Encyclopedia of Criminology and Criminal Justice

2014 Edition
| Editors: Gerben Bruinsma, David Weisburd

Inferential Crime Mapping

Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-5690-2_318

Synonyms

Overview

Crime mapping has become extremely popular as an analytic technique in the last 20 years and is arguably the default mode of analysis for law enforcement officers. Spencer Chainey (this volume) explores the driving forces behind this rise as well as what is current best practice. A more detailed treatment can be found at Chainey and Ratcliffe (2005).

The purpose of this entry is to outline a number of ways that the practice of crime mapping could be improved. Many analysts receive incomplete training in spatial analysis, and most in-house training consists of how to interrogate the various crime databases with very little or no time spent on theories of crime and criminality nor how to conduct analysis (generating and testing hypotheses, drawing inferences). There is currently a paucity of guidance on how to construct the analysis endeavor with notable exceptions being Ekblom (1988), Weisel (2003...

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Recommended Reading and References

  1. Baumer E, Wright R (1996) Crime seasonality and serious scholarship: a comment on Farrell and Pease. Brit J Criminol 36:579–581Google Scholar
  2. BBC (2004) Woman ‘blessed by the holy toast’. http://news.bbc.co.uk/2/hi/americas/4019295.stm
  3. Beavon DJK, Brantingham PL, Brantingham PJ (1994) The influence of street networks on the patterning of property offenses. In: Ronald V, Clarke G (eds) Crime prevention studies, vol 2. Criminal Justice Press, MonseyGoogle Scholar
  4. Bichler G, Balchak S (2007) Address matching bias: ignorance is not bliss. Pol Int J Police Strat Manag 30:32–60Google Scholar
  5. Boggs SL (1965) Urban crime patterns. Am Sociol Rev 30:899–908Google Scholar
  6. Brantingham PJ, Dyreson DA, Brantingham PL (1976) Crime seen through a cone of resolution. Am Behav Sci 20:261–273Google Scholar
  7. Chainey S (2012) Improving the explanatory content of analysis products using hypothesis testing. Policing 6(2):108-121Google Scholar
  8. Chainey S, Desyllas J (2008) Modelling pedestrian movement to measure on-street crime risk. In: Liu L, Eck JE (eds) Artificial crime analysis systems: using computer simulations and geographic information systems. Idea Group, Hershey, pp 71–91Google Scholar
  9. Chainey S, Ratcliffe JH (2005) GIS and crime mapping. Wiley, ChichesterGoogle Scholar
  10. Clarke RV (1984) Opportunity-based crime rates: the difficulties of further refinement. Brit J Criminol 24:74–83Google Scholar
  11. Dawid PA (2005) Probability and proof. In: Anderson TJ, Schum DA, Twining WL (eds) Analysis of evidence. Cambridge University Press, CambridgeGoogle Scholar
  12. Eck JE (1997) What do those dots mean? Mapping theories with data. In: Weisburd DL, McEwen T (eds) Crime mapping and crime prevention, vol 8. Criminal Justice Press, Monsey, pp 377–406Google Scholar
  13. Eck JE, Ronald V, Clarke G, Guerette RT (2007) Risky facilities: crime concentration in homogeneous sets of establishments and facilities. In: Farrell G, Bowers KJ, Johnson SD, Townsley M (eds) Imagination for crime prevention: essays in honour of Ken Pease, vol 21. Criminal Justice Press, Monsey, pp 225–264Google Scholar
  14. Ekblom P (1988) Getting the best out of crime analysis. Crime prevention unit. Home Office, LondonGoogle Scholar
  15. FBI (2011) Crime in the United States by metropolitan statistical area, 2010 (Table 6).”Google Scholar
  16. Harries KD (1981) Alternative denominators in conventional crime rates. In: Brantingham PJ, Brantingham PL (eds) Environmental criminology. Sage, Beverly Hills, pp 147–165Google Scholar
