of Georeferencing………………………………………………..31

Table 5 Tools and Software Used……………………………………………………...32

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List of Figures

Fig 2.1GPS Data Acquisition Model………………..……………………………..….16

Fig 2.2 Super Shock Proof Armored Ruggedized High Speed 1/4" Sony CCTV..……19

Fig 2.3 Sony 1/4" EX-View 6" High Speed CCTV camera…………………………...20

Fig2.4 Rotating CCTV cameras………………………………………………………..21

Fig 3.1 Map of Nairobi Area……………………………………………………….......22

Fig 3.2 Data Collection Workflow……………………………………………………..27

Fig 3.4 Data Extraction and Verification……………………………………………....32

Fig 3.5.2 Georeferencing Procedure…………………………………………………...33

Fig 3.6 Adding Attributes to Shape files…………………………………………........36

Fig 4.1.1 Crime Hotspots Map………………………………………………………...40

Fig 4.1.2 Hotspots Buffers At 55metres………………………………………….……41

Fig4.1.3 Buffered Hotspots at 60metres………………………………………….…....42

4.1.4 Proposed CCTV Sites………………………………………………………........43

Fig 4.1.5 Crime Hotspots and Related CCTV Sites……………………………….......44

Fig 4.1.6 Graduated Crime Hotspots…………………………..…………………........45

Fig4.1.7 Buffered CCTV Sites…………………………………………………….......46

Fig 4.2.1 Crime Rates Per Months of the Years…………………………………..…...47

Fig 4.22 Crime Incidences from January to March………………………………..…..48

Fig 4.2.3 Crime Incidences from April to June…………………………………..…....49

Fig 4.2.4 Crime Incidences from July to September……………………………...…..50

Fig4.2.5 Crime Incidences by Gender by Months of the Year………………….….....51

Fig 4.2.6 Crime Incidences at the Place of Occurrence………………………….…...52

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List of Abbreviations

CCTV: Closed Circuit Television

CBD: Central Business District

OCPD: Officer Commanding Police Station

NPPH: Nairobi Province Police Headquarters

GIS: Geographic Information Systems

GPS: Global Positioning Systems

UN: United Nations

MAPS: Mapping Analysis for Public Safety

CAD: Computer Aided Design

MAUP: Modified Aerial Unit Problem

VCA: Video Content Analysis

FOV: Field of View.

RDBMS: Relational Database Management Systems

SQL: Standard Query Language

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CHAPTER ONE

1.0 INTRODUCTION

1.1 Background

Crime is present not only in the majority of societies of one particular species but in all societies

of all types. There is no society that is not confronted with the problem of criminality. Its forms

changes; the acts thus characterized are not the same everywhere but always they have been

observed in such a way as to draw themselves penal repression. If in proportion as societies pass

from lower to higher types, the rate of criminality i.e. the relation between the annual number of

crimes and the population tended to decline, it might be believed that crime while still normal, is

tending to lose this character of normality. However there is no reason to believe that such a

regression is substantiated.

Since time immemorial statistics enable us to follow the cause of criminality. It has increased

everywhere globally. There is no phenomenon that represents indisputably all the symptoms of

normality, since it appears closely connected to conditions of all collective life. To make of

crime a form of social morbidity, would be to admit that morbidity is not something accidental

but on the contrary, that in certain cases it grows out of fundamental constitution of the living

organism.

Currently advanced as well as developing countries have a growing problem of crime and

delinquency (Reckless C, 1973). The magnitude of the problem is registered in increased public

concern about the safety of individuals and property, the current escalation of offenses which

manage to escape detection in a fluid world, the growing willingness of victims observers to

report criminal deeds to the police, owing to the development of communication technology, the

state of greater police coverage in every state and even attention given to criminal activities by

the news media.

1.2 Problem Statement

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Crime in Kenya especially in cities and big towns has been on the rise. It is perceived to be as a

result of the fast urbanization progress in not only in Kenya but Africa as a continent. Crime is

associated with high economic costs, sociological costs and psychological effects. With this

increase of crime, it is a high time that the department of internal security should incorporate

modern ways of crime monitoring which calls for the use of Geographic Information Systems

and increase surveillance using the closed circuit television in addition to historical patrolling

and manning of the streets. With the alarming population growth rate in Kenya, it is a challenge

to assign a policeman to every street. On the other hand crime surveillance devices and

monitoring of the same can be done at a central monitoring unit.

Nairobi metropolitan has a population of about four million people with a higher concentration in

the informal settlements and slums. With a high rate of poverty and unemployment, these places

have turned out to be a beehive of activities, whereby individuals are trying to make ends meet

by whatever means consequently compromising on security. Due to the overwhelming

population growth, crime management capacity tends to be overcome unproportionally. It is a

high time we realized the need of technological measures such as GIS and GPS to minimize the

traditional system of manual maintenance of crime records which is no longer adequate in

addressing the current resolution needs. In addition to that, it is not only enough to identify the

crime hot spots but also to keep analyzing the trends and patterns of those activities in time and

space.

1.3 Objectives of the Study

The objectives of this study are as follows:

Main Objectives

i. To identify the major crime prone areas or the crime hotspots in Nairobi CBD and the

environs.

ii. To identify the optimum sites for CCTV installation.

Specific Objective

i. To analyze the crime pattern trend based on spatial-temporal variables.

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1.4 Scope of Study

Nairobi is the most populous city in East Africa, with a current estimated population of about 4

million. According to the 2009 Census, in the administrative area of Nairobi, 3,138,295

inhabitants lived within 696 km2 (269 sq mi). Nairobi is currently the 12th largest city in Africa,

including the population of its suburbs. The study focuses mainly on the Nairobi Central

Business District and the immediate environs particularly towards the areas of Ngara, Kamkunji

and Muthurwa. Since crime is a broad aspect covering various vices, for this particular study the

data collected was basically from reported cases in the city. The data was acquired from Central

Police Division for the period between June 2010 and December 2012. The data contents include

the type of crime, time of the day, the month, identity of the victim or perpetrator and the

location of committed crime.

1.5 Organization of the Report

Chapter 1-Provides the background information on crime in the study area, problem statement,

objectives and the scope of study.

Chapter 2-Gives a general overview on Crime, GIS, GPS, CCTV and the use of GIS in crime

mapping and analysis.

Chapter 3-Gives the methodology or the steps followed in the data collection, preparation,

editing, verification and database creation.

Chapter 4-Deals with the actual manipulation of data to give results and also their visual displays

which enable the analysis of the same.

Chapter 5-Statement of the conclusions and recommendations.

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CHAPTER TWO

2.0 LITERATURE REVIEW

2.1 Definition of Crime

Crimes are acts that are against the law. A crime is any act or omission prohibited by the law for

the protection of the public and made punishable by state in a judicial proceeding in its own

name. It is a public wrong as distinguished from a mere private wrong or civil injury to an

individual (Marshall L, 1999). There are many different types of crimes, from crimes against

persons to victimless crimes, violent crimes to white collar crimes.

2.1.1 Classification of crime

Crimes against Persons

Crimes against persons, also called personal crimes, include murder, aggravated assault, rape,

and robbery. Personal crimes are unevenly distributed in not only Kenya but also the rest of the

world, with young, urban, poor committing these crimes more than others. (United Nations

Report 2007).

Crimes against Property

Property crimes involve theft of property without bodily harm, such as burglary, larceny, auto theft, and

arson. Like personal crimes, young, urban, poor, and alien minorities generally commit these crimes more

than others.

Crimes against Morality

Crimes against morality are also called victimless crimes because there is no complainant, or

victim. Prostitution, illegal gambling, and illegal drug use are all examples of victimless crimes.

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White-Collar Crime

White-collar crimes are crimes that are committed by people of high social status who commit

these crimes in the context of their occupation. Examples of such crimes are the pyramid

schemes of 2006 and the Anglo-Leasing which cost the individuals and the country billions of

money. This includes embezzling (stealing money from one’s employer), insider trading, and

tax evasion and other violations of income tax laws.

White-collar crimes generally generate less concern in the public mind than other types of crime,

however in terms of economic effect; white-collar crimes are even more consequential for the

society. Nonetheless, these crimes are generally the least investigated and least prosecuted.

Organized Crime

Organized crime is crime committed by structured groups typically involving the distribution of

illegal goods and services to others. The term can refer to any group that exercises control over

large illegal enterprises such as the drug trade, illegal gambling, prostitution, weapons

smuggling, or money laundering.

A key sociological concept in the study of organized crime is that these industries are organized

along the same lines as legitimate businesses and take on a corporate form. There are typically

senior partners who control the business’ profits, workers who manage and work for the

business, and clients who buy the goods and services that the organization provides.

2.2 Crime in Kenya

The most common serious crime in urban Kenya is carjacking in order to commit an armed

robbery. In early 2007, foreign citizens were killed and one critically injured in two separate

carjacking incidents. Nairobi averages about ten vehicle hijackings per day, while Kenyan

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authorities have limited capacity to deter or investigate such acts. Public service vehicles tend to

be targeted since they carry up to 14 passengers or more especially in the evenings. Although

these attacks are often violent, victims are generally not injured if they do not resist (Gimode, E

A. 2001). However, victims are sometimes tied up and put in the back seat or trunk of their own

car. Criminals who commit these crimes will not hesitate to shoot a victim who is the least bit

uncooperative or who may appear to hesitate before complying with their assailant.

Theft and banditry

Pickpockets and thieves carry out "snatch and run" crimes on city streets and near crowds

(Google, Inc. 2013).There have been reports of safes being stolen from hotel rooms and hotel

desk staff being forced to open safes. Thieves routinely snatch jewelry and other objects from

open vehicle windows while motorists are either stopped at traffic lights or in heavy traffic.

Thieves on matatus, buses and trains may steal valuables from inattentive passengers. Many

scams, perpetrated against unsuspecting tourists, are prevalent in and around the city of Nairobi.

Many of these involve people impersonating police officers and using fake police ID badges and

other credentials. Nevertheless, police checkpoints are common in Kenya and all vehicles are

required to stop if directed to do so. There has been an increase in armed banditry in or near

many of Kenya’s national parks and game reserves, particularly the Samburu, Leshaba, and

Maasai Mara game reserves. In response, the Kenya Wildlife Service and police have taken

some steps to strengthen security in the affected areas, but the problem has not been eliminated.

Travelers who do not use the services of reputable travel firms or knowledgeable guides or

drivers are especially at risk.

