Welcome to the InfoSci Platform
Reference Hub3
Geographic Analysis of Domestic Violence Incident Locations and Neighborhood Level Influences

Geographic Analysis of Domestic Violence Incident Locations and Neighborhood Level Influences

Rick L. Bunch, Christine Murray, Xiaoli Gao, Eleazer D. Hunt
Copyright: © 2018 |Volume: 9 |Issue: 2 |Pages: 19
ISSN: 1947-9654|EISSN: 1947-9662|EISBN13: 9781522544494|DOI: 10.4018/IJAGR.2018040102
Cite Article Cite Article

MLA

Bunch, Rick L., et al. "Geographic Analysis of Domestic Violence Incident Locations and Neighborhood Level Influences." IJAGR vol.9, no.2 2018: pp.1-19. http://doi.org/10.4018/IJAGR.2018040102

APA

Bunch, R. L., Murray, C., Gao, X., & Hunt, E. D. (2018). Geographic Analysis of Domestic Violence Incident Locations and Neighborhood Level Influences. International Journal of Applied Geospatial Research (IJAGR), 9(2), 1-19. http://doi.org/10.4018/IJAGR.2018040102

Chicago

Bunch, Rick L., et al. "Geographic Analysis of Domestic Violence Incident Locations and Neighborhood Level Influences," International Journal of Applied Geospatial Research (IJAGR) 9, no.2: 1-19. http://doi.org/10.4018/IJAGR.2018040102

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Domestic violence is an important public health issue, and there is limited research to date that examines community-level influences on this serious form of violence. This article investigates the neighborhood characteristics of domestic violence incidents in the city of Greensboro, North Carolina. US Census block group boundaries and corresponding tables were used as proxies for neighborhoods. The article addresses an important gap in domestic violence research by combining geographic and statistical analyses at the block group level. Geographic data were analyzed using an Optimized Hot Spot Analysis (OHA) along with features selected by penalized Poisson regression model. The OHA was used to identify spatial clusters of high and low values while the penalized Poisson regression model was used to select the important variables from over 7000 candidates. The results of high-dimensional analysis produced six categories and 20 variables that were used to examine the characteristics of spatial clusters.