Spatial distributions and use patterns of user groups in urban forest parks: An examination utilizing GPS tracker

Abstract

Understanding the spatial distributions of visitors is critical for urban forest park planning and management. Utilizing GPS trackers and a survey, this study focuses on two large urban forest parks in Shanghai to identify the main user groups and explore their use patterns, spatial distribution patterns, and preferred park settings. Valid data of 304 users’ movement and spatial location were collected using GPS trackers with a 10-second interval. The results indicate that Chinese users usually visit urban forest parks in small groups with families and friends. The three major user groups are those come to play with children, have contact with nature and relax, and get together with families and friends. The average nearest neighbor and standard deviational ellipse analyses indicate that users who come to have contact with nature and relax engage in more vigorous activities, are spatially more dispersed and explore larger areas of forest parks. Logistic regression and hotspot analyses suggest that users who come to play with children and get together with families and friends prefer to stay in areas that can accommodate group activities, such as amusement areas, lawns, and barbeque sites. Points of interest in the park tend to attract users who want to have contact with nature and relax. Findings of the study provides empirical evidence on the associations between users’ motivations and their spatial distribution patterns in urban forest park, which has direct implications to park planning and design. In practice, designers may provide preferred park settings and spacious activity zones to accommodate group activities, and disperse park features preferred by different user groups to avoid potential conflicts between visitors with social-oriented and nature-oriented motivations. The study is unique in that it proposes an objective approach to analyze users’ movement in nature recreation area using GPS data and spatial statistics techniques.