Comparing Human Activity Density and Green Space Supply Using the Baidu Heat Map in Zhengzhou, China

Abstract

Rapidly growing cities often struggle with insufficient green space, although information on when and where more green space is needed can be difficult to collect. Big data on the density of individuals in cities collected from mobile phones can estimate the usage intensity of urban green space. Taking Zhengzhou’s central city as an example, we combine the real-time human movement data provided by the Baidu Heat Map, which indicates the density of mobile phones, with vector overlays of different kinds of green space. We used the geographically weighted regression (GWR) method to estimate differentials in green space usage between weekdays and weekends, utilizing the location and the density of the aggregation of people with powered-up mobile phones. Compared with weekends, the aggregation of people in urban green spaces on workdays tends to vary more in time and be more concentrated in space, while the highest usage is more stable on weekends. More importantly, the percentage of weekday green space utilization is higher in small parks and green strips in the city, with the density increasing in those small areas, while the green space at a greater distance to the city center is underutilized. This study validates the potential of applying Baidu Heat Map data to provide a dynamic perspective of green space use, and highlights the need for more green space in city centers.

Postearthquake Casualty Prediction Based on Heatmaps and Wavelet Supporting Vector Machines

“…erefore, Baidu heatmaps also dynamically change in real time and are updated every 15 minutes. Relevant research shows that the deviations in the living and working populations obtained from Baidu heatmap big data from real statistical data are less than 5% [20,22]; thus, the Baidu heatmaps are generally consistent with the real situation and provide an important data basis for postearthquake loss prediction, demand assessment, and emergency rescue management.…”

Measuring the Correlation between Human Activity Density and Streetscape Perceptions: An Analysis Based on Baidu Street View Images in Zhengzhou, China

“…A total of 208 pieces of data were collected during the study's hour-long capture period. According to the previous article [36], the data distribution is as follows (Figure 4). it is a commercial product.…”

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

“…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].…”

“…The BHM data, as an important population-oriented visualization product, could directly indicate real-time population density as distinguished by colors on the map [45,64]. This data ranges from 0 to 7 and the larger value means more human activities.…”