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A brief history of the analysis of crime concentration

Published online by Cambridge University Press:  15 April 2010

SHANE D. JOHNSON
SHANE D. JOHNSON*
Affiliation:
Department of Security and Crime Science, UCL Jill Dando Institute, University College London, Second Floor Brook House, 2-16 Torrington Place, London WC1E 7HN, UK email: Shane.johnson@ucl.ac.uk
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Abstract

Decades of research demonstrate that crime is concentrated at a range of spatial scales. Such findings have clear implications for crime forecasting and police resource allocation models. More recent work has also shown that crime clusters in space and time with a regularity that might improve methods of crime prediction. In this paper I review some of the available evidence and provide illustrations of the types of analysis – spatial and spatio-temporal – conducted hitherto. With a few exceptions, the application of formal Mathematics in the study of space–time patterns of crime has been rather limited, and so a central aim of the paper is to stimulate interest in this area of research.

Type
Papers
Information
European Journal of Applied Mathematics , Volume 21 , Issue 4-5: Criminality , October 2010 , pp. 349 - 370
Copyright
Copyright © Cambridge University Press 2010

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References

Agnew, R. (1985) A revised strain theory of delinquency. Soc. Forces 64, 151–167.Google Scholar
Beavon, D. J., Brantingham, P. & Brantingham, P. (1994) The influence of street networks on the patterning of property offenses. In: Clarke, R. (editor), Crime Prevention Studies Vol II, Willow Tree Press, New York.Google Scholar
Bernasco, W. (2008) Them again? Same offender involvement in repeat and near repeat burglaries. Eur. J. Criminol. 5, 411–431.CrossRefGoogle Scholar
Besag, J. & Diggle, P. (1977) Simple Monte Carlo tests for spatial pattern. Appl. Stat. 26, 327–333.Google Scholar
Bowers, K. J. & Johnson, S. D. (2005) Domestic burglary repeats and space-time clusters: The dimensions of risk. Eur. J. Criminol. 105, 67–92.Google Scholar
Bowers, K. J., Johnson, S. D. & Pease, K. (2004) Prospective hot-spotting: The future of crime mapping? Br. J. Criminol. 105, 641–658.Google Scholar
Braga, A. & Bond, B. (2008). Policing crime and disorder hot spots: A randomized controlled trial. Criminology 46, 577–607.CrossRefGoogle Scholar
Brantingham, P. & Brantingham, P. (1981) Environmental Criminology, Sage, Beverly Hills.Google Scholar
Brantingham, P. & Brantingham, P. (1993a) Environment, routine and situation: Toward a pattern theory of crime. In Clarke, R. V. & Felson, M. (editors), Routine Activity and Rational Choice, Transaction Publishers, New Brunswick, pp. 259–294.Google Scholar
Brantingham, P. & Brantingham, P. (1993b) Nodes, paths and edges: Considerations on the complexity of crime and the physical environment. J. Environ. Psychol. 13, 3–28.Google Scholar
Brantingham, P. & Brantingham, P. (1995) Criminality of place: Crime generetors and crime attractors. Eur. J. Criminal Policy Res. 105, 5–26.Google Scholar
Chainey, S. & Ratcliffe, J. H. (2005) GIS and Crime Mapping, Wiley, London.Google Scholar
Cohen, L. & Felson, M. (1979) Social change and crime rate trends: A routine activity approach. Am. Sociol. Rev. 105, 588–608.Google Scholar
Cornish, D. (1994) The procedural analysis of offending and its relevance for situational prevention. Crime Prevention Studies Volume 3, Criminal Justice Press, New York.Google Scholar
Cornish, D. & Clarke, R. V. (1986) The Reasoning Criminal: Rational Choice Perspectives on Offending, Springer-Verlag, New York.Google Scholar
Farrell, G. (2005) Progress and prospects in the prevention of repeat victimization. In: Tilley, N. (editor), Handbook of Crime Prevention and Community Safety, Willan, Cullompton, pp. 145–172.Google Scholar
Farrell, G. & Pease, K. (1994) Crime seasonality: Domestic disputes and residential burglary in Merseyside 1988–90. Br. J. Criminol. 34, 487–498.CrossRefGoogle Scholar
Farrell, G., Tilley, N., Tseloni, M. & Mailley, J. (2008) The crime drop and the security hypothesis. Br. Soc. Criminol. Newslett. 62, 17–21.Google Scholar
Getis, A. (1964) Temporal land-use pattern analysis with the use of nearest neighbour and quadrate methods. Ann. Assoc. Am. Geogr. 54 (3), 391–399.CrossRefGoogle Scholar
