Wolff3 D

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Third International Workshop on "Geographical Analysis, Urban Modeling, Spatial Statistics"

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Wolff3 D

  1. 1. Geospatial Modelling of Urban Security: A novel approach with Virtual 3D City Models Markus Wolff, Hartmut Asche 3D Geoinformation Research Group, University of Potsdam ICCSA | Perugia | July 2008
  2. 2. <ul><li>1 Introduction: Cities at risk </li></ul><ul><li>2 Semantic extension of 3D geodata base </li></ul><ul><li>Integration thematic information </li></ul><ul><li>Establishing a workflow </li></ul><ul><li>Populating the city model </li></ul><ul><li>3 Geoanalysis of urban risk exposure </li></ul><ul><li>Mapping levels of urban threat exposure </li></ul><ul><li>Visibilty for specific buildings </li></ul><ul><li>4 Conclusion </li></ul>0 Presentation overview
  3. 3. 1 Introduction
  4. 4. Cities at risk <ul><li>Metropolitan areas </li></ul><ul><li>High concentration of technical, social and traffic infrastructure </li></ul><ul><li>Importance in politics, culture, economy and finance </li></ul><ul><li>Vulnerable environments </li></ul><ul><li>Different urban regions > different levels and spatial distribution of vulnerability compared to possible security threats </li></ul><ul><li>Identifying vulnerable urban regions </li></ul><ul><li>GIS-based analysis coupled with visualisation potential of virtual 3D city models: GIS+VIS </li></ul><ul><li>Mapping, analysis and assessment of different threat levels in urban environments </li></ul>1 Introduction
  5. 5. <ul><li>Applying combined GIS/VIS methods </li></ul><ul><li>Aim: to investigate geoanalytical potential of GIS+VIS analysis Approach: application of GIS+VIS methods to model urban risks in the centre of the German capital Berlin </li></ul><ul><li>Base data Berlin </li></ul><ul><li>Built-up area of approx. 57,000 buildings </li></ul><ul><li>Street network </li></ul><ul><li>Topographic map K5 (1:5,000 scale) </li></ul><ul><li>Digital terrain model (25m resolution) </li></ul><ul><li>Digital high resolution aerial photography (HRSC, 20cm resolution) </li></ul>Cities at risk the Berlin case 1 Introduction
  6. 6. <ul><li>Study area: central Berlin transsect </li></ul>Cities at risk the Berlin case 1 Introduction <ul><li>13x6 km strip of city centre inside inner metropolitan train ring be-tween S-Bahn stations West-kreuz and Ost-kreuz </li></ul>
  7. 7. 2 Populating existing city models
  8. 8. <ul><li>Using existing city models databases </li></ul><ul><li>Availability of 3D city models significantly increased: various cities have been generating own digital 3D representations </li></ul><ul><li>Databases include: topographic and cadastral information </li></ul><ul><li>Databases do not include: application-specific thematic content linked to topographic and 3D data layers </li></ul><ul><li>Approach requires integration of application-specific thematic information into existing city model databases </li></ul>Integrating thematic information 2 Semantic extension of geodata base
  9. 9. <ul><li>Establishing a workflow </li></ul>Integrating thematic information 2 Semantic extension of geodata base <ul><li>Specifically developed workflow integrates GIS, to which thematic information is added. Therefore analysis of pedestrian flows and demographic parameters is facilitated. The visualisation system facilitating visual analysis through 3D visualisations </li></ul>
  10. 10. <ul><li>Extending 3D city model: integrating activity flows </li></ul>Populating the city model Frequency data provided by FAW 2 Semantic extension of geodata base <ul><li>Detailed information on inner city activity flows facilitates security related analysis: identification of areas, streets and single buildings frequented by few or many people </li></ul>
  11. 11. <ul><li>Assigning frequency values to buildings from road segments </li></ul>Populating the city model Frequency data provided by FAW 2 Semantic extension of geodata base <ul><li>Average passenger frequency information is transferred from road segments to adjacent buildings </li></ul>
  12. 12. <ul><li>Extending the 3D city model: integrating population data </li></ul>Populating the city model 2 Semantic extension of geodata base <ul><li>Socio-demographic data on building block basis: important for identifying & analysing vulnerable city regions ( e.g. population density, family income or purchasing power) </li></ul><ul><li>Knowledge of these patterns: draw conclusions from exposure concerning a possible hazardous event </li></ul>
  13. 13. 3 Analysing urban risk exposure
