Crime Mapping: Spatial and Temporal Challenges

  • Jerry Ratcliffe


Crime opportunities are neither uniformly nor randomly organized in space and time. As a result, crime mappers can unlock these spatial patterns and strive for a better theoretical understanding of the role of geography and opportunity, as well as enabling practical crime prevention solutions that are tailored to specific places. The evolution of crime mapping has heralded a new era in spatial criminology, and a re-emergence of the importance of place as one of the cornerstones essential to an understanding of crime and criminality. While early criminological inquiry in France and Britain had a spatial component, much of mainstream criminology for the last century has labored to explain criminality from a dispositional perspective, trying to explain why a particular offender or group has a propensity to commit crime. This traditional perspective resulted in criminologists focusing on individuals or on communities where the community extended from the neighborhood to larger aggregations (Weisburd et al. 2004). Even when the results lacked ambiguity, the findings often lacked policy relevance. However, crime mapping has revived interest and reshaped many criminologists appreciation for the importance of local geography as a determinant of crime that may be as important as criminal motivation. Between the individual and large urban areas (such as cities and regions) lies a spatial scale where crime varies considerably and does so at a frame of reference that is often amenable to localized crime prevention techniques. For example, without the opportunity afforded by enabling environmental weaknesses, such as poorly lit streets, lack of protective surveillance, or obvious victims (such as overtly wealthy tourists or unsecured vehicles), many offenders would not be as encouraged to commit crime.


Crime Prevention Location Quotient Repeat Victimization Crime Problem Modifiable Areal Unit Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The author would like to thank the Philadelphia Police Department for continued support and provision of data over many years, and Ralph B. Taylor, Martin Andresen, Shane Johnson, George Rengert, Liz Groff and Travis Taniguchi for comments on an earlier draft of this chapter; however, opinions, omissions and errors remain firmly the fault of the author.


  1. Andresen MA (2006) Crime measures and the spatial analysis of criminal activity. Br J Criminol 46(2):258–285CrossRefGoogle Scholar
  2. Anselin L (1988) Spatial econometrics: methods and models. Kluwer, DordrechtGoogle Scholar
  3. Anselin L (1995) Local indicators of spatial association – LISA. Geogr Anal 27(2):93–115CrossRefGoogle Scholar
  4. Anselin L (1996) The Moran scatterplot as an ESDA tool to assess local instability in spatial association. In: Fischer M, Scholten HJ, Unwin D (eds) Spatial Analytical Perspectives on GIS. Taylor and Francis, London, pp 111–125Google Scholar
  5. Anselin L, Bera A (1998) Spatial dependence in linear regression models with an introduction to spatial econometrics. In: Ullah A, Giles D (eds) Handbook of applied economic statistics. Marcel Dekker, New York, pp 237–289Google Scholar
  6. Anselin L, Griffiths E, Tita G (2008) Crime mapping and hot spot analysis. In: Wortley R, Mazerolle L (eds) Environmental criminology and crime analysis. Willan Publishing, Cullompton, Devon, pp 97–116Google Scholar
  7. Arbia G (2001) The role of spatial effects in the empirical analysis of regional concentration. Geogr Syst 3(3): 271–281CrossRefGoogle Scholar
  8. Assuncao RM, Reis EA (1999) A new proposal to adjust Moran’s I for population density. Stat Med 18:2147–2162CrossRefGoogle Scholar
  9. Bailey TC, Gatrell AC (1995) Interactive spatial data analysis, 2nd edn. Longman, LondonGoogle Scholar
  10. Besag J, Diggle PJ (1977) Simple Monte Carlo tests for spatial pattern. Appl Stat 26(3):327–333CrossRefGoogle Scholar
  11. Bichler G, Balchak S (2007) Address matching bias: ignorance is not bliss. Policing: An Int J Police Strateg Manage 30(1):32–60CrossRefGoogle Scholar
  12. Boggs SL (1965) Urban crime patterns. Am Sociol Rev 30(6):899–908CrossRefGoogle Scholar
