# Human Clustering Based on Graph Embedding and Space Functions of Trajectory Stay Points on Campus
## 5 Conclusions  
In this paper, we proposed a methodology for human clustering based on feature vector representations, which are generated by the graph embedding of human networks of space functions of trajectory stay points. Using trajectory data generated from the Wi-Fi log data of 3000 individuals over nine days on a university campus, three human networks were built for typical time durations. The networks' sizes and key statistical data of node degrees are closely related to the corresponding time duration and agree with the general pattern of public activities in the application context. With help of clustering performance indicators, appropriate biased random walk strategies were determined for K-means clustering. Corresponding analysis of the clustering results was conducted, revealing distinct behavioral patterns of typical campus inhabitants. A general pattern is that more groups of individuals can be revealed as the time duration decreases. The individuals in typical clusters are in agreement with the background information about campus inhabitants, and space usage analysis also matches individuals' spatial and temporal activity patterns. By incorporating space function into human mobility analysis, more research can be conducted to improve this work and further explore applicationsin wider contexts. In our research, we set three typical time durations to build the corresponding human networks. In reality, there are many other spontaneous and planned activities happening on campus, which may not last for the specific time duration we set in this research. It would be meaningful to validate patterns found in our current work by investigating clustering characteristics with a fine time duration sequence. The search strategies used in graph embedding processing of the human network could be enhanced by incorporating edge weights--for example, exact time durations or sizes of individual groups. The semantics of time attribute is also an important factor in human mobility; for example, a two-hour duration in the morning is different from one in the afternoon. This was not fully considered in our current work and can be explored further. Space function is one type of categorical information, and the resulting clusters can be taken as categorical data as well. Correspondence analysis can be introduced to explore associations based on a contingency table [30]. This research was designed to reveal the general relational patterns between human clusters and space functions by grouping each individual into a specific cluster. In the future, other clustering algorithms, such as hierarchical clustering algorithms and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), can be employed to extract clusters adaptively and detect outliers. Of course, it is necessary to implement optimization algorithms to determine the suitable threshold values. In a broader urban context, a different space function system should be developed, and a fine adaptive time duration sequence should be introduced, considering very diverse human mobilities at large scales [12]. Privacy and ethical issues should be considered to protect individuals' rights and guarantee responsible big data use. The data used in this paper were anonymized and shared with restrictions. Verification of the results can be conducted at coarse granularity levels. This work can be used to derive more individuals' spatial and temporal characteristics and detailed interactions with spaces, with further implications for public decision making. Conceptualization, K.X. and T.W. ; methodology, K.X. and T.W. ; software, K.X. and P.Z. ; validation, K.X., T.W., and Z.W. ; formal analysis, K.X. and T.W. ; investigation, K.X., P.Z. and T.W. ; resources, T.W. and Z.Z. ; data curation, K.X., Z.Z., P.Z. and Z.W. ; writing--original draft preparation, K.X. and T.W. ; writing--review and editing, K.X. and T.W. ; visualization, K.X. ; supervision, T.W. ; project administration, T.W. ; funding acquisition, T.W.