  17. Hirschfield A (2005) Analysis for intervention. In: Tilley N (ed) Handbook of crime prevention and community safety. Willan, Cullompton, pp 629–673Google Scholar
  18. Johnson SD, Bowers KJ (2010) Permeability and crime risk: are cul-de-sacs safer? J Quant Criminol 26:89–111Google Scholar
  19. Jowell R, Hedges B, Lynn P, Farrant G, Heath A (1993) Review: the 1992 British election: the failure of the polls. Public Opin Q 57:238–263Google Scholar
  20. Keeter S (2006) The impact of cell phone noncoverage bias on polling in the 2004 presidential election. Public Opin Q 70:88–98Google Scholar
  21. Kurland J, Kautt P (2011) The event effect: demonstrating the impact of denominator selection on ‘floor’ and ‘ceiling’ crime rate estimates in the context of public events. In: Morina AD (ed) Crime rates, types and hot-spots. Nova, Hauppauge, pp 115–144Google Scholar
  22. Langan PA, Farrington DP (1998) Crime and justice in the United States and in England and Wales, 1981–96. US Department of Justice, Office of Justice Programs, Bureau of Justice StatisticsGoogle Scholar
  23. London Borough of Enfield (2011) Safe as houses: reducing domestic burglary project. Finalist, Goldstein POP Awards, http://www.popcenter.org/library/awards/goldstein/2011/11-09(F).pdf
  24. Maguire M (2012) Crime data and statistics. In: Maguire M, Morgan R, Reiner R (eds) The Oxford handbook of criminology. Oxford University Press, Oxford, pp 241–301Google Scholar
  25. McDowall D, Loftin C, Pate M (2011) Seasonal cycles in crime, and their variability. J Quant Criminol 28(3):389–410Google Scholar
  26. Openshaw S (1983) The modifiable areal unit problem. Geo Books, NorwichGoogle Scholar
  27. Pawson R, Tilley N (1997) Realistic evaluation. Sage, New YorkGoogle Scholar
  28. Phillips PD (1973) Risk-related crime rates and crime patterns. Proc Assoc Am Geog 5:221–224Google Scholar
  29. Pyle GF, Hanten EW (1974) The spatial dynamics of crime. Research Paper Series. Department of Geography, University of Chicago, ChicagoGoogle Scholar
  30. Ratcliffe JH (2001) On the accuracy of tiger-type geocoded address data in relation to cadastral and census areal units. Int J Geog Inform Sci 15:473–485Google Scholar
  31. Ratcliffe JH (2006) A temporal constraint theory to explain opportunity-based spatial offending patterns. J Res Crime Del 43:261Google Scholar
  32. Ratcliffe JH (2008) Intelligence-led policing. Willan, CullomptonGoogle Scholar
  33. Rengert GF (1997) Auto theft in Central Philadelphia. In: Homel R (ed) Policing for prevention: reducing crime, public intoxication and injury. Criminal Justice Press, Monsey, pp 199–220Google Scholar
  34. Roncek DW, Maier PA (1991) Bars, blocks, and crimes revisited: linking the theory of routine activities to the empiricism of hot spots. Criminology 29:725–753Google Scholar
  35. Sagan C (1995) The demon-haunted world: science as a candle in the dark. Random House, New YorkGoogle Scholar
  36. Saperstein B (1972) The generalized birthday problem. J Am Stat Assoc 67(338):425–428Google Scholar
  37. Squire P (1988) Why the 1936 literary digest poll failed. Public Opin Q 52:125–133Google Scholar
  38. Tobler WR (1987) Experiments in migration mapping by computer. Cartogr Geog Inform Sci 14:155–163Google Scholar
  39. Townsley M (2009) Spatial autocorrelation and impacts on criminology. Geog Anal 41:450–459Google Scholar
  40. Townsley M, Mann M, Garrett K (2011) The missing link of crime analysis: a systematic approach to testing competing hypotheses. Policing 5:158–171Google Scholar
  41. Weisel DL (2003) The sequence of analysis in solving problems. In: Knutsson J (ed) Problem oriented policing: from innovation to mainstream, vol 15. Criminal Justice Press, Monsey, pp 115–146Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.School of Criminology and Criminal JusticeGriffith UniversityBrisbaneAustralia