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

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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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Detalles Bibliográficos
Autores: Nieto-Chaupis, Huber, Alfaro-Acuña, Anthony
Formato: artículo
Fecha de Publicación:2022
Institución:Universidad Autónoma del Perú
Repositorio:AUTONOMA-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.autonoma.edu.pe:20.500.13067/1753
Enlace del recurso:https://hdl.handle.net/20.500.13067/1753
https://doi.org/10.1007/978-3-030-94514-5_33
Nivel de acceso:acceso restringido
Materia:Machine learning
Urban cities
Latin American cities
https://purl.org/pe-repo/ocde/ford#2.02.04
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spelling Nieto-Chaupis, HuberAlfaro-Acuña, Anthony2022-03-10T16:33:25Z2022-03-10T16:33:25Z2022-01-01Nieto-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_33978-3-030-94514-5https://hdl.handle.net/20.500.13067/1753Lecture Notes in Civil Engineeringhttps://doi.org/10.1007/978-3-030-94514-5_33We 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.application/pdfengSpringerPEinfo:eu-repo/semantics/restrictedAccesshttps://creativecommons.org/licenses/by-nc-nd/4.0/AUTONOMA226325337reponame:AUTONOMA-Institucionalinstname:Universidad Autónoma del Perúinstacron:AUTONOMAMachine learningUrban citiesLatin American citieshttps://purl.org/pe-repo/ocde/ford#2.02.04Machine Learning to Assess Urbanistic Development in the South Pole of Lima Cityinfo:eu-repo/semantics/articlehttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85125230044&doi=10.1007%2f978-3-030-94514-5_33&partnerID=40&md5TEXTMachine Learning to Assess Urbanistic Development in the South Pole of Lima City.pdf.txtMachine Learning to Assess Urbanistic Development in the South Pole of Lima City.pdf.txtExtracted texttext/plain501http://repositorio.autonoma.edu.pe/bitstream/20.500.13067/1753/4/Machine%20Learning%20to%20Assess%20Urbanistic%20Development%20in%20the%20South%20Pole%20of%20Lima%20City.pdf.txt18da64ca9a2fca9aa67e853b50dbb413MD54THUMBNAILMachine Learning to Assess Urbanistic Development in the South Pole of Lima City.pdf.jpgMachine Learning to Assess Urbanistic Development in the South Pole of Lima City.pdf.jpgGenerated Thumbnailimage/jpeg5607http://repositorio.autonoma.edu.pe/bitstream/20.500.13067/1753/5/Machine%20Learning%20to%20Assess%20Urbanistic%20Development%20in%20the%20South%20Pole%20of%20Lima%20City.pdf.jpg7eaa601785070ca2e91f57e855b2e300MD55LICENSElicense.txtlicense.txttext/plain; charset=utf-885http://repositorio.autonoma.edu.pe/bitstream/20.500.13067/1753/2/license.txt9243398ff393db1861c890baeaeee5f9MD52ORIGINALMachine Learning to Assess Urbanistic Development in the South Pole of Lima City.pdfMachine Learning to Assess Urbanistic Development in the South Pole of Lima City.pdfVer fuenteapplication/pdf99335http://repositorio.autonoma.edu.pe/bitstream/20.500.13067/1753/3/Machine%20Learning%20to%20Assess%20Urbanistic%20Development%20in%20the%20South%20Pole%20of%20Lima%20City.pdfa33cfe1310f6742bd17811390b26208fMD5320.500.13067/1753oai:repositorio.autonoma.edu.pe:20.500.13067/17532022-03-11 03:00:20.214Repositorio de la Universidad Autonoma del Perúrepositorio@autonoma.pe
dc.title.es_PE.fl_str_mv Machine Learning to Assess Urbanistic Development in the South Pole of Lima City
title Machine Learning to Assess Urbanistic Development in the South Pole of Lima City
spellingShingle Machine Learning to Assess Urbanistic Development in the South Pole of Lima City
Nieto-Chaupis, Huber
Machine learning
Urban cities
Latin American cities
https://purl.org/pe-repo/ocde/ford#2.02.04
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
author Nieto-Chaupis, Huber
author_facet Nieto-Chaupis, Huber
Alfaro-Acuña, Anthony
author_role author
author2 Alfaro-Acuña, Anthony
author2_role author
dc.contributor.author.fl_str_mv Nieto-Chaupis, Huber
Alfaro-Acuña, Anthony
dc.subject.es_PE.fl_str_mv Machine learning
Urban cities
Latin American cities
topic Machine learning
Urban cities
Latin American cities
https://purl.org/pe-repo/ocde/ford#2.02.04
dc.subject.ocde.es_PE.fl_str_mv 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.
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-03-10T16:33:25Z
dc.date.available.none.fl_str_mv 2022-03-10T16:33:25Z
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dc.identifier.citation.es_PE.fl_str_mv 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
dc.identifier.isbn.none.fl_str_mv 978-3-030-94514-5
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.13067/1753
dc.identifier.journal.es_PE.fl_str_mv Lecture Notes in Civil Engineering
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1007/978-3-030-94514-5_33
identifier_str_mv 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
Lecture Notes in Civil Engineering
url https://hdl.handle.net/20.500.13067/1753
https://doi.org/10.1007/978-3-030-94514-5_33
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Machine Learning to Assess Urbanistic Development in the South Pole of Lima City
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