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Modelling Crime: A Spatial Microsimulation Approach. Charatdao Kongmuang School of Geography University of Leeds. Supervisors Dr. Graham Clarke, Dr. Andrew Evans, Dr. Dimitris Ballas. What is Crime?.

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slide1

Modelling Crime:A Spatial Microsimulation Approach

Charatdao Kongmuang

School of Geography

University of Leeds

Supervisors

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

what is crime
What is Crime?

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

(Mannheim 1965: 22)

why crime
Why crime?
  • It is one of the most important problems facing the UK today.
  • Adds stress to people lives and impairs the quality of life of individuals and communities.
geography of crime
Geography of Crime
  • Crime Mapping

Spatial patterns of crime

  • Ecological Analysis

relationship between crime and socio-economic / environmental factors

  • Spatial Analysis- using GIS

Hot spot areas

microsimulation
Microsimulation

A methodology aimed at building large-scale datasets on the attributes of individual units and analysing policy impacts on these micro units.

(Clarke, 1996)

why spatial microsimulation
Why Spatial Microsimulation?
  • Criminal behaviour is related to current attributes of individuals.
  • Can be used to conduct policy simulations and forecasting.
  • Can generate spatial outcomes at a detailed level of resolution.
  • It has not yet been applied to study

crime.

advantages of spatial microsimulation
Advantages of Spatial Microsimulation
  • Data linkage ability
  • Spatial flexibility
  • Efficiency of storage
  • Ability to update and forecast

(Clarke, 1996)

drawbacks
Drawbacks
  • The difficulty to validating the model

outputs

  • Large requirements of computational

power

(Clarke, 1996)

objectives
Objectives
  • Build a spatial microsimulation model

for crime

  • Use this model for forecasting crime

- The effect on crime rates

- What types of area tend to have high

crime rates?

- Estimate individuals’ propensity to

commit crime and to be a victim.

methodology
Methodology

1. Construct a population microdata set.

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

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

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

3. Simulate

Simulation of crime on the basis of individual propensities to commit crime

4. Validate

Compare simulation outputs with actual data

(e.g. from West Yorkshire Police)

slide12

Low

Socio-Economic Status

crime data
Crime Data

The official statistics do not represent the total crime.

Only 27% of the total offences are recorded

by the police (Home Office, 1995).

Reported crime

Unreported crime

types of crime
Types of Crime
  • Robbery
  • Burglary

- Burglary Dwelling

- Burglary Other

  • Vehicle Crime
  • Theft
  • Criminal Damage
crime in leeds
Crime in Leeds
  • In West Yorkshire, 40.9% of all crime committed takes place in Leeds
  • Crime Rate 2000/2001

Crimes/1000 pop.

Leeds: 146

West Yorkshire: 124

England: 102

(Leeds Community Safety, 2001)

  • Burglary and vehicle crime are the highest crimes in Leeds.
offenders in leeds
Offenders in Leeds
  • Predominantly male, white
  • 56% are unemployed
  • Offender characteristics are related to

drug, alcohol, financial problems, and

unemployment

(Leeds Community Safety, 2001)

victims in leeds
Victims in Leeds
  • The most common age

30-39 (1999-2000)

over 40 (2000-2001)

  • The number of older people experiencing

crime has been increased.

  • Victims over 40 are most likely to be victims

of burglary, criminal damage, theft, and

vehicle crime.

(Leeds Community Safety, 2001)

slide19

Headingley

University

City and Holbeck

Burmantofts