Explosive growth in geospatial and temporal data as well as the emergence of new technologies emphasize the need for automated discovery of spatiotemporal knowledge. Spatiotemporal data mining studies the process of discovering interesting and previously unknown, but potentially useful patterns from large spatiotemporal databases. It has broad application domains including ecology and environmental management, public safety, transportation, earth science, epidemiology, and climatology. The complexity of spatiotemporal data and intrinsic relationships limits the usefulness of conventional data science techniques for extracting spatiotemporal patterns. In this survey, we review recent computational techniques and tools in spatiotemporal data mining, focusing on several major pattern families: spatiotemporal outlier, spatiotemporal coupling and tele-coupling, spatiotemporal prediction, spatiotemporal partitioning and summarization, spatiotemporal hotspots, and change detection. Compared with other surveys in the literature, this paper emphasizes the statistical foundations of spatiotemporal data mining and provides comprehensive coverage of computational approaches for various pattern families.ISPRS Int. J. Geo-Inf. 2015, 4 2307We also list popular software tools for spatiotemporal data analysis. The survey concludes with a look at future research needs.

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Given a spatial network and a collection of activities (e.g., pedestrian fatality reports, crime reports), Significant Linear Hotspot Discovery (SLHD) finds all shortest paths in the spatial network where the concentration of activities is statistically significantly high. SLHD is important for societal applications in transportation safety or public safety such as finding paths with significant concentrations of accidents or crimes. SLHD is challenging because 1) there are a potentially large number of candidate paths (∼ 10 16 ) in a given dataset with millions of activities and road network nodes and 2) test statistic (e.g., density ratio) is not monotonic. Hotspot detection approaches on Euclidean space (e.g., SaTScan) may miss significant paths since a large fraction of an area bounded by shapes in Euclidean space for activities on a path will be empty. Previous network-based approaches consider only paths between road intersections but not activities. This paper proposes novel models and algorithms for discovering statistically significant linear hotspots using the algorithms of neighbor node filter, shortest path tree pruning, and Monte Carlo speedup. We present case studies comparing the proposed approaches with existing techniques on real data. Experimental results show that the proposed algorithms yield substantial computational savings without reducing result quality.

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Recent developments in data mining and machine learning approaches have brought lots of excitement in providing solutions for challenging tasks (e.g., computer vision). However, many approaches have limited interpretability, so their success and failure modes are difficult to understand and their scientific robustness is difficult to evaluate. Thus, there is an urgent need for better understanding of the scientific reasoning behind data mining and machine learning approaches. This requires taking a transdisciplinary view of data science and recognizing its foundations in mathematics, statistics, and computer science. Focusing on the geospatial domain, we apply this crucial transdisciplinary perspective to five common geospatial techniques (hotspot detection, colocation detection, prediction, outlier detection and teleconnection detection). We also describe challenges and opportunities for future advancement.