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

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.

COVID-19: A Comparative Study of Population Aggregation Patterns in the Central Urban Area of Tianjin, China

“…It represented an operating state under the constraint of various control measures. Therefore, we selected 29 November 2019 and 27 February 2020 as the representative days of the urban area in the pre-epidemic and epidemic periods, respectively [ 10 , 31 ].…”

“…We obtained the BHM data every half hour, and then conducted a projection conversion and reclassification to build population distribution dataset. Because seven colors represented seven population density levels, the original pictures were transformed from colors to 0–7 value tiff rasters [ 10 , 31 ].…”

“…Under the concept of people-oriented urban design [ 1 ], the interaction between human behavior and urban material space is the most important dimension to study the regulation of urban processes [ 2 , 3 , 4 , 5 , 6 ]. Urban population distribution [ 7 , 8 , 9 ], like population activity density [ 10 ] and urban vitality distribution [ 11 ], is the dependent variable of urban spatial behavior that can be used to characterize the operational state of an urban area [ 12 ]. The population distribution and flow in a high-density urban area are the consequence of material space, functional space, and public policy.…”

Analyzing the distribution of researchers in China: An approach using multiscale geographically weighted regression

“…The bandwidth parameter in GWR is the parameter used to indicate the scale over which spatial processes vary (Zhang et al., 2020). The number of points selected to calibrate a GWR model at a single point is weighted according to a kernel, such as a bi‐square or Gaussian kernel, where nearer points are weighted more strongly than farther points and the gradient follows the kernel function.…”

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

“…The "heat" of the map represents the density (humans per area) per cell of the map, and the density is presented as different colors and continuously updated every 15 min. Early applications of these data for spatially explicit urban activity have used mobile phone data to examine how people move within and between cities [21][22][23][24][25], while others utilized user-generated geographic information to calculate core density and analyze the distribution and supply of infrastructure [26][27][28].…”