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Investigating functional consistency of mobility-related urban zones via motion-driven embedding vectors and local POI-type distributions
Urban morphology and human mobility are two sides of the complex mixture of elements that implicitly define urban functionality. By leveraging the emerging availability of crowdsourced data, we aim for novel insights on how they relate to each other, which remains a substantial scientific challenge....
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Nature Singapore
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9239969/ https://www.ncbi.nlm.nih.gov/pubmed/35783355 http://dx.doi.org/10.1007/s43762-022-00049-8 |
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author | Crivellari, Alessandro Resch, Bernd |
author_facet | Crivellari, Alessandro Resch, Bernd |
author_sort | Crivellari, Alessandro |
collection | PubMed |
description | Urban morphology and human mobility are two sides of the complex mixture of elements that implicitly define urban functionality. By leveraging the emerging availability of crowdsourced data, we aim for novel insights on how they relate to each other, which remains a substantial scientific challenge. Specifically, our study focuses on extracting spatial-temporal information from taxi trips in an attempt on grouping urban space based on human mobility, and subsequently assess its potential relationship with urban functional characteristics in terms of local points-of-interest (POI) distribution. Proposing a vector representation of urban areas, constructed via unsupervised machine learning on trip data’s temporal and geographic factors, the underlying idea is to define areas as “related” if they often act as destinations of similar departing regions at similar points in time, regardless of any other explicit information. Hidden relations are mapped within the generated vector space, whereby areas are represented as points and stronger/weaker relatedness is conveyed through relative distances. The mobility-related outcome is then compared with the POI-type distribution across the urban environment, to assess the functional consistency of mobility-based clusters of urban areas. Results indicate a meaningful relationship between spatial-temporal motion patterns and urban distributions of a diverse selection of POI-type categorizations, paving the way to ideally identify homogenous urban functional zones only based on the movement of people. Our data-driven approach is intended to complement traditional urban development studies on providing a novel perspective to urban activity modeling, standing out as a reference for mining information out of mobility and POI data types in the context of urban management and planning. |
format | Online Article Text |
id | pubmed-9239969 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Nature Singapore |
record_format | MEDLINE/PubMed |
spelling | pubmed-92399692022-06-30 Investigating functional consistency of mobility-related urban zones via motion-driven embedding vectors and local POI-type distributions Crivellari, Alessandro Resch, Bernd Comput Urban Sci Original Paper Urban morphology and human mobility are two sides of the complex mixture of elements that implicitly define urban functionality. By leveraging the emerging availability of crowdsourced data, we aim for novel insights on how they relate to each other, which remains a substantial scientific challenge. Specifically, our study focuses on extracting spatial-temporal information from taxi trips in an attempt on grouping urban space based on human mobility, and subsequently assess its potential relationship with urban functional characteristics in terms of local points-of-interest (POI) distribution. Proposing a vector representation of urban areas, constructed via unsupervised machine learning on trip data’s temporal and geographic factors, the underlying idea is to define areas as “related” if they often act as destinations of similar departing regions at similar points in time, regardless of any other explicit information. Hidden relations are mapped within the generated vector space, whereby areas are represented as points and stronger/weaker relatedness is conveyed through relative distances. The mobility-related outcome is then compared with the POI-type distribution across the urban environment, to assess the functional consistency of mobility-based clusters of urban areas. Results indicate a meaningful relationship between spatial-temporal motion patterns and urban distributions of a diverse selection of POI-type categorizations, paving the way to ideally identify homogenous urban functional zones only based on the movement of people. Our data-driven approach is intended to complement traditional urban development studies on providing a novel perspective to urban activity modeling, standing out as a reference for mining information out of mobility and POI data types in the context of urban management and planning. Springer Nature Singapore 2022-06-28 2022 /pmc/articles/PMC9239969/ /pubmed/35783355 http://dx.doi.org/10.1007/s43762-022-00049-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Paper Crivellari, Alessandro Resch, Bernd Investigating functional consistency of mobility-related urban zones via motion-driven embedding vectors and local POI-type distributions |
title | Investigating functional consistency of mobility-related urban zones via motion-driven embedding vectors and local POI-type distributions |
title_full | Investigating functional consistency of mobility-related urban zones via motion-driven embedding vectors and local POI-type distributions |
title_fullStr | Investigating functional consistency of mobility-related urban zones via motion-driven embedding vectors and local POI-type distributions |
title_full_unstemmed | Investigating functional consistency of mobility-related urban zones via motion-driven embedding vectors and local POI-type distributions |
title_short | Investigating functional consistency of mobility-related urban zones via motion-driven embedding vectors and local POI-type distributions |
title_sort | investigating functional consistency of mobility-related urban zones via motion-driven embedding vectors and local poi-type distributions |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9239969/ https://www.ncbi.nlm.nih.gov/pubmed/35783355 http://dx.doi.org/10.1007/s43762-022-00049-8 |
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