Exploring Affecting Factors of Park Use Based on Multisource Big Data: Case Study in Wuhan, China

Abstract

The exploration of park use and its affecting factors is necessary for urban park planning and management. Using big data, 70 urban parks in Wuhan were selected as the study objects in the present study. Two groups of linear regression models were developed to examine the effects of various factors on park use. The total numbers of visits and public demands in urban parks were recorded as 1,291,894 and 4,022, respectively. Social media data reflect visits, while public demand data reflect demands. First, a large area and sufficient facilities greatly help improve park use; moreover, we should pay attention to old urban parks in disrepair. More squares need to be considered in urban parks; attention should be paid to the water pollution and safety concerns that may arise in urban parks. Second, the influence of accessibility on park use is stable. It is beneficial for park use to construct greenways connecting residential areas and urban parks. The layout of pocket parks in the city center is also a feasible way to improve park use. Third, to improve park use, we should arrange commercial entertainment facilities and services in urban parks in point of interest (POI)-rich areas, as well as facilities and services for residents’ daily activities in urban parks in population-rich areas. This study provides a way to combine multisource data that reflect the number of park visits and park demands to explore affecting factors of park use.