Detecting and Evaluating Urban Clusters with Spatiotemporal Big Data
AbstractThe design of urban clusters has played an important role in urban planning, but realizing the construction of these urban plans is quite a long process. Hence, how the progress is evaluated is significant for urban managers in the process of urban construction. Traditional methods for detecting urban clusters are inaccurate since the raw data is generally collected from small sample questionnaires of resident trips rather than large-scale studies. Spatiotemporal big data provides a new lens for understanding urban clusters in a natural and fine-grained way. In this article, we propose a novel method for Detecting and Evaluating Urban Clusters (DEUC) with taxi trajectories and Sina Weibo check-in data. Firstly, DEUC applies an agglomerative hierarchical clustering method to detect urban clusters based on the similarities in the daily travel space of urban residents. Secondly, DEUC infers resident demands for land-use functions using a naïve Bayes’ theorem, and three indicators are adopted to assess the rationality of land-use functions in the detected clusters—namely, cross-regional travel index, commuting direction index, and fulfilled demand index. Thirdly, DEUC evaluates the progress of urban cluster construction by calculating a proposed conformance indicator. In the case study, we applied our method to detect and analyze urban clusters in Wuhan, China in the years 2009, 2014, and 2015. The results suggest the effectiveness of the proposed method, which can provide a scientific basis for urban construction. View Full-Text
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Tang, L.; Gao, J.; Ren, C.; Zhang, X.; Yang, X.; Kan, Z. Detecting and Evaluating Urban Clusters with Spatiotemporal Big Data. Sensors 2019, 19, 461.
Tang L, Gao J, Ren C, Zhang X, Yang X, Kan Z. Detecting and Evaluating Urban Clusters with Spatiotemporal Big Data. Sensors. 2019; 19(3):461.Chicago/Turabian Style
Tang, Luliang; Gao, Jie; Ren, Chang; Zhang, Xia; Yang, Xue; Kan, Zihan. 2019. "Detecting and Evaluating Urban Clusters with Spatiotemporal Big Data." Sensors 19, no. 3: 461.
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