YouTube Video Link: CS Seminar 15 YouTube Video
Speaker
Dr. Shashi Shekhar, McKnight Distinguished University Professor at University of Minnesota
Abstract
Rise of spatial big data (e.g., trajectories, remote-sensing) is fueling growth of Geo-AI (e.g., geo-imagery analysis automation) for making previously unimaginable maps, answering trail-blazing geo-content based queries, and understanding spatiotemporal patterns of our lives, etc. Applications span from apps for navigation, ride-sharing, and delivery to monitoring global crops, climate change, diseases, and smart cities to understanding cellular or urban patterns of life.
However, one-size-fit-all machine learning performs poorly (e.g., salt-n-pepper noise, inaccuracy) due to spatial autocorrelation and variability, which violate the common independent and identically distributed random variable assumption (i.e. data samples are generated independently and from identical distribution). Furthermore, high cost of spurious patterns requires guardrails such as noise tolerance, and modeling of spatial concepts (e.g., polygons) and implicit relationships (e.g., distance, inside). In addition, methods discretizing continuous space face the modifiable areal unit problem (e.g., gerrymandering).
Thus, the talk suggests spatial data science approaches and describes methods for spatial classification and prediction (e.g., spatial auto-regression, spatial decision trees, spatial variability aware neural networks) along with techniques for discovering patterns such as noise-robust hotspots (e.g., SaTScan, linear, arbitrary shapes), interactions (e.g., co-locations, tele-connections ), spatial outliers, and their spatio-temporal counterparts (e.g., cascade , mixed-drove co-occurrence ). It concludes by calling for inclusion of spatial perspectives in data science courses and curricula.
Biography
Dr. Shashi Shekhar is a leading scholar of spatial computing and Geographic Information Systems (GIS). Contributions include scalable algorithms for eco-routing, evacuation route planning and spatial pattern (e.g., colocation, linear hotspots) mining, along with an Encyclopedia of GIS, a Spatial Databases textbook, and a spatial computing book for professionals. Dr. Shekhar is a McKnight Distinguished University Professor, a Distinguished University Teaching Professor, ADC Chair at the University of Minnesota (UMN). He is serving as an Associate Director of the Data Science Initiative in the UMN College of Science and Engineering. He is also serving as a co-Editor-in-Chief of the Geo-Informatica journal (Springer), a general co-chair of SIAM Data Mining Conference (2023), and a program co-chair of the ACM SIG-SPATIAL Intl. Conference (2022). Earlier, he served as the President of the University Consortium for GIS (UCGIS), and a member of many National Academies' committees and the Computing Research Association (CRA) board. Recognitions include IEEE-CS Technical Achievement Award, UCGIS Education Award, IEEE Fellow and AAAS Fellow. More details can be found on his webpage.
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