Improving the Accuracy of Urban Environmental Quality Assessment Using Geographically-Weighted Regression Techniques
thesisposted on 08.06.2021, 12:44 by Kamil Faisal
Population growth around the world may cause an adverse impact on the environment and the human life. Thus, modeling the Urban Environmental Quality (UEQ) becomes indispensable for a better city planning and an efficient urban sprawl control. To evaluate the impact of city development, this study aims to utilize remote sensing and Geographic Information System (GIS) techniques to assess the UEQ in two major cities in Ontario, Canada. The main objectives of this research are: 1) to examine the relationship of multiple UEQ parameters derived from remote sensing, GIS and socio-economic data; 2) to evaluate some of the existing methods (e.g. linear regression, GIS overlay and Principal Component Analysis (PCA)) for assessing and integrating multiple UEQ parameters; 3) to propose a new method to weight urban and environmental parameters obtained from different data sources; 4) to develop a new method to validate the UEQ results with respect to three socio-economic indicators. Remote sensing, GIS and census data were first obtained to calculate various environmental, urban parameters and socio-economic indicators. The derived parameters and indicators were tested to emphasize their relationship to UEQ. Three geographically-Weighted Regression (GWR) techniques were used to integrate all these environmental, urban parameters and socio-economic indicators. Three key indicators including family income, the 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 three indicators. The findings showed that the GWR with spatial lag model represents an improved precision and accuracy up to 20% with respect to GIS overlay and PCA techniques. The final outcomes of the research can help the authorities and decision makers to understand the empirical relationships among regional science, urban morphology, real estate economics and economic geography.