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Using geographically weighted regression to explore neighborhood‐level predictors of domestic abuse in the UK
Author(s) -
Weir Ruth
Publication year - 2019
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/tgis.12570
Subject(s) - geographically weighted regression , psychological intervention , domestic violence , geography , regression analysis , regression , space (punctuation) , public economics , demographic economics , econometrics , psychology , environmental health , economics , statistics , medicine , human factors and ergonomics , computer science , poison control , mathematics , psychiatry , psychoanalysis , operating system
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.