Political crime

Kenya is generally a peaceful and friendly country in its political activism, it is nonetheless

common during elections, referendums and other political votes for campaign violence to occur

around the country, and ethnic clashes account for much of Kenya's problems. On 29th

December 2007, the day after Kenya’s National Parliamentary and presidential elections,

violence erupted in major cities cross Kenya, including Nairobi, Mombasa, and Kisumu. Political

instability throughout Kenya was reported, which resulted in the deaths of over 600 Kenyans.

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Kenya was ranked 150th out of 179 countries for corruption in the year 2007. (Corruption

Perceptions Index 2007). On a scale of 0 to 10, with 0 the most corrupt and 10 the most

transparent, Transparency International rated Kenya 2.1.

Terrorism

At the urging of the Al-Shabaab militant group, a significant and increasing number of terrorist

attacks in Kenya have been carried out by local Kenyans, many of whom are recent converts to

the unpopular cult. Estimates in 2012 placed the figure of Kenyan fighters at around 10% of Al-

Shabaab's total forces (Google, inc. 2013). Referred to as the "Kenyan Mujahideen" by Al-

Shabaab's core members, the converts are typically young, overzealous with poverty making

them easier targets for the outfit's recruitment activities. Because the Kenyan insurgents have a

different profile from the Somali and Arab militants that allows them to blend in with the general

population of Kenya, they are also often harder to track. The mastermind the Kampala bombings

who now cooperates with the Kenyan police believes that in doing so, the group is essentially

trying to use local Kenyans to do its "dirty work" for it while its core members escape unscathed.

According to diplomats, Islamic areas in coastal Kenya and Tanzania, such as Mombasa and

Zanzibar, are also especially vulnerable for recruitment.

Drug abuse

Drug abuse has become a major issue in Kenya, especially in Mombasa which is affected by this

issue more than any other part of the country. Young men in their early 20s have been the most

affected demographically. Women in Mombasa have held public protests, asking the government

to move quickly to arrest young people using narcotics. Bhang smoking has until recently been

the drug of choice, but heroin injection is becoming increasingly popular. Seventy percent of

drug abusers have admitted that they are using heroin. In addition to drug abuse, the trafficking

of illegal drugs in the country has become a major issue as well. An estimated 100 million

dollars' worth is trafficked within the country each year.

2.3 Crime in Nairobi

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According to the UN Habitat report 2007 on crime in Nairobi, the following observations were

made. The most common causes of crime given were identified by respondents as unemployment

and poverty, although general idleness and the quick rewards that crime brings were also noted.

A very small minority mentioned the increase in foreigners as the major cause of crime. Just over

half of the all the residents of Nairobi worry about crime all the time, whilst a further one-third

thinks of it sometimes. There was very little difference between men and women and across

different age cohorts. Generally, people think that most crimes are caused by people within their

neighborhood. About 75% of all respondents feel unsafe in their homes during the night and

more than half feel unsafe during the day. Just under half consider that they live under siege and

would avoid going out during the day unless it was absolutely necessary, whilst just under three-

quarters feel the same about travelling and working after dark. The vast majority of residents

would not go into the City Centre during the evening at all.

Nearly two-thirds of all respondents’ link crime to the fact that they felt that during the past year

the number of illegal firearms had increased. The vast majority of these respondents felt that

these increases were due to smuggling from Somalia. To substantiate their claims a little over

half of all the residents of Nairobi claim to regularly hear gunshots. Only a tiny fraction of all

respondents admitted that occasionally they carried a firearm, but ten acknowledge knowing a

friend that owns a firearm. Most disturbing was the fact that one-third of all Nairobi’s residents

would own a firearm if they had the opportunity to acquire one.

On the police force, half of all respondents argue that although the terms of service and

conditions of the police force are consistently reviewed by Government, efficiency levels of

police service had worsened during the past few years. The overwhelming majority of

respondents suggest that the police institution is one of the major casualties of bribery at the

individual level, attributing one in three crimes either directly or indirectly to police officers.

Nearly two-thirds of all respondents argued for the need to set-up some form of assistance group

to help people who are victims of crime. Just over one-third felt that complementary measures

such as security guards or vigilante groups or simply setting up neighbourhood watches is

essential to enhance community policing in addressing crime, for the police alone were incapable

of dealing with crime.

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The issue of street children is an emotional one in Nairobi, and it is not uncommon to find

residents attributing a good proportion of crime to this group of Nairobi citizens (Gimode E. A,

2001). However, the portion of crime perceived to have occurred in the respondent’s

neighbourhoods bears very little resemblance to the kind of crime people think street children

commit elsewhere. Yet they feel that street children may become the criminals of the future.

Because of this, one-quarter of all respondents felt that the best solution to the problem of street

children was to forcibly remove them from the city. This however was balanced by the views of

the rest of the respondents who feel that positive interventions are possible and must be

attempted if the street children problem is to be solved.

Almost all the respondents felt that that bribery has assumed alarming levels of acceptability

among residents in Nairobi, with half admitting to having actively participated in some form of

behaviour that might be classified under the broad category of bribery. Furthermore, nearly one-

quarter of all residents felt that residents of the city are catalyzing the culture of bribery among

the police force, with just over 25% claiming to have in fact bribed a police officer during the

past year. While noting government efforts on the poverty reduction strategy, respondents

emphasized the creation of employment opportunities and policies to reduce poverty should take

precedence in government policy.

2.4 Management of Information on Criminal Activities

With the high rate of poverty and unemployment, Nairobi especially within informal settlements

has turned out to be a beehive of all activities with individuals trying to make the ends meet by

whatever means. Owing to its overwhelming population growth, crime management capacity

tends to be overcome unproportionally thus calling on for other mitigation measures such as

surveillance monitoring using CCTV which are strategically positioned. Traditional system of

record maintenance is no longer adequate in addressing the current resolution needs and is a high

time modern technology was embraced.

Since 1960, GIS has emerged a discipline on its own right. From its land use application in

Canada to all pervasive technologies used today in navigation, retail site location, risk

management, and military planning.GIS is ubiquitous in modern life. Reduction in computer

hardware and development of software enabled its use in crime pattern and trend analysis.

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Computerization of police criminal records has come with a realization that this material can be

used for crime and intelligence analysis (Ratcliffe 2004).Early use of GIS on crime mapping had

geocoding and technical problems (Hirschfield et al 1995) unlike now. Much innovation on

crime mapping was driven in USA by Mapping Analysis for Public Safety (MAPS), which has

been the foundation of crime mapping in many countries e.g.UK, SA, Australia.

2.5 Use of GIS in Crime Mapping

Crime in any form is connoted to an inherent geographical or spatial quality which is of interest to a Geospatial Engineer (Alexander M, 1994). In turn this location could be of relative position to the

epicenter of crime perpetration.

In around 1970s,the realization that crime could not be solely understood and explained deeply by

exploring geographical components.New technology and techniques were needed to tackle these

challenges through critical identification of crime patterns and hotspots; exploration of environment and

crime including socio-economic variables. Thanks to GIS which is a more comprehensive tool not only

for this study but also other aspects.

Practical examples of GIS in crime mapping are as follows;

Analyzing the impact of crime-reduction activities.

Identifying the crime hotspots for targeting, deploying and allocating suitable responses.

Helping to understand the crime distribution through pattern analysis and other local data e.g.

demography.

Use of information for resource allocation by both public and police.

All the spatially related matters of crime are entered into records as geodatabases on which the GIS

analyst can perform data query consequently displaying the desired results. Information can then be

derived through time, dates, modus operandi, and crime type amongst others. Strategic decisions can then

be made, such as deployment of more security persons to high crime areas.

2.6 Components of GIS Technology

Geographic information system (GIS) is a system designed to capture, store, manipulate,

analyze, manage, and present all types of geographical data. (Mulaku, G. C 2013). In the

simplest terms, GIS is the merging of cartography, statistical analysis, and database technology. This

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system therefore requires computer hardware, software, and people in order to manage geospatial

databases.

A GIS can be thought of as a system which digitally creates and manipulates spatial areas that

may be jurisdictional, purpose, or application-oriented. Generally, a GIS is custom-designed for

an organization. Hence, a GIS developed for an application, jurisdiction, enterprise, or purpose

may not be necessarily interoperable or compatible with a GIS that has been developed for some

other application and purpose. What goes beyond a GIS is a spatial data infrastructure, a concept

that has no such restrictive boundaries. In a general sense, the term describes any information

system that integrates stores, edits, analyzes, shares, and displays geographic information for

informed decision making. GIS applications are tools that allow users to create interactive

queries, analyze spatial information, edit data in maps, and present the results of all these

operations. Below are the basic components of GIS.

a. Hardware

The basic items of hardware for GIS include the computer, printer, digitizer, keyboard,

mouse, monitor and external storage media. Others may include DVD writers, a scanner and a

modem for internet link. The choice of the hardware to install may depend on the type of

software requirement such as the computer’s RAM size and speed.

b. Software

These are the set of instructions written in formal programming languages which can be understood

by the programmer and the computer. The operating system is the chief coordinator of all the

instructions given to computer.GIS software enables the input of data through folder connections,

data management and manipulation through editing tools. It is also necessary to put protective

software like the antivirus.

c. GIS data

Data in a GIS environment is kept in a database and is managed by a database management system.

These data may include scanned maps which are used to construct shape files and their attributes.

Building a GIS database may take up to 70% of the total cost of setting up a GIS. (Arnoff, 1989).

d. Organizational Procedures

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Includes the creation of awareness about GIS technology and how it will fit appropriately in

the overall operation of the organization in question. It generally includes development of

standards, access protocols, database administration, quality assurance and system security.

e. The people

These are the individuals who operate the system, use and also maintain the databases.

Normally in a large geospatial setup, the following personnel are required; GIS manager, GIS

analyst, GIS programmer among others.

2.7 GIS techniques and technology

Modern GIS technologies use digital information, for which various digitized data creation

methods are used. The most common method of data creation is digitization, where a hard copy

map or survey plan is transferred into a digital medium through the use of a CAD program, and

geo-referencing capabilities. With the wide availability of ortho-rectified imagery (from satellite

and aerial sources), heads-up digitizing is becoming the main avenue through which geographic

data is extracted which involves the tracing of geographic data directly on top of the aerial

imagery.

GIS uses spatial-temporal location as the key index variable for all other information. Just as a

relational database containing text or numbers can relate many different tables using common

key index variables, GIS can relate unrelated information by using location as the key index

variable. The key is the location and/or extent in space-time.

