RT Article
T1 Reducing Bias in Estimates for the Law of Crime Concentration
JF Journal of quantitative criminology
VO 35
IS 4
SP 747
OP 765
A1 Mohler, George
A2 Brantingham, P. Jeffrey 1970-
A2 Carter, Jeremy G. 1982-
A2 Short, Martin B.
LA English
YR 2019
UL https://krimdok.uni-tuebingen.de/Record/1691742805
AB 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.
K1 Poisson process
K1 Negative binomial
K1 Crime concentration
K1 Crime hotspot
K1 Gini index
K1 CrimCrime concentration
DO 10.1007/s10940-019-09404-1