The role of alcohol outlet visits derived from mobile phone location data in enhancing domestic violence prediction at the neighborhood level |
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| Institution: | 1. Department of Geography, University at Buffalo, The State University of New York, USA;2. Department of Mathematics, University at Buffalo, The State University of New York, USA;3. Department of Medicine, University at Buffalo, The State University of New York, USA;1. Fernandes Figueira Institute, Oswaldo Cruz Foundation, Fiocruz, Rio de Janeiro, Brazil;2. Department of Human Geography, University of Toronto Scarborough, Toronto, Canada;1. School of Global Health Management and Informatics, University of Central Florida, Orlando, FL, USA;2. School of Public Administration, University of Central Florida, Orlando, FL, USA;3. Doctoral Program in Public Affairs, University of Central Florida, Orlando, FL, USA;1. Department of Sociology and Criminology, Pennsylvania State University, University Park, PA, USA;2. Population Research Institute, Pennsylvania State University, University Park, PA, USA;3. College of Information Science and Technology, Pennsylvania State University, University Park, PA, USA;4. Department of Statistics, Pennsylvania State University, University Park, PA, USA;5. Center for Big Data Analytics and Discovery Informatics, Pennsylvania State University, University Park, PA, USA;6. Institute for Computational and Data Sciences, Pennsylvania State University, University Park, PA, USA;1. College of Architecture and Urban Planning, Fujian University of Technology, Fuzhou, Fujian Province, China;2. Department of Geography, University at Buffalo, Buffalo, NY, USA;3. Department of Political Science, University at Buffalo, Buffalo, NY, USA;4. Hansjorg Wyss Department of Plastic Surgery, NYU Grossman School of Medicine, New York, NY, USA |
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| 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. |
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| Keywords: | Domestic violence Alcohol outlet visit Mobile phone location data Points of interest Machine learning |
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