Crime Risk Models: Specifying Boundaries and Environmental

Backcloths

Kate Bowers

Introduction• Crime Risk Model specification

– Boundaries• Units of Analysis

– Environmental backcloth• Land use• Housing• Accessibility

– Crime Risk Model Accuracy• Determining map accuracy and utility• Testing against chance models

– Future Projects• CA modelling of risk• Area linking models• Multi-level models

MAUP- The Modifiable Areal Unit Problem

• 'the areal units (zonal objects) used in many geographical studies are arbitrary, modifiable, and subject to the whims and fancies of whoever is doing, or did, the aggregating.' (Openshaw, 1984 p.3).

• Staggering number of different options for aggregating data– Administrative boundaries– Automatic non-overlapping boundaries

• Grids and polygons

• Two problems exist– Scale- variation which occurs when data from one scale of areal unit is

aggregated into more or less areal units.

– Aggregation- wide variety of different possible areal units

Burglaries per 100 households

Burglaries per 100 households

Hot beats

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0.9 0 0.9 1.8 Miles

Hotspot00 - 1.5641.564 - 3.0933.093 - 5.7215.721 - 33.094

# Next week# next 2 days

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Yellow= burglaries within two days

Green= burglaries within 7 days

Traditional Hotspot Map

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0.9 0 0.9 1.8 Miles

12prosp501270.231 - 1270.9421270.942 - 1274.8171274.817 - 1282.9211282.921 - 1298.2931298.293 - 1479.488

# Next week# next 2 days

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Yellow= burglaries within two days

Green= burglaries within 7 days

Prospective Map

Map Evaluation

• Map accuracy:– Number of “hits”– Search efficiency (hits per unit area)

• Map practicality:– Number of hot areas– Size of hot areas

Map Evaluation: accuracy

2 days (26) 1 week (70) Area covered Search efficiency (2 day per km2)

Prospective Map

62% 64% 5.4km2 2.96

Traditional Hotspot Map

46% 56% 5.4km2 2.22

Beat Map 12% 24% 5.1km2 0.59

Map evaluation: practicality

Prospective Map Traditional Hotspot Map

Mean area 12778m2 56502m2

Mean perimeter 377 m 925 m

No. of hotspots 79 19

Mean AP ratio 10 51

Friction surfaces/opportunity structure

• Opportunity structure (Flow enablers)– Land use, distribution of houses, house type and tenure (see Groff & La

Vigne, 2001)

• Friction– distance, topology (water, railways etc), crime prevention activity, social

factors (affluence and cohesion)

• Facilitators– Proximity to bus stops and roads (see Brantinghams)

Accounting for Background: Method• GIS- vector grid mapping- 50 metre grid squares

• Housing- OS Land Line– Number of houses in each square– Average area of houses– Physical area of square used covered by housing

• Roads– Number of sections of roads running through grid square– Length of road running through square– Classification of road (Major, Minor)

• Weighting squares– Housing alone– Roads alone– Combinations

Mapping Layers: Land Use and Crime Risk

Accuracy concentration curve for the promap algorithm and chance expectation

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Accuracy concentration curve for the KDE algorithm and chance expectation

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Retrospective KDE

Simulation 95th percentile

Chance simulation mean (N=99)

Accuracy concentration curve for the Beat map generated for the rate of burglary per 1000 households

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Beats by rate per HHSimulation 95th PercentileSimulation Mean (N=99)

Accuracy concentration curve for the promap algorithm (including both opportunity surfaces) and chance expectation

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Promap*Rds*HousesSimulation 95th percentileChance simulation mean (N=99)

Median mapping algorithm accuracy

Percentage of burglaries identified

10 25 50 75 90

Prospective: Promap 1.39 5.09 14.39 30.89 55.36

Percen

tage of cells searched

Promap*Houses 1.59 5.09 14.39 28.39 48.88

Promap*RDs 1.39 4.89 13.39 29.09 52.57

Promap*Houses*RDs 1.59 4.59 12.59 29.39 56.35

Chance: Simulation 95th Percentile 3.8 11.5 27.3 44.8 56.8Simulation Mean 7.0 17.0 34.3 51.3 61.3

