Abstract
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.
Highlights
The terminology “quality of life” has been continuously discussed in the literature, so as to lay a foundation to serve the subsequent quantification of Urban Environmental Quality (UEQ).Szalai [1] emphasized that quality of life represents the degree of satisfaction with life and the feeling of well-being, which can be measured by exogenous and endogenous factors
Three approaches were demonstrated to integrate the aforementioned environmental and urban parameters. These two existing approaches (PCA and Geographic Information System (GIS) overlay) were first implemented, and subsequently, we investigated the use of Geographically-Weighted Regression (GWR) techniques to integrate all of the aforementioned parameters, which can lead to an improved estimation of UEQ
UEQ zones are the consequence of the summation of all of the positive parameters that are located within Zones A–D
Summary
The terminology “quality of life” has been continuously discussed in the literature, so as to lay a foundation to serve the subsequent quantification of Urban Environmental Quality (UEQ). Szalai [1] emphasized that quality of life represents the degree of satisfaction with life and the feeling of well-being, which can be measured by exogenous and endogenous factors. Concluded the meaning of the quality of life by the satisfaction of life. Raphael et al [3] further echoed and agreed that quality of life tends more to be the enjoyable degree of a person toward the important responsibilities of his/her life. Kamp et al [4] described the quality of life by physical and immaterial equipment, such as health, education, justice, work, family, etc. UEQ is the consequence of the combination of environmental parameters, including nature, open space, infrastructure, built environment, physical environment amenities and natural resources, and Sensors 2017, 17, 528; doi:10.3390/s17030528 www.mdpi.com/journal/sensors
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