An Automated Framework for Explaining Facts Extracted From Mobility Datasets

When a data scientist analyzes mobility data (e.g., using a data visualization tool), she may find out some interesting facts in the dataset. An example of a fact can be: "The number of Taxi trips in NYC on January 23, 2016, dropped drastically as compared to other days of the same month". However, the data scientist may be left clueless if they cannot find a crisp explanation to such a fact. Furthermore, the tedious task of finding an explanation by manually scraping the data becomes even impossible with big data. Existing techniques are designed for non-spatial data which cannot be applied to spatial data because it does not consider the spatial proximity. In this paper, we propose an automatic framework which guides the data scientist to explain the fact discovered from mobility data. Our approach expands on the aggravation and intervention techniques while using spatial partitioning/clustering to improve explanations for spatial data. Experiments show that the proposed approach outperforms the state-of-the-art approaches in finding the explanation for facts extracted from NYC taxi real mobility dataset.