ModellingModelling Crime: Crime:A Spatial Microsimulation A Spatial Microsimulation

ApproachApproach

Charatdao KongmuangCharatdao KongmuangSchool of Geography

University of Leeds

SupervisorsSupervisorsDr. Graham Clarke, Dr. Andrew Evans, Dr. Dimitris Ballas

What is Crime?What is Crime?

‘‘Crime is, first of all, a legal conception, Crime is, first of all, a legal conception, human human behaviourbehaviour punishable under the punishable under the criminal law’criminal law’

(Mannheim 1965: 22)(Mannheim 1965: 22)

Why crime?Why crime?

► It is one of the most important It is one of the most important

problems facing the UK today. problems facing the UK today.

►Adds stress to people lives and impairs Adds stress to people lives and impairs

the quality of life of individuals and the quality of life of individuals and

communities.communities.

Study Of CrimeStudy Of Crime

SociologySociology

BiologyBiology

EconomicsEconomics

PoliticPolitic PsychologyPsychology

LawLaw

PsychiatryPsychiatry

CriminologyCriminology

GeographyGeography

CrimeCrime

Geography of CrimeGeography of Crime

Crime MappingCrime Mapping

Spatial patterns of crimeSpatial patterns of crime Ecological AnalysisEcological Analysis

relationship between crime and socio-relationship between crime and socio-economic / environmental factorseconomic / environmental factors

Spatial Analysis- Spatial Analysis- using using GISGIS

Hot spot areas Hot spot areas

MicrosimulationMicrosimulation

A methodology aimed at building A methodology aimed at building

large-scale datasets on the attributes large-scale datasets on the attributes

of individual units and analysing of individual units and analysing

policy impacts on these micro units. policy impacts on these micro units.

(Clarke, (Clarke, 1996)1996)

Why Spatial Why Spatial Microsimulation?Microsimulation?

Criminal Criminal behaviourbehaviour is related to is related to current attributes of individuals.current attributes of individuals.

Can be used to conduct policy Can be used to conduct policy simulations and forecasting.simulations and forecasting.

Can generate spatial outcomes at a Can generate spatial outcomes at a detailed level of resolution. detailed level of resolution.

It has not yet been applied to study It has not yet been applied to study

crime.crime.

Advantages of Advantages of Spatial MicrosimulationSpatial Microsimulation

Data linkage abilityData linkage ability

Spatial flexibilitySpatial flexibility

Efficiency of storageEfficiency of storage

Ability to update and forecastAbility to update and forecast

(Clarke, (Clarke, 1996)1996)

DrawbacksDrawbacks

The difficulty to validating the model The difficulty to validating the model

outputsoutputs

Large requirements of Large requirements of

computational computational

powerpower

(Clarke, 1996)(Clarke, 1996)

ObjectivesObjectives

Build a spatial microsimulation Build a spatial microsimulation model model

for crime for crime Use this model for forecasting crime Use this model for forecasting crime

- The effect on crime rates - The effect on crime rates

- What types of area tend to have high - What types of area tend to have high

crime rates?crime rates?

- Estimate individuals’ propensity to - Estimate individuals’ propensity to

commit crime and to be a victim.commit crime and to be a victim.

MethodologyMethodology1. Construct a population microdata set.1. Construct a population microdata set.

- A list of individuals along with associated attributes on the - A list of individuals along with associated attributes on the basis of Census and Survey data (e.g. British Crime Survey) basis of Census and Survey data (e.g. British Crime Survey)

- - Conditional probabilities, calculated from available known Conditional probabilities, calculated from available known data, will be used to reconstruct detailed micro-level data, will be used to reconstruct detailed micro-level populations.populations.

2. Create the sample of individuals based on set of 2. Create the sample of individuals based on set of probabilitiesprobabilities

3. Simulate3. Simulate Simulation of crime on the basis of individual propensities to Simulation of crime on the basis of individual propensities to

commit crimecommit crime

4. Validate4. Validate Compare simulation outputs with actual data Compare simulation outputs with actual data

(e.g. from West Yorkshire Police)(e.g. from West Yorkshire Police)

Category Category IndicatorIndicator

High PropensityHigh Propensity

Demographic Demographic Characteristics of Characteristics of OffenderOffender

AgeAge

Sex Sex

Marital Status Marital Status

Ethnic StatusEthnic Status

Family Status Family Status Family SizeFamily Size

Young adultYoung adult

MaleMale

SingleSingle

Minority GroupMinority Group

Broken Home , divorce (weak family life)Broken Home , divorce (weak family life)

LargeLarge

Socio-Economic Socio-Economic Status of OffenderStatus of Offender

Income Income

OccupationOccupation

EmploymentEmployment

Education Education

Social Class Social Class

Type of tenureType of tenure

Low incomeLow income

UnskilledUnskilled

Unemployment Unemployment

LessLess

LowLow

RentedRented

Physical Features of Physical Features of the householdthe household

HousingHousing Density of livingDensity of living

EnvironmentEnvironment

SubstandardSubstandard

High densityHigh density

PoorPoor

Local/Regional Local/Regional characteristicscharacteristics

UrbanisationUrbanisation

Population densityPopulation densityHighHigh

High High

Characteristics of Characteristics of victimvictim

Age Age

SexSex

LifestyleLifestyle

TenureTenure

Young adultYoung adult

MaleMale

Away homeAway home

Council estateCouncil estate

Proximity to Proximity to opportunityopportunity

Neighbourhood Neighbourhood Inner cityInner cityBorder zone located close to disadvantage Border zone located close to disadvantage areasareas

LowLowSocio-Economic Socio-Economic

StatusStatus

Crime DataCrime Data

The official statistics do not The official statistics do not represent the total crime.represent the total crime.

