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Using aggregated mobile phone location data to compare the realised foodscapes of different socio-economic groups.
Institution:1. School of Social Sciences, The University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia;2. School of Population and Global Health, The University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia;3. Business School, The University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia;1. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA;2. Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA;3. School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA;4. Department of Public Policy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA;5. Department of Biological Sciences, Center for Human Health and the Environment, North Carolina State University, Raleigh, NC, USA;6. Department of Geography, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA;1. Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan;2. Graduate School of Medicine, Kyoto University, Yoshida-konoe-cho, Sakyo-ku, Kyoto-shi, Kyoto, 606-8315, Japan;3. Faculty of Health and Sport Sciences, University of Tsukuba, 3-29-1 Otsuka, Bunkyo-ku, Tokyo, 112-0012, Japan;4. Center for Preventive Medicine Sciences, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba-shi, Chiba-ken, 260-0856, Japan;5. Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-cho, Obu-shi, Aichi-ken, 474-8511, Japan;1. Institute of Health Equity and Social Justice Research, Northeastern University, 360 Huntington Ave., Boston, MA, 02115, USA;2. Department of Health Sciences, Northeastern University, 360 Huntington Ave., Boston, MA, 02115, USA;3. Department of Sociology and Anthropology, Northeastern University, 360 Huntington Ave., Boston, MA, 02115, USA;1. Center for Advancing Population Science (CAPS), Medical College of Wisconsin, Milwaukee, WI, USA;2. Division of General Internal Medicine, Department of Medicine, Froedtert & the Medical College of Wisconsin, Milwaukee, WI, USA;3. Institute of Health and Equity, Department of Public and Community Health, The Medical College of Wisconsin, Milwaukee, WI, USA;1. Department of Geography, McGill University, 705-805 Sherbrooke Street West, Montreal, Quebec, H3A 0B9, Canada;2. Dalla Lana School of Public Health, University of Toronto, Health Sciences Building 155 College Street, 6th Floor Toronto, ON M5T 3M7, Canada;3. Statistics Canada, Health Analysis Division, 100 Tunney''s Pasture Driveway, Ottawa, Ontario, K1A 0T6, Canada;4. Divisions of Internal Medicine, Clinical Epidemiology and Endocrinology and Metabolism, McGill University Health Centre, 1001 Decarie Boulevard, D02.3312, Montreal, Quebec, H4A 3J1, Canada;5. Department of Public Health Sciences, School of Medicine, Queen''s University, Carruthers Hall, 62 Fifth Field Company Lane, Kingston, ON, K7L 3N6, Canada;1. Center for Public Health Systems Science, Brown School at Washington University in St. Louis, 1 Brookings Drive, MSC 1196-0251-46, St. Louis, MO, 63130, USA;2. School of Public and International Affairs, The University of Georgia, 180 Baldwin Hall, Athens, GA, 30602, USA;3. Department of Health Behavior, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599-7440, USA;4. Lineberger Comprehensive Cancer Center, University of North Carolina, 101 Manning Drive, Chapel Hill, NC, 27599-7295, USA;5. Stanford Prevention Research Center, Stanford University School of Medicine, 3300 Hillview Ave, Mail Code 5537, Palo Alto, CA, 94304-1334, USA
Abstract:The foodscape (the built food environment) is considered one of the driving factors of the higher burden of obesity and chronic disease observed in low socio-economic status (SES) groups. Traditional data collection methods struggle to accurately capture actual access and exposure to the foodscape (realised foodscape). We assess the use of anonymised mobile phone location data (location data) in foodscape studies by applying them to a case study in Perth, Western Australia to test the hypothesis that lower SES groups have poorer realised foodscapes than high SES groups. Kernel density estimation was used to calculate realised foodscapes of different SES groups and home foodscape typologies, which were compared to home foodscapes of the different groups. The location data enabled us to measure realised foodscapes of multiple groups over an extended period and at the city scale. Low SES groups had poor availability of food outlets, including unhealthy outlets, in their home and realised foodscapes and may be more susceptible to a poor home foodscape because of low mobility.
Keywords:Mobile phone location data  Food environment  Activity spaces  Kernel density estimation  Daily mobility  Socio-economic status (SES)
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