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

2021 ◽  
Author(s):  
Kamil Faisal ◽  
Ahmed Shaker
Keyword(s):  
Environmental Quality ◽  
Geographically Weighted Regression ◽  
Family Income ◽  
Census Data ◽  
Principal Component ◽  
Urban Morphology ◽  
Weighted Regression ◽  
Spatial Lag Model ◽  
Economic Parameters ◽  
The City

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.

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

2021 ◽  
Author(s):  
Kamil Faisal ◽  
Ahmed Shaker
Keyword(s):  
Environmental Quality ◽  
Geographically Weighted Regression ◽  
Family Income ◽  
Census Data ◽  
Principal Component ◽  
Urban Morphology ◽  
Weighted Regression ◽  
Spatial Lag Model ◽  
Economic Parameters ◽  
The City

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.

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

2021 ◽  
Author(s):  
Kamil Faisal ◽  
Ahmed Shaker
Keyword(s):  
Remote Sensing ◽  
Environmental Quality ◽  
City Planning ◽  
Family Income ◽  
Census Data ◽  
Negative Impact ◽  
Human Life ◽  
Principal Component ◽  
Economic Parameters

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.

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

2021 ◽  
Author(s):  
Kamil Faisal ◽  
Ahmed Shaker
Keyword(s):  
Remote Sensing ◽  
Environmental Quality ◽  
City Planning ◽  
Family Income ◽  
Census Data ◽  
Negative Impact ◽  
Human Life ◽  
Principal Component ◽  
Economic Parameters

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.

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

2021 ◽  
Author(s):  
Kamil Faisal
Keyword(s):  
Remote Sensing ◽  
Environmental Quality ◽  
Geographically Weighted Regression ◽  
City Planning ◽  
Urban Morphology ◽  
New Method ◽  
Economic Indicators ◽  
Weighted Regression ◽  
The Impact ◽  
The Relationship

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.

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

2021 ◽  
Author(s):  
Kamil Faisal
Keyword(s):  
Remote Sensing ◽  
Environmental Quality ◽  
Geographically Weighted Regression ◽  
City Planning ◽  
Urban Morphology ◽  
New Method ◽  
Economic Indicators ◽  
Weighted Regression ◽  
The Impact ◽  
The Relationship

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.

Spatial modelling of soil organic carbon stocks with combined principal component analysis and geographically weighted regression

2018 ◽  
Vol 156 (6) ◽  
pp. 774-784 ◽  
Author(s):  
Long Guo ◽  
Mei Luo ◽  
Chengsi Zhangyang ◽  
Chen Zeng ◽  
Shanqin Wang ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Geographically Weighted Regression ◽  
Environmental Variables ◽  
Prediction Models ◽  
Principal Component ◽  
Weighted Regression ◽  
Auxiliary Variables ◽  
Soc Stocks

AbstractWith the development of remote sensing and geostatistical technology, complex environmental variables are increasingly easily quantified and applied in modelling soil organic carbon (SOC). However, this emphasizes data redundancy and multicollinearity problems adding to the difficulty in selecting dominant influential auxiliary variables and uncertainty in estimating SOC stocks. The current paper considers the spatial characteristics of SOC density (SOCD) to construct prediction models of SOCD on the basis of reducing the data dimensionality and complexity using the principal component analysis (PCA) method. A total of 260 topsoil samples were collected from Chahe town, China. Eight environmental variables (elevation, aspect, slope, normalized difference vegetation index, normalized difference moisture index, nearest distance to construction area and road, and land use degree comprehensive index) were pre-analysed by PCA and then extracted as the main principal component variables to construct prediction models. Two geostatistical approaches (ordinary kriging and ordinary co-kriging) and two regression approaches (ordinary least squares and geographically weighted regression (GWR)) were used to estimate SOCD. Results showed that PCA played an important role in reducing the redundancy and multicollinearity of the auxiliary variables and GWR achieved the highest prediction accuracy in these four models. GWR considered not only the spatial characteristics of SOCD but also the related valuable information of the auxiliary attributes. In summary, PCA-GWR is a promising spatial method used here to predict SOC stocks.

scholarly journals PEMODELAN GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) PADA PERSENTASE KRIMINALITAS DI PROVINSI JAWA TIMUR TAHUN 2017

2020 ◽  
Vol 4 (1) ◽  
pp. 156-163
Author(s):  
Dessy Wulandari Syahputri Yusuf ◽  
Elvira Mustikawati Putri Hermanto ◽  
Wara Pramesti
Keyword(s):  
Geographically Weighted Regression ◽  
Human Development Index ◽  
Human Population ◽  
Weighted Regression ◽  
Coefficient Of Determination ◽  
Variable Population ◽  
North Sumatra ◽  
The City ◽  
Poor Population ◽  
Better Than

Crime is everything that exists in Indonesia. Based on BPS data in 2018, East Java Province ranks first in the Province of North Sumatra and the Special Capital Region of Jakarta. This research was conducted to determine the factors that support crime in each Regency / City of East Java Province. The method used in this research is Weighted Geographic Regression (GWR). Geographically Weighted Regression (GWR) is one of the statistical methods used to model variable responses with regional or area-based predictor variables. Based on the GWR results, it is recognized as a variable Population Density Percentage (X1), Open Unemployment Rate (X2), Poor Population (X3), Population who are Victims of Drug Abuse (X4), Human Development Index (X5), and Married Human Population (X6) ) importance in the city of Surabaya. The coefficient of determination (R2) and AIC from GWR is better than the OLS model. This refers to the optimal R2 and AIC values ​​of 91.40% and 129.293.

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