Understanding neighbourhood level predictors of domestic abuse and their variation over space

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 neighbourhood-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 victimisation 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 victimisation, allowing targeted policy interventions and efficiently allocated resources.

For further information and the article, please see Using geographically weighted regression to explore neighborhood‐level predictors of domestic abuse in the UK – Weir – 2019 – Transactions in GIS – Wiley Online Library

Photo caption: Andrii Yalanskyi /

Patterns and Predictors of Stranger Rape Locations

This paper examines the spatial, environmental, and temporal patterns of 10,488 stranger rapes committed over a 15-year period in Greater London, UK.

We distinguished between two types of stranger rapes according to perpetrator method of approach, i.e. absent/fleeting interaction with victim on approach (S1) or extended interaction with victim on approach (S2). There were a range of locational settings in which perpetrators both encountered their victims and where the offence took place, and these differed by method of approach. The highest number of S1 offences occurred outdoors, with 74% of approaches and 55% of offences located recorded as outside. For S2 rapes, there was more variety in approach locations with only 32% outside. The level of locational correspondence between approach and offence location was 71% for S1 rapes and 28% for S2 rapes. A series of negative binomial regression models identified variables predictive of stranger rape offence location. There were significant associations with transport connections and the night-time economy for both S1 and S2 rapes. Other significant predictors were deprivation score, the percentage of one person properties, and the percentage of private rented properties in a location. The percentage of green space was a significant predictor for S1 rapes only. The current findings challenge the popular narrative that stranger rape occurs in a specific setting (i.e. outside in a secluded location at night) and have implications for place-based crime prevention policy.

…these findings challenge the popular narrative that stranger rape occurs in a specific setting…