Urban attractors: Discovering patterns in regions of attraction in cities
<jats:p>Understanding the dynamics by which urban areas attract visitors is important in today’s cities that are continuously increasing in population towards higher densities. Identifying services that relate to highly attractive districts is useful to make policies regarding the placement of...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021
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| Online Access: | https://hdl.handle.net/1721.1/133382 |
| _version_ | 1811072555194253312 |
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| author | Alhazzani, May Alhasoun, Fahad Alawwad, Zeyad González, Marta C |
| author2 | Massachusetts Institute of Technology. Center for Computational Engineering |
| author_facet | Massachusetts Institute of Technology. Center for Computational Engineering Alhazzani, May Alhasoun, Fahad Alawwad, Zeyad González, Marta C |
| author_sort | Alhazzani, May |
| collection | MIT |
| description | <jats:p>Understanding the dynamics by which urban areas attract visitors is important in today’s cities that are continuously increasing in population towards higher densities. Identifying services that relate to highly attractive districts is useful to make policies regarding the placement of such places. Thus, we present a framework for classifying districts in cities by their attractiveness to daily commuters and relating Points of Interests (POIs) types to districts’ attraction patterns. We used Origin-Destination matrices (ODs) mined from cell phone data that capture the flow of trips between each pair of places in Riyadh, Saudi Arabia. We define the attraction profile for a place based on three main statistical features: The number of visitors a place received, the distribution of distance traveled by visitors on the road network, and the spatial spread of locations from where trips started. We used a hierarchical clustering algorithm to classify all places in the city by their features of attraction. We discovered three main types of Urban Attractors in Riyadh during the morning period: <jats:italic>G</jats:italic>lobal, which are significant places in the city, <jats:italic>D</jats:italic>owntown, which contains the central business district, and Residential attractors. In addition, we uncovered what makes districts possess certain attraction patterns. We used a statistical significance testing approach to quantify the relationship between Points of Interests (POIs) types (services) and the patterns of Urban Attractors detected.</jats:p> |
| first_indexed | 2024-09-23T09:07:47Z |
| format | Article |
| id | mit-1721.1/133382 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:07:47Z |
| publishDate | 2021 |
| publisher | Public Library of Science (PLoS) |
| record_format | dspace |
| spelling | mit-1721.1/1333822023-02-24T21:18:18Z Urban attractors: Discovering patterns in regions of attraction in cities Alhazzani, May Alhasoun, Fahad Alawwad, Zeyad González, Marta C Massachusetts Institute of Technology. Center for Computational Engineering <jats:p>Understanding the dynamics by which urban areas attract visitors is important in today’s cities that are continuously increasing in population towards higher densities. Identifying services that relate to highly attractive districts is useful to make policies regarding the placement of such places. Thus, we present a framework for classifying districts in cities by their attractiveness to daily commuters and relating Points of Interests (POIs) types to districts’ attraction patterns. We used Origin-Destination matrices (ODs) mined from cell phone data that capture the flow of trips between each pair of places in Riyadh, Saudi Arabia. We define the attraction profile for a place based on three main statistical features: The number of visitors a place received, the distribution of distance traveled by visitors on the road network, and the spatial spread of locations from where trips started. We used a hierarchical clustering algorithm to classify all places in the city by their features of attraction. We discovered three main types of Urban Attractors in Riyadh during the morning period: <jats:italic>G</jats:italic>lobal, which are significant places in the city, <jats:italic>D</jats:italic>owntown, which contains the central business district, and Residential attractors. In addition, we uncovered what makes districts possess certain attraction patterns. We used a statistical significance testing approach to quantify the relationship between Points of Interests (POIs) types (services) and the patterns of Urban Attractors detected.</jats:p> 2021-10-27T19:52:27Z 2021-10-27T19:52:27Z 2021 2021-06-16T18:54:34Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/133382 en 10.1371/journal.pone.0250204 PLoS ONE Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Public Library of Science (PLoS) PLoS |
| spellingShingle | Alhazzani, May Alhasoun, Fahad Alawwad, Zeyad González, Marta C Urban attractors: Discovering patterns in regions of attraction in cities |
| title | Urban attractors: Discovering patterns in regions of attraction in cities |
| title_full | Urban attractors: Discovering patterns in regions of attraction in cities |
| title_fullStr | Urban attractors: Discovering patterns in regions of attraction in cities |
| title_full_unstemmed | Urban attractors: Discovering patterns in regions of attraction in cities |
| title_short | Urban attractors: Discovering patterns in regions of attraction in cities |
| title_sort | urban attractors discovering patterns in regions of attraction in cities |
| url | https://hdl.handle.net/1721.1/133382 |
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