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Urban Visualization Study Group
Digital Design Center, College of Architecture,
University of North Carolina at Charlotte
The Charlotte Visualization Center, College of
Computing and Informatics, University of North
Carolina at Charlotte
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Urban Visualization Study Group
Remco Chang, Research Scientist, Vis Center
Eric Sauda, Professor, DDC
Ginette Wessel, Graduate Research Assistant, DDC
Dr. Mark A. Livingston, Naval Research Lab
Consultants
Dr. William Ribarsky, Vis Center
Dr. Zachary Wartell, Vis Center
Dr. Robert Kosara, Vis Center
Dr. Jose Gamez, DDC
Dr. Zhong-jie Lin, DDC
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Urban Modeling
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Definition
What is Urban Modeling?
– Geometric Modeling
• The display of urban models
– Information Modeling
• Information visualization of people and
socioeconomic structures in a city
– Cognitive Modeling
• Individuals’ perspectives and
understandings of a city
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Urban Modeling
Architecture and Computer Science
•
Understanding urban environments geometrically, informationally, and cognitively
– There are hundreds of years of research in Architecture
– But Architects tend to be qualitative in their theories
– On-going discourse on urban theories
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Urban theories in Computer Science
– Navigating Virtual Worlds [1]
– Building Virtual Worlds [2]
– Information Visualization and Data Organization
– Procedural Building Generation [4]
[3]
[1] Darken, RP, Allard, T. 1999, Spatial Orientation and Wayfinding in Large-Scale Virtual Spaces II. Presence 8(6): iii-vi.
[2] Parish, Y and Muller, P: 2001, Procedural Modeling of Cities. Computer Graphics ACM SIGGRAPH: 301-308
[3] R. Ingram and S. Benford. Legibility enhancement for information visualization. In IEEE Visualization, 1995.
[4] P. Müller, P. Wonka, S. Haegler, A. Ulmer and L. Van Gool. 2006. Procedural Modeling of Buildings. In Proceedings of SIGGRAPH 2006 / ACM
Transactions on Graphics (TOG), ACM Press, Vol. 25, No. 3, pages 614-623.
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Urban Modeling
Importance
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Google Earth and Map (Geometric Modeling)
– Enhanced Keyhole with analytical capabilities (Information Modeling)
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ESRI is the leader in Geographical Information Systems (Geometric Modeling)
– The new push is in visual analytics (Information Modeling)
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Problem is only getting bigger and more important
– IEEE Spectrum (June 2007)
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Difficult Problem
– Large Scale (Geometric Modeling)
– Very fine details (Geometric Modeling)
– Multiple variables (Information Modeling)
– Competing perspectives and interests (Cognitive Modeling)
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Urban Modeling
Beneficial Apps?
Urban Analysis
Urban Model Visualization
and Evaluation
Creating Intelligent Maps
Urban Training for Soldiers
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benefits
Urban Analysis (1/3)
New York City
Washington DC
•
Charlotte
Quantifying cities allows us to perform… [5]
– Analysis
– Comparison
– Improvement
[5] T. Butkiewicz, R. Chang, Z. Wartell, W. Ribarsky. Visual Analysis of Urban Terrain Dynamics. UCGIS Dynamic Workshop 2006
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benefits
Urban Analysis (2/3)
300
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How “structured” is a city?
– Measures distances between
clusters of buildings
– “Grid-like” structures will have
slower rises in the graphs
– Concept based on Kevin
Lynch [6]
250
200
150
Series1
100
50
0
1 43 85 127 169 211 253 295 337 379 421 463
Atlanta, Georgia
450
400
“[Legibility is] the ease with which
[a city’s] parts may be recognized
and can be organized into a
coherent pattern.” – Lynch 1960
350
300
250
Series1
200
150
100
50
0
1
[6] K. Lynch. The Image of the City. The MIT Press, 1960.
Xinxiang, China
3335 6669 10003 13337 16671 20005 23339
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benefits
•
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Urban Analysis (3/3)
AlphaWorld
– Axial lines depicting roads [7]
– Color indicates “integration”
– Quantification based on Hillier’s
Intelligibility concept
Integration vs. Connectivity
“An intelligible system is one in which wellconnected spaces also tend to be wellintegrated spaces. An unintelligible system
is one where well-connected spaces are not
well integrated” – Hillier 1996
[7] Dalton, R. C. 2002. Is spatial intelligibility critical to the design of large-scale virtual environments? In Journal of Design Computing 4. Special
Issue on Designing Virtual Worlds.
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benefits
Evaluating Urban Models (1/2)
Original Model
45% polygons
18% polygons
Create simplified urban models that retain the
“image of the city” from any view angle and
distance.
Question: How “good” are the simplified
models?
[8] R. Chang, T. Butkiewicz, C. Ziemkiewicz, Z. Wartell, N. Pollard, and W. Ribarsky. Hierarchical simplification of city models to maintain urban
legibility. Technical Report CVC-UNCC-06-01, Visualization Center, UNC Charlotte, 2006.
