Analyses of crime patterns in NIBRS data based on a novel graph theory clustering method: Virginia as a case study.
This paper suggests a novel clustering method for analyzing the National Incident-Based Reporting System (NIBRS) data, which include the determination of correlation of different crime types, the development of a likelihood index for crimes to occur in a jurisdiction, and the clustering of jurisdictions based on crime type. The method was tested by using the 2005 assault data from 121 jurisdictions in Virginia as a test case. The analyses of these data show that some different crime types are correlated and some different crime parameters are correlated with different crime types. The analyses also show that certain jurisdictions within Virginia share certain crime patterns. This information assists with constructing a pattern for a specific crime type and can be used to determine whether a jurisdiction may be more likely to see this type of crime occur in their area.
School of Management, Shandong University, Jinan, Shandong, China.
Peixin
0000-0002-1107-6882
P
Zhao
Department of Mathematics, West Virginia University, Morgantown, WV, USA.
Marjorie
0000-0003-3224-4643
M
Darrah
Department of Sociology and Anthropology, West Virginia University, Morgantown, WV, USA.
Jim
J
Nolan
Department of Mathematics, West Virginia University, Morgantown, WV, USA.
Cun-Quan
0000-0001-5583-4481
CQ
Zhang
D016000:Cluster Analysis; D003415:Crime / Q000331:legislation & jurisprudence / Q000706:statistics & numerical data*; D008962:Models, Theoretical; D014768:Virginia