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- W2912081967 abstract "This research leverages computer vision with statistical,computational, and urban design techniques to reveal insightful and helpfulrelations between spatial features and patterns of spatial use. Three casestudies are developed in two locations, in Madrid and Pittsburgh, withdistinct spatial and utilization characteristics for covering diverse conditions through two types of experiments. Thefirst one develops a bivariate analysis between parameters from data that describe the layout and use of each location for identifying correlations between spatial features and utilization patterns. The second experiment uses machine-learning techniques for spatialclustering supported by matching temporal signatures of the detectedoccupations by the developed computer vision algorithms. The proposed design framework yields new types of data about people’s interactions with their urban built environment for generating new knowledge to inform design and policy decisions more effectively and expanding the capabilities of practitioners for focusing on exploring new scenarios. This thesisadvocates for expanding design through data." @default.
- W2912081967 created "2019-02-21" @default.
- W2912081967 creator A5068809197 @default.
- W2912081967 date "2018-05-10" @default.
- W2912081967 modified "2023-09-27" @default.
- W2912081967 title "What You See Is What You Get: Data-Informed Workflow in Design for Architecture and Urbanism" @default.
- W2912081967 doi "https://doi.org/10.1184/r1/7063361.v1" @default.
- W2912081967 hasPublicationYear "2018" @default.
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