The document describes a study that used spatial statistics methods to analyze fear of crime and vulnerability to residential burglary in a central European city. The study used survey data from 1,505 citizens about their fear of burglary and security measures in their homes. Spatial analysis techniques like kernel density estimation and nearest neighbor clustering were used to identify "hot spots" of high fear and vulnerability, finding a fear hot spot in the Westside and a vulnerability hot spot downtown. The analysis helped understand patterns of crime risk across the city.

1. Am I Safe in My Home? Fear of Crime
Analyzed with Spatial Statistics Methods
in a Central European City
Daniel Lederer | 19.6.2012 | ICCSA 2012, Salvador de Bahia, Brazil

2. Presentation Overview
• Introduction
• Methods and Techniques Used
• Analysis and Results
• Fear of Residential Burglary
• Vulnerability to Residential Burglary
• Conclusion and Future Research
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3. Main Project: Urban Crime Analysis and Mapping
citizen‘s personal
police-reported
perception of
crime
crime
comprehensive report
on the urban crime
situation
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4. Introduction
Research 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?
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5. Methods and Techniques Used
Quantitative 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 property
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6. Methods and Techniques Used
Quantitative 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 security
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7. Methods and Techniques Used
Spatial-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)
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8. Methods and Techniques Used
Descriptive and Exploratory Spatial Data Analysis:
• Spatial Autocorrelation
• Kernel Density Estimation (KDE)
• Nearest Neighbor Hierarchical Clustering (NNHC)
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9. Methods and Techniques Used
Spatial 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 events
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10. Methods and Techniques Used
Kernel 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)
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11. Methods and Techniques Used
Nearest 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 cluster
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16. 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 result
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17. 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 crime
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18. THANK YOU FOR YOUR ATTENTION!
KFV (Austrian Road Safety Board)
Mag. Daniel Lederer
Research & Knowledge Management
Austria | 1100 Vienna | Schleiergasse 18
Tel: +43-(0)5 77 0 77-1405 | Fax: +43-(0)5 77 0 77-1186
E-Mail: kfv@kfv.at | www.kvf.at

19. • 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)
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