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

1 Introduction: Cities at risk 2 Semantic extension of 3D geodata base Integration thematic information Establishing a workflow Populating the city model 3 Geoanalysis of urban risk exposure Mapping levels of urban threat exposure Visibilty for specific buildings 4 Conclusion 0 Presentation overview

1 Introduction: Cities at risk

2 Semantic extension of 3D geodata base

Integration thematic information

Establishing a workflow

Populating the city model

3 Geoanalysis of urban risk exposure

Mapping levels of urban threat exposure

Visibilty for specific buildings

4 Conclusion

1 Introduction

Cities at risk Metropolitan areas High concentration of technical, social and traffic infrastructure Importance in politics, culture, economy and finance Vulnerable environments Different urban regions > different levels and spatial distribution of vulnerability compared to possible security threats Identifying vulnerable urban regions GIS-based analysis coupled with visualisation potential of virtual 3D city models: GIS+VIS Mapping, analysis and assessment of different threat levels in urban environments 1 Introduction

Metropolitan areas

High concentration of technical, social and traffic infrastructure

Importance in politics, culture, economy and finance

Vulnerable environments

Different urban regions > different levels and spatial distribution of vulnerability compared to possible security threats

Identifying vulnerable urban regions

GIS-based analysis coupled with visualisation potential of virtual 3D city models: GIS+VIS

Mapping, analysis and assessment of different threat levels in urban environments

Applying combined GIS/VIS methods 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 Base data Berlin Built-up area of approx. 57,000 buildings Street network Topographic map K5 (1:5,000 scale) Digital terrain model (25m resolution) Digital high resolution aerial photography (HRSC, 20cm resolution) Cities at risk the Berlin case 1 Introduction

Applying combined GIS/VIS methods

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

Base data Berlin

Built-up area of approx. 57,000 buildings

Street network

Topographic map K5 (1:5,000 scale)

Digital terrain model (25m resolution)

Digital high resolution aerial photography (HRSC, 20cm resolution)

Study area: central Berlin transsect Cities at risk the Berlin case 1 Introduction 13x6 km strip of city centre inside inner metropolitan train ring be-tween S-Bahn stations West-kreuz and Ost-kreuz

Study area: central Berlin transsect

13x6 km strip of city centre inside inner metropolitan train ring be-tween S-Bahn stations West-kreuz and Ost-kreuz

2 Populating existing city models

Using existing city models databases Availability of 3D city models significantly increased: various cities have been generating own digital 3D representations Databases include: topographic and cadastral information Databases do not include: application-specific thematic content linked to topographic and 3D data layers Approach requires integration of application-specific thematic information into existing city model databases Integrating thematic information 2 Semantic extension of geodata base

Using existing city models databases

Availability of 3D city models significantly increased: various cities have been generating own digital 3D representations

Databases include: topographic and cadastral information

Databases do not include: application-specific thematic content linked to topographic and 3D data layers

Approach requires integration of application-specific thematic information into existing city model databases

Establishing a workflow Integrating thematic information 2 Semantic extension of geodata base 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

Establishing a workflow

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

Extending 3D city model: integrating activity flows Populating the city model Frequency data provided by FAW 2 Semantic extension of geodata base Detailed information on inner city activity flows facilitates security related analysis: identification of areas, streets and single buildings frequented by few or many people

Extending 3D city model: integrating activity flows

Detailed information on inner city activity flows facilitates security related analysis: identification of areas, streets and single buildings frequented by few or many people

Assigning frequency values to buildings from road segments Populating the city model Frequency data provided by FAW 2 Semantic extension of geodata base Average passenger frequency information is transferred from road segments to adjacent buildings

Assigning frequency values to buildings from road segments

Average passenger frequency information is transferred from road segments to adjacent buildings

Extending the 3D city model: integrating population data Populating the city model 2 Semantic extension of geodata base Socio-demographic data on building block basis: important for identifying & analysing vulnerable city regions ( e.g. population density, family income or purchasing power) Knowledge of these patterns: draw conclusions from exposure concerning a possible hazardous event

Extending the 3D city model: integrating population data

Socio-demographic data on building block basis: important for identifying & analysing vulnerable city regions ( e.g. population density, family income or purchasing power)

Knowledge of these patterns: draw conclusions from exposure concerning a possible hazardous event

