A geographical detector study on factors influencing urban park use in Nanjing, China

How Did Built Environment Affect Urban Vitality in Urban Waterfronts? A Case Study in Nanjing Reach of Yangtze River

“…Wu et al [25] suggested that social media check-in data can be used as a proxy for characterizing spatiotemporal patterns of urban vitality in Shenzhen. Recently, BHM data, as a kind of crowdsourced data regarding human activity, provide a new angle to portray population distribution and urban dynamics [43,44]. Numerous novel studies have tapped into the BHM data as a crucial tool in the research of green spaces and parks [43,45,46], urban population aggregation characteristics [47,48], and urban structure and land use [49].…”

“…Recently, BHM data, as a kind of crowdsourced data regarding human activity, provide a new angle to portray population distribution and urban dynamics [43,44]. Numerous novel studies have tapped into the BHM data as a crucial tool in the research of green spaces and parks [43,45,46], urban population aggregation characteristics [47,48], and urban structure and land use [49]. In contrast to social media data and other traditional datasets, BHM data can provide real-time analysis for the dynamics of human activities on daily or hourly intervals [43].…”

“…Numerous novel studies have tapped into the BHM data as a crucial tool in the research of green spaces and parks [43,45,46], urban population aggregation characteristics [47,48], and urban structure and land use [49]. In contrast to social media data and other traditional datasets, BHM data can provide real-time analysis for the dynamics of human activities on daily or hourly intervals [43]. However, comprehensive studies using BHM data to characterize urban vitality are rare, thus this study attempts to adopt the BHM data as the proxy for describing the spatiotemporal characteristics of urban vitality in urban waterfronts.…”

“…), and then displays the relative population distinguished by colors, where red represents high density, and blue represents low density (Figure 2). Recent studies have verified that BHM data could be used as a reasonable proxy for measuring the dynamics of human activities in different areas [29,43]. In this study, therefore, the BHM data at hourly intervals from 6:00 to 22:00 across the Nanjing city were collected on a weekday (October 14th in 2020, Wednesday) and a weekend (October 17th in 2020, Saturday) (Figure 2).…”

“…This data ranges from 0 to 7 and the larger value means more human activities. Fan et al [43] proposed a method to assess the vitality of urban parks through BHM data. This study adopted the BHM data-based method for measuring urban vitality and calculated the average urban vitality value of each spatial unit.…”