Discovery of hot regions about crowd activities based on mobility data

Mobility data records the change of location and time about crowd activities, showing semantic knowledge about human mobility. From the perspective of regional semantic knowledge, mining the hot regions visited frequently by moving crowds is essential to understand regional characteristics in the smart city applications. This paper studied how to discover hot regions and how to constraint their coverage size. Based on an analysis of the location sequence of moving crowd, a discovery method for discovering hot regions based on kernel function was proposed. This discovery method uses the grid as a spatial data indexing structure and the Top-k sorting method. A discovery algorithm of hot regions was presented based on the discovery method. Finally, experimental results validate accurately the feasibility and effectiveness of the method on practical datasets.