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Urban human activity density spatiotemporal variations and the relationship with geographical factors: An exploratory Baidu heatmaps‐based analysis of Wuhan, China

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  • Zuo Zhang
  • Yangxiong Xiao
  • Xiang Luo
  • Min Zhou

Abstract

With the development and popularity of mobile Internet technology, data sources of human activity in urban centers are rapidly updated and play an important role in supporting urban planning and management. Therefore, it is critical to integrate different data sources and detect spatially implicit information in the spatial pattern of relationships between urban human activity and related geographical factors. A new analytical framework is first proposed to integrate multisource location‐based big data and use these data to analyze dynamic real‐time human activity density (HAD). Taking Wuhan, the largest city in central China as an example, using the Baidu’s thermal data, this paper analyzes spatiotemporal characteristics of HAD distributions at different points on weekends and weekdays, and further combines the relevant cities’ points of interest data to analyze the correlations between different spatial elements and HAD distributions. The results show that: (a) Using a new indicator and data processing method can simply achieve effective utilization of Baidu’s thermal data; (b) Combined with standardized grids, spatial density estimation can match the two different data sources in this study; (c) The greater the HAD, the greater is the elasticity of change, and in the active population area, the densities of human activity on weekends and weekdays at different times have significant differences; and (d) Different geographically weighted regression models effectively distinguish the influence of different urban elements on weekdays and weekends. In particular, the impact patterns of the workplace, education, and cityscape reflect the unique spatial patterns of research cases. These findings, as well as visual analytics, help in the understanding of the potential value of Baidu heatmaps in urban study and provide support for more scientific and accurate urban planning and space management for the better consideration of real‐time changes in human activity.

Suggested Citation

  • Zuo Zhang & Yangxiong Xiao & Xiang Luo & Min Zhou, 2020. "Urban human activity density spatiotemporal variations and the relationship with geographical factors: An exploratory Baidu heatmaps‐based analysis of Wuhan, China," Growth and Change, Wiley Blackwell, vol. 51(1), pages 505-529, March.
  • Handle: RePEc:bla:growch:v:51:y:2020:i:1:p:505-529
    DOI: 10.1111/grow.12341
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    Cited by:

    1. Shumei Zhang & Wenshi Zhang & Ying Wang & Xiaoyu Zhao & Peihao Song & Guohang Tian & Audrey L. Mayer, 2020. "Comparing Human Activity Density and Green Space Supply Using the Baidu Heat Map in Zhengzhou, China," Sustainability, MDPI, vol. 12(17), pages 1-14, August.
    2. Hengyu Gu & Hanchen Yu & Mehak Sachdeva & Ye Liu, 2021. "Analyzing the distribution of researchers in China: An approach using multiscale geographically weighted regression," Growth and Change, Wiley Blackwell, vol. 52(1), pages 443-459, March.
    3. Liwei Qin & Wenke Zong & Kai Peng & Rongpeng Zhang, 2024. "Assessing Spatial Heterogeneity in Urban Park Vitality for a Sustainable Built Environment: A Case Study of Changsha," Land, MDPI, vol. 13(4), pages 1-24, April.
    4. Yi Shi & Yi Zheng & Daijun Chen & Junyan Yang & Yue Cao & Ao Cui, 2024. "Research on the Correlation between the Dynamic Distribution Patterns of Urban Population Density and Land Use Morphology Based on Human–Land Big Data: A Case Study of the Shanghai Central Urban Area," Land, MDPI, vol. 13(10), pages 1-18, September.
    5. Peng Zeng & Zongyao Sun & Yuqi Chen & Zhi Qiao & Liangwa Cai, 2021. "COVID-19: A Comparative Study of Population Aggregation Patterns in the Central Urban Area of Tianjin, China," IJERPH, MDPI, vol. 18(4), pages 1-15, February.
    6. Zhenwei Wang & Xiaochun Wang & Zijin Dong & Lisan Li & Wangjun Li & Shicheng Li, 2023. "More Urban Elderly Care Facilities Should Be Placed in Densely Populated Areas for an Aging Wuhan of China," Land, MDPI, vol. 12(1), pages 1-13, January.
    7. Feilong Hao & Ming Lu & Tingting Yu & Shijun Wang, 2024. "Identification and characterization of urban polycentric structure based on points of interest in Shenyang, China," Growth and Change, Wiley Blackwell, vol. 55(1), March.

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