Any variable that can be located spatially, and increasingly also temporally, can be referenced

using a GIS. Locations or extents in earth space–time may be recorded as dates/times of

occurrence, and x, y, and z coordinates representing, longitude, latitude, and elevation,

respectively. Units applied to recorded temporal-spatial data can vary widely even when using

exactly the same data, but all earth-based spatial–temporal location and extent references should,

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ideally, be relatable to one another and ultimately to a "real" physical location or extent in space

and time.

Related by accurate spatial information, an incredible variety of real-world and projected past or

future data can be analyzed, interpreted and represented to facilitate education and decision

making. This key characteristic of GIS has begun to open new avenues of scientific inquiry into

behaviors and patterns of previously considered unrelated real-world information.

2.8 Identifying Crime Hotspots

In order to identify the crime hotspots we use a range of techniques which involve visualizing the

crime patterns in terms of location, size, shape, extent and the magnitudes of these hotspots.

There are more advanced technologies which can be used however they are not available in the

common GIS packages (Ratcliffe J, 2004).

A Crime hotspot is a geographical area of higher than average crime that is known for repetitive

crime concentration with respect to the whole or particular region of interest. As mentioned

earlier hotspots are clusters of varying magnitudes of activities. Knowing the exact locations of

these locations is an important step when figuring out why these areas record persistent crime

reports. The process of crime mapping is not a straight forward procedure and may be biased

during interpretation.GIS software capabilities on spatial data analysis becomes handy in

buffering the hotspots thereby allowing the picking of the exact hotspot with the assistance of

GPS.A good example of additional functionality is the Modifiable Aerial Unit Problem(MAUP),

the range of classes and parameters to set in map legend, designing of the map and visualization

aspect of spatial patterns and whether the crime data needs to be normalized against underlying

population.

2.8.1 Hotspot Categories

Spatial categories

i. Dispersed-A crime hotspot where events are distributed around the hotspot are e.g.

vehicles across a car park.

ii. Clustered- Crime spots surrounding a particular feature e.g. a bar.

23

iii. Hotpoint-Repeated victimization at a certain location.

Temporal categories

i. Diffused –No discernible pattern to the time of occurrence.

ii. Focused-Characterized by block of time or peak hours e.g. between

10-11 p.m.

iii. Acute-Crime taking place at exact span of time e.g.12 O’clock midday.

2.9 Crime Analysis

Crime analysis is a law enforcement function that involves systematic analysis for identifying

and analyzing patterns and trends in crime and disorder. Information on patterns can help law

enforcement agencies deploy resources in a more effective manner, and assist detectives in

identifying and apprehending suspects. Crime analysis also plays a role in devising solutions to

crime problems, and formulating crime prevention strategies. Quantitative social science data

analysis methods are part of the crime analysis process; though qualitative methods such as

examining police report narratives also play a role.

Crime analysis can occur at various levels, including tactical, operational, and strategic. Crime

analysts study crime reports, arrests reports, and police calls for service to identify emerging

patterns, series, and trends as quickly as possible. They analyze these phenomena for all relevant

factors, sometimes predict or forecast future occurrences, and issue bulletins, reports, and alerts

to their agencies. They then work with their police agencies to develop effective strategies and

tactics to address crime and disorder. Other duties of crime analysts may include preparing

statistics, data queries, or maps on demand; analyzing beat and shift configurations; preparing

information for community or court presentations; answering questions from the public and the

press; and providing data and information support for a police department's Comp Stat process.

Sociodemographics, along with spatial and temporal information, are all aspects that crime

analysts look at to understand what's going on in their jurisdiction. Crime analysis employs data

mining, crime mapping, statistics, research methods, desktop publishing, charting, presentation

skills, critical thinking, and a solid understanding of criminal behavior. In this sense, a crime

24

analyst serves as a combination of an information systems specialist, a statistician, a researcher, a

criminologist, a journalist, and a planner for a local police department.

2.10 Geostatistical Crime Analysis

Geostatistical methods of crime pattern analysis make use of samples picked over a wide area of

coverage and interpolate missing points to create a continuous surface. These sample points

could be measurements of some geographic phenomenon such as crime or epidemiology.

Deterministic method of interpolation uses purely mathematical models whereas geostatistical

interpolation technique uses both mathematical and statistical model in order to create

predictable surface events.

2.10.1 Use of Morans I in Crimestat

Morans I is a classic measure of global spatial dependence and can be applied to both polygons

and points which have attribute data attached to them (Chaney S, Ratcliffe J, 2004). One major

advantage of Morans I is that it allows the analyst to measure clustering in points meaning that

the process could determine the clustering of burglary or carjacking distribution even when both

are aggregated to the same set of polygons. Programs like Crimestat require each area to be to be

described as x and y coordinates and then gives the greatest influence to points that are located

closest to the location being tested.

2.10.2 G-Statistic for Measuring High or Low Clustering

The Morans I either global or local can only detect the presence of clustering of similar values. It

cannot tell whether the clustering is made of high or low values. This led to the use of G-statistic

which could separate clusters of high values from those of low (Getis and Ord 1992) .The

general G-statistic based on a specified distance d is defined as follows;

G(d)= ∑∑Wij(d)

∑∑ xixj,i≠j

Where xj=value of location i

25

Epoch

Receiver

Xj=value of j if j is within d of i and Wij(d)is the spatial weight. The weight can be based on

some weighted distance e.g. inverse distance

Expected value of G (d) is

E (G) = ∑∑Wij(d)

n(n−1) E (G) is typically small when n is large. A high G (d) value suggests a

clustering of high values and low G (d) clustering of low values.A z score can be computed for G

(d) to evaluate its statistical significance.

Similarly to Morans I, the local G-Statistic G (d) is often used as a tool for hotspot analysis. A

cluster of high positive z-scores suggests the presence of a cluster of high values or hotspot and

low z scores for a cold spot. G-statistic also allows the use of a distance threshold d, defined as

distance beyond which no discernible increase in clustering of high/low values exist.

2.11 GPS Intervention on Crime Mapping

The GPS or global positioning system is an electronic navigation device that makes use of

network of satellites found on earth’s orbit to locate specific positions and placements. Originally

created for military applications, the GPS was first used by the US department of defense. It is

during 1980’s when the US government allowed its public use.

Because it takes advantage of the satellite systems above the earth, GPS units can be used

anywhere in the world for 24hrs a day. It works in any whether condition and does not require

any subscription charge, GPS units come in a wide variety of specifications and are

manufactured by several companies.GPS units work by receiving information from GPS

satellites that revolve the earth in specific frequency. These satellites transmit information to the

earth through the GPS unit or receiver. Using triangulation to calculate and identify the exact

location of the user.

26

GPS receivers also compute and compare the time when the signals were transmitted and

received, and such time difference also help in locating the user’s exact position. (Lange,

1992).GPS units feature a screen that serves as an electronic map where the user’s location is to

be shown.

Major Types of GPS

1. Portable GPS. This type of GPS unit is portable enough to be carried along while

travelling by foot or car. However it is not small enough to be kept inside the pocket. It

typically measures 4 inches wide and weighs about 10 ounces.

2. Pocket GPS. This type of GPS unit is designed to fit inside the pocket of pants or

shirts.

It weighs about 5 ounces with a screen measuring 3.5 inches. It costs more than the

portable units because of its small, slim, light weight feature.

3. In-dash GPS. This type of GPS unit is built into the automobile’s dash board, thus

adding security to the unit and avoiding loss of the property.

4. Fitness and cycling GPS. It is specially created for people who walk, jog, run or ride

bicycles. It is designed to fit snuggly on the wrist like a watch. It can also track the

athletes pace, distance speed and even calories burnt!

5. Motorcycle GPS. This type of GPS is almost the same as that used in cars but is

designed to fit in the motorcycle consoles, waterproof, and vibration resistant. Most

units come with blue tooth hands free technology feature.

27

Fig2.1 shows GPS data acquisition model.

6. Marine GPS. This is specifically designed for marine use and comes with plotting

functionality. It is equipped with special marine database and navigational aids such as

sound signals, buoys and day beacons among others.

2.12 Application of GPS to Crime Activities

Currently in Kenya, the use of GPS in crime monitoring and response is not yet fully developed.

Much of the work done using the assistance of the GPS technology is mainly navigation and

surveying. Companies that install car tracking systems are coming up; in this manner the GPS

gadget is fixed on the vehicle such that wherever the vehicle moves it can be monitored. In case

the vehicle gets stolen the GPS gadget will assist the police in tracking the vehicles movement.

Globally, one of the most recent developments has been fleet management through AVL

(automatic vehicle locator).The system provides efficiency of response and helps to ensure an

officers safety. By comparing GPS and the roads coordinates, AVL becomes a navigational

guide, thus facilitating the police officers with accurate information concerning the best response

route to an incident. It also facilitates resource allocation e.g. the immediate dispatch of the

closest patrol officers to the site.

2.13 CCTV Surveillance Systems

Closed-circuit television (CCTV) is the use of video cameras to transmit a signal to a

specific place, on a limited set of monitors. It differs from broadcast television in that the

signal is not openly transmitted, though it may employ point to point (P2P), point to

multipoint, or mesh wireless links. Though almost all video cameras fit this definition,

the term is most often applied to those used for surveillance in areas that may need

monitoring such as banks, casinos, airports, military installations, and convenience stores.

A more advanced form of CCTV, utilizing digital video recorders (DVRs), provides

recording for possibly many years, with a variety of quality and performance options and

extra features (such as motion-detection and email alerts). More recently, decentralized

IP-based CCTV cameras, some equipped with megapixel sensors, support recording

directly to network-attached storage devices, or internal flash for completely stand-alone

operation.

28

There is strong anecdotal evidence that CCTV aids in detection and conviction of offenders;

indeed UK police forces routinely seek CCTV recordings after crimes. (Google, inc. 2013).

Moreover CCTV has played a crucial role in tracing the movements of suspects or victims and is

widely regarded by antiterrorist officers as a fundamental tool in tracking terrorist suspects.

Large-scale CCTV installations have played a key part of the defenses against terrorism since the

1970s. Cameras have also been installed on public transport in the hope of deterring crime, and

in mobile police surveillance vans, often with automatic number plate recognition. Video

Content Analysis (VCA) is the capability of automatically analyzing video to detect and

determine temporal events not based on a single image. As such, it can be seen as the automated

equivalent of the biological visual cortex.

