Use of Big Data software in architecture and urban-territorial planning

Use of Big Data software in architecture and urban-territorial planning

A frequent problem in architecture and urban-territorial planning is to be able to find groups of elements with homogeneous characteristics. In architecture, building/construction classifications are deduced from a number of parameters or variables; and if the urban structure is analyzed, it is poss...

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Główni autorzy: Barbero, Dante Andrés; IIPAC, CONICET-UNLP., Chévez, Pedro Joaquín; IIPAC, CONICET-UNLP, Discoli, Carlos Alberto; IIPAC, CONICET-UNLP, Martini, Irene; IIPAC, CONICET-UNLP
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Wydane: Universidad Nacional del Nordeste. Facultad de Arquitectura y Urbanismo 2020
Dostęp online:https://revistas.unne.edu.ar/index.php/crn/article/view/4624
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spelling oai:ojs.revistas.unne.edu.ar:article-46242020-12-22T20:06:12Z Use of Big Data software in architecture and urban-territorial planning Utilización de software de Big Data en la arquitectura y la planificación urbano-territorial Barbero, Dante Andrés; IIPAC, CONICET-UNLP. Chévez, Pedro Joaquín; IIPAC, CONICET-UNLP Discoli, Carlos Alberto; IIPAC, CONICET-UNLP Martini, Irene; IIPAC, CONICET-UNLP Big Data Data Mining architecture urban planning. Big Data minería de datos arquitectura planificación urbana A frequent problem in architecture and urban-territorial planning is to be able to find groups of elements with homogeneous characteristics. In architecture, building/construction classifications are deduced from a number of parameters or variables; and if the urban structure is analyzed, it is possible to identify homogeneous areas according to the type of land use, services coverage, among other possible aspects. When the volume of data to be processed is such that it cannot be analyzed by conventional methods, it is necessary to use Big data techniques. In this work, a framework for Big data (Apache Spark) will be used to discover homogeneous areas in terms of coverage of urban basic services of infrastructure and sanitation. Identifying such areas will allow to locate places with similar benefits, infer new demands based on possible urban growths and identify places on the periphery where the city can grow, among other possible uses. Un problema recurrente en arquitectura y planificación urbano-territorial es poder encontrar grupos de elementos con características homogéneas. En arquitectura, las clasificaciones edilicias/constructivas se deducen a partir de un número de parámetros o variables, y si se analiza la estructura urbana es posible identificar áreas homogéneas según el tipo de uso de suelo, cobertura de servicios, entre otros aspectos. Cuando el volumen de datos para procesar es tal que no se pueden analizar mediante métodos convencionales, es necesario recurrir a técnicas de Big Data. En este trabajo se utilizará un framework para Big Data (Apache Spark) para descubrir áreas homogéneas en cuanto a cobertura de servicios urbanos básicos de infraestructura y saneamiento. Identificar tales áreas permitirá localizar lugares con similares prestaciones, inferir nuevas demandas en función de posibles crecimientos urbanos e identificar lugares de la periferia hacia donde puede crecer la ciudad, entre otros posibles usos. Universidad Nacional del Nordeste. Facultad de Arquitectura y Urbanismo 2020-12-22 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://revistas.unne.edu.ar/index.php/crn/article/view/4624 10.30972/crn.29294624 Cuaderno Urbano; Vol. 29, Núm. 29 (2020); 99-118 1853-3655 1666-6186 spa https://revistas.unne.edu.ar/index.php/crn/article/view/4624/4341 Copyright (c) 2020 Dante Andrés Barbero, Pedro Joaquín Chévez, Carlos Alberto Discoli, Irene Martini https://creativecommons.org/licenses/by-nc-sa/4.0
institution Universidad Nacional del Nordeste
collection OJS
language spa
format Online
author Barbero, Dante Andrés; IIPAC, CONICET-UNLP.
Chévez, Pedro Joaquín; IIPAC, CONICET-UNLP
Discoli, Carlos Alberto; IIPAC, CONICET-UNLP
Martini, Irene; IIPAC, CONICET-UNLP
spellingShingle Barbero, Dante Andrés; IIPAC, CONICET-UNLP.
Chévez, Pedro Joaquín; IIPAC, CONICET-UNLP
Discoli, Carlos Alberto; IIPAC, CONICET-UNLP
Martini, Irene; IIPAC, CONICET-UNLP
Use of Big Data software in architecture and urban-territorial planning
author_facet Barbero, Dante Andrés; IIPAC, CONICET-UNLP.
Chévez, Pedro Joaquín; IIPAC, CONICET-UNLP
Discoli, Carlos Alberto; IIPAC, CONICET-UNLP
Martini, Irene; IIPAC, CONICET-UNLP
author_sort Barbero, Dante Andrés; IIPAC, CONICET-UNLP.
title Use of Big Data software in architecture and urban-territorial planning
title_short Use of Big Data software in architecture and urban-territorial planning
title_full Use of Big Data software in architecture and urban-territorial planning
title_fullStr Use of Big Data software in architecture and urban-territorial planning
title_full_unstemmed Use of Big Data software in architecture and urban-territorial planning
title_sort use of big data software in architecture and urban-territorial planning
description A frequent problem in architecture and urban-territorial planning is to be able to find groups of elements with homogeneous characteristics. In architecture, building/construction classifications are deduced from a number of parameters or variables; and if the urban structure is analyzed, it is possible to identify homogeneous areas according to the type of land use, services coverage, among other possible aspects. When the volume of data to be processed is such that it cannot be analyzed by conventional methods, it is necessary to use Big data techniques. In this work, a framework for Big data (Apache Spark) will be used to discover homogeneous areas in terms of coverage of urban basic services of infrastructure and sanitation. Identifying such areas will allow to locate places with similar benefits, infer new demands based on possible urban growths and identify places on the periphery where the city can grow, among other possible uses.
publisher Universidad Nacional del Nordeste. Facultad de Arquitectura y Urbanismo
publishDate 2020
url https://revistas.unne.edu.ar/index.php/crn/article/view/4624
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