Abstract

Given the importance of urban parks for recreation, it is critical to understand how they are used and perceived. Currently, relatively few studies have examined the public’s activities and preferences at the same time. Social media data are increasingly recognized as a promising data source to study these two aspects. However, little is known regarding the utility and representativeness of social media data for urban parks. In particular, a lack of understanding exists on the comparability of research results measured by bottom-up social media data and top-down official survey data in urban park studies. This research used social media data from Ctrip and Dianping and survey data from the local government agency with park visits statistics and park satisfaction surveys to understand the public’s visitation and satisfaction of 102 urban parks in central Shanghai. We first assessed the similarities between the social media data and survey data by correlation analysis. We then used the negative binomial regression model and beta regression model to examine the matches and mismatches of the different data sources in investigating factors influencing park visitation and park satisfaction. Our correlation results showed that the social media data significantly correlated with the survey data and that social media data performed better in more frequently visited parks. Our regression results showed that the water bodies, recreational facilities, and surrounding commercial facilities were the common influencing factors of park visitation and park satisfaction for all parks. While significant correlations were noticed between these two data sources, we found that the surrounding population density was negatively associated with park visitation measured by social media data but positively associated with park visitation measured by survey data. This study provides a comparative perspective to study park visitation and park satisfaction by combining social media data and official survey data. Clarifying the consistencies and inconsistencies between the social media data and official survey data is important because it could help us to understand the representativeness and potential biases of the social media data when used in visitation monitoring and satisfaction monitoring.

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