A system using VCA can recognize changes in the environment and even identify and compare

objects in the database using size, speed, and sometimes colour. The camera’s actions can be

programmed based on what it is “seeing”. For example; an alarm can be issued if an object has

moved in a certain area, or if a painting is missing from a wall, or if a smoke or fire is detected,

or if running people are detected, or if fallen people are detected and if someone has spray

painted the lens, as well as video loss, lens cover, defocuses and other so called camera

tampering events.VCA analytics can also be used to detect unusual patterns in a videos

environment. The system can be set to detect anomalies in a crowd of people, for instance a

person moving in the opposite direction in airports where passengers are only supposed to walk

in one direction out of a plane or in a subway where people are not supposed to exit through the

entrances.

VCA also has the ability to track people on a map by calculating their position from the images.

It is then possible to link many cameras and track a person through an entire building or area.

This can allow a person to be followed without having to analyze many hours of film. Currently

the cameras have difficulty identifying individuals from video alone, but if connected to a key-

card system, identities can be established and displayed as a tag over their heads on the video.

Facial recognition system is a computer application for automatically identifying or verifying a

person from a digital image or a video frame from a video source. One of the ways to do this is

by comparing selected facial features from the image and a facial database. The combination of

29

CCTV and facial recognition has been tried as a form of mass surveillance, but has been

ineffective because of the low discriminating power of facial recognition technology and the

very high number of false positives generated. This type of system has been proposed to compare

faces at airports and seaports with those of suspected terrorists or other undesirable entrants.

On average, CCTV cameras have a field of view of about 150 feet (Google, inc. 2013) which

translates to about 50 metres. However there are special case cameras e.g. those used in military

bases and highly secured armory. Below are examples of CCTV cameras that can be annexed to

various surfaces.

Super Shock Proof Armored Ruggedized High Speed 1/4" Sony EX-View HAD 530TVL WDR Day/Night PTZ, IP66 weatherproof, IR up to

300 ft, 3.4-122mm lens

Sony 1/4" EX-View 6" High Speed Day/Night WDR PTZ CCTV camera, 36x optical 12x digital (420X zoom), NTSC/PAL option/tilt speed 0-1200 per sec/tilt range 1800 with tilt flip/pan speed 0-3000 per sec/picture elements 380,000 pixels.

As an optical zoom example, a man of 5’2” tall at 200 feet away, would be 118% of the screen

and the FOV would be 5.9’wide x 4.5’ tall.

30

Fig 2.3

Fig 2.2

2.14 Application of CCTVs in Kenya

Installation of CCTVS in Kenya is mostly done in public places and buildings in order to

monitor large crowds of people. It can be said that most of the cameras are targeted at places

where there are entrances and exit doors. For example, banking halls and ATMs have cameras

that are strategically mounted in various locations especially facing the counters where money is

delivered. Busy shopping malls are also common areas for CCTV installation, supermarkets,bus

and train terminals included. Other places include car parks, busy streets and even libraries. It is

interesting that nowadays even churches have been placed under surveillance.

2.15 CCTV Site Rationalization

Rationalization means choosing the most efficient and effective means of accomplishing a task.

CCTV rationalization therefore means the effective use of CCTVS to accomplish the task of

crime monitoring. Rationalization also means the optimum selection of the best sites for making

the best use of the available resources in monitoring crime. For instance, instead of using three

surveillance cameras over three crime hotspots, two of them may be used (considering some

factors like areas of overlap) to monitor that place effectively. In essence rationalization can be

said to be making the best out of the available resources to exhaustively deliver the desired

objectives.

31

Fig 2.4 Rotating CCTV cameras

The cameras above can rotate 3600.This type is more preferable for installation especially at open

places like bus stations, markets and road junctions.

CHAPTER THREE

3.0 METHODOLOGY

3.1 Study Area

Nairobi is the most populous city in East Africa, with a current estimated population of about 3.5

million. According to the 2009 Census, in the administrative area of Nairobi, 3,138,295

inhabitants lived within 696 km2 (269 sq mi). Nairobi is currently the 12th largest city in Africa,

32

including the population of its suburbs. Nairobi lies between the latitudes 1010’S and1025’S and

longitudes 360 40’E and 37010’E.

Covering a large area, Nairobi area is divided into different divisions and units in order to curb

crime effectively. In October 2004 Nairobi was extended to a Greater Nairobi covering Magadi,

Mlolongo, Syokimau, Kiamumbi and Kinoo taking up parts of Rift valley, Eastern and Central

provinces respectively. Currently we have a total of nine Police divisions, which include:

1. Gigiri Police Division

2. Langata Police Division

3. Ngong Police Division

33

Fig 3.1 Showing Nairobi Area

4. Kasarani Police Division

5. Embakasi Police Division

6. Central Police Division

7. Kayole Police Division

8. Buruburu Police Division

9. Kasarani Police Division

3.2 Data Collection

3.2.1 Data Identification Process

This refers to the identification, collection, digitization and correction of errors for the necessary

data used to build GIS databases. It is the most expensive and critical phase for the success of

any GIS project. Both primary and secondary geographic data may be obtained in digital or

analog format. Analog data for example the crime incidences recorded at the police station for

this case required to be entered in computer spreadsheets. Analog maps were scanned and then

imported to ArcGIS as geographic database. Before importing into GIS environment

considerable formatting and restructuring may be required. The datasets required for this

particular study were collected and their sources identified as follows.

Table1: Datasets and their sources

DATASETS DATA DESCRIPTION DATA SOURCE

34

Topographic Maps and

Administrative Maps

Kenya admin. Maps at

scale1:50000 and Nairobi

topomaps at scales 1:5000

and 1:20000

Survey of Kenya(SOK)

JICA, Japanese International

Corporation Agency

Crime Incidence and Reports Crime incidences and reports

as recorded in the Police

occurrence book. The type of

crime, date and time of attack

and gender of victim

Nairobi Central Police Division.

Crime Hotspots Locations. Geographic Coordinates Picked using a hand held GPS,

Garmin, Model

GPSMAP60CSX

The maps that were acquired were in hard copy and therefore they were scanned and also

georeferenced. The crime incidence reports were recorded in the daily occurrence book and later

transferred into large book files. Despite of the manual record keeping, the files were arranged in

a chronological manner thus making an easier access to those needed for the study.

3.2.2 Attribute Data Entry

The lowest level at which the user interacts with a geospatial database is object class normally

referred to as a layer or feature class.

The data obtained was actually recorded as statements stating the place of crime, gender of the

victim, type of crime ,date and time of occurrence .It was necessary to summarize that

information first in Microsoft Excel spreadsheets then into tables as shown by the sample below.

This data was then imported as delimited text file formats into ArcGIS working environment.

35

3.2.3 Geographic Data Acquisition

The geographic data obtained was in form of a digital topographic map covering Nairobi area.

The maps were of scale 1:5000 and also 1:50000.Since the area of study only covered the

Central Business District and the environs it was necessary to crop out that area of interest using

the Global Mapper. These maps were georeferenced and digitized to show the major road

arteries and buildings. For map unit symbols and features, they were directly uploaded into the

ArcGIS environment since they could be selected in the view window to see if that hotspot was

located within the base map by entering its symbol or identity in a dialog box.

36

Table2: Showing tabulated crime data

Planning

Evaluation

Editing Digitizing

Preparation

3.2.4 GPS Receiver Operation.

There are several GPS receivers currently in the market one being the Germin 60CSX handheld

GPS. While picking the actual features for this case study, the GPS which uses the common dry

cells as power source, was switched on at the actual sites of the crime hotspots and held for about

five minutes to allow it acquire maximum satellite detection. The GPS automatically picked the

geographical coordinates and altitude of the point which was entered and saved as waypoints.

The date and time of recording was also stored. Later the whole set of marked points was on a

computer, exported and displayed as point overlays on the base map.

Table 3: Shows an excerpt of the geographic data collected

PLACE CODE DATE/TIME LONGI/LATIT ALTITUDE

Ambassador Amba1 3/27/2013 12:37 37 M 257997 9858095 1672 m

Central bus

station Cbs1 3/27/2013 15:18 37 M 258320 9857944 1648 m

Cbs2 3/27/2013 15:19 37 M 258285 9858008 1649 m

Cbs3 3/27/2013 15:22 37 M 258262 9858014 1654 m

Cbs4 3/27/2013 15:29 37 M 258248 9858017 1666 m

Central Police Cent 2/28/2013 8:23 37 M 256139 9858718 1732 m

37

Figure3.2. data collection workflow

Station

City Hall Cityh1 3/27/2013 16:37 37 M 257727 9858026 1660 m

Cityh2 3/27/2013 16:39 37 M 257625 9858007 1658 m

Cityh3 3/27/2013 16:42 37 M 257549 9857944 1658 m

Police Control

Room contrl 3/26/2013 17:01 37 M 255935 9856803 1736 m

Cross Road Cros1 3/27/2013 14:14 37 M 258507 9858344 1672 m

Cros2 3/27/2013 14:17 37 M 258388 9858414 1690 m

Cros5 3/27/2013 14:22 37 M 258212 9858545 1695 m

Cros6 3/27/2013 14:23 37 M 258176 9858554 1693 m

Gaberone

Gab1 3/27/2013 14:53 37 M 258163 9858318 1643 m

Globe

Roundabout Glb1 3/27/2013 12:02 37 M 257553 9858842 1704 m

Glb2 3/27/2013 12:04 37 M 257590 9858886 1698 m

Glb3 3/27/2013 12:07 37 M 257525 9858976 1695 m

Glb4 3/27/2013 12:10 37 M 257437 9858970 1699 m

Glb5 3/27/2013 12:13 37 M 257375 9858854 1698 m

Gen Post Office Gpo 3/28/2013 10:35 37 M 257244 9858062 1711 m

Haile Selassie Hai1 3/27/2013 12:55 37 M 258319 9857761 1666 m

Hai2 3/27/2013 12:59 37 M 258491 9857857 1675 m

Hai3 3/27/2013 13:02 37 M 258610 9857923 1681 m

Kencom Bus

station Ken1 3/28/2013 10:18 37 M 257495 9858209 1695 m

Ken10 3/28/2013 10:29 37 M 257045 9858028 1723 m

Ken11 3/28/2013 10:32 37 M 257157 9858037 1719 m

Ken3 3/27/2013 16:31 37 M 257805 9858113 1665 m

Ken4 3/27/2013 16:32 37 M 257813 9858109 1665 m

Kenc1 3/27/2013 16:28 37 M 257909 9858092 1666 m

Kimathi Street Kim1 3/28/2013 9:31 37 M 257788 9858187 1689 m

Kim2 3/28/2013 9:32 37 M 257769 9858193 1693 m

Kim3 3/28/2013 9:34 37 M 257648 9858309 1695 m

3.3 Geocoding the Map

38

This was done in order to assign the particular tabular data to its specific location on the earth’s

surface in order to visualize the spatial characteristics of the features for which in this case were

entered as shape files i.e. name of roads and avenues and also magnitudes of crime in the

hotspots. Normally in geocoding procedure, addresses and zip codes are used but in this case

features have been identified using their name labels.

