关键词: 城市时空热点, 大数据, 数据组织, Spark

Abstract: City hot-spots are a spatial-temporal area with frequent trips and larger traffic flow. It has a large number of applications in the areas of public services, such as urban infrastructure construction, transportation planning, selection of shop location, and crime prevention. The current methods for hot-spots detection usually adopt the Getis-Ord statistical method to divide the trajectories into spatial-temporal areas and calculate the hot value of the areas covered by all the trajectory data as city hot-spots. Due to the large amount of data and complex calculation, the current methods focus on how to deal with massive amounts of data. With the expansion of the application field, hot-spots detection does not have to use all data, and proper data organization can make hot-spots detection efficient. This paper proposes a hot-spots query to better meet the flexible needs of users, which calculates the hot value of the areas according to the user-specified parameters（geographical range, date range, size of hot-spots and way of time organization） and returns the TOP-K hot areas as city hot-spots. For different query parameters, the hot-spots query needs to process different data. This paper proposes a trajectory data organization method with small-scale grid index to quickly extract the trajectory data that need to be processed. Using the New York City taxi trajectory dataset to conduct query experiments in the Spark cluster, the results show that the proposed indexing method and storage strategy can greatly reduce the time.

Key words: city hot-spots, big data, data management, Spark