Improving the Accuracy of Urban Environmental Quality Assessment Using Geographically-Weighted Regression Techniques

Urban Environmental Quality (UEQ) can be treated as a generic indicator that objectively represents the physical and socio-economic condition of the urban and built environment. The value of UEQ illustrates a sense of satisfaction to its population through assessing different environmental, urban an...

Descripción completa

Detalles Bibliográficos
Autores principales: Faisal, Kamil, Shaker, Ahmed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5375814/
https://www.ncbi.nlm.nih.gov/pubmed/28272334
http://dx.doi.org/10.3390/s17030528
_version_ 1782519062629187584
author Faisal, Kamil
Shaker, Ahmed
author_facet Faisal, Kamil
Shaker, Ahmed
author_sort Faisal, Kamil
collection PubMed
description Urban Environmental Quality (UEQ) can be treated as a generic indicator that objectively represents the physical and socio-economic condition of the urban and built environment. The value of UEQ illustrates a sense of satisfaction to its population through assessing different environmental, urban and socio-economic parameters. This paper elucidates the use of the Geographic Information System (GIS), Principal Component Analysis (PCA) and Geographically-Weighted Regression (GWR) techniques to integrate various parameters and estimate the UEQ of two major cities in Ontario, Canada. Remote sensing, GIS and census data were first obtained to derive various environmental, urban and socio-economic parameters. The aforementioned techniques were used to integrate all of these environmental, urban and socio-economic parameters. Three key indicators, including family income, higher level of education and land value, were used as a reference to validate the outcomes derived from the integration techniques. The results were evaluated by assessing the relationship between the extracted UEQ results and the reference layers. Initial findings showed that the GWR with the spatial lag model represents an improved precision and accuracy by up to 20% with respect to those derived by using GIS overlay and PCA techniques for the City of Toronto and the City of Ottawa. The findings of the research can help the authorities and decision makers to understand the empirical relationships among environmental factors, urban morphology and real estate and decide for more environmental justice.
format Online
Article
Text
id pubmed-5375814
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-53758142017-04-10 Improving the Accuracy of Urban Environmental Quality Assessment Using Geographically-Weighted Regression Techniques Faisal, Kamil Shaker, Ahmed Sensors (Basel) Article Urban Environmental Quality (UEQ) can be treated as a generic indicator that objectively represents the physical and socio-economic condition of the urban and built environment. The value of UEQ illustrates a sense of satisfaction to its population through assessing different environmental, urban and socio-economic parameters. This paper elucidates the use of the Geographic Information System (GIS), Principal Component Analysis (PCA) and Geographically-Weighted Regression (GWR) techniques to integrate various parameters and estimate the UEQ of two major cities in Ontario, Canada. Remote sensing, GIS and census data were first obtained to derive various environmental, urban and socio-economic parameters. The aforementioned techniques were used to integrate all of these environmental, urban and socio-economic parameters. Three key indicators, including family income, higher level of education and land value, were used as a reference to validate the outcomes derived from the integration techniques. The results were evaluated by assessing the relationship between the extracted UEQ results and the reference layers. Initial findings showed that the GWR with the spatial lag model represents an improved precision and accuracy by up to 20% with respect to those derived by using GIS overlay and PCA techniques for the City of Toronto and the City of Ottawa. The findings of the research can help the authorities and decision makers to understand the empirical relationships among environmental factors, urban morphology and real estate and decide for more environmental justice. MDPI 2017-03-07 /pmc/articles/PMC5375814/ /pubmed/28272334 http://dx.doi.org/10.3390/s17030528 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Faisal, Kamil
Shaker, Ahmed
Improving the Accuracy of Urban Environmental Quality Assessment Using Geographically-Weighted Regression Techniques
title Improving the Accuracy of Urban Environmental Quality Assessment Using Geographically-Weighted Regression Techniques
title_full Improving the Accuracy of Urban Environmental Quality Assessment Using Geographically-Weighted Regression Techniques
title_fullStr Improving the Accuracy of Urban Environmental Quality Assessment Using Geographically-Weighted Regression Techniques
title_full_unstemmed Improving the Accuracy of Urban Environmental Quality Assessment Using Geographically-Weighted Regression Techniques
title_short Improving the Accuracy of Urban Environmental Quality Assessment Using Geographically-Weighted Regression Techniques
title_sort improving the accuracy of urban environmental quality assessment using geographically-weighted regression techniques
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5375814/
https://www.ncbi.nlm.nih.gov/pubmed/28272334
http://dx.doi.org/10.3390/s17030528
work_keys_str_mv AT faisalkamil improvingtheaccuracyofurbanenvironmentalqualityassessmentusinggeographicallyweightedregressiontechniques
AT shakerahmed improvingtheaccuracyofurbanenvironmentalqualityassessmentusinggeographicallyweightedregressiontechniques