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.