Data mining time series with applications to crime analysis

Author

Brown, Donald E. ; Oxford, Rosemary B.

Author_Institution

Dept. of Syst. & Inf. Eng., Virginia Univ., Charlottesville, VA, USA

Volume

3

fYear

2001

fDate

2001

Firstpage

1453

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

Keywords

data mining; police data processing; time series; Richmond, Virginia; alcohol sales; breaking and entering; crime; crime analysis; data mining; police; previous incidents; subcity regions; time series; unemployment rates; Cities and towns; Data analysis; Data mining; Pattern analysis; Regression analysis; Resource management; Systems engineering and theory; Time series analysis; Unemployment; Windows;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 2001 IEEE International Conference on

Conference_Location

Tucson, AZ

ISSN

1062-922X

Print_ISBN

0-7803-7087-2

Type

conf

DOI

10.1109/ICSMC.2001.973487

Filename

973487