Deciphering the recreational use of urban parks: Experiments using multi-source big data for all Chinese cities

Abstract

China’s rapid urbanization process has accentuated the disparity between the demand for and supply of its park recreational services. Estimations of park use and an understanding of the factors that influence it are critical for increasing these services. However, the data traditionally used to quantify park use are often subjective as well as costly and laborious to procure. This paper assessed the use of parks through an analysis of check-in data obtained from the Weibo social media platform for 13,759 parks located in all 287 cities at prefecture level and above across China. We investigated how park attributes, accessibility, and the socioeconomic environment affected the number and density of park check-ins. We used multiple linear regression models to analyze the factors influencing check-ins for park visits. The results showed that in all the cities, the influence of external factors on the number and density of check-in visits, notably the densities of points of interest (POIs) and bus stops around the parks was significantly positive, with the density of POIs being the most influential factor. Conversely, park attributes, which included the park service area and the landscape shape index (LSI), negatively influenced park use. The density of POIs and bus stops located around the park positively influenced the density of the recreational use of urban parks in cities within all administrative tiers, whereas the impact of park service areas was negative in all of them. Finally, the factors with the greatest influence varied according to the administrative tiers of the cities. These findings provide valuable inputs for increasing the efficiency of park use and improving recreational services according to the characteristics of different cities.