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

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 no research in the UK that has focused on neighbourhood level predictors of domestic abuse and their variation across space. This paper uses Geographically Weighted Regression (GWR) 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 victimisation rate at the Lower Super Output Area (LSOA) 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’ wellbeing, but it also enables policy makers to gain a better understanding of the geography of victimisation, allowing targeted policy interventions and efficiently allocated resources