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Detecting and Evaluating Urban Clusters with Spatiotemporal Big Data

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 urb...

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Autores principales: Tang, Luliang, Gao, Jie, Ren, Chang, Zhang, Xia, Yang, Xue, Kan, Zihan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6387028/
https://www.ncbi.nlm.nih.gov/pubmed/30678066
http://dx.doi.org/10.3390/s19030461
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author Tang, Luliang
Gao, Jie
Ren, Chang
Zhang, Xia
Yang, Xue
Kan, Zihan
author_facet Tang, Luliang
Gao, Jie
Ren, Chang
Zhang, Xia
Yang, Xue
Kan, Zihan
author_sort Tang, Luliang
collection PubMed
description 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.
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spelling pubmed-63870282019-02-26 Detecting and Evaluating Urban Clusters with Spatiotemporal Big Data Tang, Luliang Gao, Jie Ren, Chang Zhang, Xia Yang, Xue Kan, Zihan Sensors (Basel) Article 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. MDPI 2019-01-23 /pmc/articles/PMC6387028/ /pubmed/30678066 http://dx.doi.org/10.3390/s19030461 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tang, Luliang
Gao, Jie
Ren, Chang
Zhang, Xia
Yang, Xue
Kan, Zihan
Detecting and Evaluating Urban Clusters with Spatiotemporal Big Data
title Detecting and Evaluating Urban Clusters with Spatiotemporal Big Data
title_full Detecting and Evaluating Urban Clusters with Spatiotemporal Big Data
title_fullStr Detecting and Evaluating Urban Clusters with Spatiotemporal Big Data
title_full_unstemmed Detecting and Evaluating Urban Clusters with Spatiotemporal Big Data
title_short Detecting and Evaluating Urban Clusters with Spatiotemporal Big Data
title_sort detecting and evaluating urban clusters with spatiotemporal big data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6387028/
https://www.ncbi.nlm.nih.gov/pubmed/30678066
http://dx.doi.org/10.3390/s19030461
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