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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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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. |
format | Online Article Text |
id | pubmed-6387028 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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