Am I Safe in My Home? Fear of Crime Analyzed with Spatial Statistics Methods in a Central European City  Daniel Lederer - KFV (Austrian Road Safety Board), Research and Knowledge Management
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Am I Safe in My Home? Fear of Crime Analyzed with Spatial Statistics Methods in a Central European City Daniel Lederer - KFV (Austrian Road Safety Board), Research and Knowledge Management

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geog-an-mod 12 - iccsa 2012

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    Am I Safe in My Home? Fear of Crime Analyzed with Spatial Statistics Methods in a Central European City  Daniel Lederer - KFV (Austrian Road Safety Board), Research and Knowledge Management Am I Safe in My Home? Fear of Crime Analyzed with Spatial Statistics Methods in a Central European City Daniel Lederer - KFV (Austrian Road Safety Board), Research and Knowledge Management Presentation Transcript

    • Am I Safe in My Home? Fear of CrimeAnalyzed with Spatial Statistics Methodsin a Central European CityDaniel Lederer | 19.6.2012 | ICCSA 2012, Salvador de Bahia, Brazil
    • Presentation Overview• Introduction• Methods and Techniques Used• Analysis and Results • Fear of Residential Burglary • Vulnerability to Residential Burglary• Conclusion and Future Research19.06.2012 Urban Crime Analysis and Mapping 2
    • Main Project: Urban Crime Analysis and Mapping citizen‘s personal police-reported perception of crime crime comprehensive report on the urban crime situation19.06.2012 Urban Crime Analysis and Mapping 3
    • IntroductionResearch Questions of the Present Study:• Are there differences in the level of fear of becoming a victim of a residential burglary between the districts in the city?• Within the city, are there certain areas with a lack of technical safety measures, which may lead to an increased vulnerability to burglary?19.06.2012 Urban Crime Analysis and Mapping 4
    • Methods and Techniques UsedQuantitative Survey in a Central European City• Computer Assisted Telephone Interviews of 1,505 randomly selected citizens• respondents were asked about different topics to personal security• special selection in the present study: • fear of residential burglary • anti-victimizations strategies to protect personal property19.06.2012 Urban Crime Analysis and Mapping 5
    • Methods and Techniques UsedQuantitative Survey in a Central European City• dataset includes two important characteristics for spatial analysis: 1. 35 inhabitants were selected in a disproportional stratified random sampling from every district 2. the use of personal addresses• important for measuring local differences in personal security19.06.2012 Urban Crime Analysis and Mapping 6
    • Methods and Techniques UsedSpatial-based Information is Available on 2 Levels:• level of polygon data (districts)• level of point data (addresses)Advantages:• possibility to analyze the data with different spatial statistics methods• reduces certain sources of errors (e.g. Modifiable Areal Unit Problem)19.06.2012 Urban Crime Analysis and Mapping 7
    • Methods and Techniques UsedDescriptive and Exploratory Spatial Data Analysis:• Spatial Autocorrelation• Kernel Density Estimation (KDE)• Nearest Neighbor Hierarchical Clustering (NNHC)19.06.2012 Urban Crime Analysis and Mapping 8
    • Methods and Techniques UsedSpatial Autocorrelation• Global Moran’s I • useful to understand general spatial patterns • measures the deviation from spatial randomness by comparing the value at any one location with the value at all other locations • Moran’s I statistic varies from -1 to +1• Local Indicator of Spatial Association (LISA) • useful to identify statistically significant local spatial clusters • e.g. hot or cold spots • compares local averages to global averages and assesses the association of certain events19.06.2012 Urban Crime Analysis and Mapping 9
    • Methods and Techniques UsedKernel Density Estimation (KDE)• interpolation method, which creates a smooth surface of the point data with a variation in the density of enclosed points• areas with a high quantity of points result in a high density• based on two parameters: • grid cell size • bandwidth (search radius)19.06.2012 Urban Crime Analysis and Mapping 10
    • Methods and Techniques UsedNearest Neighbor Hierarchical Clustering (NNHC)• grouping spatially close points into hierarchical clusters• depends on the Nearest Neighbor Index test, which compares the distances between the points of the actual distribution against a random distributed data set of the same sample size• depending on two parameters: • threshold distance • minimum number of points for each cluster19.06.2012 Urban Crime Analysis and Mapping 11
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    • Conclusion• spatial analysis methods help to better understand special topics in fear of crime in the selected European city• by using different aggregation levels and techniques in clustering spatial data, a large amount of complex information could be compressed in thematic maps• identifying of important clusters: • fear-of-residential-burglary hot spot in the Westside • vulnerability-to-residential-burglary hot spot in downtown • combination of hot spots matches in the Westside an overlapping result19.06.2012 Urban Crime Analysis and Mapping 16
    • Future Research• Why are the hot spots located in these specific areas?• enlarging the spatial analysis by using confirmatory spatial statistical methods• investigating links between fear of crime, vulnerability to crime and the actual occurrence of crime19.06.2012 Urban Crime Analysis and Mapping 17
    • THANK YOU FOR YOUR ATTENTION!KFV (Austrian Road Safety Board)Mag. Daniel LedererResearch & Knowledge ManagementAustria | 1100 Vienna | Schleiergasse 18Tel: +43-(0)5 77 0 77-1405 | Fax: +43-(0)5 77 0 77-1186E-Mail: kfv@kfv.at | www.kvf.at
    • • Anselin, L.: Local Indicators of Spatial Association-LISA. Geographical Analysis, vol. 27 (2), pp. 93—115 (1995)• Anselin, L., Cohen, J., Cook, D., Gorr, W., Tita, G.: Spatial Analyses of Crime. Criminal Justice 2000, vol. 4, pp. 213—262 (2000)• Bailey, T.C., Gatrell, A.C.: Interactive Spatial Data Analysis. Longman (1995)• Eck, J., Chainey, S.P., Cameron, J., Leitner, M., Wilson, R. (eds.): Mapping Crime: Understanding Hotspots. National Institute of Justice, Washington DC (2005)• 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)• 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. http://www.icpsr.umich.edu/CrimeStat/download.html (2004)• Openshaw, S.: The Modifiable Areal Unit Problem. Concepts and Techniques in Modern Geography, vol. 38 (1984) 19.06.2012 Urban Crime Analysis and Mapping 19