Characterizing mixed-use buildings based on multi-source big data |
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| Authors: | Xiaoping Liu Xingjian Liu He Jin Jinpei Ou Limin Jiao |
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| Institution: | 1. Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou, China;2. Department of Urban Planning and Design, University of Hong Kong, Hong Kong;3. Department of Geography, Texas State University, San Marcos, TX, USA;4. School of Resource and Environment Science, Wuhan University, Wuhan, China;5. key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan, China |
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| Abstract: | To-date few research has successfully integrated big data from multiple sources to characterize urban mixed-use buildings. In this paper, we introduce a probabilistic model to integrate multi-source and geospatial big data (social network data, taxi trajectories, Points of Interest and remote sensing images) to characterize urban mixed-use buildings. The usefulness of our model is demonstrated with a case study of the Tianhe District in megacity Guangzhou, China. The model predicted building functions at 85% accuracy based on ground truth data from field surveys. We further explored the spatial patterns of the identified building functions. Most mixed-use buildings are located along major streets. Our proposed model can identify mixed-use buildings in a city; information is useful for planning evaluation and urban policymaking. |
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| Keywords: | Mixed-use buildings geospatial big data Bayes’ theorem |
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