Spatiotemporal Study of Park Sentiments at Metropolitan Scale Using Multiple Social Media Data

Abstract

Creating wonderful emotional experiences is the critical social function and cultural service of urban parks. Park sentiment patterns in rapidly urbanizing metropolitan areas need to be understood and interpreted thoroughly. This research aims to systematically study park sentiment patterns in metropolitan areas. By focusing on parks in Shanghai city and using the local mainstream social media data (SMD) of Dazhong Dianping, Ctrip, and Weibo, we created a series of score-related indicators to estimate park sentiment. We then applied statistical analyses to systematically interpret sentiment patterns in the spatial, temporal, and spatiotemporal domains, explored their related factors, and compared the performance of different SMD sources. The results proved that Shanghai parks generally bring positive emotions to visitors but showed uneven sentiment patterns citywide. Park sentiment distributions differed from various SMD sources, but the SMD sets of Dazhong Dianping and Ctrip showed significant correlations. For these two SMD sets, visitors have greater and more stable happiness in parks on a workday than on a non-workday and in spring than in other seasons. Parks with higher positive sentiments are scattered citywide, whereas those with lower emotions are clustered in the downtown area. For Weibo, more positive emotions occurred on non-workdays or in autumn, and the lower mood clustering did not exist. Moreover, the quality-related internal factors of the park itself, rather than external factors such as location and conditions, were identified to influence park sentiment. The innovations of park sentiment methods in this study included using multiple SMD sets, creating more accurate sentiment indexes, and applying statistics in temporal, spatial, and spatiotemporal domains. These enhanced sentiment analyses for urban parks to obtain more systematic, comprehensive, and thorough results. The defects and improvements for urban park construction were explored by interpreting park sentiment patterns and possible causes and effects. This motivates better park management and urban development, and enlightens urban planners, landscape designers, and policymakers.

Keywords

• social media
• sentiment analysis
• sentiment pattern
• park
• green space
• big data