Identifying Divergent Building Structures Using Fuzzy Clustering of Isovist Features
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Identifying Divergent Building Structures Using Fuzzy Clustering of Isovist Features Sebastian Feld*, Hao Lyu^, Andreas Keler° *Mobile and Distributed Systems Group, LMU Munich ^Department of Cartography, TUM Munich °Applied Geoinformatics, University of Augsburg [email protected] 13th ICA LBS 2016, Vienna, Austria November 14th, 2016 MOTIVATION “Alternative routes in complex environments” Indoor Navigation ▪ Assistance in hospitals, airports, fairs, … ▪ Mobile robots (smart city, ambient assistant living) ▪ Non-player character in computer games Alternative routes ▪ What is an alternative route? How to find them? ▪ Is there a quality of alternative routes? ▪ How to get preferably diverse routes? Leaving the application level ▪ Similarity or distance metrics of geospatial trajectories ▪ In particular regarding indoor scenarios, i.e. floor plans of buildings Archetypal Isovists | 14.11.2016 Slide | 2 TWO CONTRIBUTIONS Basically ▪ A background service for LBS Maps ▪ Insights about building structure using plain floorplan ▪ Classification of indoor areas and surroundings (entrance areas, corridors, halls, streets) ▪ Clustering of isovist features via archetypal analysis Routes ▪ Analyze effect of isovist features on archetypal routes ▪ Alternative routes based on perception Archetypal Isovists | 14.11.2016 Slide | 3 ISOVISTS 퐴푥 area 푃푥 real-surface perimeter (amount of visible obstacle surface) 푄푥 occlusivity (length of occluding radial boundary) 푀2,푥 variance (distribution of radials length) 푀3,푥 skewness (distribution’s asymmetry) 푁푥 circularity (isoperimetric quotient) Benedikt M (1979) To take hold of space: isovist and isovist fields. Environment and Planning B 6(1): 47-65 Archetypal Isovists | 14.11.2016 Slide | 4 ARCHETYPAL ANALYSIS Archetypal analysis (AA) ▪ Focusses on extrema, prototypes, originals, pure types “preferably diverse” ▪ Approximates dataset’s convex hull in feature space using 푘 points (=archetypes) Error decreases with different number of 푘 archetypes ▪ 푘 = 1 average ▪ 푘 = 푛 each observation is an archetype Use screeplot and “elbow criterion” to identify suitable value of 푘 Cutler A, Breiman L (1994) Archetypal analysis. Technometrics 36(4): 338-347 Archetypal Isovists | 14.11.2016 Slide | 5 ARCHETYPAL ANALYSIS Observations Features Archetypes Wayne Gretzky GP Games Played Archetype A Mark Messier G Goals Archetype B Gordie Howe A Assists Archetype C Jaromir Jagr PIM Penalty Minutes … Ron Francis PPG Power Play Goals Marcel Dionne S Shots Steve Yzerman TOI/GP Time On Ice Per Game Interpretation Mario Lemieux Shifts/GP Shifts Per Game Joe Sakic FOW% Faceoff Win Percentage Forward Phil Esposito … Defensemen … Goaltenders … Archetypal Isovists | 14.11.2016 Slide | 6 RESULTS – CLUSTERING THE MAP Input ▪ Map with 푟 rows and 푐 columns ▪ 푁 × 푚 matrix 푋 ▪ 푁 = 푟 × 푐 observations ▪ 푚 = 6 attributes 퐴푥, 푃푥, 푄푥, 푀2,푥, 푀3,푥, 푁푥 Archetypal Isovists | 14.11.2016 Slide | 7 RESULTS – CLUSTERING THE MAP Parallel coordinates plot ▪ Isovist features on abscissae, corresponding values on ordinate ▪ Each gray line represents a walkable pixel ▪ 푘 = 3, i.e. three archetypes Archetypal Isovists | 14.11.2016 Slide | 8 RESULTS – CLUSTERING THE MAP Interpretation ▪ Good lookout with extremely high area 퐴푥 ▪ Moderate real-surface 푃푥, high occlusivity 푄푥 and high variance 푀2,푥 indicate diverse line of sight ▪ Rather lower values ▪ Particularly low variance (푀2,푥) indicates regular shape ▪ Moderate area 퐴푥, plenty of walls to be seen (high 푃푥), and low occlusivity 푄푥 suggest uniform and simplistic structure Archetypal Isovists | 14.11.2016 Slide | 9 RESULTS – CLUSTERING THE MAP Colored pixels with threshold 훼 > 0.5 ▪ Open space, quite compact ▪ Restricted and regular view, rooms or smaller halls ▪ Very restricted in two directions, very wide otherwise, like narrow halls or streets around the building ▪ White area as an interesting effect Archetypal Isovists | 14.11.2016 Slide | 10 RESULTS – CLUSTERING THE MAP 푘 = 4 푘 = 5 Deep view into entrances (very Spectator would see much area high occlusivity 푄 , i.e. the length 푥 while having a wall behind the back of the occluding radial boundary) Archetypal Isovists | 14.11.2016 Slide | 11 RESULTS – IDENTIFYING ALTERNATIVE ROUTES Setup ▪ 400 routes representing the observations ▪ Each observation has got 5 × 6 = 30 attributes (min, max, mean, median, variance) ▪ Scree plot suggests 푘 = 3 Feld S, Werner M, Schönfeld M, Hasler S (2015) Archetypes of Alternative Routes in Buildings. In: 6th International Conference on Indoor Positioning and Indoor Navigation (IPIN 2015), pp. 1-10 Archetypal Isovists | 14.11.2016 Slide | 12 RESULTS – IDENTIFYING ALTERNATIVE ROUTES Interpretation 푘 = 3, threshold 훼 > 0.8 ▪ Traversing consistently through patio having several variations at start/end ▪ Very straight through streets or long and narrow halls ▪ Variations (shortcuts or detours) are located in rather narrow spaces ▪ Variable, following variations of rooms and doors Archetypes based on impression, not only their geographic location Archetypal Isovists | 14.11.2016 Slide | 13 RESULTS – FEATURES VS. (NO) TIME Archetypal routes Stacked isovist features Stacked isovists. Time dimension for 푘 = 2 over the time is lost, but patterns are visible Archetypal Isovists | 14.11.2016 Slide | 14 CONCLUSION AND FUTURE WORK Conclusion ▪ Fuzzy clustering using computed perception of space ▪ Different environment areas and alternative routes based on visibility properties Future Work ▪ Focus on measurements along the routes using stacked isovist visualization and visual analytics ▪ Incorporate different route creation algorithms and random start/goals Archetypal Isovists | 14.11.2016 Slide | 15 Identifying Divergent Building Structures Using Fuzzy Clustering of Isovist Features Sebastian Feld*, Hao Lyu^, Andreas Keler° *Mobile and Distributed Systems Group, LMU Munich ^Department of Cartography, TUM Munich °Applied Geoinformatics, University of Augsburg [email protected] 13th ICA LBS 2016, Vienna, Austria November 14th, 2016 .