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

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.

The role of alcohol outlet visits derived from mobile phone location data in enhancing domestic violence prediction at the neighborhood level

“…They measured the concentrated disadvantage by a combination of variables, such as the proportion of households below poverty level, households on public assistance, and female-headed households with kids below age 18; they measured residential instability based on the proportion of renter-occupied housing units. Weir (2019) found that neighborhood-level income index, antisocial behaviors rate, the proportion of Black, Asian, and minority ethnic population, and population density are predictive of DV at the neighborhood level. The socioeconomic and demographic variables used in neighborhood-level studies are typically obtained from census surveys.…”

The Spatial Heterogeneity of Factors of Drug Dealing: A Case Study from ZG, China

“…Once the local parameters are obtained, they can be mapped and their spatial patterns could be explored. Therefore, we plotted the coefficients of each explanatory variable to exam its spatial pattern, and explore its possible causes, as many previous studies using GWPR models have done [2,22]. The application of GWPR to assessing drug dealings demonstrated the spatial variation of regression coefficients between the dependent variable and independent variables across PSMA areas in ZG city (Figure 4).…”

“…For example, 450,000 people died directly or indirectly from drug abuse in 2015 [1]. In addition, the economic cost of drug abuse is also high, for example, the annual expenditure for controlling drug abuse in the UK is as high as £15.7 billion [2]. Illegal drugs can also lead to other types of crimes, such as violence, general incivilities, and property crime [3,4].…”

“…The main purpose of this research is to apply GWPR modeling in assessing the spatial pattern of drug dealing in a large Chinese city. Specifically, this study seeks to answer two research questions: (1) whether the relationship between drug dealing and its impact factors will change over space; (2) whether GWPR model is better than GLM models in analyzing drug dealing crimes. Such knowledgeable gain is not only conductive to academic progress, but also allows law enforcement departments to understand the geographical demand for their services, and then intervene to reduce the negative impact of drug dealing.…”

Exploring the Effects of Contextual Factors on Residential Land Prices Using an Extended Geographically and Temporally Weighted Regression Model

“…In addition to building structural and locational requirements, contextual attributes, obviously, also affect changes in residential land prices. For instance, two schools are near each other, but one has better educational facilities and resources; the selling prices of houses near these schools would be influenced by the neighborhood-level attribute space [33][34][35][36]. Rich Harris [30] proposed a contextualized geographically weighted regression (CGWR) to integrate attribute correlations between neighborhood-level observations and found that it was significant in a real estate context, but temporal information was ignored.…”