An Investigation of GIS Overlay and PCA Techniques for Urban Environmental Quality Assessment: A Case Study in Toronto, Ontario, Canada

Abstract

Abstract:The United Nations estimates that the global population is going to be double in the coming 40 years, which may cause a negative impact on the environment and human life. Such an impact may instigate increased water demand, overuse of power, anthropogenic noise, etc. Thus, modelling the Urban Environmental Quality (UEQ) becomes indispensable for a better city planning and an efficient urban sprawl control. This study aims to investigate the ability of using remote sensing and Geographic Information System (GIS) techniques to model the UEQ with a case study in the city of Toronto via deriving different environmental, urban and socio-economic parameters. Remote sensing, GIS and census data were first obtained to derive environmental, urban and socio-economic parameters. Two techniques, GIS overlay and Principal Component Analysis (PCA), were used to integrate all of these environmental, urban and socio-economic parameters. Socio-economic parameters including family income, higher education and land value were used as a reference to assess the outcomes derived from the two integration methods. The outcomes were assessed through evaluating the relationship between the extracted UEQ results and the reference layers. Preliminary findings showed that the GIS overlay represents a better precision and accuracy (71% and 65%), respectively, comparing to the PCA technique. The outcomes of the research can serve as a generic indicator to help the authority for better city planning with consideration of all possible social, environmental and urban requirements or constraints.

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

“…The dependent parameter indicates the UEQ outcome, which was derived from GIS overlay method. That is mainly because the GIS overlay was found to be more emblematic for UEQ in some previous studies and one of our parallel studies [5,15]. The independent parameters are the urban and environmental parameters, which were derived from the remote sensing and GIS data, such as population density, building density, NDVI, public transportation, etc.…”

“…These integration techniques are able to assess the current and predict/estimate the future UEQ, which are modelled by the municipal and city planners [4]. Thus, the assessment of UEQ can be an efficient tool to provide effective information of urban conditions, sustainable development and regional planning [5]. UEQ can be modelled using satellite remote sensing techniques through analysing multi-temporal and multi-resolution data, which are able to give a clear vision for visualizing and understanding the land cover, Land Surface Temperature (LST), water conditions and vegetation in urban areas [6,7].…”

“…The PCA analytical technique has several potential disadvantages: (1) it produces unweighted components, which may not highlight those important parameters; (2) PCA does not work properly in nonlinear relationships; and finally, (3) the minimum number of components is indeterminable [5]. The GIS overlay method does not consider correlation among parameters, nor give weight to the parameters.…”

Special Issue: “Maintaining Ecosystem Services to Support Urban Needs”

“…Integration techniques, including GIS overlay and Principle Component Analysis, were used to integrate environmental, urban, and socio-economic parameters. The approach can provide fruitful information to model UEQ; however, due to the lack of significance the practical application of this indicator may be limited [24].…”

“…Klimas et al [16] confirmed that incorporating knowledge of small-scale variability in ESs and in disservices on parcel-sized lots (private or public) may improve sustainable planning in urban areas. Faisal and Shaker [24] modeled the Urban Environmental Quality (UEQ) for better city planning and efficient urban sprawl control. Integration techniques, including GIS overlay and Principle Component Analysis, were used to integrate environmental, urban, and socio-economic parameters.…”