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Motivation: Availability of Urban Data Motivation: Availability of Urban Data

Motivation: Availability of Urban Data - PowerPoint Presentation

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Motivation: Availability of Urban Data - PPT Presentation

http bloomingtoningovdocumentsviewDocumentphpdocumentid2455dirbuildingbuildingfootprintsshape https datacityofchicagoorgBuildingsBuildingFootprintsw2v3isjw A lot of POI datasets eg in Google Earth are becoming available now ID: 242361

objects red buildings collocated red objects collocated buildings building compute objects

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Slide1

Motivation: Availability of Urban Data

http://bloomington.in.gov/documents/viewDocument.php?document_id=2455;dir=building/buildingfootprints/shapehttps://data.cityofchicago.org/Buildings/Building-Footprints/w2v3-isjw A lot of POI datasets (e.g. in Google Earth) are becoming available now. Buildings of the City of Chicago (830,000 Polygons) :Challenges:Extract Valuable Knowledge from such datasets Data MiningFacilitate Querying and Visualizing of such dataset HPC / BigData InitiativeSlide2

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Project5 Questions for Dataset Zinj

Are buildings randomly distributed or is there some clustering?

Are buildings of the same building type collocated, anti-collocated or

not?

Are

building belonging to different building types collocated, anti-collocated or not—for example, you will try to answer the question if garages are collocated with commercial buildings.

Idea to answer question: create curves based on number of objects within the radius of another object/kNN-distance,… and obtain answers by comparing curves generated for different contexts.

See:

http://

wiki.landscapetoolbox.org/doku.php/spatial_analysis_methods:ripley_s_k_and_pair_correlation_function

Slide3

Example: Collocation Red and Green Objects

FOR radii r1,…,rn DO FOR all green objects g DO Compute #-of-red objects within radius rj of g ENDDO Compute average roj of values observed in previous loop Put entry (rj, (roj/total_number_of_red_objects)) into Curve ENDDO Slide4

An Alternative Approach Using

k-Nearest-Neighbor DistanceFOR k=k1,…,kr DO FOR all green objects gp DO Compute distance rdp to k-nearest red object to g ENDDO Compute average rdi of values observed in previous loop Put entry (ki, rdi) into the Curve ENDDORemark: For k-values use 0.1% of the red objects; 0.1*1.5 of the red objects

, 0.1%*1.52 of the red objects ,

0.1%*1.5

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of the red objects

,…, until at most 50% of the red objects—with x being the ceiling function computing the smallest integer that is greater equal than x.

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