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Modelling Crime: A Spatial Microsimulation Approach

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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Modelling Crime: A Spatial Microsimulation Approach

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

  2. What is Crime? ‘Crime is, first of all, a legal conception, human behaviour punishable under the criminal law’ (Mannheim 1965: 22)

  3. 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.

  4. Study Of Crime

  5. 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

  6. 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)

  7. 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.

  8. Advantages of Spatial Microsimulation • Data linkage ability • Spatial flexibility • Efficiency of storage • Ability to update and forecast (Clarke, 1996)

  9. Drawbacks • The difficulty to validating the model outputs • Large requirements of computational power (Clarke, 1996)

  10. 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.

  11. 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)

  12. Low Socio-Economic Status

  13. 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

  14. Sources of Data

  15. Types of Crime • Robbery • Burglary - Burglary Dwelling - Burglary Other • Vehicle Crime • Theft • Criminal Damage

  16. 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.

  17. Offenders in Leeds • Predominantly male, white • 56% are unemployed • Offender characteristics are related to drug, alcohol, financial problems, and unemployment (Leeds Community Safety, 2001)

  18. 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)

  19. Headingley University City and Holbeck Burmantofts

  20. Headingley

  21. Sawasdee(sa-wat-dee)

  22. Leedsward

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