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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Bibliographische Detailangaben
1. VerfasserIn: Mohler, George (VerfasserIn)
Beteiligte: Brantingham, P. Jeffrey 1970- ; Carter, Jeremy G. 1982- ; Short, Martin B.
Medienart: Elektronisch Aufsatz
Sprache:Englisch
Veröffentlicht: 2019
In: Journal of quantitative criminology
Jahr: 2019, Band: 35, Heft: 4, Seiten: 747-765
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Schlagwörter:
Poisson process
Negative binomial
Crime concentration
Crime hotspot
Gini index
CrimCrime concentration
Beschreibung
Zusammenfassung: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.
ISSN:1573-7799
DOI:10.1007/s10940-019-09404-1