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benefits
Original Model
Our Textured Model
Evaluating Urban Models (2/2)
Simplified Model using
QSlim
Our Model
Visually and cognitively different, but quantitatively similar
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benefits
Main Ave
Intelligent Maps
Downtown
Main Ave
Downtown
City Park
City Park
Industrial
District
Industrial
District
Canal
Canal
E Street
You are here!
•
•
You are here!
Position-based Intelligent Labeling
Generating Mental Maps
E Street
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benefits
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Urban Training
Training soldiers for urban combat [9]
– Existing technologies (GPS, maps) are dangerous and
difficult to use at times
– New technologies (Augmented Reality) are cumbersome
and unproven
– Mental map of urban environment is the last line of
defense
[9] M. Livingston, L. Rosenblum, S. Julier, D. Brown, Y. Baillot, J. Swan, J. Gabbard, and D. Hix. An augmented reality system for military
operations in urban terrain. In Proceedings of the Interservice / Industry Training, Simulation, and Education Conference, page 89, 2002.
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Urban Modeling
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What We’ve Done
Collaboration between Computer Science and Architecture
– Going beyond “data sharing”
Survey of Urban Theories
– How they benefit urban modeling and visualization
Simplification of Urban Models
– Understanding the geometric layout of a city
Visualization of a City
– Combines geometric modeling with information modeling
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project
Survey of Urban Theories
Survey of Alternate Urban Theories
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Cognitive Mapping (Lynch, Sitte)
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Space Syntax (Hillier)
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Experiential (Cullen)
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Typological (Krier, Rossi)
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Rule (Alexander)
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City.Org (Venturi, Koolhaas)
[10] Allen, Stan. Points and Lines: Diagrams and Projects for the City. New York, Princeton Architectural Press, 1999.
[11] Mitchell, William J. City of Bits. Cambridge: MIT Press, 1995
[12] Mitchell, William J. Placing Words: Symbols, Space, and the City. Cambridge: The MIT Press, 2005.
[13] Koolhaas, Rem et al. Mutations: Harvard Project on the City. Barcelona: ACTAR, 2000.
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project
Urban Simplification (1/3)
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Simplification based on
Kevin Lynch’s concept
of “Urban Legibility”
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Preserves
– Paths
– Edges
– Nodes
– Districts
– Landmarks
Downtown Charlotte
Changing pixel tolerance affects the amount of abstraction
[14] R. Chang, T. Butkiewicz, C. Ziemkiewicz, Z. Wartell, N. Pollard, and W. Ribarsky. Hierarchical simplification of city models to maintain urban
legibility. In SIGGRAPH ’06: ACM SIGGRAPH 2006 Sketches, page 130. ACM Press, 2006
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project
Urban Simplification (2/3)
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project
Urban Simplification (3/3)
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Simplification of geometry (polygons) works well
– Using the Lynchian guideline, evaluation
shows that users can understand the
abstracted buildings intuitively
– Performance of the simplification and
rendering measured using frame rates and
polygon count is good
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Simplification of texture doesn’t work well
– Texture sizes and memory requirement
exceed capabilities of 3D graphics cards
– No guidelines to follow for abstraction,
instead we rely on traditional concepts from
computer graphics
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Shows using architectural theory is helpful in
simplifying and rendering urban models
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project
Urban Visualization
Focus Dependent, Multi-Resolution Visualization of Urban Relationships
[15] R. Chang, G. Wessel, R. Kosara, E. Sauda, and W. Ribarsky. Legible Cities: Focus-Dependent Multi-Resolution Visualization of Urban
Relationships. To Appear: InfoVis 2007, IEEE Transactions on Computer Graphics.
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Urban Modeling
CreativeIT
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Our inter-disciplinary collaboration is unique and has been successful
– Quantifying the abstract theories
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Blazing new trails
– There is no existing conference or journal dedicated to urban modeling.
However, people are starting to notice the importance of the field
• SIGGRAPH 2006 course of urban models
– Need new evaluation metrics and methods
• Existing metrics such as frame rate and polygon count are
inadequate
• Similar to non-photorealistic rendering (NPR), new methods and
metrics are needed to determine “goodness”
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Discovering creative perspectives and solutions to existing problems
– Implement urban theories for understanding urban environments
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Urban Modeling
Thoughts…
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Geometric Modeling
– Continue the work on Urban Simplification and Modeling
– Analyze cities with quantifiable measurements
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Information Modeling
– Incorporate orthogonal data layers into urban visualization
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Cognitive Modeling
– Create mental maps
– Evaluate legibility in urban battlefields
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Long Term Goals
– Testing validity and feasibility of urban theories
– Perspective-based urban visualization
– Urban model depiction based on City.org concepts
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Questions and Comments?
Remco Chang
Eric Sauda
Ginette Wessel
www.coa.uncc.edu
www.viscenter.uncc.edu
[rchang, ejsauda, gmwessel]@uncc.edu