3 Analysing urban risk exposure

Identifying urban regions with different levels of exposure Buildings potentially exposed to an increased security risk not evenly distributed in city space Assumption: not every area of urban environment equally exposed to same level of potential threat Regional variations of threat levels in urban environments 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

Identifying urban regions with different levels of exposure

Buildings potentially exposed to an increased security risk not evenly distributed in city space

Assumption: not every area of urban environment equally exposed to same level of potential threat

Regional variations of threat levels in urban environments

Visualising different levels of urban threat exposure Mapping levels of urban threat exposure 3 Geoanalysis of urban risk exposure overlaying city model with grid of user defined cell size

Visualising different levels of urban threat exposure

overlaying city model with grid of user defined cell size

Mapping levels of urban threat exposure Visualising different levels of urban threat exposure 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. Calculating distance of each grid cell to closest building exposed (e.g. embassy) Resulting grid: cells which con-tain one distance

Visualising different levels of urban threat exposure

Calculating distance of each grid cell to closest building exposed (e.g. embassy)

Resulting grid: cells which con-tain one distance

Mapping levels of urban threat exposure Visualising different levels of urban threat exposure 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 Reclassifying according to proximity: grid cells closer to exposed building are assigned a higher exposure value than cells with greater distance

Visualising different levels of urban threat exposure

Reclassifying according to proximity: grid cells closer to exposed building are assigned a higher exposure value than cells with greater distance

Visualising different levels of urban threat exposure 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 GIS-based grid analysis: combination of function-specific grids into single “exposure grid” building uses > different levels of threat exposure Combination by summated weighting of threat exposure grids

Visualising different levels of urban threat exposure

GIS-based grid analysis: combination of function-specific grids into single “exposure grid”

building uses > different levels of threat exposure

Combination by summated weighting of threat exposure grids

Visualising threat exposure Mapping levels of urban threat exposure 3 Geoanalysis of urban risk exposure Combined weighted exposure grid visualised in different user-centered ways: 3D visualisation of a virtual threat surface

Visualising threat exposure

Combined weighted exposure grid visualised in different user-centered ways: 3D visualisation of a virtual threat surface

Developing tools for automated processing Mapping levels of urban threat exposure 3 Geoanalysis of urban risk exposure automated workflow by pro-gramming of specialised GIS tools facilitate automated and fast processing of the single grids

Developing tools for automated processing

automated workflow by pro-gramming of specialised GIS tools

facilitate automated and fast processing of the single grids

Using 3D city model as an analysis tool Visibility of objects (building, area) from specific positions Example: British embassy near Brandenburg Gate Viewing position: roof of a high-rise building, distance 1.3 km Analysis: which parts of embassy building are visible from this perspective? Visibility analysis for specific buildings 3 Geoanalysis of urban risk exposure

Using 3D city model as an analysis tool

Visibility of objects (building, area) from specific positions

Example: British embassy near Brandenburg Gate

Viewing position: roof of a high-rise building, distance 1.3 km

Analysis: which parts of embassy building are visible from this perspective?

4 Conclusion

Summary Approach presented combines GIS-based spatial analysis with innovative 3D visualisations using virtual three-dimensional city models for applications in civil security 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 Combining function-specific grids with threat exposure levels facilitates visualisation of spatial distribution of threat levels Resulting geographic distribution can be combined with additional socio-demographic or infrastructure data for further geovisual analysis 4 Conclusion

Approach presented combines GIS-based spatial analysis with innovative 3D visualisations using virtual three-dimensional city models for applications in civil security

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

Combining function-specific grids with threat exposure levels facilitates visualisation of spatial distribution of threat levels

Resulting geographic distribution can be combined with additional socio-demographic or infrastructure data for further geovisual analysis

Dipl.-Geogr. Markus Wolff [email_address] 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 GI Group, Department of Geography, University of Potsdam www.geographie.uni-potsdam.de Computer Graphics Group, Hasso Plattner Institute (HPI) at University of Potsdam www.hpi.uni-potsdam.de/3d Speaker and Affiliation Project Information

Dipl.-Geogr. Markus Wolff [email_address]

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

GI Group, Department of Geography, University of Potsdam

www.geographie.uni-potsdam.de

Computer Graphics Group, Hasso Plattner Institute (HPI) at University of Potsdam

www.hpi.uni-potsdam.de/3d

Thank you for your attention! Questions? Comments? Feedback? Contact: Markus.Wolff@hpi.uni-potsdam.de Visit our website: www.3dgi.de