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

Hot beats

Yellow= burglaries within two days Green= burglaries within 7 days Traditional Hotspot Map

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 coveredSearch efficiency (2 day per km 2 ) Prospective Map 62%64%5.4km Traditional Hotspot Map 46%56%5.4km Beat Map 12%24%5.1km

Map evaluation: practicality Prospective MapTraditional Hotspot Map Mean area12778m m 2 Mean perimeter377 m925 m No. of hotspots7919 Mean AP ratio1051

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

Accuracy concentration curve for the KDE algorithm and chance expectation

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

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

Median mapping algorithm accuracy Percentage of burglaries identified Prospective: Promap Percentage of cells searched Promap*Houses Promap*RDs Promap*Houses*RDs Chance: Simulation 95 th Percentile Simulation Mean Retrospective: KDE Choropleth (concentration) Choropleth (rate per area) Choropleth (rate per homes)

Relative vulnerability of different housing types April Households burgled Total number of houses of type Prevalence rate Total number of incidents Incidence rate Semi-detached Detached Terraced Flats

April 95-00Housing Type Prevalence rateSemiDetachedTerracedFlat Quintile (6176) (1793) (498) (318) Quintile (6179) (1038) (2485) (1018) Quintile (5206) (579) (6150) (1838) Quintile (3965) (336) (7751) (2701) Quintile (3377) (391) (6924) (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 prevalence time prevalence time 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), 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