Abstract

Mobility data is being produced in massive amounts way larger than any time in history due to the widespread of new mobile data sources such as mobile smart devices and Internet of Things (IoT) devices. This adds new challenges to analyze data in both space and time. Spatial regionalization is a process of grouping spatial areas into a set of regions to analyze and draw conclusions about spatial phenomena at different points of time. Almost all the existing regionalization techniques develop approximate solutions that are limited to small-sized data. However, the massive amount of data being produced nowadays calls for scalable and efficient regionalization techniques to handle the contagiously growing and changing data. Moreover, using approximation techniques requires having some kind of measure to assess the quality of those techniques. This Ph. D research mainly addresses the scalability issue of spatial regionalization techniques to explore new frontiers and applications. First, we introduce parallel scalable techniques to support regionalization techniques and measure their quality on large datasets. Then, we build upon our scalable techniques to support high-level spatial inference applications that are not possible with the existing limitations. We conduct extensive experiments to evaluate the performance of our proposed techniques in terms of runtime and solution quality.

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