Machine Learning to Assess Urbanistic Development in the South Pole of Lima City

We employ Machine Learning through the Mitchell’s criteria to carry out an assessment on the potential spatial configurations at the south pole of Lima city, at Perú. Based at both qualitative and quantitative facts, an model has been proposed that targets to measure the success of spatial expansion...

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Main Authors: Nieto-Chaupis, Huber, Alfaro-Acuña, Anthony
Format: Article in Journal/Newspaper
Language:English
Published: Springer 2022
Subjects:
Machine learning
Urban cities
Latin American cities
https://purl.org/pe-repo/ocde/ford#2.02.04
South Pole
South pole
Online Access:https://hdl.handle.net/20.500.13067/1753
https://doi.org/10.1007/978-3-030-94514-5_33
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spelling ftuautonomaperu:oai:repositorio.autonoma.edu.pe:20.500.13067/1753 2023-05-15T18:21:55+02:00 Machine Learning to Assess Urbanistic Development in the South Pole of Lima City Nieto-Chaupis, Huber Alfaro-Acuña, Anthony 2022-01-01 application/pdf https://hdl.handle.net/20.500.13067/1753 https://doi.org/10.1007/978-3-030-94514-5_33 eng eng Springer PE https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125230044&doi=10.1007%2f978-3-030-94514-5_33&partnerID=40&md5 Nieto-Chaupis H. & Alfaro-Acuña, A. (2022) Machine Learning to Assess Urbanistic Development in the South Pole of Lima City. In: Mendonça P., Cortiços N.D. (eds) Proceedings of the 7th International Conference on Architecture, Materials and Construction. ICAMC 2021. Lecture Notes in Civil Engineering, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-030-94514-5_33 978-3-030-94514-5 https://hdl.handle.net/20.500.13067/1753 Lecture Notes in Civil Engineering https://doi.org/10.1007/978-3-030-94514-5_33 info:eu-repo/semantics/restrictedAccess https://creativecommons.org/licenses/by-nc-nd/4.0/ CC-BY-NC-ND AUTONOMA 226 325 337 Machine learning Urban cities Latin American cities https://purl.org/pe-repo/ocde/ford#2.02.04 info:eu-repo/semantics/article 2022 ftuautonomaperu https://doi.org/20.500.13067/1753 https://doi.org/10.1007/978-3-030-94514-5_33 2022-03-13T19:29:30Z We employ Machine Learning through the Mitchell’s criteria to carry out an assessment on the potential spatial configurations at the south pole of Lima city, at Perú. Based at both qualitative and quantitative facts, an model has been proposed that targets to measure the success of spatial expansion of districts based at distances and number of habitants. In this manner Machine Learning appears as a robust tool with capabilities to anticipate the possible achievements as well as issues along the time the city is under spatial growth. The efficiency of sustained growth is measured in terms of success probability. Therefore, we can claim that the ongoing growth of Villa el Salvador engages to some extent the philosophy of Mitchell’s criteria. Article in Journal/Newspaper South pole Repositorio de la Universidad Autonoma del Perú South Pole 325 337
institution Open Polar
collection Repositorio de la Universidad Autonoma del Perú
op_collection_id ftuautonomaperu
language English
topic Machine learning
Urban cities
Latin American cities
https://purl.org/pe-repo/ocde/ford#2.02.04
spellingShingle Machine learning
Urban cities
Latin American cities
https://purl.org/pe-repo/ocde/ford#2.02.04
Nieto-Chaupis, Huber
Alfaro-Acuña, Anthony
Machine Learning to Assess Urbanistic Development in the South Pole of Lima City
topic_facet Machine learning
Urban cities
Latin American cities
https://purl.org/pe-repo/ocde/ford#2.02.04
description We employ Machine Learning through the Mitchell’s criteria to carry out an assessment on the potential spatial configurations at the south pole of Lima city, at Perú. Based at both qualitative and quantitative facts, an model has been proposed that targets to measure the success of spatial expansion of districts based at distances and number of habitants. In this manner Machine Learning appears as a robust tool with capabilities to anticipate the possible achievements as well as issues along the time the city is under spatial growth. The efficiency of sustained growth is measured in terms of success probability. Therefore, we can claim that the ongoing growth of Villa el Salvador engages to some extent the philosophy of Mitchell’s criteria.
format Article in Journal/Newspaper
author Nieto-Chaupis, Huber
Alfaro-Acuña, Anthony
author_facet Nieto-Chaupis, Huber
Alfaro-Acuña, Anthony
author_sort Nieto-Chaupis, Huber
title Machine Learning to Assess Urbanistic Development in the South Pole of Lima City
title_short Machine Learning to Assess Urbanistic Development in the South Pole of Lima City
title_full Machine Learning to Assess Urbanistic Development in the South Pole of Lima City
title_fullStr Machine Learning to Assess Urbanistic Development in the South Pole of Lima City
title_full_unstemmed Machine Learning to Assess Urbanistic Development in the South Pole of Lima City
title_sort machine learning to assess urbanistic development in the south pole of lima city
publisher Springer
publishDate 2022
url https://hdl.handle.net/20.500.13067/1753
https://doi.org/10.1007/978-3-030-94514-5_33
geographic South Pole
geographic_facet South Pole
genre South pole
genre_facet South pole
op_source AUTONOMA
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Nieto-Chaupis H. & Alfaro-Acuña, A. (2022) Machine Learning to Assess Urbanistic Development in the South Pole of Lima City. In: Mendonça P., Cortiços N.D. (eds) Proceedings of the 7th International Conference on Architecture, Materials and Construction. ICAMC 2021. Lecture Notes in Civil Engineering, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-030-94514-5_33
978-3-030-94514-5
https://hdl.handle.net/20.500.13067/1753
Lecture Notes in Civil Engineering
https://doi.org/10.1007/978-3-030-94514-5_33
op_rights info:eu-repo/semantics/restrictedAccess
https://creativecommons.org/licenses/by-nc-nd/4.0/
op_rightsnorm CC-BY-NC-ND
op_doi https://doi.org/20.500.13067/1753
https://doi.org/10.1007/978-3-030-94514-5_33
container_start_page 325
op_container_end_page 337
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