{"id":1264,"date":"2021-06-11T15:04:20","date_gmt":"2021-06-11T15:04:20","guid":{"rendered":"https:\/\/bwali.org\/?page_id=1264"},"modified":"2021-07-07T04:04:44","modified_gmt":"2021-07-07T04:04:44","slug":"tracking-environment-health-behaviours","status":"publish","type":"page","link":"https:\/\/bwali.org\/index.php\/projects\/tracking-environment-health-behaviours\/","title":{"rendered":"Tracking Environment & Health Behaviors"},"content":{"rendered":"\t\t
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Tracking Environment & Health Behaviors<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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A novel evidence-based methodology is presented for determining place-based thresholds of objectively measured built environment features\u2019 relationships with active travel. Using an innovative machine-learning based Generalized Additive Modeling framework, systematic heterogeneity fundamental to the development of well-justified and objective environmental thresholds is accounted for. The methodology is employed to model an individual\u2019s likelihood of transport walking as a function of environmental factors using California Household Travel Survey linked with comprehensive built environment data.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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The results reveal strong and complex non-linear dependencies of likelihood of transport walking on environmental features that cannot be quantified using standard threshold detection methods. Thresholds for key environmental features to enhance active travel vary significantly across different socioeconomic groups. Accounting for strong income-based differences in development of environmental benchmarks is emphasized. The thresholds can serve as a useful guiding tool for policymakers, planners, engineers, and public health officials to track existing environmental conditions and healthy behaviors.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Major Contributors:
Lawrence Frank, <\/strong>Ph.D. Professor, Urban Studies & Planning, UC San Diego.\u00a0
Jim Chapman<\/strong>, MSCE. Managing Principal, Urban Design 4 Health.\u00a0
Eric Fox<\/strong>, MScP, Senior Planner, Urban Design 4 Health.\u00a0<\/p>

Publications:
<\/strong>Wali, B., Frank, L. D., Chapman, J. E., & Fox, E. H. Developing policy thresholds for objectively measured environmental features to support active travel.\u00a0Transportation Research Part D: Transport and Environment<\/em>,\u00a090<\/em>, 102678. PDF<\/a><\/strong><\/p>

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The authors would like to acknowledge the Robert Wood Johnson Foundation (RWJF), especially Dr. Tracy Orleans, Senior Program Officer and Carolyn Miller, Senior Program Officer for supporting this study. This research was conducted by Urban Design 4 Health, Inc. (UD4H) as part of research funded by RWJF (Grant ID 74518) — National Environmental Database \u2013 Walk Index, Benchmarks & Investments.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Tracking Environment & Health Behaviors A novel evidence-based methodology is presented for determining place-based thresholds of objectively measured built environment features\u2019 relationships with active travel. Using an innovative machine-learning based Generalized Additive Modeling framework, systematic heterogeneity fundamental to the development of well-justified and objective environmental thresholds is accounted for. The methodology is employed to model …<\/p>\n

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