  13. Bowers KJ, Johnson SD (2004) Who commits near repeats? A test of the boost explanation. West Criminol Rev 5(3):12–24Google Scholar
  14. Bowers KJ, Johnson SD, Pease K (2004) Prospective hot-spotting: the future of crime mapping? Br J Criminol 44(5):641–658CrossRefGoogle Scholar
  15. Brantingham PL, Brantingham PJ (1990) Situational crime prevention in practice. Can J Criminol 32(1):17–40Google Scholar
  16. Brantingham PL, Brantingham PJ (1993) Environment, routine, and situation: toward a pattern theory of crime. In: Clarke RV, Felson M (eds) Routine activity and rational choice, Vol 5. Transaction, New Brunswick, pp 259–294Google Scholar
  17. Cahill M, Mulligan G (2007) Using geographically weighted regression to explore local crime patterns. Soc Sci Comput Rev 25(2):174–193CrossRefGoogle Scholar
  18. Chainey S, Ratcliffe JH (2005) GIS and crime mapping. Wiley, LondonGoogle Scholar
  19. Chainey S, Reid S, Stuart N (2003) When is a hotspot a hotspot? A procedure for creating statistically robust hotspot maps of crime. In: Kidner DB, Higgs G, White SD (eds) Socio-economic applications of geographic information science. Taylor and Francis, London, pp 21–36Google Scholar
  20. Chainey S, Tompson L, Uhlig S (2008) The utility of hotspot mapping for predicting spatial patterns of crime. Secur J 21(1–2):4–28CrossRefGoogle Scholar
  21. Clarke, RV (ed) (1992) Situational crime prevention: successful case studies. Harrow and Heston, Albany, NYGoogle Scholar
  22. Clarke RV (2004) Technology, criminology and crime science. Eur J Crim Policy Res 10(1):55–63CrossRefGoogle Scholar
  23. Clarke RV (2008) Situational crime prevention. In: Wortley R Mazerolle L (eds) Environmental criminology and crime analysis. Willan Publishing, Cullompton, Devon, pp 178–194Google Scholar
  24. Clarke RV, Felson M (1993) Introduction: criminology, routine activity, and rational choice. In: Clarke RV, Felson M (eds) Routine activity and rational choice. Vol 5. Transaction, New Brunswick, pp 259–294Google Scholar
  25. Cliff AD, Ord JK (1969) The problem of spatial autocorrelation. In: Scott AJ (ed) London papers in regional science. Pion, London, pp 25–55Google Scholar
  26. Cohen LE, Felson M (1979) Social change and crime rate trends: a routine activity approach. Am Sociol Rev 44: 588–608CrossRefGoogle Scholar
  27. Cornish DB, Clarke RV (1987) Understanding crime displacement: an application of rational choice theory. Criminology 25(4):933–947CrossRefGoogle Scholar
  28. Cornish D, Clarke R (1986) The reasoning criminal: rational choice perspectives on offending. Springer, New YorkGoogle Scholar
  29. Cozens P (2008) Crime prevention through environmental design. In: Wortley R, Mazerolle L (eds) Environmental criminology and crime analysis. Willan Publishing, Cullompton, Devon, pp 153–177Google Scholar
  30. Craglia M, Haining R, Wiles P (2000) A comparative evaluation of approaches to urban crime pattern analysis. Urban Stud 37(4):711–729CrossRefGoogle Scholar
  31. Dorling D, Openshaw S (1992) Using computer animation to visualize space-time patterns. Environ Plann B Plann Des 19(6):639–650CrossRefGoogle Scholar
  32. Eck JE (1997) What do those dots mean? Mapping theories with data. In D. Weisburd T. McEwen (eds) Crime mapping and crime prevention, Vol 8. Criminal Justice Press, Monsey, NY, pp 379–406Google Scholar
  33. Eck JE, Chainey S, Cameron JG, Leitner M, Wilson RE (2005) Mapping crime: understanding hot spots (Special Report). National Institute of Justice, Washington DCGoogle Scholar
  34. Ekblom P, Tilley N (2000) Going equipped: criminology, situational crime prevention and the resourceful offender. Br J Criminol 40(3):376–398CrossRefGoogle Scholar
  35. Farrell G, Chenery S, Pease K (1998) Consolidating police crackdowns: findings from an anti-burglary project (Police Research Series paper 113). Policing and Reducing Crime Unit, Research, Development and Statistics Directorate, Home Office, LondonGoogle Scholar
  36. Farrell G, Pease K (1993) Once bitten, twice bitten: repeat victimisation and its implications for crime prevention. Police Res Group: Crime Prev Unit Ser Pap 46:32Google Scholar