Groff, E., Weisburd, D. & Morris, A. (2008) Where the action is at places: Examining spatio-temporal patterns of juvenile crime at places using trajectory analysis. In: Weisburd, D., Bernasco, W. & , B. G. J (editors), Putting Crime in its Place, Springer, New York.Google Scholar
Hillier, B. (2004) Can streets be made safe? Urban Des. Int. 9, 31–45.Google Scholar
Hird, C. A. (2007) Seasonaility in recorded crime: Preliminary findings, Home Office, London.Google Scholar
Jeffrey, C. R. (1971) Crime Prevention Through Environmental Design, Sage, Beverly Hills.Google Scholar
Johnson, S. D. (2008) Repeat burglary victimization: A tale of two theories. J. Exp. Criminol. 4, 215–240.Google Scholar
Johnson, S. D. (2009) Potential uses of computational methods in th evaluation of crime reduction activity. In: Knuttson, J. & Tilley, N. (editors), Crime Prevention Studies vol 24, Willan, Cullompton.Google Scholar
Johnson, S. D., Bernasco, W., Bowers, K. J., Elffers, H., Ratcliffe, J., Rengert, G., et al. (2007a) Near repeats: A cross national assessment of residential burglary. J. Quant. Criminol. 105, 201–219.Google Scholar
Johnson, S. D., Birks, D. J., McLaughlin, L., Bowers, K. J. & Pease, K. (2007b) Prospective Mapping in Operational Context. Home Office, London.Google Scholar
Johnson, S. D. & Bowers, K. J. (2004a) The burglary as clue to the future: The beginnings of prospective hot-spotting. Eur. J. Criminol. 105, 237–255.Google Scholar
Johnson, S. D. & Bowers, K. J. (2004b) The stability of space-time clusters of burglary. Br. J. Criminol. 105, 55–65.Google Scholar
Johnson, S. D. & Bowers, K. J. (2007) Burglary prediction: The roles of theory flow and friction. In: Farrell, G., Bowers, K. J., Johnson, S. D. & Townsley, M. (editors), Imagination for Crime Prevention: Essays in Honour of Ken Pease, Willan, Cullompton.Google Scholar
Johnson, S. D. & Bowers, K. J. (2010) Permeability and Burglary Risk: Are Cul-de-sacs safer? J. Quant. Criminol., in pressGoogle Scholar
Johnson, S. D., Bowers, K. J., Birks, D. & Pease, K. (2008a) Predictive mapping of crime by ProMap: Accuracy, units of analysis and the environmental backcloth. In: Weisburd, D., Bernasco, W. & Bruinsma, G. (editors), Putting Crime in its Place: Units of Analysis in Spatial Crime Research. Springer, New York.Google Scholar
Johnson, S. D., Bowers, K. J. & Hirschfield, A. F. (1997) New insights in to the spatial and temporal distribution of repeat victimization. Br. J. Criminol. 37, 224–241.Google Scholar
Johnson, S. D. & Braithwaite, A. (2009) Spatio-Temporal Modelling of Insurgency in Iraq. In: Freilich, J. D. & Newman, G. R. (editors), Reducing Terrorism Through Situational Crime Prevention. Willan, Cullompton.Google Scholar
Johnson, S. D., Lab, S. & Bowers, K. J. (2008b) Stable and fluid hotspots of crime: Differentiation and identification. Built Environ. 34, 32–45.Google Scholar
Johnson, S. D., Summers, L., & Pease, K. (2006) Vehicle Crime: Communicating Spatial and Temporal Patterns, Home Office Report, March 2006.Google Scholar
Johnson, S. D., Summers, L. & Pease, K. (2009) Offenders as forager: A direct test of the boost account of victimization. J. Quant. Criminol. 25, 181–200.Google Scholar
Kleemans, E. R. (2001) Repeat burglary victimization: Results of empirical research in the Netherlands. In: Farrell, G. A. (editor), Crime Prevention Studies 12, Willan, Cullompton, pp. 53–68.Google Scholar
Knox, G. (1964) Epidemiology of childhood Leukaemia in Northumberland and Durham. Br. J. Preventitive Soc. Med. 105, 17–24.Google Scholar
Merton, R. (1938) Social structure and anomie. Am. Sociol. Rev. 3, 672–682.Google Scholar
Mitchell, H. & Babb, P. (2007) Crimes Detected in England and Wales 2006/07. Home Office Statistical Bulletin 15/07. Home Office, London.Google Scholar
Mohler, G., Short, M. B., Brantingham, P. J., Schoenberg, F. P. & Tita, G. (2010) Self-exciting point processes explain spatial-temporal patterns in crime. Submitted for Review, accessed 17 February from, URL: http://www.math.ucla.edu/~mbshort/papers/crime3.pdf.Google Scholar
Monmonier, M. (1996) How to Lie With Maps. University of Chicago Press, Chicago.Google Scholar
Moran, P. (1950) Notes on continuous stochastic phenomena. Biometrika 37, 17–33.Google Scholar
Morgan, F. (2001) Repeat burglary in a Perth suburb: Indicator of short-term or long-term risk? In: Pease, G. F. (editor), Repeat Victimization: Crime Prevention Studies volume 12, Criminal Justice Press, Monsey NY.Google Scholar
Nicholas, S., Kershaw, C. & Walker, A. (2007) Home Office Statistical Bulletin: Crime in England and Wales 2006/07, Home Office, London.Google Scholar