  14. 14. <ul><li>Identifying urban regions with different levels of exposure </li></ul><ul><li>Buildings potentially exposed to an increased security risk not evenly distributed in city space </li></ul><ul><li>Assumption: not every area of urban environment equally exposed to same level of potential threat </li></ul><ul><li>Regional variations of threat levels in urban environments </li></ul>Mapping levels of urban threat exposure 3 Geoanalysis of urban risk exposure assumed increased highly increased threat level Buildings shopping centres embassies petrol stations consulates police posts government offices power/transformer stations
  15. 15. <ul><li>Visualising different levels of urban threat exposure </li></ul>Mapping levels of urban threat exposure 3 Geoanalysis of urban risk exposure <ul><li>overlaying city model with grid of user defined cell size </li></ul>
  16. 16. Mapping levels of urban threat exposure <ul><li>Visualising different levels of urban threat exposure </li></ul>3 Geoanalysis of urban risk exposure value of closest exposed building: embassy grid, e.g., contains distance cell values of closest buildings: embassies, consulate offices etc. <ul><li>Calculating distance of each grid cell to closest building exposed (e.g. embassy) </li></ul><ul><li>Resulting grid: cells which con-tain one distance </li></ul>
  17. 17. Mapping levels of urban threat exposure <ul><li>Visualising different levels of urban threat exposure </li></ul>3 Geoanalysis of urban risk exposure distance from exposure basis building [m] level 0 to 25 1 > 25 to 50 0.5 > 50 to 100 0.2 >100 to 200 0.1 >200 0 <ul><li>Reclassifying according to proximity: grid cells closer to exposed building are assigned a higher exposure value than cells with greater distance </li></ul>
  18. 18. <ul><li>Visualising different levels of urban threat exposure </li></ul>Mapping levels of urban threat exposure grid weight embassies 4 consulates 4 shopping centres 2 petrol stations 1 government offices 4 police posts 1 power or transformer stations 1 3 Geoanalysis of urban risk exposure <ul><li>GIS-based grid analysis: combination of function-specific grids into single “exposure grid” </li></ul><ul><li>building uses > different levels of threat exposure </li></ul><ul><li>Combination by summated weighting of threat exposure grids </li></ul>
  19. 19. <ul><li>Visualising threat exposure </li></ul>Mapping levels of urban threat exposure 3 Geoanalysis of urban risk exposure <ul><li>Combined weighted exposure grid visualised in different user-centered ways: 3D visualisation of a virtual threat surface </li></ul>
  20. 20. <ul><li>Developing tools for automated processing </li></ul>Mapping levels of urban threat exposure 3 Geoanalysis of urban risk exposure <ul><li>automated workflow by pro-gramming of specialised GIS tools </li></ul><ul><li>facilitate automated and fast processing of the single grids </li></ul>
  21. 21. <ul><li>Using 3D city model as an analysis tool </li></ul><ul><li>Visibility of objects (building, area) from specific positions </li></ul><ul><li>Example: British embassy near Brandenburg Gate </li></ul><ul><li>Viewing position: roof of a high-rise building, distance 1.3 km </li></ul><ul><li>Analysis: which parts of embassy building are visible from this perspective? </li></ul>Visibility analysis for specific buildings 3 Geoanalysis of urban risk exposure
  22. 22. 4 Conclusion
  23. 23. Summary <ul><li>Approach presented combines GIS-based spatial analysis with innovative 3D visualisations using virtual three-dimensional city models for applications in civil security </li></ul><ul><li>Augmenting existing spatial database of virtual 3D city models by variety of parameters including building occupancy, frequency values and socio-demographic parameters, allows for identification of areas and objects exposed to specific levels of threat </li></ul><ul><li>Combining function-specific grids with threat exposure levels facilitates visualisation of spatial distribution of threat levels </li></ul><ul><li>Resulting geographic distribution can be combined with additional socio-demographic or infrastructure data for further geovisual analysis </li></ul>4 Conclusion
  24. 24. <ul><li>Dipl.-Geogr. Markus Wolff [email_address] </li></ul><ul><li>Research Group “3D Geoinformation – modelling, processing and system integration” at University of Potsdam (2006-2011, Döllner & Asche), funded by German Federal Ministry of Edu-cation and Research BMBF www.3dgi.de </li></ul><ul><li>GI Group, Department of Geography, University of Potsdam </li></ul><ul><li>www.geographie.uni-potsdam.de </li></ul><ul><li>Computer Graphics Group, Hasso Plattner Institute (HPI) at University of Potsdam </li></ul><ul><li>www.hpi.uni-potsdam.de/3d </li></ul>Speaker and Affiliation Project Information
  25. 25. Thank you for your attention! Questions? Comments? Feedback? Contact: Markus.Wolff@hpi.uni-potsdam.de Visit our website: www.3dgi.de

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