Visualizing Wi-Fi accesses from city-scale population for urban analysis
With the concept of big data being widely adopted in various areas, smart urban computing based on big data analysis has been a hot topic in recent years. As an important part of most people's daily life, Wi-Fi accessing can provide large-scale valuable user mobility data for urban computing, which indicates a new research field. A multi-layer visualizing model is proposed in this paper, based on which urban regional function and feature analysis is performed to find out valuable patterns from Wi-Fi accessing records generated by city-scale population. Moreover, by partitioning the city into region blocks, the association between different regions can be characterized by spatio-temporal population flows between them. Potential problems caused by inappropriate urban planning can be revealed by inspecting and comparing the multi-scale mobility visualizations. The experimental results show that important urban dynamic patterns can be visualized and this can be utilized to interpret potential urban planning problems reflected by large scale Wi-Fi connection data.