  37. Farrell G, Pease K (1994) Crime seasonality – domestic disputes and residential burglary in Merseyside 1988–90. Br J Criminol 34(4):487–498Google Scholar
  38. Feins JD, Epstein JC, Widom R (1997) Solving crime problems in residential neighborhoods: comprehensive changes in design, management, and use. NIJ Issues Pract 157Google Scholar
  39. Felson M (1998) Crime and everyday life: impact and implications for society 2nd edn. Pine Forge Press, Thousand Oaks, CAGoogle Scholar
  40. Felson M, Poulsen E (2003) Simple indicators of crime by time of day. Int J Forecast 19(4):595–601CrossRefGoogle Scholar
  41. Field S (1992) The effect of temperature on crime. Br J Criminol 32(3):340–351Google Scholar
  42. Forrester D, Chatterton M, Pease K (1988) The Kirkholt burglary prevention project, Rochdale (No. 13). Crime Prevention Unit (Home Office), LondonGoogle Scholar
  43. Fotheringham AS, Brunsdon C, Charlton M (2002) Geographically weighted regression. Wiley, Chichester, UKGoogle Scholar
  44. Fotheringham SA, Brunsdon C (2004) Some thoughts on inference in the analysis of spatial data. Int J Geogr Inf Sci 18(5):447–457CrossRefGoogle Scholar
  45. Getis A, Ord JK (1992) The analysis of spatial association by use of distance statistics. Geogr Anal 24(3):189–206CrossRefGoogle Scholar
  46. Getis A, Ord JK (1996) Local spatial statistics: an overview. In: Longley P, Batty M (eds) Spatial analysis: modelling in a gis environment, 1st edn. GeoInformation International, London p 374Google Scholar
  47. Guerry A-M (1833) Essai sur la statistique morale de la France: precede d’un rapport a l’Academie de sciences. Chez Crochard, ParisGoogle Scholar
  48. Hagerstrand T (1970) What about people in regional science? Pap Reg Sci 24:7–21CrossRefGoogle Scholar
  49. Harries KD (1980) Crime and the environment. Charles C. Thomas, Springfield, ILGoogle Scholar
  50. Harries KD (1981) Alternative denominators in conventional crime rates. In: Brantingham PJ, Brantingham PL (eds) Environmental criminology. Sage, London, pp 147–165Google Scholar
  51. Harries KD (1999) Mapping crime: principles and practice. US Department of Justice, Washington DCGoogle Scholar
  52. Hope ACA (1968) A simplified Monte Carlo significance test procedure. J R Stat Soc Ser B 30:583–598Google Scholar
  53. Hope T (1995) The flux of victimization. Br J Criminol 35(3):327–342Google Scholar
  54. Johnson SD, Bowers KJ (2004a) The burglary as clue to the future: the beginnings of prospective hot-spotting. Eur J Criminol 1(2):237–255CrossRefGoogle Scholar
  55. Johnson SD, Bowers KJ (2004b) The stability of space-time clusters of burglary. Br J Criminol 44(1):55–65CrossRefGoogle Scholar
  56. Johnson SD, Bernasco W, Bowers KJ, Elffers H, Ratcliffe JH, Rengert GF, Townsley M (2007) Space-time patterns of risk: a cross national assessment of residential burglary victimization. J Quant Criminol 23(3):201–219CrossRefGoogle Scholar
  57. Johnson SD, Bowers KJ, Birks D, Pease K (2009) Predictive mapping of crime by ProMap: accuracy, units of analysis and the environmental backcloth. In: Weisburd D, Bernasco W, Bruinsma G (eds) Putting crime in its place: units of analysis in spatial crime research Springer, New York, pp 165–192Google Scholar
  58. Land K, Deane G (1992) On the large-sample estimation of regression models with spatial effect terms: a two-stage least squares approach. Sociol Methodol 22:221–248CrossRefGoogle Scholar
  59. Laycock G (2001) Hypothesis-based research: the repeat victimization story. Crim Justice 1(1):59–82Google Scholar
  60. Lersch KM (2004) Space, time, and crime. North Caroline Press, Durham, NCGoogle Scholar
  61. Levine N (2006) Crime Mapping and the Crimestat Program. Geogr Anal 38(1):41–56CrossRefGoogle Scholar
  62. Levine N (2007) CrimeStat: a spatial statistics program for the analysis of crime incident locations (v 3.1). Ned Levine & Associates, Houston, TX, and the National Institute of Justice, Washington, DC. Mar []. Chapter 8
  63. MacEachren A (1994) Time as a cartographic variable. In: Hearnshaw H, Unwin D (eds) Visualisation in geographical information systems. Wiley, London, pp 115–130Google Scholar