3.4 Database Creation

The type of data determines how it is going to be stored in a GIS database i.e. Attribute or

geographic. However, interrelated geographical and attribute tables can equally be handled by a

relational database. Unlike hierarchical or network DBMS, relational structures do not have

pointers or hierarchy. RDBMS were preferably used in this project because it was flexible to

handle real world geographical objects as well as its flexibility in handling queries. A search

would be made on any tables using any of the attribute fields and even a combination of them.

Standard query language was then used to retrieve information like the crime hotspots that had

incidences beyond a certain value based on the matching of attribute data entries. The key entry

in the relational tables was the type of crime committed.

Below is a summary of data extraction and manipulation process.

39

DATA IDENTIFICATION

DATA COLLECTION AND DATA CAPTURE

NON SPATIAL DATASPATIAL DATA

DATA PROCESSING AND VERIFICATION Georeferencing, Editing, Topology

GIS DATABASE

RESULTS AND ANALYSIS

DATA CORRECT?

Fig 3.4 Data flow diagram

3.5 Georeferencing

40

YES

NO

Its obvious that all features on the earth surface are attached to a specific location

(coordinates).Without locations, data are said to be non spatial and would be of very little help in

a Geographic Information System. The act of assigning locations to features is called

georeferencing. In this project four control points were identified near the corners of the image.

Most of the control points were identified from outstanding map features like the centres of

roundabouts and the corners of major buildings. The software used to georeference Nairobi map

for this case was the Global Mapper 10. Each identified point was zoomed into and the

corresponding coordinates were entered in the ground control point entries. The software

provides for the adjustments of the projection and the datum. Universal Transverse Mercator and

Arc 1960 were used as projection and datum respectively.

Fig3.5.1 Georeferencing in Progress

Table4: The coordinates used for georeferencing are shown in the table below

41

EASTING NORTHING POINT DESCRIPTION

249468.14 9863599.32 Top left corner of Nairobi map

265343.70 9866361.50 Top right corner of map

265355.55 9853854.71 Bottom right corner of map

249495.85 9853841.71 Bottom left corner of map

3.6 Map Digitization Process

The features on the topographical map were digitized as point, line or polygon features as

follows:

The datasets such as the digital topographic maps were loaded into the file location of the

computer as a folder in drive C.

The Arc catalog in the ArGIS software was then used to create shape files which were

then loaded in ArcMap ready for digitization.

The shape files created were buildings, roads, open grounds, rivers and crime hotspots.

Since buildings were in geometrical shapes, they were then digitized as polygons by use

of the digitizer tool from the editor catalog by simply tracing the outermost bounds of the

buildings. A similar procedure was done for open grounds .Roads and rivers were

digitized as line features by tracing along them.

The hotspots had been picked by the GPS as coordinate points and were then exported

and overlaid in the map as point features.

Each time features in a shape file were being digitized, the specific shape file was

activated and before moving to digitize the next, all edited features were saved. The

errors identified included undershoots, overshoots, dangling lines and sliver polygons.

The following are the tools and software used in the data editing process.

Table5: Tools and software used

42

Apart from editing the shape files, the necessary attributes could be added to them using the add

fields option. This would allow the entry of sufficient information which would enable the

database to be queried adequately. The figure below shows how the attributes were added to

crime hotspots shape file.

43

Tool Specifications Purpose

Desktop

Computer

Processor Intel(R)

Pentium 4,2.5GHz

2 GB RAM

Data And Information Processing

ArcGIS Version 10.1 Vectorization, Database Query And

Analysis

Global Mapper Version 10.0 Georeferencing and Clipping

Scanner Hp Scanner

deskjet

Scanning Topographic Maps

Fig3.6: Addition of attributes to a shape file

3.7 GIS Database Query

All the shape files created were attached to an attribute table showing all the details about it. For

example the crime hotspots were attributed to the place names, the time of occurrence, type of

victim and the number of recorded incidences. In ArcGis software program, query expressions

were created to derive the desired information from the map and the attribute tables. If the query

was directed to display the locations of highest crime, they would then be displayed on the map

depending on the set parameters. The querying of the database was done by activating the theme

by clicking on it on the table of contents showing different shape files. Simple queries could be

performed by using the identity tool whereby you simply click on any feature e.g. the hotspot

and would display its name and any other information about that feature as long as it is in the

attribute table. An example of a query was the selection of hotspots with more than forty crime

incidences which would be appropriate for CCTV coverage.

3.8 Cartographic Display and Visualization

Maps as an interface to GIS were created so that they could enable viewing, querying and also

analysis of events. From the ArcMap window, the layout view option enabled the creation of

maps by inserting the legend, north arrow bars scale and neat line. Map symbols were then used

to represent various features which were created as shape files from the topographical base map.

Red circular point symbols were used to show the crime hotspots, line symbols for roads and

polygon features for buildings and open grounds.

3.8.1 Thematic Mapping

In the representation of features various visual variables were used for data display. They

included the variation of hue, value, chroma, size, texture, shape and pattern. For instance a

yellow color was used in buffering the crime hotspots as it stands out from the red colour. Base

map features were digitized using desaturated colours e.g. brown for the roads and light blue for

the river. It is on the derived base map that the crime hotspots and CCTV sites were overlaid.

3.8.2 Graduated Maps

44

Graduated/proportional/quantitative point symbols have long been used in thematic mapping

(Njoroge T, 2013).In this case of study, geometric symbols e.g. circles of different sizes were

used to show data at specific locations. This was done by first digitizing the base map features

like major roads, buildings and drainage system in order to give a map user some orientation of

the map. Proportional point symbols show relative magnitudes of crime at various locations. The

features of interest were displayed by freezing the base map itself leaving the digitized features

active on the ArcMap window.

. 3.9 Site Selection for CCTV Installation

Two criteria were used in the identification of CCTV sites. One of them was the use of the

cluster analysis obtained from the database query using the query builder either by attributes or

location. Database interrogation involved pointing at the feature using identity tool, typing and

by the use of a formal Structured Query Language (SQL). Some of the queries performed were

to;

Find the most committed type of crime.

Find the places that have more than forty crime incidences.

Find all the crime hotspots along Ronald Ngala.

Places with the highest number of crime incidences were allocated more values /weight in terms

of site identification since they would require more surveillance. Regions covered by the radial

buffer of fifty five metres would require a surveillance system. Where the buffers overlapped, it

was an indication of clustered hotspots which would require more surveillance. Since the

interest was in out-door surveillance most of the perpetrators would use roads and avenues as

escape routes; it was therefore essential for all the road junctions in those areas to be put under

surveillance. For instance, a hit and run vehicle tries to escape after killing an individual along

Moi Avenue and tries to exit through Haile Selassie it must go through a junction at some point

and would be probably detected by at least one of the cameras. The locations where the buffers

overlapped gave an ideal location for CCTV installation i.e. rationalization. This means that the

more the overlaps the higher the necessity for installation. Considering the high cost of these

cameras, building sites were given the first priority so as to keep them not easily identifiable by

the public. This would assist in maintenance of the cameras as well because they would not be

45

subject to easy vandalism. Open places were also mapped as proposed sites after all other factors

e.g. no adjacency to buildings or structures were considered. City council of Nairobi took an

initiative to light open areas e.g. markets and streets by tower lamps and these were also

considered as installation sites.

CHAPTER 4

4.0 RESULTS AND ANALYSIS

Spatial analysis was necessary because it includes all to do with transformations, manipulation

and other methods that were needed to add value to all geographic data collected. That in turn

would assist in informed decision making since the situations are displayed as they are on the

ground. Some examples of spatial analysis used for this case study include querying and

reasoning, measurements and transformations.

The results obtained from the spatial data analysis reveal that criminal activities are not evenly

distributed within the CBD and its environs but are rather concentrated at certain places than

others. From the geostatistical results as displayed on the point pattern maps we find that

clustering of the incident points increases towards the northern part of the city towards the lower

outskirts of the town as one approaches Ngara, Kariokor, Kamkunji and Muthurwa.

46

4.1 Generation of Crime Distribution Maps.

47

48

Figure 4.1.1 thematic map showing crime hotspots in various locations of the city

Fi

g 4.

1.2

show

s crim

e ho

tspo

ts b

uffer

ed a

t 55

met

res r

adiu

s

49

50

Fig4

.1.3

show

s crim

e bu

ffers

at 6

0met

re ra

dius

Fig 4.1.4 Showing Nairobi proposed CCTV sites

Fig 4.1.5: Shows the position of Crime Hotspots and CCTV sites.

51

52

Fig

4.1.

6 G

radu

ated

crim

e ho

tspo

ts

53

Fig 4.1.7 Buffered CCTV Sites

The crime hotspots were buffered at a radius of 50 metres so as to show how well the hotspots

would be covered by the CCTV cameras. Initially the ArcGIS buffering command was to cover

each identified region. It is evident that there are places with overlapping buffers meaning that

the place is under intense surveillance because of the clustered crime patterns. This could assist

the police in allocating or giving more attention to those areas.

4.2 CRIME STATISTICAL ANALYSIS BY OTHER ATTRIBUTES

4.2 .1 Crime Incidences by Months of the Year

It is evident from the graph that stealing was the most common crime committed throughout the

year. In the months of Jan to March robbery with violence, car jacking, assault and fraud were

the highest crimes compared to other months. Stealing, felony, burglary and drugs were the

highest committed crimes in the months of April to June but recorded the least number of car

jacking. Stealing and pretence were the most committed crimes in July to September and the

least recorded was forgery.

STEALIN

G

ROBBERY W

ITH VIO

CAR JACKING

FRAUD

DRUGS

FORGER

Y

ASAULT

BURGLARY

PRETENCE

FELONY

FIGHTS

0

10

20

30

40

50

60

70

80

90

JAN-MARCH 2011APRIL-JUNE 2011JULY-SEPT 2011

54

Fig 4.2.1 Showing different crime rates.

