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Using geographically weighted regression to explore neighborhood-level predictors of domestic abuse in the UK

Weir, R. ORCID: 0000-0002-5554-801X (2019). Using geographically weighted regression to explore neighborhood-level predictors of domestic abuse in the UK. Transactions in Geographic Information Systems (GIS), 23(6), pp. 1232-1250. doi: 10.1111/tgis.12570

Abstract

Reducing domestic abuse has become a priority for both local and national governments in the UK, with its substantial human, social, and economic costs. It is an interdisciplinary issue, but to date there has been no research in the UK that has focused on neighborhood-level predictors of domestic abuse and their variation across space. This article uses geographically weighted regression to model the predictors of police-reported domestic abuse in Essex. Readily available structural and cultural variables were found to predict the domestic abuse rate and the repeat victimization rate at the lower super output area level and the model coefficients were all found to be non-stationary, indicating varying relationships across space. This research not only has important implications for victims' well being, but also enables policy makers to gain a better understanding of the geography of victimization, allowing targeted policy interventions and efficiently allocated resources.

Publication Type: Article
Additional Information: © 2019 John Wiley & Sons Ltd. This is the peer reviewed version of the following article: Weir, R. (2019). Using geographically weighted regression to explore neighborhood-level predictors of domestic abuse in the UK. Transactions in Geographic Information Systems (GIS), 23(6), pp. 1232-1250, which has been published in final form at https://doi.org/10.1111/tgis.12570. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
Departments: City, University of London (-2022) > School of Arts & Social Sciences
School of Arts & Social Sciences
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