Land use interpretation for cellular automata models with socioeconomic heterogeneity
Cellular automata models for simulation of urban development usually lack the social heterogeneity that is typical of urban environments. In order to handle this shortcoming, this paper proposes the use of supervised clustering analysis to provide socioeconomic intra-urban land use classification at...
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ANTAC - Associação Nacional de Tecnologia do Ambiente Construído
2011
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ojs-article-190992011-12-31T19:42:38Z Land use interpretation for cellular automata models with socioeconomic heterogeneity Furtado, Bernardo Alves Cellular automata models; Urban development; RMBH; Supervised clustering analysis Cellular automata models for simulation of urban development usually lack the social heterogeneity that is typical of urban environments. In order to handle this shortcoming, this paper proposes the use of supervised clustering analysis to provide socioeconomic intra-urban land use classification at different levels to be applied to cellular automata models. An empirical test in a highly diverse context in the Greater Metropolitan Area of Belo Horizonte (RMBH) in Brazil is provided. The results show that a reliable division into different socioeconomic land-use classes at large scale enable detailed urban dynamic analysis. Furthermore, the results also allow the quantification of the proportion of urban space occupation for different levels of income; (2) and their pattern in relation to the city centre. ANTAC - Associação Nacional de Tecnologia do Ambiente Construído 2011-12-05 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf https://seer.ufrgs.br/ambienteconstruido/article/view/19099 Ambiente Construído; v. 11, n. 4 (2011); 167-183 Ambiente Construído; v. 11, n. 4 (2011); 167-183 Ambiente Construído; v. 11, n. 4 (2011); 167-183 1678-8621 1415-8876 por https://seer.ufrgs.br/ambienteconstruido/article/view/19099/14570 Direitos autorais 2016 Ambiente Construído https://creativecommons.org/licenses/by/4.0 |
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Universidade Federal do Rio Grande do Sul |
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language |
por |
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Online |
author |
Furtado, Bernardo Alves |
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Furtado, Bernardo Alves Land use interpretation for cellular automata models with socioeconomic heterogeneity |
author_facet |
Furtado, Bernardo Alves |
author_sort |
Furtado, Bernardo Alves |
title |
Land use interpretation for cellular automata models with socioeconomic heterogeneity |
title_short |
Land use interpretation for cellular automata models with socioeconomic heterogeneity |
title_full |
Land use interpretation for cellular automata models with socioeconomic heterogeneity |
title_fullStr |
Land use interpretation for cellular automata models with socioeconomic heterogeneity |
title_full_unstemmed |
Land use interpretation for cellular automata models with socioeconomic heterogeneity |
title_sort |
land use interpretation for cellular automata models with socioeconomic heterogeneity |
description |
Cellular automata models for simulation of urban development usually lack the social heterogeneity that is typical of urban environments. In order to handle this shortcoming, this paper proposes the use of supervised clustering analysis to provide socioeconomic intra-urban land use classification at different levels to be applied to cellular automata models. An empirical test in a highly diverse context in the Greater Metropolitan Area of Belo Horizonte (RMBH) in Brazil is provided. The results show that a reliable division into different socioeconomic land-use classes at large scale enable detailed urban dynamic analysis. Furthermore, the results also allow the quantification of the proportion of urban space occupation for different levels of income; (2) and their pattern in relation to the city centre. |
publisher |
ANTAC - Associação Nacional de Tecnologia do Ambiente Construído |
publishDate |
2011 |
url |
https://seer.ufrgs.br/ambienteconstruido/article/view/19099 |
work_keys_str_mv |
AT furtadobernardoalves landuseinterpretationforcellularautomatamodelswithsocioeconomicheterogeneity |
_version_ |
1709370599702265856 |