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Presentation at AAG 2011 Space-Time Symposium

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  1. 1. Geovisual Analytics and the Space-Time Cube<br />Otto Huisman, Menno-Jan Kraak and Bas Retsios<br /><br />
  2. 2. Introduction<br />We are currently witnessing rapid advances in integrated positioning technologies such as GPS, cellular positioning, and RFID tracking, resulting in the increasing availability of large datasets describing human movement. <br />As scientists, we are constrained by our tools for scientific inquiry. This is evidenced by increasing ‘implementations’ of various space-time analytical toolkits.<br />In the ongoing search to better understand our environment and the impacts that human activities have upon it, we need flexible environments for the integration of visualization, analysis and models.<br />These should help to create knowledge and understanding through facilitating the transition from raw geodata to contextualisedgeoinformation<br />The Space-Time Cube is both a concept and an interactive environment for data mamangement, visualisation and analysis (plugins)<br />Here we demonstrate Geovisual Analytics for spatio-temporal data in a range of application settings involving human movement and dynamics.<br />
  3. 3. Space-time data describing movement: objects and events<br />x,y,t,id - trajectories<br />x,y,t - events<br />Main problem is no longer the issue of data availability. Rather, it is issues relating to:<br /><ul><li>The lack of attribute/semantic data
  4. 4. Unknowns relating to accuracy and quality
  5. 5. Scale / resolution / granularity
  6. 6. ‘Data integration’</li></ul>Given increases in data volumes and focus on ‘automated’ approaches, these issues require appropriate strategies – and appropriate tools.<br />
  7. 7. The Space-Time Cube<br />
  8. 8. The Space-Time Cube<br />Software environment<br />Concept<br />Data model<br />
  9. 9. Data management, visualisation and analysis<br />Context map<br />scatterplot<br />PCP<br />???<br />
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  12. 12. Space-time data describing movement / behaviour<br />Key issues: different ‘levels’ of investigation – which represent different levels of interest, and at the same time, complexity:<br />Examining behaviour in small to medium datasets<br />Identifying patterns from small to massive datasets<br />Pattern analysis and interpretation – a ‘hot’ frontier of research.<br />Applications:<br />Post-earthquake movement behaviour<br />‘Moving flock’ patterns (pedestrians)<br />
  13. 13. Examining behaviour: post-earthquake evacuation paths<br />Kawaguchi town – hit with 6.8 on 23rd October 2004 at 17:56<br />
  14. 14. Identifying patterns in trajectory data: ‘moving flocks’ in Dwingelerveld Park<br />
  15. 15. Identifying patterns in trajectory data: ‘moving flocks’ in Dwingelerveld Park<br />Moving flock algorithm source: based upon source code from KDD Laboratory, University of Pisa.<br />
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  18. 18. Interpretation?<br />
  19. 19. Possible movement: alternative data models<br />Incorporation of (loosely-coupled) simulation models to generate movement possibilities using Time-Geographic concepts from Hagerstrand (1970).<br />Uses taxel/voxel data model for data management and analysis developed by Forer (1998), and methodology in Huisman (2006)<br />
  20. 20. Possible movement: discrete space-time<br />
  21. 21. Space-time ‘events’ and ‘phenomena’<br />When we do not have {x,y,t,id}, but instead the data refer to some (unique) event happening at x,y,t. <br />Examples include (reported) disease cases, accidents, and so forth.<br />Here will look at 2 very different examples:<br />Synthetic data on illegal migrant arrests [VAST2008]<br />Archaeological inquiry<br />
  22. 22. VAST2008 Challenge dataset: illegal migrant arrests<br />The data include interdiction by U.S. Coast Guard cutters as well as information about successful landings.<br />Some attribute information attached.<br />Objective: to understand more about the migration during these years. <br />Solution (stage 1 only) : find clusters of common interdictions/landings <br />Algorithm: extended from WEKA <br />opensourceimplementation <br />
  23. 23. Identifying patterns: space-time clustering with OPTICS<br />
  24. 24. Identifying patterns: space-time clustering with OPTICS<br />
  25. 25. Identifying patterns: space-time clustering with OPTICS<br />Interpretation?<br />
  26. 26. Identifying relationshipsbetween space-time events: Archaeological inquiry<br />This is preliminary work on a large database of excavation sites in Puerto Rico.<br />Some additional functionality was implemented to explicitly visualise ‘relationships’ between space-time events:<br /><ul><li>Discovery of general patterns in data;
  27. 27. Identification of clusters of related observations or finds (represented as nodes in a linked graph); and
  28. 28. Exploration of represented relationships to aid in further understanding of individual observations.</li></li></ul><li>
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  31. 31. Summary<br />Space-time data capturing aspects of human dynamics is highly differentiated, in terms of:<br />Resolution, granularity, sampling interval<br />Accuracy / quality<br />Compare ‘cellphone tracking’ datasets with space-time ‘event’ datasets, and GPS tracking data. These differ enormously in their sampling properties, can be interpolated to different degrees, and hence require appropriate methods to handle them. <br />The Space-Time Cube is one tool to aid in management, (interactive) analysis and visualisation– as such, it is one component of a complete visual analytics toolkit.<br />In progress: everything, but focus on space-time ‘sampling’ methodology to handle massive datasets.<br />