North, B. V., Curtis, D. & Sham, P. C. (2002) A note on the calculation of empirical p values fom Monte Carlo procedures. Am. J. Hum. Gen. 71, 439–441.Google Scholar
Openshaw, S. (1984) The Modifiable Areal Unit Problem, Geo Books, Norwich.Google Scholar
Pease, K. (1998) Repeat Victimization: Taking Stock. Home Office Police Research Group, Crime Detection and Prevention Series, Paper 90, Home Office, London.Google Scholar
Pitcher, A. B. (2010) Adding police to a mathematical model of burglary. Eur. J. Appl. Math. 21, 401–419.Google Scholar
Polvi, N., Looman, T., Humphries, C. & Pease, K. (1991) The time course of repeat burglary victimisation. Br. J. Criminol. 31, 411–414.Google Scholar
Pyke, G. (1984) Optimal foraging theory: A critical review. Annu. Rev. Ecol. Syst. 15, 523–575.Google Scholar
Ratcliffe, J. (2002) Aoristic signatures and the temporal analysis of high volume crime patterns. J. Quant. Criminol. 18, 23–43.Google Scholar
Ratcliffe, J. (2004) The hotspot matrix: A framework for the spatio-temporal targeting of crime reduction. Police Pract. Res. 5, 5–23.Google Scholar
Ratcliffe, J. H. & Rengert, G. (2008) Near-repeat patterns in Philadelphia shootings. Security J. 21, 58–76.Google Scholar
Robinson, W. (1950) Ecological correlations and the behavior of individuals. Am. Sociol. Rev. 15, 351–357.Google Scholar
Sagovsky, A. & Johnson, S. D. (2007) When does repeat victimisation occur? Aust. New Zealand J. Criminol. 40, 1–26.Google Scholar
Sampson, R. J. & Raudenbush, S. W. (1999) Systematic observation of public spaces: A new look at disorder in urban neighbourhoods. Am. J. Sociol. 105, 603–651.Google Scholar
Shaw, C. & McKay, H. (1969) Juvenile Delinquency and Urban Areas, University of Chicago Press, Chicago.Google Scholar
Sherman, L., Gartin, P. & Buerger, M. (1989) Hot spots of predatory crime: Routine activities and the criminology of place. Criminology 27, 27–55.Google Scholar
Sherman, L. W. & Weisburd, D. (1995) General deterrent effects of police patrol in crime “hot spots”: A randomized controlled trial. Justice Q. 12, 625–648.Google Scholar
Short, M. B., D'Orsogna, M. R., Brantingham, J. & Tita, G. E (2009) Measuring and modeling repeat and near-repeat burglary effects. J. Quant. Criminol. 25, 325–339.CrossRefGoogle Scholar
Summers, L., Johnson, S. D. & Rengert, G. (2010) The use of maps in offender interviewing. In: Bernasco, W. (editor), Offenders on Offending, Willan. Cullompton.Google Scholar
Tobler, W. (1970) A computer movie simulating urban growth in the Detroit Region. Econ. Geography 46, 234–240.Google Scholar
Townsley, M., Homel, R. & Chaseling, J. (2003) Infectious burglaries: A test of the near repeat hypothesis. Br. J. Criminol. 43, 615–633.Google Scholar
Townsley, M., Johnson, S. D. & Ratcliffe, J. H. (2008) Space-time dynamics of insurgent activity in Iraq. Security J. 21, 139–146.Google Scholar
Trickett, A., Osborn, D., Symour, J. & Pease, K. (1992) What is different about high crime areas? Br. J. Criminol. 32, 81–89.Google Scholar
Tseloni, M. & Pease, K. (2005) Population inequality: The case of repeat crime victimization. Int. Rev. Victimol. 12, 75–90.Google Scholar
Vaupel, J. W. & Yashin, A. I. (1985) Heterogeneity's ruses: Some surprising effects of selection on population dynamics. Am. Stat. 39, 176–185.Google Scholar
Weisburd, D., Bruinsma, G. J. & Bernasco, W. (2008) Units of analysis in geographic Criminology: historical developement, critical issues and open questions. In: Weisburd, D., Bernasco, W. & Bruinsma, G. J. (editors), Putting Crime in its Place: Units of Analysis in Geographic Criminology, Springer, New York. pp. 3–34.Google Scholar
Weisburd, D., Bushway, S., Lum, C. & Yang, S. (2004) Trajectories of crime at places: A longitudinal study of street segments in the city of Seattle. Criminology 42, 283–321.Google Scholar
Weisburd, D. & Eck, J. (2004) What can police do to reduce crime, disorder and fear? Ann. Am. Acad. Political Soc. Sci. 593, 42–65.Google Scholar
Weisburd, D. & Green, L. (1995) Policing drug hotspots: The Jersey city DMA experiment. Justice Q. 12, 711–735.CrossRefGoogle Scholar
Wright, R. T. & Decker, S. (1994) Burglars in the Job: Streetlife and Residential Break-ins, Northeastern University Press, Boston.Google Scholar
Xie, M., Pogarsky, G., Lynch, J. P. & McDowall, D. (2006) Prior police contact and subsequent victim reporting: Results from the NCVS. Justice Q. 23, 481–501.CrossRefGoogle Scholar