Reducing Bias in Estimates for the Law of Crime Concentration
The law of crime concentration states that half of the cumulative crime in a city will occur within approximately 4% of the city's geography. The law is demonstrated by counting the number of incidents in each of N spatial areas (street segments or grid cells) and then computing a parameter bas...
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| Contributors: | ; ; |
| Format: | Electronic Article |
| Language: | English |
| Published: |
2019
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| In: |
Journal of quantitative criminology
Year: 2019, Volume: 35, Issue: 4, Pages: 747-765 |
| Online Access: |
Presumably Free Access Volltext (Resolving-System) |
| Journals Online & Print: | Drawer...
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| Check availability: | HBZ Gateway |
| Keywords: |
MARC
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| 520 | |a The law of crime concentration states that half of the cumulative crime in a city will occur within approximately 4% of the city's geography. The law is demonstrated by counting the number of incidents in each of N spatial areas (street segments or grid cells) and then computing a parameter based on the counts, such as a point estimate on the Lorenz curve or the Gini index. Here we show that estimators commonly used in the literature for these statistics are biased when the number of incidents is low (several thousand or less). Our objective is to significantly reduce bias in estimators for the law of crime concentration. | ||
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