Zixiang Wang

Spatial big data about human mobility have been employed intensively in understanding human spatial activity patterns, which is a central topic in many applications. Available research on spatial clustering patterns of human activities has been investigated mainly based on similarities of locations and temporal attributes of spatial trajectories. These methods are not effective in revealing human groups who move among spaces at different locations but with the same functions. Function, as one semantic attribute of spaces, is a major driver of most human movements. This work investigates human clustering based on space functions of trajectory stay points in human mobility data using graph embedding. Firstly, typical functions of spaces are categorized into 35 types in our research area, which is a university campus. Human trajectories based on Wi-Fi networks were collected as test data. Then, human networks are built among human individuals. Each individual is taken as a node in the network, and an edge is built between two nodes if the corresponding individuals stay in spaces of the same type of function longer than a specific time duration. The graph embedding algorithm is used to calculate feature vector representations of nodes in the network, which can capture complex relationships among nodes through biased random walks. K-means clustering is applied to classify the feature vectors, which reveals potential behavioral pattern similarities of individuals concerning the functions of their staying spaces. The elbow method and silhouette score of clusters are used to determine an appropriate number of clusters. Three scenarios were designed based on three specific time durations, and random walk-biased parameters were fine-tuned to improve the clustering performance. Results reveal typical clusters and correlation between clusters and typical space functions.

As a typical and special type of urban setting, the university campus usually faces similar challenges as cities raised by high-density inhabitants. The smart campus has been introduced based on the smart city, as concepts, technologies, and solutions to improve livability and energy efficiency. Inhabitants’ occupancy in buildings and open spaces on campus is critical to optimize campus management and services. Information about spatial occupancy of campus inhabitants can be produced based on various location-based solutions, such as global navigation satellite systems (GNSS), campus cameras, Bluetooth, and Wi-Fi. As an essential component in campus information infrastructure, Wi-Fi network covers almost the entire university campus and has advantages in collecting locations of campus inhabitants. In this paper, geo-visualization of spatial occupancy of campus inhabitants is designed and implemented using anonymized Wi-Fi network log data. First, 3-dimension building models are reconstructed based on LiDAR point clouds and construction drawings. Then, the Wi-Fi network log data are cleaned and preprocessed. Campus inhabitants’ locations are extracted from structural Wi-Fi data. Geo-visualization at room, floor, and building levels is designed and implemented. On a temporal dimension, spatial occupancy can be visualized by each second, minute, hour, or day of the week in 3D buildings. The implementation of the geo-visualization is based on CesiumJS, which offers an interface for 3D-animated visualization and interaction. The research can be used to support university management and educators to implement the smart campus and optimize pedagogical research.