Only 27% of the total offences are recorded Only 27% of the total offences are recorded

by the police by the police (Home Office, 1995)(Home Office, 1995)..

Reported crime

Unreported crime

Sources of DataSources of DataDataData SourceSource DetailDetail

Crime in LeedsCrime in Leeds West YorkshireWest Yorkshire Police Police

Types of crimeTypes of crime

Date and time occurDate and time occur

Police division, BeatPolice division, Beat

House number, Street numberHouse number, Street number

PostcodePostcode

OS referencesOS references

Easting & NorthingEasting & Northing

Victim characteristics Victim characteristics (Gender,Age, (Gender,Age,

Ethnicity) Ethnicity)

Offender Offender CharacteristicsCharacteristics

Pre-Sentence Pre-Sentence Reports (PSRs) Reports (PSRs)

Employment StatusEmployment Status

AgeAge

EthnicityEthnicity

VictimisationVictimisation British Crime British Crime SurveySurvey

Age, Gender, Marital StatusAge, Gender, Marital Status

Income, Tenure, EmploymentIncome, Tenure, Employment

Accommodation Type,Accommodation Type,

LifestyleLifestyle

Conviction/Conviction/

Criminal Criminal StatisticsStatistics

Home OfficeHome Office Gender, Age, Court Proceeding, Gender, Age, Court Proceeding,

Sentencing Sentencing

Types of CrimeTypes of Crime

RobberyRobbery BurglaryBurglary

- Burglary Dwelling- Burglary Dwelling

- Burglary Other- Burglary Other

Vehicle Crime Vehicle Crime Theft Theft Criminal DamageCriminal Damage

Crime in LeedsCrime in Leeds In West Yorkshire, In West Yorkshire, 40.9%40.9% of all crime of all crime

committed takes place in Leedscommitted takes place in Leeds

Crime Rate Crime Rate 2000/2001 2000/2001

Crimes/1000 pop.Crimes/1000 pop.

Leeds: Leeds: 146 146

West Yorkshire: West Yorkshire: 124 124

England: 102England: 102 (Leeds Community Safety, 2001)(Leeds Community Safety, 2001)

BurglaryBurglary and and vehicle crimevehicle crime are the are the highest crimes in Leeds.highest crimes in Leeds.

Offenders in LeedsOffenders in Leeds

Predominantly male, whitePredominantly male, white

56% are unemployed56% are unemployed

Offender characteristics are related to Offender characteristics are related to

drug, alcohol, financial problems, and drug, alcohol, financial problems, and

unemploymentunemployment (Leeds Community Safety, 2001)(Leeds Community Safety, 2001)

Victims in LeedsVictims in Leeds The most common ageThe most common age

30-39 (1999-2000)30-39 (1999-2000)

over over 40 (2000-2001)40 (2000-2001)

The number of older people experiencing The number of older people experiencing

crime has been increased.crime has been increased.

Victims over 40 are most likely to be victims Victims over 40 are most likely to be victims

of burglary, criminal damage, theft, and of burglary, criminal damage, theft, and

vehicle crime.vehicle crime. (Leeds Community Safety, 2001)(Leeds Community Safety, 2001)

Total Crime0 - 1,9992,000 - 3,9994,000 - 5,9996,000- 7,999>7,999

7 0 7 14 Miles

N

EW

S

Total CrimeApril 2001-March 2002

University

City and Holbeck Burmantofts

Headingley

Crime Rate <100100 - 199200 - 299300 - 399>399

7 0 7 14 Miles

N

EW

S

Crime Rate per 1000 PopulationApril 2001- March 2002

Burglary 338 - 461462 - 723724 - 910911 - 11301131 - 2155

7 0 7 14 Miles

N

EW

S

Buglary

Burglary Dwelling150 - 267268 - 360361 - 660661 - 960961 - 1386

7 0 7 14 Miles

N

EW

S

Burglary Dwelling

Headingley

Burglary Other168 - 219220 - 293294 - 463464 - 638639 - 1543

7 0 7 14 Miles

N

EW

S

Burglary Other

Criminal Damage181 - 348349 - 577578 - 884885 - 12461247 - 2171

7 0 7 14 Miles

N

EW

S

Criminal Damage

Robbery7 - 2627 - 5051 - 9091 - 270271 - 716

7 0 7 14 Miles

N

EW

S

Robbery

Vehicle Crime215 - 380381 - 673674 - 12261227 - 23172318 - 4880

7 0 7 14 Miles

N

EW

S

Vehicle Crime

Other Theft190 - 389390 - 601602 - 957958 - 14151416 - 8369

7 0 7 14 Miles

N

EW

S

Other Theft

Population Density<1,000 person/km.1,001 - 2,000 person/km.2,001 - 3,000 person/km.3,001 - 4,000 person/km.>4,000person/km.

Total CrimeTotal Crime

7 0 7 14 Miles

N

EW

S

Population Density & Crime

Unemployed470 - 554555 - 664665 - 899900 - 13281329 - 1879

Total CrimeTotal Crime

7 0 7 14 Miles

N

EW

S

Unemployment & Crime

Young Adults1788 - 20042005 - 24562457 - 27522753 - 31163117 - 4248

Property CrimeTotal Crime

7 0 7 14 Miles

N

EW

S

Young Adults & Crime

Males Young Adult_Unemployed89 - 103104 - 140141 - 206207 - 265266 - 400

Property CrimeTotal Crime

7 0 7 14 Miles

N

EW

S

Male Young Adults and Unemployed

SawasdeeSawasdee(sa-wat-dee)(sa-wat-dee)

Leeds ward