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...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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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 |
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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 |