The amenity mix of urban neighborhoods
Advances in computational urbanism have stimulated the rise of generative and parametric approaches to urban design. Yet, most generative and parametric approaches focus on physical characteristics, such as a neighborhoods walkability, energy efficiency, and urban form. Here, we study the colocation...
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Format: | Article |
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Elsevier BV
2020
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Online Access: | https://hdl.handle.net/1721.1/128824 |
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author | Hidalgo, César A. Castañer, Elisa Sevtsuk, Andres |
author2 | Massachusetts Institute of Technology. Department of Urban Studies and Planning |
author_facet | Massachusetts Institute of Technology. Department of Urban Studies and Planning Hidalgo, César A. Castañer, Elisa Sevtsuk, Andres |
author_sort | Hidalgo, César A. |
collection | MIT |
description | Advances in computational urbanism have stimulated the rise of generative and parametric approaches to urban design. Yet, most generative and parametric approaches focus on physical characteristics, such as a neighborhoods walkability, energy efficiency, and urban form. Here, we study the colocation patterns of more than one million amenities in 47 U.S. cities to model the amenity mix of neighborhoods, and to identify the amenities that are over- or under-supplied in a neighborhood. We build this model by combining a clustering algorithm, designed to identify amenity-dense neighborhoods, and a network, connecting amenities that are likely to collocate. Our findings extend generative and parametric urban design approaches to the amenity mix of neighborhoods, by leveraging the idea of relatedness from the economic geography literature, to evaluate and optimize a neighborhood's amenity mix. |
first_indexed | 2024-09-23T10:22:32Z |
format | Article |
id | mit-1721.1/128824 |
institution | Massachusetts Institute of Technology |
last_indexed | 2024-09-23T10:22:32Z |
publishDate | 2020 |
publisher | Elsevier BV |
record_format | dspace |
spelling | mit-1721.1/1288242022-12-10T07:48:54Z The amenity mix of urban neighborhoods Hidalgo, César A. Castañer, Elisa Sevtsuk, Andres Massachusetts Institute of Technology. Department of Urban Studies and Planning Massachusetts Institute of Technology. School of Architecture and Planning Advances in computational urbanism have stimulated the rise of generative and parametric approaches to urban design. Yet, most generative and parametric approaches focus on physical characteristics, such as a neighborhoods walkability, energy efficiency, and urban form. Here, we study the colocation patterns of more than one million amenities in 47 U.S. cities to model the amenity mix of neighborhoods, and to identify the amenities that are over- or under-supplied in a neighborhood. We build this model by combining a clustering algorithm, designed to identify amenity-dense neighborhoods, and a network, connecting amenities that are likely to collocate. Our findings extend generative and parametric urban design approaches to the amenity mix of neighborhoods, by leveraging the idea of relatedness from the economic geography literature, to evaluate and optimize a neighborhood's amenity mix. 2020-12-11T21:56:08Z 2020-12-11T21:56:08Z 2020-08 2020-06 Article http://purl.org/eprint/type/JournalArticle 0197-3975 https://hdl.handle.net/1721.1/128824 Hidalgo, César A. et al. "The amenity mix of urban neighborhoods." Habitat International 106 (August 2020): 102205 © 2020 Elsevier Ltd http://dx.doi.org/10.1016/j.habitatint.2020.102205 Habitat International Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier BV Prof. Sevtsuk |
spellingShingle | Hidalgo, César A. Castañer, Elisa Sevtsuk, Andres The amenity mix of urban neighborhoods |
title | The amenity mix of urban neighborhoods |
title_full | The amenity mix of urban neighborhoods |
title_fullStr | The amenity mix of urban neighborhoods |
title_full_unstemmed | The amenity mix of urban neighborhoods |
title_short | The amenity mix of urban neighborhoods |
title_sort | amenity mix of urban neighborhoods |
url | https://hdl.handle.net/1721.1/128824 |
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