Adding Census Geographical Detail into the British Crime Survey for Modelling Crime

Adding Census Geographical Detail into Adding Census Geographical Detail into the British Crime Survey for Modelling the British Crime Survey for Modelling

CrimeCrimeCharatdao KongmuangCharatdao Kongmuang

Naresuan University, ThailandNaresuan University, Thailand

Graham Clarke and Andrew EvansGraham Clarke and Andrew EvansUniversity of Leeds, UKUniversity of Leeds, UK

BackgroundBackground

Crime and risk of victimisationCrime and risk of victimisationUnevenly distributed across population,

time and spaceVaries dramatically with demographic,

socio-economic and area characteristics Crimes at the small area scale

often do not match expectations based on national averages

Background (cont.)Background (cont.)

Although, the British Crime Survey (BCS) provides rich information about levels of crime and crime victimisation, it cannot be used to explain crime victimisation for small geographical units (not currently released below the national level)

SolutionSolution

Attach the information from the BCS to the more geographically disaggregated census data using spatial microsimulation technique.

What is Microsimulation?What is Microsimulation?

A methodology aimed at building large-scale datasets on individual units such as persons, households or firms and can be used to simulate the effect of changes in policy or other changes on these micro units

What is Spatial What is Spatial Microsimulation?Microsimulation?

A microsimulation that takes space into account

It contains geographical information that can be used to investigate the policy impacts

SimCrime ModelSimCrime Model

A static spatial microsimulation model designed to estimate the likelihood of being a victim of crime and crime rates at the small area level in Leeds.

SimCrime Model (cont.)SimCrime Model (cont.)

Combines individual microdata from the British Crime Survey (BCS) with spatially small area aggregated census data to create synthetic microdata estimates for output areas (OAs) in Leeds using a Combinatorial Optimisation Simulated AnnealingSimulated Annealing method.

SimCrime Model SimCrime Model SpecificationSpecification

The synthetic microdata dataset was generated at the census output area for Leeds with the use of ‘Simulated ‘Simulated Annealing-Based Reweighting Annealing-Based Reweighting Program’Program’.

514,523 individuals aged 16-74 living in households found in Leeds in the UK 2001 Census were recreated

Simulated Annealing Based Reweighting Program

(generate a population microdata dataset at the Output Area Level)

Implemented in Java.

The process involves selecting the combination of individuals from the BCS microdata which best fits the known constraints in the selected small areas (of the 2001 UK Census).

The process is repeated with the aim of gradually improving fitgradually improving fit between the observed data and the selected combination of individual from the BCS.

Data for generating Data for generating synthetic micro-population:synthetic micro-population:

Census Area Statistics of the 2001 UK CensusConstrained TablesNumber of total population in small-

areas

Microdata from the 2001 British Crime Survey

Census Area Statistics Census Area Statistics (CAS)(CAS)

Equivalent to the Small Area Statistics (SAS) of the 1971, 1981, and 1991 Censuses.

Available for geographical levels down to output area (OA), the smallest unit of the 2001 Census.

Note:Note: Each OA contains approximately 290 persons or 125 households

Category Indicator High Propensity

Demographic Characteristics of

Offender

AgeSex Marital Status Family Status Family Size

Young adultMaleSingleBroken Home , divorce (weak family life)Large

Socio-Economic Status of Offender

Income Employment statusEducation Deprivation

Low incomeUnemployedLessHigh level of deprivation

Household Characteristics

Density of livingTenure

SubstandardRented

Victim Characteristics

Age SexEthnicityLifestyleTenure

Young adultMaleMinority GroupAway homeRented, not owner occupied

Neighbourhood types and characteristics

UrbanisationPopulation Density Proximity

HighHighInner city, proximity to disadvantage areas

Variables related to crimeVariables related to crime

Constrained Tables:Constrained Tables:

CS004:CS004: Age by Sex and Living Arrangements (16 categories)

CS047:CS047: National Statistic-Socioeconomic Classification by Tenure

(18 categories) CS061CS061:: Tenure and Car or Van

Availability by Economic Activity (24 categories)

SimCrime SimCrime Constrained Constrained

VariablesVariables

CategoriesCategories

AgeAge

Aged 16-24Aged 25-34Aged 35-49Aged 50-74

SexSex MaleFemale

Living ArrangementLiving Arrangement CoupleNot couple

Economic ActivityEconomic ActivityEmployedUnemployedInactiveFull-time Student

Tenure TypeTenure Type OwnedRented

Car or Van availabilityCar or Van availability No CarOne CarTwo or more car

Socio-economic Socio-economic ClassificationClassification

Higher Managerial and professional occupations

Lower Managerial and professional occupationsIntermediate occupationsSmall employers and own account workersLower supervisory and technical occupationsSemi-routine occupationsRoutine occupationsNever worked and long-term unemployedNot classified

Discrepancies in census counts between tablesDiscrepancies in census counts between tables Source: 2001 Census Area StatisticsSource: 2001 Census Area Statistics

Note: Note: Each cell shows the number of people aged 16-74 living in households

The constraint tables should be adjusted to minimise discrepancies between the total populations in small areas.