Crime Incidences by Time from January -March 2011

7.00AM-11.48AM

11.48AM-4.36PM

4.36PM-9.24PM

9.24PM-2.12AM

2.12AM-7.00AM

0

5

10

15

20

25

30

35

40

45

JAN-MARCH 2011

JAN-MARCH

Fig 4.2.2

It is evident from the graph above that the highest number of crime incidences from Jan to March

2011 took place in the day especially morning hours until noon and afternoon. From evening

around 5 p.m. there was a drop in the reported cases and a very few of them were reported in the

night around 10 p.m. and 7 a.m.

55

Crime Incidences by Time from April-June 2011

7.00AM-11.48AM

11.48AM-4.36PM

4.36PM-9.24PM

9.24PM-2.12AM

2.12AM-7.00AM

0

5

10

15

20

25

30

35

40

APRIL-JUNE 2011

APRIL-JUNE

Time

Fig 4.2.3

From April to June 2011 most crime incidences were reported to have occurred from noon to the

early evening around 5p.m.There were relatively more incidences in the morning and fewer

incidences were reported from 5pm until late night. Incidences started increasing slightly

towards morning hours.

56

Crime Incidences by Time from July-September 2011

7.00AM-11.48AM

11.48AM-4.36PM

4.36PM-9.24PM

9.24PM-2.12AM

2.12AM-7.00AM

0

10

20

30

40

50

60

JULY-SEPT 2011

JULY-SEPT

Fig 4.2.4

In the months of July to September 2011 most crime incidences occurred from morning till noon.

There was a successive drop of crime incidences from the afternoon till morning.

57

Time

Crime Incidences by Gender by Months of the Year

JUNE-AUGT JAN-MARCH APRIL-JUNE JULY-AUGST0

20

40

60

80

100

120

140

MALE

FEMALE

Number of victims

Fig 4.2.5

The graph displays the crime incidence by gender victimization and shows that most victims

were male people. The months of June to August 2010 recorded the highest number of male

victims whereas the months of January to March 2011 recorded the highest number of female

victims. Apparently, there was less victimization to both genders between the months of April to

June 2011.

58

Crime Incidences at Place of Occurrence

RIVER ROAD

LANDHIES

ROAD

LUTH

ULI

MACHAKOS C BUS

RACE COURSE

RONALD NGALA

MUTHURWA

NEW PUMWANI

TOM M

BOYA

MOI AVEN

UE

CENTR

AL BUS S

TATN

KIRINYAGA ROAD

0

10

20

30

40

50

60

70

JAN-MARCH 2011APRIL-JUNE 2011JULY-SEPT 2011TOTAL ENTRIES

Fig 4.2.6

4.3 DISCUSSION

Figures 4.2.1,4.2.3,4.2.4and 4.2.5 show the crime incidences reported between the months of

January and September 2011.There are six major crime hotspots identified in hierarchy of crime

prevalence namely Race Course, River Road, Machakos Country Bus, Luthuli, Muthurwa,

Landhies Road. River Road exhibited the highest number of reported case from January to

March 2011. Between April and June 2011, Race Course had the highest number of reported

incidences. From July to September 2011 it was Muthurwa that had the highest reports of crime.

The most recurrent incident was stealing during the day and this could be attributed to the fact

that most people are busy trading and there is a heavy cash flow exchanging hands for example

in River Road and Muthurwa market. Most of the perpetrators around Muthurwa barely escape

59

alive when the hands of the angry mob lay on them! Robbery with violence is most prominent in

the CBD owing to the presence of high profile businesses like commercial banks and also tourist

hotels. Cases of carjacking mostly occur towards the outskirts of the towns and in poorly lit

places and places next to bushes like Quarry Road. Much of the assault and mugging happen in

places near bank ATMs and poorly lit avenues, dark alleys and corridors. People should be

advised to avoid fly-overs, underpasses and bridges especially during evenings and late night

hours.

Drug trafficking and other psychotropic substances were mostly reported in Muthurwa, Landhies

Road and Kariokor market. Drugs like cannabis sativa/bhang were mostly brought from the rural

areas. It is worth mentioning that police dogs have played a major role in curbing drug

trafficking

The issue of fraud, bad cheques and forgery were mostly prominent in the city center which is a

herb of large commercial activities. In city hall, several cases were reported concerning

individuals who forged documents to get employed (mystery of the ghost workers), or to acquire

City Council’s assets with intention to steal or misuse for self interest. Fake currency notes have

also been witnessed and all these call for much scrutiny and careful verification of documents

wherever possible.

60

CHAPTER 5

5.0 CONCLUSION

It is evident from this analysis that indeed crime can happen anywhere although there are those

particular places where several crime events seem to occur more frequently than others and such

places are called crime hotspots. Most crimes occur mostly during the day in the CBD mainly

because it is a hub of commercial activities and Nairobi being the capital city of Kenya as well as

administrative headquarters. Stealing, fraud and robbery with violence were displayed as the

most recurrent crimes. These observations concur with the crime place theories that offenders

make rational decisions with respect to committing a crime influenced by disposing factors

between motivated perpetrators, victims and targets in space and time.

The use of Geographic Information System in study of crime patterns and distribution has proven

much efficient over the traditional manual methods. Through the spatial analysis of real time

events other technological considerations have been implemented for example selection of

CCTV sites based on the GIS analysis which proved important in meeting the objectives of this

study.

Amongst the aims of the project was to analyze the trends of crime in the CBD and use the

information thereafter to assist the security firms and also create awareness to the public to avoid

uncompromising situations which could land them as victims of crime. Thematic maps produced

assisted in integrating various layers of information which enabled quick onscreen display of the

desired features. The buffered crime hotspots assisted in the site selection of CCTV installation

sites and also in the allocation of resources in the most effective manner.

The Kenya police headquarters at Vigilance house has made efforts to embrace GIS in their

crime analysis however implementation of GIS in various police stations is not yet fully

considered.

Statistical analysis done for all crimes revealed that places at the northern and eastern part of the

city recorded more crime events more than the CBD itself because of various reasons below.

61

The CBD constitutes mainly of administration offices and large commercial businesses

such as banks which are guarded by heavily armed police officers and are under 24hr

CCTV surveillance. The reported crimes mostly took place outside the buildings along

the streets.

In the north eastern part of the city along River Road and Race Course roads, most

business premises lacked security personnel and in any case they were only armed with

clubs which were not efficient to confront perpetrators armed with guns and pistols.

Apart from that, most of the premises had no coverage of surveillance systems.

Most people go to the CBD to work during the day and later commute back to their

places of residence in the evening. These residential places are located in the outskirts

towards the northern and eastern parts of the city. Many cases reported involved people

either going home on foot or walking to a bus station.

There is little public awareness on where to avoid when walking home and at what time

of the day.

There was no clear indication of the exact time when these crimes were committed but from

the graphical analysis, it was found that most of the crime incidences took place within the

day. At day time less serious crimes like stealing and pretence were reported as opposed to

mugging, burglary, robbery with violence and car jacking which mostly occurred in the late

evenings and at night.

Through the statistical graph displays, the crime hotspots have been successfully identified

and this would give informed decision on where to increase security vigilance.

62

RECOMMENDATIONS

1. All the Governmental and non-Governmental organizations handling security issues should

incorporate GIS technology by setting up and maintaining GIS departments to help in better

management and close monitoring of particular crime hotspots and also in predictive crime

pattern analysis.

2. Keen recording of crime incidences should be particulate with respect to the exact point of

occurrence to avoid generalization of places like in this case of study most crimes were only

associated with roads and avenues without the name of exact place consequently spending

more time in conducting oral interviews. Exhaustive information collection should therefore

be inquired from the victim when entering them in the occurrence books at the police

stations.

3. The CCTV cameras should be installed strategically at various places of the city. This would

be a real time witness and the prosecution processes in courts are speculated to take shorter

periods, which would otherwise be spent on looking for eye witnesses.

4. All the CCTVS should be integrated at a central monitoring unit by the police with

appropriate computer mechanisms to enable a link-up between police immediate response,

the magnitude of crime and the exact crime sites.

5. Public awareness should be created concerning the trends of crime to reduce crime

victimization. They should also be educated about community policing to assist security

officers concerning crime matters.

6. Proper street lighting and refurbishment of the same should be implemented to discourage

criminal activities.

7. The information obtained from the crime analysis by whichever means should be used in

resource allocation. Examples are where to assign more police on patrol, police bases and

where to increase street lights to reduce crime bottlenecks.

63

REFERENCES

1. Aki, Stavrou (2002): UN Habitat: United Nations Development Programme: Crime in

Nairobi-Results of Citywide Victim Survey.

2. Alexander, M. and Xiang, W. N (1994): Crime pattern analysis using GIS.

3. Arnoff, S (1989): Geographic Information Systems.

4. Chaney S,and Ratcliffe, J.( 2004): GIS and Crime Mapping.

5. Getis A, Ord J K (1992): Hotspot Analysis.

6. Gimode, E. A. (2001): An Anatomy of Violent Crime and Insecurity in Kenya:the case of

Nairobi 1985-1999.Research Findings on City/Street Crimes in Nairobi.

7. GPS.gov (2013): Official US Government Information about the Global Positioning

System and Related Topics. http://www.gps.gov/systems/gps/space[accessed in 23rd April

2013].

8. Hirschfield A, Bowers A, Pease K. (1995): Use of GIS on Crime Mapping.

9. Kenya Police. (2011): Annual Crime Report.

10. Mulaku, G. C. (2012): Land Information Systems Notes (Unpublished).

11. Njoroge, T. M (2012): Cartographic Map And Design Notes (Unpublished).

12. Playfair, G.( 1957): Crime In Our Century.

13. Ratcliffe, J. (2004): Use of GIS on Crime and Intelligence Analysis.

14. Ratcliffe, J. H. and M. J. Mc Cullagh (1998): Perceptions of Crime Hotspots.

15. Reckless, W. C (1973): The Crime Problem. ``A New Theory Of Delinquency And Crime”

Published By Russell Sage Foundation.

16.United Nations Report. (2007): United Nations Office on Drugs and Crime, "Marred By

Violence and Fraud.”

17.Wecom CCTV Surveillance Systems (2013):http://www.wecusurveillance.com[Accessed

on 23rd April 2013].

