Advertisement

Journal of Quantitative Criminology

, Volume 26, Issue 1, pp 113–138 | Cite as

Modeling Micro-Level Crime Location Choice: Application of the Discrete Choice Framework to Crime at Places

  • Wim Bernasco
Original Paper

Abstract

Discrete choice recently emerged as a new framework for analyzing criminal location decisions, but has thus far only been used to study the choice amongst large areas like census tracts. Because offenders also make target selection decisions at much lower levels of spatial aggregation, the present study analyzes the location choices of offenders at detailed spatial resolutions: the average unit of analysis is an area of only 18 residential units and 40 residents. This article reviews the discrete choice and spatial choice literature, justifies the use of geographic units this small, and argues that because small spatial units depend strongly on their environment, models are needed that take into account spatial interdependence. To illustrate these points, burglary location choice data from the Netherlands are analyzed with discrete choice models, including the spatial competition model.

Keywords

Discrete choice Spatial competition Sampling-from-alternatives Burglary 

Notes

Acknowledgments

Crime data were kindly made available by politie Haaglanden (Greater The Hague Police Force). I thank Henk Elffers and Gerben Bruinsma for the fruitful discussions that helped me realize this paper, and the participants of the Crime and Place Working Group, three anonymous reviewers of this journal and the editors of this special issue, for insightful comments on previous drafts.

