Crime Risk Models: Specifying Boundaries and Environmental Backcloths Kate Bowers

1. Crime Risk Models: Specifying Boundaries and Environmental Backcloths Kate Bowers

2. 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

3. 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

4. Burglaries per 100 households Burglaries per 100 households

5. Hot beats

6. TraditionalHotspot Map Yellow= burglaries within two days Green= burglaries within 7 days

7. Prospective Map Yellow= burglaries within two days Green= burglaries within 7 days

8. Map Evaluation • Map accuracy: • Number of “hits” • Search efficiency (hits per unit area) • Map practicality: • Number of hot areas • Size of hot areas

9. Map Evaluation: accuracy

10. Map evaluation: practicality

11. 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)

12. 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

13. Mapping Layers: Land Use and Crime Risk

14. Accuracy concentration curve for the promap algorithm and chance expectation

15. Accuracy concentration curve for the KDE algorithm and chance expectation

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

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

18. Median mapping algorithm accuracy

19. Relative vulnerability of different housing types

20. Prevalence rates for different types of housing in each quintile

21. Where next?- Modelling Street Network • Examples of the accessibility measure used by Beavon et al. (1994) • Quickest path analysis (connectivity of grid squares)

22. 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

23. Host-pathogen coexistence (long infectious period) prevalence time Where Next?- FCA: Local density-dependent transmission SusceptibleInfected ImmuneUnoccupied Possible outcomes: • Pathogen extinction (short infectious period) prevalence time Slide by Joanne Turner (University of Liverpool)

24. 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

25. 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.

26. Nearest Neighbour Index: Retrospective and Prospective Methods