Dr JeanClaude Thill Knight Distinguished Professor Geography UNCC Dr Remco Chang Research Scientist Vis Center UNCC Eric Sauda Professor DDC School of Architecture UNCC Ginette ID: 311960
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UrbanVis
Dr. Jean-Claude Thill, Knight Distinguished Professor, Geography, UNCCDr. Remco Chang, Research Scientist, Vis Center, UNCCEric Sauda, Professor, DDC, School of Architecture, UNCCGinette Wessel, Doctoral Student, Architecture, BerkeleyElizabeth Unruh, Research Assistant, DDC, School of Architecture, UNCC
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UrbanVisProblemcomplexity and heterogeneity of information new city formsgateway visualization through spaceProblemRapid growth of UrbanismLayers of InformationSpatial and Semantic InformationIll defined (or even Wicked) UrbanismSlide3
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UrbanVisProblemcomplexity and heterogeneity of information new city formsgateway visualization through spaceProblemRapid growth of UrbanismSlide4
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UrbanVisProblemcomplexity and heterogeneity of information new city formsgateway visualization through spaceProblemRapid growth of UrbanismSlide5
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UrbanVisProblemcomplexity and heterogeneity of information new city formsgateway visualization through spaceProblemLayers of InformationNot just more informationBut heterogenous informationSlide6
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UrbanVisProblemcomplexity and heterogeneity of information new city formsgateway visualization through spaceProblemSpatial and Semantic InformationTwo forms of informationSemantic (what)Spatial (where)Neurological basisSlide7
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UrbanVisProblemcomplexity and heterogeneity of information new city formsgateway visualization through spaceProblemIll defined (or even Wicked) UrbanismRelationship of form of the city to its contentEvidence from Urban TheoryWell definedIll DefinedWickedSlide8
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UrbanVisProblemcomplexity and heterogeneity of information new city formsgateway visualization through spaceUrban TheoryIdeal plan of Sforzinda, 1464.
Monteriggioni
Diocaesarea
Limited Size
LocalClear BoundariesHonored positions
Well definedSlide9
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UrbanVisProblemcomplexity and heterogeneity of information new city formsgateway visualization through spaceUrban TheoryGrowth of Dhaka City 1600-1980Explosive growthTransportations TechnologyRegionalBlurred edgesHonored positionsIll definedSatellite view of
BosWashSlide10
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UrbanVisProblemcomplexity and heterogeneity of information new city formsgateway visualization through spaceUrban TheoryCamillo Sitte(Rob KrierAldo RossiNew Urbanism……………)Slide11
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UrbanVisProblemcomplexity and heterogeneity of information new city formsgateway visualization through spaceUrban TheoryShibuya, Tokyo, 2009.Robotvision
MultinodalOverlay of new mediaInformationUrban-Rural goneView dependentWicked CitySlide12
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UrbanVisProblemcomplexity and heterogeneity of information new city formsgateway visualization through spaceUrban TheoryCarlo Ratti, Senseable CitiesCarlo Ratti(Rem KoolhaasBernard TschumiLandscape UrbanismHenri LeLefebvre
……………)Slide13
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UrbanVisProblemcomplexity and heterogeneity of information new city formsgateway visualization through spaceGeography & Geographic Information SciencesStudy of phenomena from the perspective of their spatial relations:Location, scale, place, and spaceSemantic generalization of the City
Defining socially coherent and homogeneous neighborhoodsUse of factor analysis and cluster analysis to reduce the data matrix to a few latent dimensions and a few regions: TypologyMore recently, use of data mining techniques such as self-organizing maps
GeodemographicsSlide14
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UrbanVisProblemcomplexity and heterogeneity of information new city formsgateway visualization through spaceFuzzy SOM regional classification of Athens, Greece (Hatzichristos, 2004)Slide15
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UrbanVisProblemcomplexity and heterogeneity of information new city formsgateway visualization through spaceQuality of life, Charlotte, NCSlide16
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UrbanViscomplexity and heterogeneity of information new city formsgateway visualization through spaceScale-dependence and generalizationCartographic representationAlgorithms that preserve spatial property of data: topology, density, geometryMultiple scale-dependent representationsAllowing for queries
Preserving consistencySlide17
research groupUrbanVis
Urban Analytics: Data IntegrationData integrationHeterogeneity of semantic data layersPoints, lines, polygons, volumesCommon data structure: data rasterApproachesGeospatial overlays
Kernel density estimation for point and line data
Dasymetric
methodsSlide18
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UrbanVisProblemcomplexity and heterogeneity of information new city formsgateway visualization through spaceUrban Analytics: Information TheoryShannon’s Information Theory:
Where
N = number of houses in a cluster
N
j
= number of houses that fit a specific criteriaSlide19
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UrbanVisProblemcomplexity and heterogeneity of information new city formsgateway visualization through spaceUrban Analytics: Applying Information Theory Hierarchicallyabcde
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UrbanVisProblemcomplexity and heterogeneity of information new city formsgateway visualization through spaceUrban Analytics: Information Theory Applied
Information Theory has been used and applied to clusteringIn particular, it has been applied to categorical data clustering where the distance measurement between clusters is difficult to define.In visualization, as well as in urban computing, when information theory is applied hierarchically,
The hierarchy is mostly applied to a grid structureWhile generalizable, it defeats the purpose of creating “legible cities”
We propose to merge the work on urban legibility with information theory to:Create hierarchies based on both spatial (geometric) information, as well as semantic information
Traverse the hierarchy to determine “neighborhoods” in a city based on both geometric and semantic information.Slide21
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UrbanVisProblemcomplexity and heterogeneity of information new city formsgateway visualization through spaceUrban Analytics: Geometric + SemanticCurrently, our algorithm works only on geometric information for creating the clusters.Clusters are created based on the geometric distances between buildingsTo integrate geometric and semantic information, the naïve method would be to add weights to the two variables, for example:
Distance between clusters = (α
* geometric distance) + (
β
* semantic similarities)
However, it’s clear that if this equation is applied to clustering buildings in a city, there will be clusters that are not geometrically contiguous (and therefore not legible)
Our proposed approach is a two-staged approach:
1. find geometric neighbors.
2. cluster them if their semantic similarities are within an acceptable range.Slide22
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UrbanVisProblemnew city formsgateway visualization through spaceUrban Analytics: Sketch Mapping StudyMore segments, less neighborhoodsMore segments, less landmarksLocal ScaleCitywide ScaleSlide23
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UrbanVisProblemnew city formsgateway visualization through spaceUrban Analytics: Sketch Mapping StudyRated Most EffectiveRated Least EffectiveSlide24
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UrbanVisEvaluation of Method through Urban MorphologyWe have claimed that our algorithm creates legible clusters. Validation through expert-user evaluation.However, a computational approach could be helpful and more informative.
How “structured” is a city?Plot the distance used in each step of the single-link clustering onto a graph.
“Grid-like” structures will have slower rises in the graphs
Atlanta, Georgia
Xinxiang, ChinaSlide25
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UrbanVisEvaluation of Method through Urban MorphologyConcept similar to that of “Space Syntax”, which is a method to compute the “intelligibility” of a city.Converts a city into a graphComputes “integration” and “connectivity”
Example: AlphaWorldAxial lines depicting roads
[7]
Color indicates “integration”
“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