Understanding Urban Environments Through Urban Legibility

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Understanding Urban EnvironmentsThrough Urban
Legibility

• Remco Chang

UNC Charlotte
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Outline

• Hierarchical Simplification of City Models to
  Maintain Urban Legibility SIGGRAPH Sketch 06
• Discussion and Future Work
• Computer Graphics
• Architecture Book Chapter UCGIS 06
• Visualizing Urban Forms

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Research DirectionKnowledge Visualization
Minimize Resources, Maximize Information

• Rendering Effective Route Maps Improving
  Usability Through Generalization Agrawala and
  Stolte 2001

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Knowledge Visualization
?
City of Xinxiang, China 30k buildings 280k
polygons
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Urban Legibility

• Kevin Lynch in The Image of the City (1960, MIT
  Press)
• categorized Urban Legibility into
• Paths highways, railroads, canals
• Edges shorelines, boundaries
• Districts industrial, residential
• Nodes Time Square in NYC
• Landmarks Empire State building
• ...the ease with which a citys parts may be
  recognized and can be organized into a coherent
  pattern.

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Using Urban Legibility in Computer
Science

• Dalton 2002 history of the use of Urban
  Legibility in computer science
• Empirical justification of Urban Legibility
• Paths, Edges, and Districts are very important to
  human navigation (Darken and Sibert 1996,
  Magliano, Cohen et al. 1995)
• Landmarks do not always improve navigation
  (Tlauka and Wilson 1994 Magliano, Cohen et al.
  1995 Steck and Mallot 2000)
• Using Urban Legibility in Graphics and
  Visualization
• Ingram and Benford navigating abstract data
  spaces

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Why Urban Legibility?
Visually different, but quantitatively similar
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Our Goal

• Create simplified urban models that retain the
  image of the city from any view angles and
  distances.

Demo!
Original Model
45 polygons
18 polygons
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Related Works in
Urban Flythrough

• Visibility and Occlusion
• Wonka et al. 2000 and Schaufler et al. 2000
• Imposters
• Marciel and Shirley 1995, Sillion et al.
  1997, and Shalabi 1998
• Procedurally Generated Buildings
• Wonka et al. 2003 Mueller et al. 2006
• Popping
• Microsoft Live 2006 Google Earth 2005

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Algorithms to Preserve Legibility

• Identify and preserve Paths and Edges
• Create logical Districts and Nodes
• Simplify model while preserving Paths, Edges,
  Districts, and Nodes
• Hierarchically apply appropriate amount of
  texture
• Highlight Landmarks and choose models to render

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Identifying and Preserving Paths
and Edges
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Identifying and Preserving Paths
and Edges (1)
bc
de
def
abc

• Single-Link Clustering
• Iteratively groups the closest clusters
  together based on Euclidean distance
• produces a binary tree (dendrogram)
• Penalizes large clusters to create a more
  balanced tree

bcdef
abcdef
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Identifying and Preserving Paths
and Edges (2)
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Creating logical
Districts and Nodes
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Creating logical
Districts and Nodes (1)
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Creating logical
Districts and Nodes (2)

• Merge two clusters by combining footprints

• (c) The resulting Merged Hull
• (d) The Introduced Error, or Negative Space

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Simplification while preserving Paths,
Edges, Nodes, and Districts
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Simplification while preserving Paths,
Edges, Nodes, and Districts (1)
6000 edges
1000 edges
Demo!
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Simplification while preserving Paths,
Edges, Nodes, and Districts (2)

• After the polylines have been simplified
• Create Cluster Meshes
• The height of the Cluster Mesh is the median
  height of all buildings in the cluster

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Hierarchical Textures
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Hierarchical Textures (1)

• Each Cluster Mesh contains 6 textures
• 1 Side Texture
• 1 top-down view of the roof texture
• 4 roof textures from 4 angles
  (south, west,
  east, north)

Side texture
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Hierarchical Textures (2)

• Clusters are divided into bins based on their
  visual importance
• Each bin contains a texture atlas
• Texture atlases from all bins have the same
  dimension

n/2
n/4
n/8

.
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Runtime Levels of Detail
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Runtime Levels of Detail

• Starting with the root node of the dendrogram
• Approximate the Negative Space as a 3D box
  shown as the red box
• Project the visible sides of the box onto screen
  space
• Reject if the number of pixel is above a
  user-defined tolerance

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Landmark and Skyline Preservation (1)
Original Skyline
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Landmark and Skyline Preservation (2)

• Project a user-defined pixel tolerance (a) onto
  the top of each cluster
• If any building within that cluster is taller
  than the projected tolerance (shown in green), it
  is drawn separately from the cluster mesh.

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Results
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Conclusion

• Per-pixel error is not indicative of the visual
  quality of simplified urban models
• Higher-level knowledge from city planning helps
  extract visually salient features
• Urban Legibility allows efficient and intuitive
  simplification of urban models

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Limitations

• The rendering engine
• Currently not using display lists, vertex arrays,
  frustum culling etc.
• The pre-processing steps
• Clustering, merging, and simplification are all
  O(n3) processes
• Deep hierarchy tree
• Binary trees are deeper than quad trees
• Allow user interactions
• Let experts manually select Districts

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Special Thanks

• Co-authors Tom Butkiewicz, Caroline Ziemkiewicz,
  Zachary Wartell, Bill Ribarsky (UNC Charlotte),
  and Nancy Pollard (Carnegie Mellon University)
• This work is supported by the Department of
  Defense's MURI program, administered by the Army
  Research Office
• Eric Sauda and Jose Gamez from the Architecture
  Department for lessons on Urban Legibility
• Sonia Leach for inspiration on the clustering
  algorithm
• Evan Suma for making this software run in stereo
• Danny Fregosi and Hunter Hale for data preparation

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Part 1 Questions and Comments?

• Full paper can be found at
• http//www.viscenter.uncc.edu

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Discussion and Future Work
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Architecture (Urban Morphology)

• Feature Extraction of Urban Legibility Elements
• Hierarchy of urban legibility elements
• Quantify and identify an urban model
• Semantic understanding of an urban model

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Discussion and Future Work
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Comparing Cities

• Comparison between Different Cities
• How are New York, Washington DC, and Charlotte
  different?
• How a City Changes Over Time
• A fundamental challenge in GIS

New York
Washington DC
Charlotte
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Discussion and Future Work
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Labeling

• Position-based Intelligent Labeling
• Generating Mental Maps

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Discussion and Future Work
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Visualizing Urban Form

• Study of Urban Form
• Hasnt changed since 19th Century
• People perceive urban form as 2D or 3D maps
• Maps do not actively help the viewer understand
  the changes or trends occurring in the city

Image of ArcGIS (courtesy of ESRI)
Image of ArcGIS (courtesy of ESRI)
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Visualizing Urban Form

• Applying Visual Analytics to Urban Form

System does not contain actual urban data
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Discussion and Future Work
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Discussion and Future Work

• Computer Graphics
• Urban Model Compression
• Progressive Streaming Meshes
• Extending to 3D Models
• 3D Model of Buildings
• Trees and Forests
• Share similarities with urban models in that
  viewing large quantities is difficult

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Questions and Comments?

• http//www.viscenter.uncc.edu
• Remco Chang
• rchang_at_uncc.edu