Constraint Tables Constraint Tables AdjustmentAdjustment

Total number of people in the small areas (GroupNumber)

Each table cell

Number of people in each cell

= Number of people from the constraint table x GroupNumber

Total Sum for each areaTotal Sum for each area

Constraint Tables Constraint Tables Adjustment (cont.)Adjustment (cont.)

Constraint Table Adjusted Table

116/230 * 228 = 115

What can we get?What can we get?

The adjustment method ensures the constraint tables are more more consistentconsistent or at least can be guaranteed to produce the smallest discrepancy.

The British Crime SurveyThe British Crime Survey

One of the largest social research surveys conducted in England and Wales (Sample 40,000 households)

A victimisation survey (whether or not reported to the police)

Covers a wide range of topics (1,642 variables)

The BCS can now provide limited information at the police force area level, but NOTNOT for smaller geographical units.

Microdata (The 2001 BCS)

1,642 variables with 32,824 records1,642 variables with 32,824 records

The Program:

The microdata filtering process Goes through the entire micro-database and

checks whether an individual fits into each column of constraining tables for the current area.

Simulated Annealing process Searches for the best combinations of

individuals based on the result of the filtering process.

Output:Output: from the Simulated from the Simulated Annealing Based Reweighting ProgramAnnealing Based Reweighting Program

Synthetic Population: A list of individuals which contains the demographic and socio-economic characteristics (crime variables from the BCS are attached).

Error Report: Provides information on the difference between distributions of constrained table and synthetic microdata at the output area level.

The absolute differences between estimated &

expected counts

Error ReportError Report

Distribution of female single, widow, or divorce aged 25-49 living in rented house

Headingley

Distribution of high class households, owner occupier having at least 1 car

Evaluation of Evaluation of Synthetic Microdata Synthetic Microdata

Evaluate in terms of their match to the constraint tables from the census at the output area level.

Evaluation of Evaluation of Synthetic Microdata (cont.)Synthetic Microdata (cont.)

The measure of difference between distributions of constrained table and synthetic microdata is the Total Absolute Total Absolute Error (TAE)Error (TAE) The sum of absolute differences between

estimated and observed counts.

To compare across the tables: Standardised Absolute Error (SAE)Standardised Absolute Error (SAE) TAE / Total expected count

SAE by Output Area0 / Perfect fit (1,318)0.001 - 0.01 (689)0.01 - 0.02 (377)0.02 - 0.03 (51)0.3-0.042 (4)

N

EW

S

Spatial Distribution of SAE for Age and Sex by Living Arrangement

SAE of 0 or perfect fit =1,318 output areasNote: The number in the bracket show number of output area for each SAE group. There are 2,439 output areas in Leeds. Source: SimCrime

SAE of all tables0 - 0.017 (1,213)0.017 - 0.036 (802)0.036 - 0.072 (367)0.072 - 0.147 (43)0.147 - 0.342 (14)

Spatial distribution of SAE for all constraints at output area level

SAE of 0 or perfect fit =1,212 output areasNote: The number in the bracket show number of output area for each SAE group. There are 2,439 output areas in Leeds. Source: SimCrime

Modelling CrimeModelling Crime

Each individual in the BCS has crime variables associated with them, the microsimulation allows us to make small area estimates victims of crime and high-risk areas.

Assume that if the synthetic population have the same characteristics as the population from the BCS, they will have the same propensity to be a victim of crime.

Victim RateLess than 6061 - 7071 - 8081 - 90More than 90

N

EW

S

Victim Rate per 1000 households of Burglary Dwelling

Headingley

University

Estimated victim rate per 1,000 households by Estimated victim rate per 1,000 households by ward of ward of

‘‘burglary dwelling’ in Leedsburglary dwelling’ in Leeds

ConclusionConclusion

SimCrime effectively adds ‘geography’ to the British Crime Survey

The spatial aspect of the data make it possible to do analysis at different spatial scales.

Demonstrated a method to minimise discrepancies between the totals of the constraint tables

Conclusion (cont.)Conclusion (cont.)

The spatial microsimulation has enabled the modelling of crime victimisation at small area levelssmall area levels. Before this the smallest area of modelling crime in the UK was at the police force area level.

More informationMore information

Modelling Crime: A Spatial Microsimulation Modelling Crime: A Spatial Microsimulation ApproachApproach

(Completed PhD thesis)(Completed PhD thesis) http://www.geog.leeds.ac.uk/people/old/c.kongmuang/

SimCrime: A Spatial Microsimulation for SimCrime: A Spatial Microsimulation for Crime in LeedsCrime in Leeds (Working Paper 06/1) (Working Paper 06/1) http://www.geog.leeds.ac.uk/wpapers/index.html

Email: Email: charatdao@gmail.comDept. Natural Resources and EnvironmentFac. of Agriculture, Natural Resources and EnvironmentNaresuan University, Muang, Phitsanulok, 65000, THAILAND

Thank youThank you