References

  1. Andresen MA (2006) Crime measures and the spatial analysis of criminal activity. Br J Criminol 46:258–285CrossRefGoogle Scholar
  2. Baller RD, Anselin L, Messner SF, Deane G, Hawkins DF (2001) Structural covariates of US county homicide rates: incorporating spatial effects. Criminology 39:561–590CrossRefGoogle Scholar
  3. Barker RG (1968) Ecological psychology: concepts and methods for studying the environment of human behavior. Stanford University Press, StanfordGoogle Scholar
  4. Ben-Akiva M, Bierlaire M (1999) Discrete choice methods and their applications to short term travel decisions. In: Hall RW (ed) Handbook of transportation science. Kluwer, Norwell, pp 5–34Google Scholar
  5. Ben-Akiva ME, Lerman SR (1985) Discrete choice analysis: theory and applications to travel demand. MIT Press, CambridgeGoogle Scholar
  6. Bennett T (1995) Identifying, explaining, and targeting burglary ‘hot spots’. Eur J Crim Policy Res 3:113–123CrossRefGoogle Scholar
  7. Bernasco W (2006) Co-offending and the choice of target areas in burglary. J Invest Psychol Offender Profiling 3:139–155CrossRefGoogle Scholar
  8. Bernasco W (2009a) Burglary. In: Tonry M (ed) The oxford handbook of crime and public policy. Oxford University Press, Oxford, pp 165–190Google Scholar
  9. Bernasco W (2009b) Foraging strategies of Homo Criminalis: lessons from behavioral ecology. Crime Patterns Anal 2:5–16Google Scholar
  10. Bernasco W (forthcoming) A sentimental journey to crime; effects of residential history on crime location choice. Criminology 48(2)Google Scholar
  11. Bernasco W, Block R (2009) Where offenders choose to attack: a discrete choice model of robberies in Chicago. Criminology 47:93–130CrossRefGoogle Scholar
  12. Bernasco W, Luykx F (2003) Effects of attractiveness, opportunity and accessibility to burglars on residential burglary rates of urban neighborhoods. Criminology 41:981–1001CrossRefGoogle Scholar
  13. Bernasco W, Nieuwbeerta P (2005) How do residential burglars select target areas? A new approach to the analysis of criminal location choice. Br J Criminol 45:296–315CrossRefGoogle Scholar
  14. Bhat C, Zhao H (2002) The spatial analysis of activity stop generation. Transp Res Part B Methodol 36:557–575CrossRefGoogle Scholar
  15. Boots BN, Kanaroglou PS (1988) Incorporating the effects of spatial structure in discrete choice models of migration*. J Reg Sci 28:495–510CrossRefGoogle Scholar
  16. Bowers KJ, Johnson SD (2005) Domestic burglary repeats and space-time clusters: the dimensions of risk. Eur J Criminol 2:67–92CrossRefGoogle Scholar
  17. Brantingham PJ, Brantingham PL (1978) A theoretical model of crime site selection. In: Krohn MD, Akers RL (eds) Crime, law and sanctions. Theoretical perspectives. Sage, Beverly Hills, pp 105–118Google Scholar
  18. Brown BB, Altman I (1981) Territoriality and residential crime: a conceptual framework. In: Brantingham PJ, Brantingham PL (eds) Environmental criminology. Sage, Beverly Hills, pp 55–76Google Scholar
  19. Ceccato V, Haining R, Signoretta P (2002) Exploring offence statistics in Stockholm city using spatial analysis tools. Ann Assoc Am Geogr 92:29–51CrossRefGoogle Scholar
  20. Chattopadhyay S (2000) The effectiveness of McFaddens’s nested logit model in valuing amenity improvement. Reg Sci Urban Econ 30:23–43CrossRefGoogle Scholar
  21. Clare J, Fernandez J, Morgan F (2009) Formal evaluation of the impact of barriers and connectors on residential burglars’ macro-level offending location choices. Aust New Zealand J Criminol 42:139–158CrossRefGoogle Scholar
  22. Cornish DB, Clarke RV (1986) Introduction. In: Cornish DB, Clarke RV (eds) The reasoning criminal: rational choice perspectives on offending. Springer, New York, pp 1–16Google Scholar
  23. Coulton C, Korbin J, Chan T, Su M (2001) Mapping residents’ perceptions of neighborhood boundaries: a methodological note. Am J Commun Psychol 29:371–383CrossRefGoogle Scholar
  24. Coupe T, Blake L (2006) Daylight and darkness targeting strategies and the risks of being seen at residential burglaries. Criminology 44:431–464CrossRefGoogle Scholar
