Implementing Hierarchical Indoor Semantic Location Identity Classification: A Case Study for COVID-19 Proximity Tracking in the Philippines

Efforts toward COVID-19 proximity tracking in closed environments focus on efficient proximity identification by combining it with indoor localization theory for location activity monitoring and proximity detection. But these are met with concerns based on existing considerations of the localization theory like costly infrastructure, multi-story support, and over-reliance on sensor networks. Semantic location identities (SLI), or location data stored with additional meaningful context, has become a feasible localizing factor especially in locations that have multiple spaces with different usage from each other. There is also a novel method of classification framework, called hierarchical classification, that leverages the hierarchical structure of the labels to reduce model complexity. The research aims to provide a solution to proximity analysis and location activity monitoring considering guidelines released in a Philippine context that addresses concerns of indoor localization and handling of geospatial data by implementing a hybrid hierarchical indoor semantic location identity classification that focuses on observable events within context-unique locations.

Application of semantic location awareness computing based on data mining in COVID-19 prevention and control system

Because of the global spread of COVID-19 in 2020, the analysis of activities and travel behavior of urban residents is the key for the prevention and control of epidemic situation. Based on this, the research on track data mining and semantic location perception is conducted. The analysis of travel behavior characteristics of urban residents is helpful to carry out epidemic prevention activities scientifically. However, the traditional manual survey and statistical analysis cannot meet the needs of the rapid development of urbanization. On the other hand, with the application and development of information technology such as communication, location and storage, a large number of mobile trajectory data of urban residents can be collected and stored. These trajectory data contain rich spatiotemporal semantic information. Through mining and analysis, a lot of valuable travel information can be get and then the daily behavior of individual users and the spatial distribution characteristics of group users’ movement can be found. The results can effectively serve the current epidemic prevention work and can be applied to the infection tracking in the process of epidemic prevention.