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Vis-A-Ware: Integrating Spatial and Non-Spatial Visualization for Domain Practice Visibility-Aware Urban Planning. High Level Overview High Level Overview Experts create visibility and Urban planning Urban planning Thomas Ortner,


  1. Vis-A-Ware: Integrating Spatial and Non-Spatial Visualization for Domain Practice Visibility-Aware Urban Planning. High Level Overview High Level Overview • Experts create visibility and • Urban planning • Urban planning Thomas Ortner, Johannes Sorger, Harald Steinlechner, Gerd Hesina, Harald landmark occlusion maps • What is the visual impact of new buildings on city scape? Piringer, Eduard Groller. • What is the visual impact of new buildings on city scape? • How will it look from multiple different perspectives? IEEE TVCG 23(2):1139-1151 2017 • How will it look from multiple different perspectives? • How can we easily compare different buildings beyond subjective perception? • How can we easily compare different buildings beyond subjective perception? • Vis-A-Ware • Qualitative and quantitative evaluation, ranking, and comparison on the different types of “visibility” of candidate buildings from various viewpoints • Links together a 3D spatial urban view with non-spatial data for more context Matthew Chun 2 3 4 Domain Practice Domain Practice Task Analysis Task Analysis • Photo montages that overlay • 3D rendering from a real images with virtual few viewpoints • With a combination of above techniques, compare candidate buildings • With a combination of above techniques, compare candidate buildings candidate buildings with respect to visual impact (Current Practices) with respect to visual impact (Current Practices) • Haptic models • Qualitative -> Potential subjective bias • Qualitative -> Potential subjective bias • Can only compare a few viewpoints at a time • Can only compare a few viewpoints at a time • Can we also compare candidate buildings in a more holistic manner? (Suggested New Practice) • Quantitative -> More specificity in details (eg. How occluded) • More comparisons possible -> Multiple viewpoints • Is it possible to combine the current and new approaches? 5 6 7 8 Related Work Related Work Related Work Design Goals • Occlusion culling • Occlusion culling • Occlusion culling • G1: Compute intuitive metrics for quantifying visual impact of • Geographic Info • Geographic Info candidates with respect to specific viewpoints System (GIS) System (GIS) • Multiple Criteria Decision Analysis (MCDA) • Coordinated Multiple Views (CMV) 9 10 11 12 Design Goals Design Goals Design Goals Design Goals • G1: Compute intuitive metrics for quantifying visual impact of • G1: Compute intuitive metrics for quantifying visual impact of • G1: Compute intuitive metrics for quantifying visual impact of • G1: Compute intuitive metrics for quantifying visual impact of candidates with respect to specific viewpoints candidates with respect to specific viewpoints candidates with respect to specific viewpoints candidates with respect to specific viewpoints • G2: Tight integration of spatial views and non-spatial views to allow for • G2: Tight integration of spatial views and non-spatial views to allow for • G2: Tight integration of spatial views and non-spatial views to allow for • G2: Tight integration of spatial views and non-spatial views to allow for a linked analysis of quantitative and qualitative data a linked analysis of quantitative and qualitative data a linked analysis of quantitative and qualitative data a linked analysis of quantitative and qualitative data • G3: Fast identification of candidates or viewpoints exhibiting high • G3: Fast identification of candidates or viewpoints exhibiting high • G3: Fast identification of candidates or viewpoints exhibiting high visual impact values visual impact values visual impact values • G4: Providing an overview of the spatial distribution of viewpoints with • G4: Providing an overview of the spatial distribution of viewpoints with high visual impact high visual impact • G5: Intuitive filtering, ranking, and comparison of candidates as well as viewpoints 13 14 15 16