  25. Coupe RT, Girling AJ (2001) Modelling police success in catching burglars in the act. Omega 29:19–27CrossRefGoogle Scholar
  26. Coupe T, Griffiths M (1996) Solving residential burglary (Crime Detection and Prevention series, No. 77). Home Office, Police Research Group, LondonGoogle Scholar
  27. Deane G, Messner S, Stucky T, McGeever K, Kubrin C (2008) Not ‘Islands, Entire of Themselves’: exploring the spatial context of city-level robbery rates. J Quant Criminol 24:337–421CrossRefGoogle Scholar
  28. Dubin RA (1998) Spatial autocorrelation: a primer. J Hous Econ 7:304–327CrossRefGoogle Scholar
  29. Duncombe W, Robbins M, Wolf DA (2001) Retire to where? A discrete choice model of residential location. Int J Popul Geogr 7:281–293CrossRefGoogle Scholar
  30. Elffers H (2003) Analysing neighbourhood influence in criminology. Stat Neerl 57:347–367CrossRefGoogle Scholar
  31. Elffers H, Reynald D, Averdijk M, Bernasco W, Block R (2008) Modelling crime flow between neighbourhoods in terms of distance and of intervening opportunities. Crime Prev Commun Saf 10:85–96CrossRefGoogle Scholar
  32. Felson M (2006) Crime and nature. Sage, Thousand OaksGoogle Scholar
  33. Fik TJ (1988) Hierarchical interaction: the modeling of a competing central place system. Ann Reg Sci 22:48–69CrossRefGoogle Scholar
  34. Fotheringham AS (1983a) A new set of spatial interaction models: the theory of competing destinations. Environ Plann A 15:15–36CrossRefGoogle Scholar
  35. Fotheringham AS (1983b) Some theoretical aspects of destination choice and their relevance to production-constrained gravity models. Environ Plann 15:1121–1132CrossRefGoogle Scholar
  36. Fotheringham AS (1985) Spatial competition and agglomeration in urban modelling. Environ Plann A 17:213–230CrossRefGoogle Scholar
  37. Fotheringham AS, Brunsdon C, Charlton M (2000) Quantitative geography, perspectives on spatial data analysis. Sage, LondonGoogle Scholar
  38. Fotheringham AS, Nakaya T, Yano K, Openshaw S, Ishikawa Y (2001) Hierarchical destination choice and spatial interaction modelling: a simulation experiment. Environ Plann A 33:901–920CrossRefGoogle Scholar
  39. Frejinger E, Bierlaire M, Ben-Akiva M (2009) Sampling of alternatives for route choice modeling. Transp Res Part B Methodol 43:984–994CrossRefGoogle Scholar
  40. Getis A (2007) Reflections on spatial autocorrelation. Reg Sci Urban Econ 37:491–496CrossRefGoogle Scholar
  41. Gitlesen JP, Thorsen I (2000) A competing destinations approach to modeling commuting flows: a theoretical interpretation and an empirical application of the model. Environ Plann A 32:2057–2074CrossRefGoogle Scholar
  42. Groff ER, La Vigne NG (2001) Mapping an opportunity surface of residential burglary. J Res Crime Delinq 38:257–278CrossRefGoogle Scholar
  43. Groff E, Weisburd D, Morris NA (2009) Where the action is at places: examining spatio-temporal patterns of juvenile crime at places using trajectory analysis and GIS. In: Weisburd D, Bernasco W, Bruinsma GJN (eds) Putting crime in its place: units of analysis in geographic criminology. Springer, New York, pp 61–86CrossRefGoogle Scholar
  44. Hensher DA, Bradley M (1993) Using stated response choice data to enrich revealed preference discrete choice models. Mark Lett 4:139–151CrossRefGoogle Scholar
  45. Hu P, Pooler J (2002) An empirical test of the competing destinations model. J Geogr Syst 4:301–323CrossRefGoogle Scholar
  46. Hunt LM, Boots B, Kanaroglou PS (2004) Spatial choice modelling: new opportunities to incorporate space into substitution patterns. Prog Hum Geogr 28:746–766CrossRefGoogle Scholar
  47. Johnson S, Summers L, Pease K (2009) Offender as forager? A direct test of the boost account of victimization. J Quant Criminol 25:181–200CrossRefGoogle Scholar
  48. Kinney JB, Brantingham PL, Wuschke K, Kirk MG, Brantingham PJ (2008) Crime attractors, generators and detractors: land use and urban crime opportunities. Built Environ 34:62–74CrossRefGoogle Scholar
  49. Kleemans ER (1996) Strategische misdaadanalyse en stedelijke criminaliteit. Een toepassing van de rationele keuzebenadering op stedelijke criminaliteitspatronen en het gedrag van daders, toegespitst op het delict woninginbraak. Universiteit Twente, Enschede, The NetherlandsGoogle Scholar
