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Data mining time series with applications to crime analysis

Authors: Donald E. Brown; Rosemary B. Oxford;

Data mining time series with applications to crime analysis

Abstract

This paper is a study of methods of predicting the number of breaking and enterings (B&Es) in subcity regions of Richmond, Virginia. In this study, predictions are made for B&Es in each of four precincts as well as in regions measuring approximately 0.64 square miles. These predictions can be helpful to police efforts by helping them more effectively allocate resources. The paper includes investigation into the distribution of incidents of breaking and entering, which concludes that B&Es are not Poisson distributed. Furthermore, in the analysis of the data, incidents of B&Es also do not show evidence of seasonal patterns. The research investigates factors that many believe are related to crime, such as unemployment rates, previous incidents of crimes, and alcohol sales.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Average
Top 10%
Average
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