  2. Design Goals Vis-A-Ware System Overview – Visual Impact Metrics (VIM) Video Spatial Non-spatial • Coded by “false colour” -> colour that Metrics - Cols • G1: Compute intuitive metrics for quantifying visual impact of candidates stands out in a scene • https://vimeo.com/183311609 with respect to specific viewpoints • Landmarks are red • G2: Tight integration of spatial views and non-spatial views to allow for a • Sky is blue Viewpoints linked analysis of quantitative and qualitative data /Candidates • Openness is green • G3: Fast identification of candidates or viewpoints exhibiting high visual - Rows • Candidate building is orange impact values • G4: Providing an overview of the spatial distribution of viewpoints with high Visual visual impact Impact - Cell • G5: Intuitive filtering, ranking, and comparison of candidates as well as viewpoints • G6: Incorporating exploration and visualization metaphors users are familiar Viewpoint POV with from existing tools 17 18 19 20 Transposable Ranking View (TRV) - Visual Encoding Transposable Ranking View (TRV) - Visual Encoding System Overview – Visual Impact Metrics (VIM) System Overview – Transposable Ranking View (TRV) Viewpoint Major Mode Viewpoint Major Mode • Coded by “false colour” -> colour that Pop-out is Pop-out is stands out in a scene • Main way to filter, rank, compare candidates based on VIM a) Bar charts show VIM for each candidate a) Bar charts show VIM for each candidate for example for example • Landmarks are red (letter) in distribution, saturation shows (letter) in distribution, saturation shows • Data Model • Sky is blue impact class impact class • Openness is green b) Stacked bar chart is compact rep. of bar • Candidate building is orange charts • To get a number, normalized on a ratio Click on Click on • # of pixels of VIM of interest/# of row to row to candidate pixels “expand it” “expand it” • Bin categories for more for more detailed detailed • Low, medium, high, very high view view • How relevant is particular viewpoint? • all candidate pixels/total number of image pixels • Bin categories • Small, medium, high 21 22 23 24 Transposable Ranking View (TRV) - Visual Encoding Transposable Ranking View (TRV) - Visual Encoding Transposable Ranking View (TRV) - Visual Encoding Transposable Ranking View (TRV) - Visual Encoding Viewpoint Major Mode Viewpoint Major Mode Viewpoint Major Mode Viewpoint Major Mode Pop-out is Pop-out is Pop-out is Pop-out is a) Bar charts show VIM for each candidate a) Bar charts show VIM for each candidate a) Bar charts show VIM for each candidate a) Bar charts show VIM for each candidate for example for example for example for example (letter) in distribution, saturation shows (letter) in distribution, saturation shows (letter) in distribution, saturation shows (letter) in distribution, saturation shows impact class impact class impact class impact class b) Stacked bar chart is compact rep. of bar b) Stacked bar chart is compact rep. of bar b) Stacked bar chart is compact rep. of bar b) Stacked bar chart is compact rep. of bar charts charts charts charts c) Linked peek brushing shows detail on c) Linked peek brushing shows detail on c) Linked peek brushing shows detail on c) Linked peek brushing shows detail on Click on Click on Click on Click on demand and current candidate across other demand and current candidate across other demand and current candidate across other demand and current candidate across other row to row to row to row to “expand it” “expand it” “expand it” “expand it” viewpoints (letter) viewpoints (letter) viewpoints (letter) viewpoints (letter) for more for more for more for more d) Any row that is ranked by distribution d) Any row that is ranked by distribution d) Any row that is ranked by distribution detailed detailed detailed detailed scores over all viewpoints scores over all viewpoints scores over all viewpoints view view view view e) Arrow icon loads into spatial view of tool e) Arrow icon loads into spatial view of tool (Map) (Map) f) A high level summary of a category of viewpoint 25 26 27 28 Transposable Ranking View (TRV) – Focus, Filter, Transpose Workflow Example Transposable Ranking View (TRV) – Focus, Filter, Transpose Workflow Example Transposable Ranking View (TRV) – Focus, Filter, Transpose Workflow Example Transposable Ranking View (TRV) – Focus, Filter, Transpose Workflow Example Filter/transpose option on focused set Select high impact Can focus again. Can Focused subset now VIM portion across expand a row (vp48) emphasized with split all viewpoints for more detailed bar heightened bar charts (Candidate Visibility) chart -> exact (left). Remaining Use filter option above to see of interest candidate VIM values distribution lowered in filtered viewpoint distributions height for context (right). now. Emphasize focused area for inspection. 29 30 31 32

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