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positioning geovisualization & geovisual analytics

positioning geovisualization & geovisual analytics. GSTI Workshop: Alan M. MacEachren GeoVISTA Center, Dept. of Geography, Penn State Geovisualization , Geovisual Analytics, Spatial Cognition, Spatial Language. Cognitive Science. Information Science. Spatially Integrated

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positioning geovisualization & geovisual analytics

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  1. positioning geovisualization& geovisual analytics GSTI Workshop: Alan M. MacEachrenGeoVISTA Center, Dept. of Geography, Penn StateGeovisualization, Geovisual Analytics, Spatial Cognition, Spatial Language Cognitive Science Information Science Spatially Integrated Social Science Computer Science GIScience Spatial Cognition Geovisual analytics Geovisualization Environmental Informatics Cartography Geography

  2. Preamble • Popular web mapping tools have made geographic information accessible to orders of magnitude more people than was the case just a decade ago. • For most people geographic visualization probably is synonymous with Google Earth. • Visualization for many is considered successful if the computer-generated renderings look like the world. • As a field of research, geovisualization has much broader roots and goals than usually recognized. Neither the label nor the disciplinary focus is primarily about what it appears to be. • Geographic information comes in a bewildering array of forms, both explicit and implicit, and the challenge is nottorender it but to represent and reason with it Alan M. MacEachren, GeoVISTA Center, Dept. of Geography, Penn State – maceachren@psu.edu

  3. from cartography, through geovisualization, to geovisual analytics: data  info  knowledge  reasoning • Visualization in Scientific Computing (VisSC): prompting a shift from cartography geovisualization – • dynamic geovisualization – (Cartography + VisSC + EDA + HCI + InfoVis): prompting hypotheses and enabling insight • geovisualization + computation – (…) + KDD: turning data to info, uncovering patterns and relationships, supporting knowledge construction • (geo)visual analytics – (visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces) – geovisual analytics: information foraging, heterogeneous info integration, analytical reasoning & sense-making, knowledge construction/management, and decision-making – facilitated by geo-visual (geospatial & geotemporal) interfaces Alan M. MacEachren, GeoVISTA Center, Dept. of Geography, Penn State – maceachren@psu.edu

  4. Visual Inquiry Toolkit Foraging loop  Sense-making loop  Search & filter  Read & extract  Schematize  Build case  Tell story  External data source Shoebox Evidence file Schema Hypotheses Presentation after Pirolli & Card Search for support Reevaluate Search for information Search for relations Search for evidence Moving beyond “traditional” infovis/geovis • linking visual & computational methods • connecting numerical data with pictures, text, concepts • supporting an iterative, extended process of analysis • evidence assembly • hypothesis formation and assessment • story-telling Improvise: Hotels.viz

  5. GSTI Workshop • What is solved? • rendering spatial o=info at global scale • many aspects of understanding enough about human perception to generate practical design guidelines for static map-based display (e.g., choosing color) – fundamental challenges remain for dynamic display • supporting interactive multiview analytical interaction for 101 – 104/105 fixed records – fundamental scaling challenges remain • What is almost solved? • serving real-time navigation info to mobile display • What has failed? • attempted to take humans completely out of the loop in strategies for addressing hard analytical problems • convincing developers/users to take advantage of what we know about perception/readability when designing map-based (and other) displays Alan M. MacEachren, GeoVISTA Center, Dept. of Geography, Penn State – maceachren@psu.edu

  6. GSTI Workshop • What is missing? • attention to the challenges of using heterogeneous data in real analytical work; • attention to challenges of implicit geographic information • What is next • visually-enabled analytical reasoning with a (roughly) geo-referenced social network of 1, 10, or 100-billion records; • understanding and integrating human and computational reasoning with large volumes of dynamic geo-spatial/temporal info • representation of geo-spatial/temporal context (a critical underpinning to solving most other questions); • moving visual-analytical power to everyday devices and tasks • integrating missing bits into the above Alan M. MacEachren, GeoVISTA Center, Dept. of Geography, Penn State – maceachren@psu.edu

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