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Ranking Buildings and Mining the Web for Popular Architectural Patterns


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Knowledge about the reception of architectural structures is crucial for architects and urban planners. Yet obtaining such information has been a challenging and costly activity. However, with the advent of the Web, a vast amount of structured and unstructured data describing architectural structures has become available publicly. This includes information about the perception and use of buildings (for instance, through social media), and structured information
about the building’s features and characteristics (for instance, through public Linked Data). Hence, first mining (i) the popularity of buildings from the social Web and (ii) then correlating such rankings with certain features of
buildings, can provide an efficient method to identify successful architectural patterns. In this paper we propose an approach to rank buildings through the automated mining of Flickr metadata. By further correlating such rankings with
building properties described in Linked Data we are able to identify popular patterns for particular building types (airports, bridges, churches, halls, and skyscrapers). Our approach combines crowdsourcing with Web mining techniques
to establish influential factors, as well as ground truth to evaluate our rankings. Our extensive experimental results depict that methods tailored to specific structure types allow an accurate measurement of their public perception.

Published in: Engineering
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Ranking Buildings and Mining the Web for Popular Architectural Patterns

  1. 1. Ranking Buildings and Mining the Web for Popular Architectural Patterns Ujwal Gadiraju, Stefan Dietze and Ernesto Diaz-Aviles Oxford, 29th June 2015
  2. 2. Outline ● Motivation ● Background ● Methodology ● Results ● Conclusions 2
  3. 3. Camillo Sitte Main works are “an aesthetic criticism” of 19th century urbanism. The whole is much more than the sum of it’s parts. “City Planning according to artistic principles.” 3
  4. 4. Form follows function VS Ornamentalism Louis Sullivan Father of Modernism. Father of Skyscrapers. “That life is recognizable in its expression, That form ever follows function. This is the law.” 4
  5. 5. Built Environment Space SyntaxIMPLICATIONS ● Urban planning ● Impact of an architectural structure ● Identify needs for restructuring, adequate maintenance and trigger retrofit scenarios ● Predict impact of building projects 5
  6. 6. What do people think about buildings? ● (On the way)/(at) home, work, play. ● Buildings invoke feelings [1,2]. ● Research has established that buildings shape the built environment. ● Built environment influences various aspects within a community. [1]. Brain electrical responses to high-and low- ranking buildings. Oppenheim et al. Clinical EEG and Neuroscience, 2009. [2]. Hippocampal contributions to the processing of architectural ranking. Oppenheim et al. NeuroImage, 2010. 6
  7. 7. Surveying Experts to establish Influential Factors Building Types - Skyscrapers - Bridges - Churches - Halls - Airports Emerging factors : ● Historic importance ● Effect on/of the surroundings/built environment ● Materials used ● Size of the building/structure ● Personal experiences ● Level of Details Emerging factors : - Ease of access to airport - Efficiency of movement/ processing inside airport - General design & Appearance 7
  8. 8. Crowdsourcing Ground Truth ● 5-point Likert Scale (Strongly Dislike - Strongly Like) ● Gold Standards and precautions to detect and curtail malicious workers or bots [1]. ● Images presented with same resolution and dimensions [2]. ● Avoid bias by using images from Wikimedia Commons. ● 18,500 trusted responses from 7,396 workers. [1]. Understanding Malicious Behavior on Crowdsourcing Platforms - The Case of Online Surveys. Ujwal Gadiraju, Ricardo Kawase, Stefan Dietze and Gianluca Demartini. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. 2015. [2]. "Size does matter: how image size affects aesthetic perception?." Chu, Wei-Ta, Yu-Kuang Chen, and Kuan-Ta Chen. In Proceedings of the 21st ACM international conference on Multimedia. ACM, 2013. 8
  9. 9. Emerging Influential Factors 9
  10. 10. Processing Pipeline for Automated Ranking of Buildings Crowdsourcing Web Mining ● News Articles and Blogs ● Tweets ● Meta-data from flickr images (title, description, tags favorites, comments) 10
  11. 11. Automated Ranking-Workflow Dataset Characteristics 11
  12. 12. Models for Ranking Buildings ● Based on perception-related metadata from relevant Flickr images. ● Sentic feature vectors using EmoLex. ● RankSVM to learn model(s). ● Feature selection for construction of different models. ● Best performing model : Weighted Model (weighted combination of feature vectors according to influential factors) 12
  13. 13. Properties 13 Influential Factors Ground Truth (Crowdsourcing) Ranking Models Ranked List CORRELATE Well-perceived patterns for Architectural Structures top-k
  14. 14. DBpedia properties corresponding to Influential Factors Caveat : ● Coverage of DBpedia properties corresponding to influential factors is limited SIZE dbpedia-owl: runwayLength dbpedia-owl: Length dbprop: architectureStyle dbprop: seatingCapacity dbpedia: floorCount 14
  15. 15. Consolidation of Patterns CHURCHES: Best-perceived Architectural Styles ● Gothic Revival ● Romanesque ● Gothic 15
  16. 16. Consolidation of Patterns 16
  17. 17. Conclusions & Future Work ● Functionalism vs Ornamentalism? ● Correlating building rankings with structured data from the Web can help us to establish popular architectural patterns. ● Building type-specific methods are important. ● Multidimensional architectural patterns through regression of influential factors. ● Using Web Data (both social and structured) in order to fill in the missing gaps. For example, buildings with x size, y uniqueness, z materials used, … are best perceived. 17
  18. 18. Summary ● Identified Influential Factors for different building types ● Ground truth construction via Crowdsourcing ● Models for ranking buildings automatically ● Correlated influential factors with structured data from DBpedia Well-perceived patterns for Architectural Structures 18
  19. 19. Contact Details : SLIDES: wal07/ 19
  20. 20. Influential Factors for Airports 20
  21. 21. America’s Favorite Architecture: AIA 150 ● 2006-2007 AIA organized a study, carried out by Harris Initiative ● In the first phase : 2,448 AIA members interviewed ● In the second phase: Survey of general public (2,214 people) ● Criticism : o List of favorites did not reflect judgments of architectural experts o AIA President said, “Rankings reflected people’s emotional connections to buildings”. 21
  22. 22. Popularity vs Perception ● POPULARITY : The state of being liked, admired or being supported by many people. E.g. Do you KNOW this building? ● PERCEPTION : The way in which something is regarded, understood, or interpreted. E.g. Do you LIKE this building? 22
  23. 23. Plutchik’s Psychoevolutionary Theory of Emotion 23
  24. 24. 24