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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?

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

                 Geography

     Sociology               Criminology




Biology                            Psychiatry
                 Crime

Economics                           Law


            Politic   Psychology
            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

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?
 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

 Data linkage ability

 Spatial flexibility

 Efficiency of storage

 Ability to update and forecast
                           (Clarke, 1996)
             Drawbacks

 The difficulty to validating the model

  outputs
 Large requirements of computational
  power
                              (Clarke, 1996)
                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
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)
    Category                  Indicator                   High Propensity
Demographic                 Age                  Young adult
Characteristics of          Sex                  Male
Offender                    Marital Status       Single
                            Ethnic Status        Minority Group
                            Family Status        Broken Home , divorce (weak family life)
                            Family Size          Large
Socio-Economic Status       Income               Low income
of Offender                 Occupation           Unskilled                  Low
                            Employment           Unemployment         Socio-Economic
                            Education            Less                      Status
                            Social Class         Low
                            Type of tenure       Rented
Physical Features of the    Housing              Substandard
household                   Density of living    High density
                            Environment          Poor
 Local/Regional             Urbanisation         High
characteristics             Population density   High
Characteristics of victim   Age                  Young adult
                            Sex                  Male
                            Lifestyle            Away home
                            Tenure               Council estate
Proximity to opportunity    Neighbourhood        Inner city
                                                 Border zone located close to disadvantage areas
                  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
                      Sources of Data
       Data                 Source                         Detail
Crime in Leeds        West Yorkshire         Types of crime
                      Police                 Date and time occur
                                             Police division, Beat
                                             House number, Street number
                                             Postcode
                                             OS references
                                             Easting & Northing
                                             Victim characteristics (Gender,Age,
                                                   Ethnicity)
Offender              Pre-Sentence           Employment Status
Characteristics       Reports (PSRs)         Age
                                             Ethnicity
Victimisation         British Crime Survey   Age, Gender, Marital Status
                                             Income, Tenure, Employment
                                             Accommodation Type,
                                             Lifestyle
Conviction/           Home Office            Gender, Age, Court Proceeding,
Criminal Statistics                          Sentencing
             Types of Crime
    Robbery
    Burglary
    - Burglary Dwelling
    - Burglary Other
    Vehicle Crime
    Theft
    Criminal Damage
               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

 Predominantly male, white

 56% are unemployed

 Offender characteristics are related to

  drug, alcohol, financial problems, and

  unemployment
                           (Leeds Community Safety, 2001)
             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)
                         Total Crime
                   April 2001-March 2002




                                                Total Crime
                                                      0 - 1,999
Headingley
                                                      2,000 - 3,999
                                                      4,000 - 5,999
                                                      6,000- 7,999
 University                                           >7,999
                                                          N
City and Holbeck                   Burmantofts
                                                     W         E
      7            0       7         14 Miles
                                                          S
    Crime Rate per 1000 Population
        April 2001- March 2002




                                    Crime Rate
                                         <100
                                         100 - 199
                                         200 - 299
                                         300 - 399
                                         >399

                                           N


                                       W        E
7    0         7         14 Miles
                                           S
        Buglary




                             Burglary
                                  338 - 461
                                  462 - 723
                                  724 - 910
                                  911 - 1130
                                  1131 - 2155

                                        N


                                   W            E
7   0     7       14 Miles
                                        S
                 Burglary Dwelling




                                          Burglary Dwelling
                                               150 - 267
                                               268 - 360
                                               361 - 660
                                               661 - 960
                                               961 - 1386
                                                    N
Headingley
                                                W       E
    7        0          7            14 Miles
                                                    S
        Burglary Other




                                Burglary Other
                                     168 - 219
                                     220 - 293
                                     294 - 463
                                     464 - 638
                                     639 - 1543



                                          N


                                     W            E
7   0      7         14 Miles
                                          S
        Criminal Damage




                                Criminal Damage
                                     181 - 348
                                     349 - 577
                                     578 - 884
                                     885 - 1246
                                     1247 - 2171
                                         N


                                     W        E
7   0       7        14 Miles
                                         S
        Robbery




                             Robbery
                                 7 - 26
                                 27 - 50
                                 51 - 90
                                 91 - 270
                                 271 - 716
                                      N


                                  W          E
7   0     7       14 Miles
                                      S
        Vehicle Crime




                                   Vehicle Crime
                                        215 - 380
                                        381 - 673
                                        674 - 1226
                                        1227 - 2317
                                        2318 - 4880
                                             N


                                        W        E
7   0      7            14 Miles
                                             S
        Other Theft




                                 Other Theft
                                      190 - 389
                                      390 - 601
                                      602 - 957
                                      958 - 1415
                                      1416 - 8369
                                           N


                                      W        E
7   0      7          14 Miles
                                           S
    Population Density & Crime




                                Total Crime
                                      Total Crime
                                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.
                                                  N


                                           W            E
7   0         7          14 Miles
                                                  S
    Unemployment & Crime


                            Total Crime
                                  Total Crime
                            Unemployed
                                  470 - 554
                                  555 - 664
                                  665 - 899
                                  900 - 1328
                                  1329 - 1879
                                     N


                                 W       E
7   0      7     14 Miles
                                     S
    Young Adults & Crime


                              Property Crime
                                   Total Crime
                              Young Adults
                                   1788 - 2004
                                   2005 - 2456
                                   2457 - 2752
                                   2753 - 3116
                                   3117 - 4248
                                      N


                                  W        E
7   0       7      14 Miles
                                       S
    Male Young Adults and Unemployed




                                    Property Crime
                                         Total Crime
                                    Males Young Adult_Unemployed
                                         89 - 103
                                         104 - 140
                                         141 - 206
                                         207 - 265
                                         266 - 400


                                                   N


                                            W            E
7        0       7       14 Miles
                                                   S
Sawasdee
(sa-wat-dee)
Leeds ward

				
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