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Sensors 2019, 19(3), 461; https://doi.org/10.3390/s19030461

Detecting and Evaluating Urban Clusters with Spatiotemporal Big Data

1
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
2
School of Urban Design, Wuhan University, Wuhan 430070, China
3
Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, China
*
Authors to whom correspondence should be addressed.
Received: 7 November 2018 / Revised: 8 December 2018 / Accepted: 16 January 2019 / Published: 23 January 2019
(This article belongs to the Section Remote Sensors)
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Abstract

The 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
Keywords: urban clusters; clustering; rationality; conformance; travel activities; spatiotemporal big data urban clusters; clustering; rationality; conformance; travel activities; spatiotemporal big data
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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.

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