Author ORCID Identifier
https://orcid.org/0000-0001-5701-3708
https://orcid.org/0000-0003-3224-4643
N/A
N/A
Document Type
Article
Publication Date
2014
College/Unit
Eberly College of Arts and Sciences
Department/Program/Center
Sociology and Anthropology
Abstract
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.
Digital Commons Citation
Zhao, Peixin; Darrah, Majorie; Nolan, Jim; and Zhang, Cun-Quan, "Analyses of Crime Patterns in NIBRS Data Based on a Novel Graph Theory Clustering Method: Virginia as a Case Study" (2014). Faculty & Staff Scholarship. 2593.
https://researchrepository.wvu.edu/faculty_publications/2593
Source Citation
Zhao, P., Darrah, M., Nolan, J., & Zhang, C.-Q. (2014). Analyses of Crime Patterns in NIBRS Data Based on a Novel Graph Theory Clustering Method: Virginia as a Case Study. The Scientific World Journal, 2014, 1–8. https://doi.org/10.1155/2014/492461
Comments
Copyright © 2014 Peixin Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.