Measuring visual enclosure for street walkability: Using machine learning algorithms and Google Street View imagery |
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| Institution: | 1. Department of Urban and Regional Planning, University at Buffalo, The State University of New York, Buffalo, NY 14214, USA;2. Center for Human-Engaged Computing, Kochi University of Technology, 185 Miyanokuchi, Tosayamada-Cho, Kami-Shi, Kochi 782-8502, Japan;1. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong;2. Hong Kong Observatory, Hong Kong;1. Department of Geography, University of Connecticut, Storrs, CT 06269, USA;2. Department of Extension, University of Connecticut, West Hartford, CT 06117-2600, USA;3. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;1. Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, MI, USA;2. Environmental Science and Policy Program, Michigan State University, East Lansing, MI, USA;3. Department of Public Health, University of Otago, Wellington, New Zealand;1. Department of Urban and Regional Planning, University at Buffalo, The State University of New York, Buffalo, NY 14214, USA;2. Department of Electronics and Information Engineering, Huazhong University of Science & Technology, Wuhan 430074, China;3. Center for Human-Engaged Computing, Kochi University of Technology, 185 Miyanokuchi, Tosayamada-Cho, Kami-Shi, Kochi 782-8502, Japan;4. The State Key Laboratory of Information Engineering on Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China |
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| Abstract: | One major limitation currently with studying street level urban design qualities for walkability is the often inconsistent and unreliable measures of streetscape features across different field surveyors even with costly training due to lack of more objective processes, which also make large scale study difficult. The recent advances in sensor technologies and digitization have produced a wealth of data to help research activities by facilitating improved measurements and conducting large scale analysis. This paper explores the potential of big data and big data analytics in the light of current approaches to measuring streetscape features. By applying machine learning algorithms on Google Street View imagery, we generated objectively three measures on visual enclosure. The results showed that sky areas were identified fairly well for the calculation of proportion of sky. The three visual enclosure measures were found to be correlated with pedestrian volume and Walk Score. This method allows large scale and consistent objective measures of visual enclosure that can be done reproducibly and universally applicable with readily available Google Street View imagery in many countries around the world to help test their association with walking behaviors. |
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| Keywords: | Street design features Enclosure Walkability Machine learning |
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