资源环境科技发展态势分析平台

Data describing how individuals use their urban environment is a valuable source of information in urban planning. In many cases, data used for these purposes have low spatial and temporal resolution, or sample size. Equally, comprehensive analytical approaches suitable for these data may be lacking. We present a statistical method borrowed from wildlife ecology and management called a resource selection function (RSF). We apply it to answer questions relating to the selection of urban green space by university students, using a dataset consisting of smartphone GPS location data volunteered by participants. We ask questions relating to urban greenspace selection by comparing used locations to a set of random locations at multiple spatial extents. We found that participants altered their selection of areas according to the surrounding recreational trail density and whether those areas were classified as green space. These relationships were also influenced by season. Our study also demonstrates how the design of an urban RSF can offer different insights by varying the extent of the domain: (1) to an individual's core area; or (2) by excluding from the domain areas that are physically unavailable. We emphasize the importance of matching availability to the research question and conclude by reviewing the opportunities presented by using FSFs combined with GPS location data in an urban context. We argue that RSFs have utility beyond wildlife ecology and management, and, given the increasing availability of smartphone GPS data, can successfully be applied to determine the use and selection of spaces by urban residents.