Flowers as attractions in urban parks: Evidence from social media data

Abstract

Urban parks are among the most important urban public services. Quantifying their visitation intensity and understanding the driving forces behind their popularity is of great relevance to urban planning. We analyze the behavior of park visitors in Beijing based on phenological information extracted from social media data. Specifically, we built a dataset utilizing natural language processing techniques and co-word analysis methods to explore the connection between flowers and park visitation. Our findings revealed that: (1) According to the changing trend of visitor volumes and their peak times, urban parks can be divided into “single-peak” (visitor volumes show a single peak, with significant seasonal characteristics) and “multi-peak” (visitor volumes show multiple peaks with no obvious seasonal characteristics) parks; (2) There is an association between flowers and visitor volumes to urban parks, with a noticeable increase in the frequency of visits to parks especially in spring (i.e., during flowering); (3) Different types of flowers have varying appeal to attract visitors. Further, parks with one or few “dominant flowers” appeal to more visitors than parks without a clear dominating flower (or flowers). Our results provide implications for urban park design and management for improving their scenic qualities.