  64. Maltz MD, Gordon AC, Friedman W (1991) Mapping crime in its community setting: event geography analysis. Springer, New YorkCrossRefGoogle Scholar
  65. Martin D (2002) Spatial patterns in residential burglary: assessing the effect of neighborhood social capital. J Contemp Crim Justice 18(2):132–146CrossRefGoogle Scholar
  66. McCord E, Ratcliffe JH (2007) A micro-spatial analysis of the demographic and criminogenic environment of drug markets in Philadelphia. Aust N Z J Criminol 40(1):43–63CrossRefGoogle Scholar
  67. Mencken FC, Barnett C (1999) Murder, nonnegligent manslaughter and spatial autocorrelation in mid-South counties. J Quant Criminol 15(4):407–422CrossRefGoogle Scholar
  68. Messner SF, Anselin L (2004) Spatial analyses of homicide with areal data. In: Goodchild MF, Janelle DG (eds) Spatially integrated social science. Oxford University Press, New York, NY, pp 127–144Google Scholar
  69. Messner SF, Anselin L, Baller RD, Hawkins DF, Deane G, Tolnay SE (1999) The spatial patterning of county homicide rates: an application of exploratory spatial data analysis. J Quant Criminol 15(4):423–450CrossRefGoogle Scholar
  70. Miller HJ (2005) A measurement theory for time geography. Geogr Anal 37(1):17–45CrossRefGoogle Scholar
  71. Monmonier M, Blij HJd (1996) How to lie with maps, 2nd edn. University of Chicago Press, ChicagoGoogle Scholar
  72. Mooney CZ (1997) Monte carlo simulation. Sage, Thousand Oaks, CAGoogle Scholar
  73. Moran PAP (1950) Notes on continuous stochastic phenomena. Biometrika 37:17–23Google Scholar
  74. Murray RK, Roncek DW (2008) Measuring diffusion of assaults around bars through radius and adjacency techniques. Crim Justice Rev 33(2):199–220CrossRefGoogle Scholar
  75. O’Shea TC, Nicholls K (2002) Crime analysis in America (Full final report), Office of Community Oriented Policing Services, Washington DCGoogle Scholar
  76. Oden N (1995) Adjusting Moran’s I for population density. Stat Med 14(1):17–26CrossRefGoogle Scholar
  77. Openshaw S (1984) The modifiable areal unit problem. Concepts Tech Mod Geogr 38:41Google Scholar
  78. Openshaw S, Cross A, Charlton M, Brunsdon C, Lillie J (1990) Lessons learnt from a Post Mortem of a failed GIS. Paper presented at the 2nd National Conference and Exhibition of the AGI, Brighton, Oct 1990Google Scholar
  79. Ord JK, Getis A (1995) Local spatial autocorrelation statistics: distributional issues and an application. Geogr Anal 27(4):286–306CrossRefGoogle Scholar
  80. Pease K (1998) Repeat victimisation: taking stock. Police Res Group: Crime Detect Prev Ser Pap 90 1–48Google Scholar
  81. Peuquet DJ (1994) It’s about time – a conceptual-framework for the representation of temporal dynamics in Geographical Information Systems. Ann Assoc Am Geogr 84(3):441–461CrossRefGoogle Scholar
  82. Polvi N, Looman T, Humphries C, Pease K (1991) The time course of repeat burglary victimization. Br J Criminol 31(4):411–414Google Scholar
  83. Quetelet A (1842) A treatise in man. Chambers, EdinburghGoogle Scholar
  84. Ratcliffe JH (2000) Aoristic analysis: the spatial interpretation of unspecific temporal events. Int J Geogr Inf Sci 14(7):669–679CrossRefGoogle Scholar
  85. Ratcliffe JH (2001) On the accuracy of TIGER-type geocoded address data in relation to cadastral and census areal units. Int J Geogr Inf Sci 15(5):473–485CrossRefGoogle Scholar
  86. Ratcliffe JH (2002) Aoristic signatures and the temporal analysis of high volume crime patterns. J Quant Criminol 18(1):23–43CrossRefGoogle Scholar
  87. Ratcliffe JH (2004a) Crime mapping and the training needs of law enforcement. Eur J Crim Policy Res 10(1):65–83CrossRefGoogle Scholar
  88. Ratcliffe JH (2004b) Geocoding crime and a first estimate of an acceptable minimum hit rate. Int J Geogr Inf Sci 18(1):61–73CrossRefGoogle Scholar
  89. Ratcliffe JH (2004c) The Hotspot Matrix: a framework for the spatio-temporal targeting of crime reduction. Police Pract Res 5(1):5–23CrossRefGoogle Scholar
  90. Ratcliffe JH (2005) Detecting spatial movement of intra-region crime patterns over time. J. Quant Criminol 21(1):103–123CrossRefGoogle Scholar