18. William, L. Marshall, Clark, L. (1970): Sociology of Crime and Delinquency.

64

APPENDIX A: Attribute table for reported crime incidences per locations

PLACE

JUNE-AUG

2010

JAN-MARCH

2011

APRIL-JUNE

2011

JULY-SEPT

2011

TOTAL

ENTRIES

RIVER ROAD 15 18 12 10 55

NEW

PUMWANI 9 8 7 7 31

LANDHIES

ROAD 9 14 10 8 41

TOM MBOYA 7 15 4 6 32

LUTHULI 17 11 6 8 42

KARIOKOR 4 5 8 7 24

MACHAKOS C

BUS 16 8 15 13 52

CENTRAL BUS

STATN 6 9 7 7 29

KIRINYAGA

ROAD 7 7 6 9 29

RACE

COURSE 11 16 20 11 58

HAILLE

SELASIE 7 9 12 6 34

RONALD

NGALA 9 10 10 11 40

KOINANGE 3 6 2 4 15

CITY HALL 12 9 5 4 30

MOI AVENUE 13 8 6 7 34

MUTHURWA 7 9 10 15 41

CROSS ROAD 6 8 4 4 22

GLOBE

ROUNDABOU

T 7 8 4 5 24

NGARA STAGE 4 2 2 4 12

CITY SQUARE 4 5 6 10 25

65

MFANGANO 5 9 7 3 24

MUINDU

MBINGU 3 7 3 4 17

AMBASSADOR 3 4 2 2 11

KENCOM 5 4 5 4 18

AFYA CENTRE 6 5 5 3 19

NAKUMATT 3 5 2 2 12

ACCRA ROAD 4 5 5 7 21

PARKLANDS

ROAD 8 6 5 6 25

UHURU

HIGHWAY 7 5 9 5 26

GPO 4 2 0 2 8

APPENDIX B: Attribute table for total crime incidences per months of the year

CRIME JUNE-AUG 2010

JAN-MARCH

2011

APRIL-JUNE

2011 JULY-SEPT

TOTAL

INCIDENCES

STEALING 79 82 62 65 288

ROBBERY

WITH VIO 15 9 6 9 39

CAR JACKING 27 15 7 11 60

FRAUD 28 19 7 5 59

DRUGS 12 9 13 8 42

FORGERY 9 11 8 4 32

ASAULT 17 23 13 20 73

BURGLARY 18 10 16 13 47

PRETENCE 4 9 11 18 42

FELONY 7 6 11 5 29

FIGHTS 4 3 5 5 17

66

APPENDIX C: Attribute table for some reported crimes at police stations

CRIME PLACE SEX/VICTIM

CRIME

COMMITTED DATE TIME

RACE COURSE F STEALING 2/1/2011 2.00PM

LUTHULI M FELONY 2/1/2011 1.30PM

CITY SQURE F PRETENCE 3/1/2011 10.00AM

NGARA STAGE M FIGHTS 3/1/2011 5.30PM

KARIOKOR M STEALING 3/1/2011 8.00AM

RACE COURSE M DRUGS 4/1/2011 4.40PM

RIVER ROAD M BURGLARY 4/1/2011 3.00AM

GPO F

ROB WIT

VIOLENCE 5/1/2011 8.30PM

NYAMAKIMA M ASSAULT 5/1/2011 10.00PM

MUTHURWA F DRUGS 6/1/2011 3.45PM

RACE COURSE F STEALING 7/10/2011 2.00PM

RIVER ROAD F CAR JACK 7/1/2011 6.00PM

NEW

PUMWANI M PRETENCE 7/1/2011 11.30AM

MACHAKOS

BUS F FELONY 7/1/2011 12.00 PM

RING ROAD M STEALING 8/1/2011 1.00PM

KOINANGE M FRAUD 8/1/2011 9.00AM

RONALD

NGALA M FORGERY 9/1/2011 8.00AM

LUTHULI F

ROB WIT

VIOLENCE 9/1/2011 11.00PM

NEW

PUMWANI M FIGHTS 9/1/2011 10.00AM

RING ROAD M DRUGS 10/1/2011 5.00PM

RACE COURSE F STEALING 10/1/2011 12.00PM

RAILWAYS M FIGHTS 11/1/2011 7.35AM

MUNYU ROAD F FORGERY 11/1/2011 8.10AM

LANDHIES M RORGERY 11/1/2011 6.30PM

67

ROAD

LANDHIES

ROAD M STEALING 11/1/2011 5.40PM

CITY HALL F PRETENCE

13/01/201

1 2.00PM

HARAMBE

AVEN M CAR JACK 13/1/2011 1.00AM

EMBASY

HOUSE F ASSAULT 14/1/2011 3.00PM

SERENA

HOTEL F DRUGS 14/1/2011 4.00PM

AFYA CENTER M STEALING 14/1/2011 4.00PM

MFANGANO M BURGLARY

15/01/201

1 10.00PM

RIVER ROAD M PRETENCE 15/1/2011 11.00AM

OTC BUS STA F STEALING 16/1/2011 7.10AM

RONALD

NGALA M FELONY 16/1/2011 2.40PM

LUTHULI M

ROB WIT

VIOLENCE 17/1/2011 8.00PM

MUINDU

MBINGU M BURGLARY 17/1/2011 10.00PM

HARAMBEE

AVEN F STEALING 17/1/2011 11.00AM

MOI AVENUE M DRUGS 18/1/2011 7.00PM

TOM MBOYA F STEALING 18/1/2011 9.00AM

GIKOMBA M STEALING 18/1/2011 7.00AM

TEA ROOM

ACCRA F PRETENCE 19/1/2011 1.00PM

HAKATI F ASSAULT 19/1/2011 3.00AM

CITY HALL M FORGERY 20/1/2011 8.50AM

NEW

PUMWANI M CAR JACK 20/1/2011 3.00PM

TOM MBOYA F DRUGS 20/1/2011 4.00AM

68

LANDHIES

ROAD M STEALING 21/1/2011 12.10PM

ACCRA ROAD F FRAUD 21/1/2011 10.30AM

TOM MBOYA F FORGERY 23/1/2011 9.00AM

ACCRA ROAD M FRAUD 23/1/2011 5.00PM

KIMATHI M FIGHTS 23/1/2011 5.40AM

ACCRA ROAD M STEALING 23/1/2011 9.00PM

LANDHIES

ROAD F STEALING 24/1/2011 4.45PM

MUTHURWA F FRAUD 24/1/2011 8.15PM

LUTHULI M

ROBBERY

WIT VIO 25/1/2011 9.00PM

MUNYU ROAD M STEALING 25/1/2011 12.00MIDNIGHT

HAILE

SELASSIE M STEALING 26/1/2011 6.00AM

ACCRA ROAD M STEALING 26/1/2011 7.45AM

CROSSROAD F CAR THEFT 27/1/2011 4.00PM

TOM MBOYA F FRAUD 27/1/2011 9.00AM

LANDHIES

ROAD F MURDER 27/1/2011 3.40AM

GIKOMBA

MARKET M FIGHTS 28/1/2011 1.00AM

OTC BUS STA F FELONY 29/1/2011 6.30PM

LANDHIES

ROAD F STEALING 29/1/2011 4.40AM

GIKOMBA F STEALING 29/1/2011 3.20PM

RIVER ROAD F STEALING 30/1/2011 1.50PM

RONALD

NGALA M ASSAULT 30/1/2011 5.15PM

TEA ROOM

ACCRA M ASSAULT 1/2/2011 5.35AM

SHEIKH

KARUME F STEALING 1/2/2011 7.00AM

69

RIVER ROAD F ASSAULT 2/2/2011 8.00PM

MFANGANO M STEALING 2/2/2011 12.25PM

RONALD

NGALA F STEALING 2/2/2011 4.50AM

TEA ROOM

ACCRA F STEALING 3/2/2011 6.30AM

GABERONE M STEALING 3/2/2011 5.15PM

LUTHULI M STEALING 4/2/2011 6.00AM

MACHAKOS

BUS M

ROBBERY

WITH VIO 4/2/2011 3.45AM

LANDHIES

ROAD M

STEALING

ASSAULT 5/2/2011 7.30PM

RIVER ROAD F STEALING 6/2/2011 9.20AM

TOM MBOYA F DRUGS 6/2/2011 10.00AM

LANDHIES

ROAD M CAR THEFT 6/2/2011 11.00AM

MUTHURWA M FIGHTS 6/2/2011 1.00PM

NEW

PUMWANI M FORGERY 7/2/2011 12.00AM

KENYA NAT

THEATRE M FRAUD 7/2/2011 1.00PM

NEW

PUMWANI F DRUGS 8/2/2011 6.45AM

QUARRY

ROAD F STEALING 8/2/2011 4.00PM

LUTHULI M ASSAULT 9/2/2011 7.00AM

UHURU PARK M STEALING 10/2/2011 6.30AM

TWIGA

TOWERS F FRAUD 10/2/2011 4.00PM

ACCRA ROAD F THEFT 10/2/2011 7.00PM

CHESTER

HOUSE F STEALING 10/2/2011 9.40PM

MFANGANO F DRUGS 11/2/2011 11.00AM

70

HAILE

SELASSIE M FRAUD 11/2/2011 7.00AM

KIRINYAGA

ROAD M STEALING 12/2/2011 7.00AM

NEW

PUMWANI M FRAUD 12/2/2011 8.00PM

GIKOMBA F FRAUD 12/2/2011 9.00PM

RIVER ROAD M STEALING 13/2/2011 10.00AM

LUTHULI F ASSAULT 13/2/2011 1.00PM

RONALD

NGALA F CHEATING 14/2/2011 12.00NOON

KIRINYAGA

ROAD M BURGLARY 14/2/2011 3.00PM

CITYHALL M FRAUD 14/2/2011 4.00PM

MACHAKOS

BUS M FRAUD 15/2/2011 7.30PM

OLD MUTUAL M PRETENCE 15/2/2011 8.00PM

TEA ROOM

ACCRA M STEALING 15/2/2011 10.00PM

EQUITY

NGARA F STEALING 15/2/2011 10.30PM

EQUITY RACE

COURSE F FORGERY 16/2/2011 8.00AM

MACHAKOS

BUS M ASSAULT 16/2/2011 10.0AM

CITYHALL F PRETENCE 16/2/2011 1.00AM

AMBASSADOR F

ROBBERY

WIT VIO 17/2/2011 8.00PM

LANDHIES

ROAD M ROBBERY 17/2/2011 11.00AM

TAMWORTH

ROAD M STEALING 18/2/2011 9.45PM

EQUITY RACE

COURSE F STEALING 18/2/2011 7.45AM

71

KUMASI ROAD M FRAUD 18/2/2011 3.00PM

CITYHALL F ASSAULT 19/2/2011 7.40PM

UON HOSTELS F STEALING 19/2/2011 10.00PM

LANDHIES

ROAD F MURDER 19/2/2011 11.00PM

MACHAKOS

BUS F FRAUD 20/2/2011 8.00AM

RACE COURSE M FORGERY 20/2/2011 10.00AM

