Decoding urban landscapes: Google street view and measurement sensitivity |
| |
| Affiliation: | 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 Architecture and Civil Engineering, City University of Hong Kong, Hong Kong Special Administrative Region;2. City University of Hong Kong Shenzhen Research Institute, Shenzhen 518057, China;5. Institute of Geography, School of GeoSciences, University of Edinburgh, Edinburgh, UK;1. School of Architecture, The Chinese University of Hong Kong, Shatin NT, Hong Kong;2. Department of Architecture, Massachusetts Institute of Technology, Cambridge, MA, USA;3. Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA;4. Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin NT, Hong Kong;5. Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA;6. Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Shatin NT, Hong Kong;7. Institute of Future Cities, The Chinese University of Hong Kong, Shatin NT, Hong Kong;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;1. Department of Architecture, National University of Singapore, Singapore;2. Department of Real Estate, National University of Singapore, Singapore |
| |
| Abstract: | While Google Street View (GSV) has been increasingly available for large-scale examinations of urban landscapes, little is known about how to use this promising data source more cautiously and effectively. Using data for Santa Ana, California, as an example, this study provides an empirical assessment of the sensitivity of GSV-based streetscape measures and their variation patterns. The results show that the measurement outcomes can vary substantially with changes in GSV acquisition parameter settings, specifically spacing and direction. The sensitivity is found to be particularly high for some measurement targets, including humans, objects, and sidewalks. Some of these elements, such as buildings and sidewalks, also show highly correlated patterns of variation indicating their covariance in the mosaic of urban space. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
