  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.