  91. Ratcliffe JH (2006) A temporal constraint theory to explain opportunity-based spatial offending patterns. J Res Crime Delinq 43(3):261–291CrossRefGoogle Scholar
  92. Ratcliffe JH (2008) Intelligence-led policing. Willan Publishing, Cullompton, DevonGoogle Scholar
  93. Ratcliffe JH (2009) The structure of strategic thinking. In: Ratcliffe JH (ed) Strategic thinking in criminal intelligence, 2nd edn. Federation Press, SydneyGoogle Scholar
  94. Ratcliffe JH, McCullagh MJ (1998a) Aoristic crime analysis. Int J Geogr Inf Sci 12(7):751–764CrossRefGoogle Scholar
  95. Ratcliffe JH, McCullagh MJ (1998b) Identifying repeat victimisation with GIS. Br J Criminol 38(4):651–662CrossRefGoogle Scholar
  96. Ratcliffe JH, McCullagh MJ (1999) Hotbeds of crime and the search for spatial accuracy. Geogr Syst 1(4):385–398CrossRefGoogle Scholar
  97. Ratcliffe JH, Rengert GF (2008) Near repeat patterns in Philadelphia shootings. Secur J 21(1–2):58–76CrossRefGoogle Scholar
  98. Rengert GF, Ratcliffe JH, Chakravorty S (2005) Policing illegal drug markets: geographic approaches to crime reduction. Criminal Justice Press, Monsey, NYGoogle Scholar
  99. Roncek DW, Maier PA (1991) Bars, blocks and crimes revisited: linking the theory of routine activities to the empiricisms of ‘Hot Spots’. Criminology 29(4):725–753CrossRefGoogle Scholar
  100. Shaw CR, McKay HD (1942) Juvenile delinquency and urban areas. Chicago University Press, ChicagoGoogle Scholar
  101. Taylor B, Kowalyk A, Boba R (2007) The integration of crime analysis Into law enforcement agencies. Police Q 10(2):154–169CrossRefGoogle Scholar
  102. Thompson SP (1898) Michael Faraday: his life and work. MacMillan, New YorkGoogle Scholar
  103. Tilley N (2004) Karl Popper: a philosopher for Ronald Clarke’s situational crime prevention. In: Shoham S, Knepper P (eds) Israeli studies in criminology, Vol 8. de Sitter, Willowdale, Ontario, pp 39–56Google Scholar
  104. Tobler W (1970) A computer movie simulating urban growth in the Detroit region. In: Economic geography, 46(Supplement: Proceedings. International Geographical Union. commission on quantitative methods. (June, 1970)) pp 234–240Google Scholar
  105. Townsley M, Homel R, Chaseling J (2003) Infectious burglaries: a test of the near repeat hypothesis. Br J Criminol 43(3):61–633Google Scholar
  106. Townsley M, Johnson SD, Ratcliffe JH (2008) Space time dynamics of insurgent activity in Iraq. Secur J 21(3): 139–146CrossRefGoogle Scholar
  107. Trickett A, Ellingworth D, Hope T, Pease K (1995) Crime victimization in the eighties – changes in area and regional inequality. Br J Criminol 35(3):343–359Google Scholar
  108. Tufte ER (2001) The visual display of quantitative information, 2nd edn. Graphics Press, LondonGoogle Scholar
  109. van Koppen PJ, De Keijser JW (1999) The time to rob: variations in time of number of commercial robberies. J Res Crime Delinq 36(1):7–29CrossRefGoogle Scholar
  110. Ward MD, Gleditsch KS (2008) Spatial regression models. (Quantitative Applications in the Social Sciences Series). Sage, Thousand Oaks, CAGoogle Scholar
  111. Weisburd D, Bernasco W, Bruinsma GJN (2009) 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 3–31Google Scholar
  112. 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(2):283–321CrossRefGoogle Scholar
  113. Weisburd D, Lum C (2005) The diffusion of computerized crime mapping in policing: linking research and practice. Police Pract Res 6(5):419–434CrossRefGoogle Scholar
  114. Weisburd D, McEwen T (1997) Crime mapping and crime prevention, Vol 8. Criminal Justice Press, New YorkGoogle Scholar
  115. White R, Sutton A (1995) Crime prevention, urban space and social exclusion. Aust N Z J Sociol 31(1):82–99CrossRefGoogle Scholar
  116. Wilson RE (2007) The impact of software on crime mapping. Soc Sci Comput Rev 25(2):135–142CrossRefGoogle Scholar
  117. Wortley R, Mazerolle L (eds) (2008) Environmental criminology and crime analysis. Willan Publishing, Cullompton, DevonGoogle Scholar

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© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Jerry Ratcliffe
    • 1
  1. 1.Department of Criminal JusticeTemple UniversityPhiladelphiaUSA

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