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

TL;DR

This study uses anonymized mobile phone location data to estimate neighborhood alcohol outlet visits and demonstrates that these visits improve the prediction of domestic violence hotspots, aiding targeted interventions.

Contribution

It introduces a novel method to derive neighborhood alcohol outlet visits from mobile data and shows their effectiveness in enhancing domestic violence prediction models.

Findings

Derived alcohol outlet visits improve DV neighborhood prediction accuracy.

Mobile data-based visits outperform traditional alcohol use proxies.

Results support policy use in DV prevention and alcohol licensing.

Abstract

Domestic violence (DV) is a serious public health issue, with 1 in 3 women and 1 in 4 men experiencing some form of partner-related violence every year. Existing research has shown a strong association between alcohol use and DV at the individual level. Accordingly, alcohol use could also be a predictor for DV at the neighborhood level, helping identify the neighborhoods where DV is more likely to happen. However, it is difficult and costly to collect data that can represent neighborhood-level alcohol use especially for a large geographic area. In this study, we propose to derive information about the alcohol outlet visits of the residents of different neighborhoods from anonymized mobile phone location data, and investigate whether the derived visits can help better predict DV at the neighborhood level. We use mobile phone data from the company SafeGraph, which is freely available to researchers and which contains information about how people visit various points-of-interest including alcohol outlets. In such data, a visit to an alcohol outlet is identified based on the GPS point location of the mobile phone and the building footprint (a polygon) of the alcohol outlet. We present our method for deriving neighborhood-level alcohol outlet visits, and experiment with four different statistical and machine learning models to investigate the role of the derived visits in enhancing DV prediction based on an empirical dataset about DV in Chicago. Our results reveal the effectiveness of the derived alcohol outlets visits in helping identify neighborhoods that are more likely to suffer from DV, and can inform policies related to DV intervention and alcohol outlet licensing.