Using multi-source big data to understand the factors affecting urban park use in Wuhan

Abstract

Urban parks have been proved to have great benefits for people and urban environments. Estimating park use and understanding its influence factors are significant for urban park planning and designing. Currently, geospatial data are used to measure park use for convenience and low cost at the city and regional levels. In this study, two sets of geospatial data were used to describe the park use of 57 urban parks in Wuhan. Nine variables for three categories affecting park use were analyzed to find the relationship with park use. The results showed that the Baidu heat map data were better than Weibo check-in data for describing park use in Wuhan. Six variables showed significant correlation with park use. The results showed that park use was mostly affected by the surrounding environment features in this study, and it was easily impacted by park attributes. However, park use was slightly affected by accessibility.