Quality of Life in Georgia: The 1980 Experience

Bederman, Sanford H.; Hartshorn, Truman A.

doi:10.1353/sgo.1984.0008

Cited by 15 publications

(5 citation statements)

References 7 publications

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“…Most of the urban environmental studies showed that NDVI is one of the most important parameters that can be used to assess UEQ, where the higher values represent the positive impact on the city [2,8]. The NDVI (ranging from −1 to 1) refers to an index that is able to monitor the vegetation activity and its annual changes, which can be calculated using Equation (6) [42]:…”

Section: Normalized Difference Vegetation Index (Ndvi)mentioning

confidence: 99%

“…Recently, satellite remote sensing techniques can help in modelling UEQ through providing continuous Earth observation images of the urban environment at different spatial, spectral and temporal resolutions [2][3][4]. A few preliminary attempts were found using multi-temporal and multi-resolution data to model UEQ [5][6][7][8], since these data can provide a clear vision for visualizing and understanding the land cover, water conditions and vegetation in urban areas [9,10]. As such,…”

Section: Introductionmentioning

confidence: 99%

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

Faisal

Shaker

2017

Sustainability

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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.

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Section: Normalized Difference Vegetation Index (Ndvi)mentioning

confidence: 99%

“…Recently, satellite remote sensing techniques can help in modelling UEQ through providing continuous Earth observation images of the urban environment at different spatial, spectral and temporal resolutions [2][3][4]. A few preliminary attempts were found using multi-temporal and multi-resolution data to model UEQ [5][6][7][8], since these data can provide a clear vision for visualizing and understanding the land cover, water conditions and vegetation in urban areas [9,10]. As such,…”

Section: Introductionmentioning

confidence: 99%

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

Faisal

Shaker

2017

Sustainability

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Section: Introductionmentioning

confidence: 99%

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

Faisal¹,

Shaker²

2024

Preprint

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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.

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“…Recently, satellite remote sensing techniques can help in modelling UEQ through providing continuous Earth observation images of the urban environment at different spatial, spectral and temporal resolutions (Nichol and Lee, 2005;Nichol and Wong, 2006;Nichol et al, 2006). A few preliminary attempts were found using multi-temporal and multi-resolution data to model UEQ (Green, 1957;Bederman and Hartshorn, 1984;Li and Weng, 2007;Nichol and Wong, 2009), since these data can provide a clear vision for visualizing and understanding the land cover, water conditions and vegetation in urban areas Siu, 2000, 2001). As such, UEQ assessment not only provides more detailed information toward urban conditions, it also serves as an efficient tool in sustainable development and urban planning.…”

Section: Introductionmentioning

confidence: 99%

“…However, it is challenging to predict and model the interrelationship and dependence of all the factors. A few preliminary attempts were found using multi-temporal and multi-resolution data to model UEQ (Green, 1957;Bederman and Hartshorn, 1984;Li and Weng, 2007;Nichol and Wong, 2009). As such, UEQ assessment not only provides more detailed information toward urban conditions, it also serves as an efficient tool in sustainable development and urban planning.…”

mentioning

confidence: 99%

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

Faisal¹

2021

Preprint

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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.

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Quality of Life in Georgia: The 1980 Experience

Cited by 15 publications

References 7 publications

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

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

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

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

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