1 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 Urban Visualization Study Group

2 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 Urban Visualization Study Group

3 Google Earth and Map –Enhanced Keyhole with analytical capabilities ESRI is the leader in Geographical Information Systems –The new push is in visual analytics Problem is only getting bigger and more important –IEEE Spectrum (June 2007) Difficult Problem –Large Scale (Geometric Modeling) –Very fine details (Geometric Modeling) –Multiple variables (Information Modeling) –Competing perspectives and interests (Cognitive Modeling) Urban Visualization Importance (Geometric Modeling) (Information Modeling) (Geometric Modeling) (Information Modeling)

4 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

5 Urban Visualization Beneficial Apps? Urban Analysis Urban Model Visualization and Evaluation Urban Training for SoldiersCreating Intelligent Maps

6 benefits Urban Analysis (1/3) Quantifying cities allows us to perform… [5] –Analysis –Comparison –Improvement New York CityWashington DC Charlotte [5] T. Butkiewicz, R. Chang, Z. Wartell, W. Ribarsky. Visual Analysis of Urban Terrain Dynamics. UCGIS Dynamic Workshop 2006

7 benefits Urban Analysis (2/3) 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] Atlanta, Georgia Xinxiang, China [6] K. Lynch. The Image of the City. The MIT Press, “[Legibility is] the ease with which [a city’s] parts may be recognized and can be organized into a coherent pattern.” – Lynch 1960

8 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 well- connected spaces also tend to be well- integrated spaces. An unintelligible system is one where well-connected spaces are not well integrated” – Hillier 1996 [7] Dalton, R. C Is spatial intelligibility critical to the design of large-scale virtual environments? In Journal of Design Computing 4. Special Issue on Designing Virtual Worlds.

9 benefits Evaluating Urban Models (1/2) Original Model45% polygons18% 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.

10 benefits Evaluating Urban Models (2/2) Original ModelSimplified Model using QSlim Our Textured ModelOur Model Visually and cognitively different, but quantitatively similar

11 benefits Intelligent Maps Canal City Park Main Ave E Street You are here! Downtown Industrial District Position-based Intelligent Labeling Generating Mental Maps You are here! Canal City Park Main Ave E Street Downtown Industrial District

12 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.

13 Urban Modeling 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

14 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

15 project Urban Simplification (2/2)

16 project Urban Visualization [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. Focus Dependent, Multi-Resolution Visualization of Urban Relationships

17 Urban Modeling Thoughts… 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

18 Urban Visualization Computer Science Cognitive Science Architecture