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

Shumei Zhang, Wenshi Zhang, Ying Wang, Xiaoyu Zhao, Peihao Song, Guohang Tian and Audrey L. Mayer
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Shumei Zhang: College of Forestry, Henan Agricultural University, Zhengzhou 450002, China
Wenshi Zhang: College of Information and Management Sciences, Henan Agricultural University, Zhengzhou 450002, China
Ying Wang: College of Forestry, Henan Agricultural University, Zhengzhou 450002, China
Xiaoyu Zhao: College of Forestry, Henan Agricultural University, Zhengzhou 450002, China
Peihao Song: College of Forestry, Henan Agricultural University, Zhengzhou 450002, China
Guohang Tian: College of Forestry, Henan Agricultural University, Zhengzhou 450002, China
Audrey L. Mayer: School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI 49931, USA

Sustainability, 2020, vol. 12, issue 17, 1-14

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.

Keywords: Baidu Heat Map; big data; central city of Zhengzhou; urban green space; human activity density (HAD) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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