CITYHALL M FORGERY 20/2/2011 8.00PM

CITYHALL M CHEATING 21/2/2011 10.00AM

CITYHALL M STEALING 21/2/2011 4.00PM

ACCRA ROAD M STEALING 21/2/2011 4.30PM

TEA ROOM

ACCRA F MURDER 22/2/2011 9.00PM

KIRINYAGA

ROAD M ASSAULT 22/2/2011 3.0AM

HAILE

SELASSIE M BURGLARY 23/2/2011 4.00AM

GIKOMBA F STEALING 23/2/2011 10.00AM

CROSSROAD M ASSAULT 24/2/2011 12.00 NOON

RONALD

NGALA M STEALING 24/2/2011 7.00AM

KICC F

CAR

JACKING 24/2/2011 7.30AM

JOGOO

HOUSE F CAR THEFT 25/2/2011 11.40 AM

PARAMOUNT

PLAZA M BURGLARY 25/2/2011 2.14PM

WAKULIMA

MKT M PRETENCE 26/2/2011 11.25AM

KARIOKOR

CEMETERY M

ROBBERY

WIT VIO 26/2/2011 9.30PM

KIRINYAGA

ROAD M STEALING 27/2/2011 6.30AM

72

NEW

PUMWANI M FIGHTS 27/2/2011 12.30PM

COMMERCIAL

HOUSE F PRETENCE 28/2/2011 6.00AM

MOI AVENUE F

CAR

JACKING 1/2/2011 8.30PM

UKWALA F FRAUD 1/3/2011 9.50AM

HAKATI ROAD M STEALING 1/3/2011 10.00PM

GIKOMBA F STEALING 2/3/2011 12.30PM

TURKANA

LANE F FELONY 2/3/2011 1.00PM

RACE COURSE M STEALING 3/3/2011 4.00AM

MOI AVENUE M STEALING 3/3/2011 11.0AM

MACHAKOS

BUS F PRETENCE 3/3/2011 5.30PM

TOM MBOYA M FORGERY 3/3/2011 12.45PM

KARIOKOR

MKT M STEALING 4/3/2011 10.30AM

HAKATI LANE F CAR THEFT 4/3/2011 11.00AM

MUTHURWA F BURGLARY 4/3/2011 3.30AM

LUTHULI M STEALING 5/3/2011 1.00AM

MACHAKOS

BUS M STEALING 5/3/2011 8.00PM

RACE COURSE M STEALING 6/3/2011 6.45AM

KIRINYAGA

ROAD M STEALING 6/3/2011 5.50PM

MOI AVENUE M ASSAULT 6/3/2011 2.00PM

HAILE

SELASSIE F

ROBBERY

WIT VIO 6/3/2011 8.00AM

NEW

PUMWANI M MURDER 7/3/2011 2.00PM

RACE COURSE F STEALING 8/3/2011 1O.05AM

NYAMAKIMA F STEALING 8/3/2011 12.32PM

73

KOINANGE M FORGERY 8/3/2011 11.00AM

KIJABE M STEALING 9/3/2011 5.30PM

KIRINYAGA

ROAD M

STEALING

BURGLARY 9/3/2011 6.00PM

CENTRAL BUS

STA M STEALING 10/3/2011 6.00AM

NEW

PUMWANI M STEALING 10/3/2011 10.00AM

MACHAKOS

BUS M FELONY 11/3/2011 3.00PM

GIKOMBA F FORGERY 12/3/2011 8.40AM

RIVER ROAD M DRUGS 12/3/2011 9.0PM

MOI AVENUE M DRUGS 12/3/2011 11.20AM

PARLIAMENT

ROAD F CHEATING 13/3/2011 4.45PM

MOI AVENUE M STEALING 13/3/2011 6.00PM

CENTRAL BUS

STA M STEALING 14/3/2011 11.00AM

RIVER ROAD F DRUGS 14/3/2011 8.45PM

MUNYU ROAD M CARJACKING 15/3/2011 10.30PM

CROSS ROAD F STEALING 16/3/2011 9.45AM

TOM MBOYA M ASSAULT 16/3/2011 10.30PM

MFANGANO M BURGLARY 16/3/2011 3.50AM

RIVER ROAD F

CAR

JACKING 17/3/2011 4.30AM

CITYHALL M CAR THEFT 17/3/2011 10.35AM

RIVER ROAD M FORGERY 17/3/2011 7.00AM

BANDA

STREET M DRUGS 17/3/2011 10.30AM

RONALD

NGALA M PRETENCE 18/3/2011 10.30AM

RACE COURSE F STEALING 18/3/2011 6.25PM

ACCRA ROAD M FRAUD 18/3/2011 5.10PM

RACE COURSE M STEALING 19/3/2011 5.00PM

74

LUTHULI M FIGHTS 19/3/2011 11.50AM

QUARRY

ROAD M ASSAULT 20/3/2011 9.30AM

RACE COURSE F STEALING 20/3/2011 11.20AM

G.P.O F STEALING 20/3/2011 2.58PM

ZIWANI M BURGLARY 21/3/2011 10.00AM

NYAMAKIMA F STEALING 21/3/2011 1.00PM

MUTHURWA M DRUGS 22/3/2011 5.00PM

GIKOMBA M CAR JACK 22/3/2011 10.30PM

U O N F STEALING 23/3/2011 11.00AM

TEMPLE

ROAD M DRUGS 23/3/2011 6.00PM

LANDHIES

ROAD M PRETENCE 23/3/2011 8.00PM

KIONANGE M FRAUD 23/3/2011 8.30PM

RONALD

NGALA F FORGERY 24/3/2011 9.40AM

CROSS TOADS M PRETENCE 25/3/2011 9.00AM

TOM MBOYA F STEALING 25/3/2011 11.00AM

TOM MBOYA F STEALING 25/3/2011 4.30PM

AMBASSADOR M PRETENCE 26/3/2011 6.00AM

APPENDIX D: Attribute table for crime hotspot locations

PLACE CODE DATE/TIME LONG/LATIT ALTITUDE

Ambassador Amba1 3/27/2013 12:37 37 M 257997 9858095 1672 m

Central bus

station Cbs1 3/27/2013 15:18 37 M 258320 9857944 1648 m

Cbs2 3/27/2013 15:19 37 M 258285 9858008 1649 m

Cbs3 3/27/2013 15:22 37 M 258262 9858014 1654 m

Cbs4 3/27/2013 15:29 37 M 258248 9858017 1666 m

75

Central Police

Station Cent 2/28/2013 8:23 37 M 256139 9858718 1732 m

City Hall Cityh1 3/27/2013 16:37 37 M 257727 9858026 1660 m

Cityh2 3/27/2013 16:39 37 M 257625 9858007 1658 m

Cityh3 3/27/2013 16:42 37 M 257549 9857944 1658 m

Police Control

Room contrl 3/26/2013 17:01 37 M 255935 9856803 1736 m

Cross Road Cros1 3/27/2013 14:14 37 M 258507 9858344 1672 m

Cros2 3/27/2013 14:17 37 M 258388 9858414 1690 m

Cros5 3/27/2013 14:22 37 M 258212 9858545 1695 m

Cros6 3/27/2013 14:23 37 M 258176 9858554 1693 m

Gaberone

Gab1 3/27/2013 14:53 37 M 258163 9858318 1643 m

Globe Round

about Glb1 3/27/2013 12:02 37 M 257553 9858842 1704 m

Glb2 3/27/2013 12:04 37 M 257590 9858886 1698 m

Glb3 3/27/2013 12:07 37 M 257525 9858976 1695 m

Glb4 3/27/2013 12:10 37 M 257437 9858970 1699 m

Glb5 3/27/2013 12:13 37 M 257375 9858854 1698 m

General Post

Office GPO 3/28/2013 10:35 37 M 257244 9858062 1711 m

Haile Sellasie Hai1 3/27/2013 12:55 37 M 258319 9857761 1666 m

Hai2 3/27/2013 12:59 37 M 258491 9857857 1675 m

Hai3 3/27/2013 13:02 37 M 258610 9857923 1681 m

Kencom Bus

Station Ken1 3/28/2013 10:18 37 M 257495 9858209 1695 m

Ken10 3/28/2013 10:29 37 M 257045 9858028 1723 m

Ken11 3/28/2013 10:32 37 M 257157 9858037 1719 m

Ken3 3/27/2013 16:31 37 M 257805 9858113 1665 m

Ken4 3/27/2013 16:32 37 M 257813 9858109 1665 m

Kenc1 3/27/2013 16:28 37 M 257909 9858092 1666 m

Kimathi Sreet Kim1 3/28/2013 9:31 37 M 257788 9858187 1689 m

Kim2 3/28/2013 9:32 37 M 257769 9858193 1693 m

Kim3 3/28/2013 9:34 37 M 257648 9858309 1695 m

76

Kim4 3/28/2013 9:38 37 M 257492 9858472 1692 m

Machakos Bus

M

cb1 3/27/2013 13:11 37 M 258940 9858071 1648 m

Mcb3 3/27/2013 13:13 37 M 258988 9858031 1651 m

Mcb4 3/27/2013 13:14 37 M 259041 9858031 1655 m

Mcb5 3/27/2013 13:16 37 M 258995 9858088 1661 m

Kirinyaga Road Kiri1 3/27/2013 11:37 37 M 258606 9858483 1677 m

Kiri2 3/27/2013 11:40 37 M 258457 9858492 1687 m

Kiri5 3/27/2013 11:53 37 M 257662 9858732 1675 m

Kiri6 3/27/2013 11:56 37 M 257572 9858782 1687 m

Koinange Street Koi1 3/28/2013 10:39 37 M 257183 9858258 1701 m

Koi3 3/28/2013 10:42 37 M 257120 9858392 1692 m

New Pumwani Pumu 3/27/2013 13:48 37 M 258623 9858127 1660 m

Pumu 3/27/2013 13:49 37 M 258577 9858160 1667 m

Pumu 3/27/2013 13:50 37 M 258551 9858163 1675 m

Pumu 3/27/2013 13:52 37 M 258611 9858116 1679 m

Landhies Road Land1 3/27/2013 13:27 37 M 259402 9858203 1656 m

Land2 3/27/2013 13:31 37 M 259359 9858178 1667 m

Land4 3/27/2013 13:34 37 M 259248 9858119 1673 m

Land5 3/27/2013 13:41 37 M 258813 9858120 1663 m

77

78

79