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(Geo) Informatics across Disciplines!

(Geo) Informatics across Disciplines!. Why Geo-Spatial Computing?. Societal: Google Earth, Google Maps, Navigation, location-based service Global Challenges facing humanity – many are geo-spatial! Future of Computer Science (CS) is to address societal challenges! Intellectual

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(Geo) Informatics across Disciplines!

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  1. (Geo) Informatics across Disciplines!

  2. Why Geo-Spatial Computing? • Societal: • Google Earth, Google Maps, Navigation, location-based service • Global Challenges facing humanity – many are geo-spatial! • Future of Computer Science (CS) is to address societal challenges! • Intellectual • Challenges CS assumptions, opporunities for new research • Ex. Shortest path problem – What algorithms are common today? • Are these correct if edge travel-times is not fixed (e.g. rush-hour, non-rush-hour)? • Is Dynamic Programming correct for spatio-temporal problems? • Ex. Classical Data Mining, e.g. Decision trees, Association Rules, Regression, … • Are these effective for spatial data? • ACM SIGSPATIAL • Special interest group started in 2008 to bring computer scientists together • Annual Conference: ACM GIS • Journals • GeoInformatica: An Intl. Jr. on Advances in Computer Science for GIS

  3. Shashi ShekharMcknight Distinguished University Professor • URLs: www.cs.umn.edu/ ~shekhar • Area: Spatial Databases, Spatial Data Mining, Geographic Info. Systems • Teaching: • Fall 2010: Csci 5980: Computational Geo-Informatics (link) • Spring 2010: Csci 5708: Database Systems • 2010-2011: Csci 8715: Spatial Databases • Books, Surveys, etc.: • Developing a book on spatial and spatio-temporal data mining • Textbook, Encyclopedia, …

  4. Research Group: Spatial Database, Data Mining, GIS • URLs: www.spatial.cs.umn.edu • Weekly Meetings: www.spatial.cs.umn.edu/schedule.html • Current Projects: • NSF: CRI:IAD Infrastructure for Research in Spatio-Temporal and Context-Aware Systems … • NSF: III-CXT: Spatio-temporal Graph Databases for Transportation Science • NSF: IGERT: Non-equilibrium Dynamics Across Space and Time • NSF: Expedition: Understanding Climate Change: A Data Driven Approach • USDOD: Spatio-Temporal Pattern Mining for Multi-Juris. Multi-Temporal Activity Datasets • USDOD: Dynamic Purpose-Aware Graph Models for Composite Networks • USDOD: Cascase Models for Multi-Scale Spatio-temporal Pattern Discovery • Current Ph.D. Students: • Pradeep Mohan • Mike Evans • Dev Oliver • Xun Zhou • KwangSoo Yang • Abdussalam Bannur • Viswanath Gunturi • Zhe Xiang

  5. Shortest Paths Storing graphs in disk blocks Evacutation Route Planning only in old plan Only in new plan In both plans Parallelize Range Queries Spatial Databases: Representative Projects

  6. Location prediction: nesting sites Spatial outliers: sensor (#9) on I-35 Nest locations Distance to open water Vegetation durability Water depth Co-location Patterns Tele connections Spatial Data Mining : Representative Projects

  7. Spatio-Temporal (ST) Questions • How do we conceptualize spatio-temporal (ST) worlds? • How do we measure ST concepts, recognize them in (remotely) sensed information or in the field, and identify their accuracy and quality? • How do we represent ST concepts with incomplete/ uncertain information, with alternative data models, and possibly with multiple representations for the same data, in digital environments? • How do we store, access, and transform ST concepts, facilitating data sharing, data transfer, and data archiving, while ensuring minimum information loss? • How do we explain ST phenomena through the application of appropriate methods of forward or inverse models of physical and human processes? • How do we visualize ST concepts on a variety of media such as maps on electronic displays or animated displays ? • How do we use ST concepts to think about spatio-temporal phenomena, and to seek explanations for spatio-temporal patterns and phenomena? • Source: Adaptation from NCGIA proposal to NSF by Goodchild et al.

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