  50. Kubrin CE (2003) Structural covariates of homicide rates: does type of homicide matter? J Res Crime Delinq 40:139–170CrossRefGoogle Scholar
  51. Kurtz EM, Koons BA, Taylor RB (1998) Land use, physical deterioration, resident-based control, and calls for service on urban streetblocks. Justice Q 15:121–149CrossRefGoogle Scholar
  52. Legendre P (1993) Spatial autocorrelation: trouble or new paradigm? Ecology 74:1659–1673CrossRefGoogle Scholar
  53. Li M-T, Chow L-F, Zhao F, Li S-C (2005) Geographically stratified importance sampling for the calibration of aggregated destination choice models for trip distribution. Transp Res Rec J Transp Res Board 1935:85–92CrossRefGoogle Scholar
  54. Logie R, Wright RT, Decker SH (1992) Recognition memory performance and residential burglary. Appl Cogn Psychol 6:109–123CrossRefGoogle Scholar
  55. Markowitz FE, Bellair PE, Liska AE, Liu JH (2001) Extending social disorganization theory: modeling the relationships between cohesion, disorder, and fear. Criminology 39:293–320CrossRefGoogle Scholar
  56. McCord ES, Ratcliffe JH (2007) A micro-spatial analysis of the demographic and criminogenic environment of drug markets in philadelphia. Aust New Zealand J Criminol 40:43–63CrossRefGoogle Scholar
  57. McFadden D (1973) Conditional logit analysis of qualitative choice behavior. In: Zarembka P (ed) Frontiers in econometrics. Academic Press, New York, pp 105–142Google Scholar
  58. McFadden D (1974) Conditional logit analysis of qualitative choice behavior. In: Zarembka P (ed) Frontiers in econometrics. Academic Press, New York, pp 105–142Google Scholar
  59. McFadden D (1978) Modeling the choice of residential location. In: Karlkvist A, Lundkvist L, Snikars F, Weibull J (eds) Spatial interaction theory and planning models. North-Holland Publ. Corp., Amsterdam, pp 75–96Google Scholar
  60. McFadden D (2001) Disaggregate behavioral travel demand’s RUM side: a 30-YEAR retrospective. In: Henscher DA (ed) Travel behavior research; the leading edge. Pergamon, Oxford, pp 17–63CrossRefGoogle Scholar
  61. Mears DP, Bhati AS (2006) No community is an Island: the effects of resource deprivation on urban violence in spatially and socially proximate communities. Criminology 44:509–548CrossRefGoogle Scholar
  62. Morenoff JD, Sampson RJ, Raudenbush SW (2001) Neighbourhood inequality, collective efficacy, and the spatial dynamics of urban violence. Criminology 29:517–559CrossRefGoogle Scholar
  63. Nerella S, Bhat C (2004) Numerical analysis of effect of sampling of alternatives in discrete choice models. Transp Res Rec J Transp Res Board 1894:11–19CrossRefGoogle Scholar
  64. Oberwittler D, Wikström P-OH (2009) Why small is better: advancing the study of the role of behavioral contexts in crime causation. In: Weisburd D, Bernasco W, Bruinsma GJN (eds) Putting crime in its place: units of analysis in geographic criminology. Springer, New York, pp 35–59CrossRefGoogle Scholar
  65. Openshaw S (1984) The modifiable areal unit problem. Geo Books, NorwichGoogle Scholar
  66. Peeters MP, Elffers H (forthcoming) Do physical barriers affect urban crime trips? The effects of a highway, a railroad, a park or a canal on the flow of crime in The Hague. Crime Patterns Anal 3Google Scholar
  67. Pellegrini PA, Fotheringham AS (2002) Modelling spatial choice: a review and synthesis in a migration context. Prog Hum Geogr 26:487–510CrossRefGoogle Scholar
  68. Pooler J (1997) Competition among destinations in spatial interaction models: a new point of view. Chin Geogr Sci 8:212–224Google Scholar
  69. Rengert GF, Wasilchick J (2000) Suburban burglary: a tale of two suburbs. Charles C. Thomas, Springfield, ILGoogle Scholar
  70. Reynald D, Averdijk M, Elffers H, Bernasco W (2008) Do social barriers affect urban crime trips? The effects of ethnic and economic neighbourhood compositions on the flow of crime in The Hague, The Netherlands. Built Environ 34:21–31CrossRefGoogle Scholar
  71. Robinson WS (1950) Ecological correlations and the behavior of individuals. Am Soc Rev 15:351–357CrossRefGoogle Scholar