Retrospective: KDE 2.09 6.59 16.89 34.87 59.04

Choropleth (concentration) 4.03 15.50 35.40 49.12 63.02

Choropleth (rate per area) 3.34 10.85 23.47 42.55 58.82

Choropleth (rate per homes) 6.41 17.62 31.70 50.02 69.11

Relative vulnerability of different housing types

April 1995-2000

Hou

sehold

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rgled

Total n

um

ber of h

ouses of typ

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Prevalen

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Total n

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ber of in

ciden

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Incid

ence rate

Semi-detached 24915 201918 24.68 26689 26.44Detached 4122 53364 15.45 4428 16.60Terraced 23824 214023 22.26 26490 24.75

Flats 12184 103199 23.61 13515 26.19

April 95-00 Housing Type

Prevalence rate Semi Detached Terraced Flat

Quintile 1 16.37(6176)

10.32(1793)

18.87(498)

12.29(318)

Quintile 2 20.39(6179)

17.85(1038)

18.44(2485)

15.87(1018)

Quintile 3 29.56(5206)

27.46(579)

21.31(6150)

20.26(1838)

Quintile 4 44.16(3965)

57.83(336)

21.95(7751)

25.69(2701)

Quintile 5 53.21(3377)

71.29(391)

25.91(6924)

27.31(6285)

Prevalence rates for different types of housing in each quintile

Where next?- Modelling Street Network

• Examples of the accessibility measure used by Beavon et al. (1994)

• Quickest path analysis (connectivity of grid squares)

Where next?- Multi-level models

• Individuals: Victims vs repeat victims– Housing type

– MO of offence

– Victim characteristics

• Small area: Cell or neighbourhood– Accessibility

– Housing details

– Crime risk levels

• Larger area: Census tract– Social and demographic information

Possible outcomes:

• Pathogen extinction (short infectious period)

Susceptible Infected

Immune Unoccupied

Where Next?- FCA: Local density-dependent transmission

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• Host-pathogen coexistence (long infectious period)

Slide by Joanne Turner (University of Liverpool)

Where Next?- CA Model Parameters

• Re-infection rates– Different levels and lengths of immunity possible

• Target hardening/ Police patrolling

• Greater susceptibility in some than others– Random short lived susceptibility

• ‘Infection’ beginning from and re-occurring in different areas– Random sparks

• Weak infectious models are possible

• Non-uniformity of contiguous cells

References

Johnson, S.D., and Bowers, K.J. (forthcoming 2007). Burglary Prediction: Theory, Flow and Friction. In Graham Farrell, Kate Bowers, Shane Johnson and Michael Townsley (Eds.), Crime Prevention studies Volume 21, Monsey NY: Criminal Justice Press

Johnson, S.D., Bowers, K.J., Birks, D.J. & Pease, K. (forthcoming 2007). Micro-Level Forecasting of Burglary: The Role of Environmental Factors. In W. Bernasco and D. Weisburd (Eds) Crime and Place, in preparation.

Johnson, S.D., McLaughlin, L., Birks, D.J., Bowers, K.J. & Pease, K. (forthcoming 2007) Prospective crime mapping in operational context. Home Office On-Line Report

Bowers, K.J., Johnson, S.D., & Pease, K. (2005). (Re)Victimisation risk, housing type and area: a study of interactions Crime Prevention and Community Safety: An International Journal 7(1), 7-17

Bowers, K.J., Johnson, S. and Pease, K. (2004) Prospective Hotspotting: The Future of Crime Mapping? British Journal of Criminology 44 (5), 641-658.

Hirschfield, A.F.G., Yarwood, D. & Bowers, K.(2001) Spatial Targeting and GIS: The Development of New Approaches for Use in Evaluating Community Safety Initiatives in M. Madden and G. Clarke, (eds) Regional Science in Business, Springer-Verlag.

Nearest Neighbour Index: Retrospective and Prospective Methods

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