Am I Safe in My Home? Fear of Crime Analyzed with Spatial Statistics Methods in a Central European City

  • Daniel Lederer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7334)


This article presents the results of a quantitative survey in a central European city, where more than 1,500 citizens were asked about their fear of becoming a victim of burglary. Additionally, vulnerabilities to crimes were measured. A large set of spatial data was analyzed with different spatial-statistic methods and visualized in maps intended to serve as a summarized overview of the citizens’ fear of crime. First results show that there are specific hot spots in fear of burglary, majorly in the core of the city, and statistically significant differences in the pattern of fear of burglary between the districts. Furthermore, areas with a lack of technical safety standards were identified. This information shall help to start local crime prevention programs to reduce fear of crime and increase the quantity of protected homes.


Environmental criminology Fear of crime Crime mapping GIS Spatial statistical analyses Urban area studies 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Anselin, L., Cohen, J., Cook, D., Gorr, W., Tita, G.: Spatial Analyses of Crime. Criminal Justice 4, 213–262 (2000)Google Scholar
  2. 2.
    Cohen, L.E., Felson, M.: Social Change and Crime Rate Trends: A Routine Activity Approach. American Sociological Review 44, 588–608 (1979)CrossRefGoogle Scholar
  3. 3.
    Cornish, D.B., Clarke, R.V. (eds.): The Reasoning Criminal: Rational Choice Perspectives on Offending. Springer, New York (1986)Google Scholar
  4. 4.
    Brantingham, P.L., Brantingham, P.J.: Mobility, Notoriety, and Crime: A Study of Crime Patterns in Urban Nodal Points. Journal of Environmental Systems 11, 89–99 (1982)Google Scholar
  5. 5.
    Harries, K.: Mapping Crime: Principle and Practice. U.S. Department of Justice, National Institute of Justice, Washington, DC (1999)Google Scholar
  6. 6.
    McIntyre, J.: Public Attitudes toward Crime and Law Enforcement. The Annals of the American Academy of Political and Social Science 374(1), 34–46 (1967)CrossRefGoogle Scholar
  7. 7.
    Doran, B.J., Burgess, M.B.: Putting Fear of Crime on the Map. Investigating Perceptions of Crime Using Geographic Information Systems. Springer, New York (2012)Google Scholar
  8. 8.
    Skogan, W.G.: Public Policy and the Fear of Crime in Large American Cities. In: Gardiner, J.A. (ed.) Public Law and Public Policy, pp. 1–18. Praeger, New York (1977)Google Scholar
  9. 9.
    Holt, J.B., Lo, C.P., Hodler, T.W.: Dasymetric Estimation of Population Density and Areal Interpolation of Census Data. Cartography and Geoinformation Science 31(2), 103–121 (2004)CrossRefGoogle Scholar
  10. 10.
    Openshaw, S.: The Modifiable Areal Unit Problem. Concepts and Techniques in Modern Geography 38 (1984)Google Scholar
  11. 11.
    Bailey, T.C., Gatrell, A.C.: Interactive Spatial Data Analysis. Longman (1995)Google Scholar
  12. 12.
    Anselin, L.: Local Indicators of Spatial Association-LISA. Geographical Analysis 27(2), 93–115 (1995)CrossRefGoogle Scholar
  13. 13.
    Eck, J., Chainey, S.P., Cameron, J., Leitner, M., Wilson, R. (eds.): Mapping Crime: Understanding Hotspots. National Institute of Justice, Washington DC (2005)Google Scholar
  14. 14.
    Levine, N.: CrimeStat 3.0. A Spatial Statistics Program for the Analysis of Crime Incident Locations. Ned Levine & Associates, Houston and U.S. Department of Justice, Washington DC (2004),
  15. 15.
    Getis, A., Ord, J.K.: Local Spatial Statistics: An Overview. In: Longley, P., Batty, M. (eds.) Spatial Analysis: Modelling in a GIS Environment. John Wiley & Sons (1996)Google Scholar
  16. 16.
    Gatrell, A.C., Bailey, T.C., Diggle, P.J., Rowlingson, B.S.: Spatial Point Pattern Analysis and Its Application in Geographical Epidemiology. Transactions of the Institute of British Geographers 21, 256–274 (1996)CrossRefGoogle Scholar
  17. 17.
    Borruso, G.: Network Density Estimation: Analysis of Point Patterns over a Network. In: Gervasi, O., Gavrilova, M.L., Kumar, V., Laganá, A., Lee, H.P., Mun, Y., Taniar, D., Tan, C.J.K. (eds.) ICCSA 2005. LNCS, vol. 3482, pp. 126–132. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  18. 18.
    Moran, P.A.P.: The Interpretation of Statistical Maps. Journal of the Royal Statistical Society. Series B. Methodological. 10(2), 243–251 (1948)MathSciNetzbMATHGoogle Scholar
  19. 19.
    Moran, P.A.P.: Notes on Continuous Stochastic Phenomena. Biometrika 37(1/2), 17–23 (1950)MathSciNetzbMATHCrossRefGoogle Scholar
  20. 20.
    Silverman, B.W., Jones, M.C., Fix, E., Hodges, J.L.: An Important Contribution to Nonparametric Discriminant Analysis and Density Estimation. Commentary on Fix and Hodges (1951). International Statistical Review 57(3), 233–238 (1951)Google Scholar
  21. 21.
    Clark, P.J., Evans, F.C.: Distance to Nearest Neighbor as a Measure of Spatial Relationships in Populations. Ecology 35, 445–453 (1954)CrossRefGoogle Scholar
  22. 22.
    King, B.: Step-Wise Clustering Procedures. Journal of the American Statistical Association 62(317), 86–101 (1967)Google Scholar
  23. 23.
    Danese, M., Lazzari, M., Murgante, B.: Kernel Density Estimation Methods for a Geostatistical Approach in Seismic Risk Analysis: The Case Study of Potenza Hilltop Town (Southern Italy). In: Gervasi, O., Murgante, B., Laganà, A., Taniar, D., Mun, Y., Gavrilova, M.L. (eds.) ICCSA 2008, Part I. LNCS, vol. 5072, pp. 415–429. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Daniel Lederer
    • 1
  1. 1.KFV (Austrian Road Safety Board), Research and Knowledge ManagementViennaAustria

Personalised recommendations