  72. Shaw WD, Ozog MT (1999) Modeling overnight recreation trip choice: application of a repeated nested multinomial logit model. Environ Res Econ 13:397–414CrossRefGoogle Scholar
  73. Sherman L, Gartin PR, Buerger ME (1989) Hot spots of predatory crime: routine activities and the criminology of place. Criminology 27:27–55CrossRefGoogle Scholar
  74. Smith TS (1976) Inverse distance variations for the flow of crime in urban areas. Social Forces 54:802–815CrossRefGoogle Scholar
  75. Smith WR, Frazee SG, Davison EL (2000) Furthering the integration of routine activity and social disorganization theories: small units of analysis and the study of street robbery as a diffusion process. Criminology 38:489–523CrossRefGoogle Scholar
  76. Snook B (2004) Individual differences in distance traveled by serial burglars. J Invest Psychol Offender Profiling 1:53–66CrossRefGoogle Scholar
  77. Snook B, Cullen RM, Mokros A, Harbort S (2005) Serial murderers’ spatial decisions: factors that influence crime location choice. J Invest Psychol Offender Profiling 2:147–164CrossRefGoogle Scholar
  78. St. Jean PKB (2007) Pockets of crime. Broken windows, collective efficacy, and the criminal point of view. University of Chicago Press, ChicagoGoogle Scholar
  79. Taylor RB (1997) Social order and disorder of street blocks and neighborhoods: ecology, microecology, and the systemic model of social disorganization. J Res Crime Delinq 34:113–155CrossRefGoogle Scholar
  80. Taylor M, Nee C (1988) The role of cues in simulated residential burglary—a preliminary investigation. Br J Criminol 28:396–401Google Scholar
  81. Thill J-C (1992) Choice set formation for destination choice modelling. Prog Hum Geogr 16:361–382CrossRefGoogle Scholar
  82. Tobler WR (1970) A computer movie simulating urban growth in the detroit region. Econ Geogr 46:234–240CrossRefGoogle Scholar
  83. Train K (1998) Recreation demand models with taste variation. Land Econ 74:230–239CrossRefGoogle Scholar
  84. Train K (2009) Discrete choice methods with simulation, 2nd edn. Cambridge University Press, New YorkGoogle Scholar
  85. Weisburd D, Bushway S, Lum C, Yang S-M (2004) Trajectories of crime at places: a longitudinal study of street segments in the city of Seattle. Criminology 42:283–322CrossRefGoogle Scholar
  86. Weisburd D, Bernasco W, Bruinsma GJN (eds) (2009a) Putting crime in its place: units of analysis in geographic criminology. Springer, New YorkGoogle Scholar
  87. Weisburd D, Bruinsma GJN, Bernasco W (2009b) Units of analysis in geographic criminology: historical development, critical issues, and open questions. In: Weisburd D, Bernasco W, Bruinsma GJN (eds) Putting crime in its place: units of analysis in geographic criminology. Springer, New York, pp 3–31CrossRefGoogle Scholar
  88. Wicker AW (1987) Behavior settings reconsidered: temporal stages, resources, internal dynamics, context. In: Stokols D, Altman I (eds) Handbook of environmental psychology. Wiley, New York, pp 613–653Google Scholar
  89. Wikström P-OH (2006) Individuals, settings, and acts of crime: situational mechanisms and the explanation of crime. In: Wikström P-OH, Sampson RJ (eds) The explanation of crime: context, mechanisms, and development. Cambridge University Press, Cambridge, pp 61–107CrossRefGoogle Scholar
  90. Wilcox P, Madensen TD, Tillyer MS (2007) Guardianship in context: implications for burglary victimization risk and prevention. Criminology 45:771–803Google Scholar
  91. Wiles P, Costello A (2000) The ‘road to nowhere’: the evidence for traveling criminals (Home Office Research Study No. 207). Home Office, Research, Development and Statistics Directorate, LondonGoogle Scholar
  92. Wright R, Logie RH, Decker SH (1995) Criminal expertise and offender decision making: an experimental study of the target selection process in residential burglary. J Res Crime Delinq 32:39–53CrossRefGoogle Scholar
  93. Zhang L, Messner SF, Liu J (2007) A multilevel analysis of the risk of household burglary in the city of Tianjin, China. Br J Criminol 47:918–937CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Netherlands Institute for the Study of Crime and Law Enforcement (NSCR)AmsterdamThe Netherlands

Personalised recommendations