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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. William Ribarsky, Vis Center
Dr. Barbara Trversky, Stanford
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Urban Visualization
Importance
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Google Earth and Map (Geometric Modeling)
– Enhanced Keyhole with analytical capabilities (Information Modeling)
•
ESRI is the leader in Geographical Information Systems (Geometric Modeling)
– The new push is in visual analytics (Information Modeling)
•
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 Visualization
Definition
Three Key Modalities
– Geometric
• The display of urban models
– Informational
• Information visualization of people and
socioeconomic structures in a city
– Cognitive
• Individuals’ perspectives and
understandings of a city
• Semantic dimension
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Urban Visualization
Urban Analysis
Urban Model Visualization
and Evaluation
Beneficial Apps?
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]
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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
•
•
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
•
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
Urban Simplification (1/2)
•
Simplification based on
Kevin Lynch’s concept
of “Urban Legibility”
•
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 (2/2)
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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
Thoughts…
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Geometric Modeling
– Automatic identification of legibility elements
– Analyze cities with quantifiable measurements
•
Information Modeling
– Intelligent labels
•
Cognitive Modeling
– Automatic generation of mental maps
– Perspective-based urban visualization
– Evaluation and evaluation metrics for urban visualization
•
Long Term Goals
– Create a foundation for understanding complex urban
environments
– New urban theory on how cities are conceptualized
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Urban Visualization
Computer Science
Cognitive Science
Architecture