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• W2784330441 abstract "Dynamic patterns of urban space reveals the interaction of human activities and urban space, and draws much attention from urban planners and decision makers. The most common way to extract dynamic patterns from city-wide level human dynamics is through Singular Value Decomposition (SVD). However, SVD constrains the columns of matrix factors to be orthonormal. Some of the entries of matrix factors must be negative which leads to difficulties in interpretation of the decomposed components when each component is physically required to be nonnegative, e.g. population density. Therefore, we propose using Semi-nonnegative Matrix Factorization (Semi-NMF) framework to extract dynamic patterns of city-wide level population density. Semi-NMF constrains dynamic patterns of population density to be nonnegative which results in parts-based representation and interpretable patterns. We apply our approach to a mobile phone dataset of Nanjing and extract interpretable dynamic patterns. Based on these patterns, we present application in urban planning to investigate commuter towns in Nanjing." @default.
• W2784330441 created "2018-01-26" @default.
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• W2784330441 date "2017-12-01" @default.
• W2784330441 modified "2023-09-25" @default.
• W2784330441 title "Discovering dynamic patterns of urban space via semi-nonnegative matrix factorization" @default.
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• W2784330441 doi "https://doi.org/10.1109/bigdata.2017.8258332" @default.
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