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.

Government failures and non-performing loans in European countries: a spatial approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ferdinando Ofria ◽  
Massimo Mucciardi
Keyword(s):  
Public Debt ◽  
Geographically Weighted Regression ◽  
European Countries ◽  
Weighted Regression ◽  
Second Step ◽  
Ols Regression ◽  
Content Type ◽  
The Impact ◽  
The Relationship ◽  
The Eu

PurposeThe purpose is to analyze the spatially varying impacts of corruption and public debt as % of GDP (proxies of government failures) on non-performing loans (NPLs) in European countries; comparing two periods: one prior to the crisis of 2007 and another one after that. The authors first modeled the NPLs with an ordinary lest square (OLS) regression and found clear evidence of spatial instability in the distribution of the residuals. As a second step, the authors utilized the geographically weighted regression (GWR) to explore regional variations in the relationship between NPLs and the proxies of “Government failures”.Design/methodology/approachThe authors first modeled the NPL with an OLS regression and found clear evidence of spatial instability in the distribution of the residuals. As a second step, the author utilized the Geographically Weighted Regression (GWR) (Fotheringham et al., 2002) to explore regional variations in the relationship between NPLs and proxies of “Government failures” (corruption and public debt as % of GDP).FindingsThe results confirm that corruption and public debt as % of GDP, after the crisis of 2007, have affected significantly on NPLs of the EU countries and the following countries neighboring the EU: Switzerland, Iceland, Norway, Montenegro, and Turkey.Originality/valueIn a spatial prospective, unprecedented in the literature, this research focused on the impact of corruption and public debt as % of GDP on NPLs in European countries. The positive correlation, as expected, between public debt and NPLs highlights that fiscal problems in Eurozone countries have led to an important rise of problem loans. The impact of institutional corruption on NPLs reports that the higher the corruption, the higher is the level of NPLs.

scholarly journals Valuing Environmental Amenities across Space: A Geographically Weighted Regression of Housing Preferences in Greenville County, SC

Land ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 147
Author(s):  
Hiebert ◽  
Allen
Keyword(s):  
Geographically Weighted Regression ◽  
City Planning ◽  
Spatial Dynamics ◽  
Ordinary Least Squares ◽  
Hedonic Model ◽  
Weighted Regression ◽  
Hedonic Price ◽  
Environmental Amenities ◽  
Forested Areas ◽  
The Impact

As global consumption and development rates continue to grow, there will be persistent stress placed on public goods, namely environmental amenities. Urban sprawl and development places pressure on forested areas, as they are often displaced or degraded in the name of economic development. This is problematic because environmental amenities are valued by the public, but traditional market analysis typically obscures the value of these goods and services that are not explicitly traded in a market setting. This research examines the non-market value of environmental amenities in Greenville County, SC, by utilizing a hedonic price model of home sale data in 2011. We overlaid home sale data with 2011 National Land Cover Data to estimate the value of a forest view, proximity to a forest, and proximity to agriculture on the value of homes. We then ran two regression models, an ordinary least squares (OLS) and a geographically weighted regression to compare the impact of space on the hedonic model variables. Results show that citizens in Greenville County are willing to pay for environmental amenities, particularly views of a forest and proximity to forested and agricultural areas. However, the impact and directionality of these variables differ greatly across space. These findings suggest the need for an integration of spatial dynamics into environmental valuation estimates to inform conservation policy and intentional city planning.

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 Application of Remote Sensing, GIS and Machine Learning with Geographically Weighted Regression in Assessing the Impact of Hard Coal Mining on the Natural Environment

2020 ◽  
Vol 12 (22) ◽  
pp. 9338
Author(s):  
Anna Kopeć ◽  
Paweł Trybała ◽  
Dariusz Głąbicki ◽  
Anna Buczyńska ◽  
Karolina Owczarz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Coal Mining ◽  
Geographically Weighted Regression ◽  
Machine Learning Algorithms ◽  
Weighted Regression ◽  
Hard Coal ◽  
Mining Operations ◽  
Hard Coal Mining ◽  
The Impact

Mining operations cause negative changes in the environment. Therefore, such areas require constant monitoring, which can benefit from remote sensing data. In this article, research was carried out on the environmental impact of underground hard coal mining in the Bogdanka mine, located in the southeastern Poland. For this purpose, spectral indexes, satellite radar interferometry, Geographic Information System (GIS) tools and machine learning algorithms were utilized. Based on optical, radar, geological, hydrological and meteorological data, a spatial model was developed to determine the statistical significance of the selected factors’ individual impact on the occurrence of wetlands. Obtained results show that Normalized Difference Vegetation Index (NDVI) change, terrain height, groundwater level and terrain displacement had a considerable influence on the occurrence of wetlands in the research area. Moreover, the machine learning model developed using the Random Forest algorithm allowed for an efficient determination of potential flooding zones based on a set of spatial variables, correctly detecting 76% area of wetlands. Finally, the GWR (Geographically Weighted Regression (GWR) modelling enabled identification of local anomalies of selected factors’ influence on the occurrence of wetlands, which in turn helped to understand the causes of wetland formation.

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 THE IMPACT OF M&A TRANSACTIONS ON THE GROWTH OF THE BANKS’ MARKET VALUE

2021 ◽  
Vol 5 (40) ◽  
pp. 45-54
Author(s):  
L. Prymostka ◽  
N. Pantielieieva ◽  
I. Krasnova ◽  
V. Lavreniuk ◽  
O. Lytvynenko
Keyword(s):  
Economic Development ◽  
Mergers And Acquisitions ◽  
Banking Sector ◽  
Jel Classification ◽  
Economic Indicators ◽  
Level Of Economic Development ◽  
Banking Business ◽  
Economic Trends ◽  
The Impact ◽  
The Relationship

Abstract. The globalization of markets, the need to comply with modern economic trends and introduce new technological solutions to increase the profitability of the banking business have significantly intensified the processes of mergers and acquisitions in the banking sector. M&A processes are long and complex, their results are difficult to forecast in lack of actual detailed research. The diversity of the results of the available research requires updating the data based on larger volumes of transactions and larger time intervals. The purpose of the article is to substantiate two hypotheses: first, the impact of M&A agreements especially on the increase in the value of banks; and impact of factors that show economic development level on the value of banks. The object of the study is the relationship between the value of commercial banks in domestic and foreign financial markets, M&A agreements, as well as economic indicators published by the World Bank and measuring the level of economic development of countries. The article uses statistical modeling method. The constructed model of linear regression allows to state that the fact of influence of M&A on growth of cost of consolidated banks is fair for 54.8% of cases. The study shows that the M&A processes have the greatest impact on the value of banks in the interval of 3—5 years after the conclusion of the agreement. Analysis of the relationship between economic indicators and the growth of bank value shows that the greatest impact on the value of banks has percent of the growth of GDP and GDP per capita, but the low value of the determinant at 22.9% indicates a low dependence of bank value on the level of economic indicators in general. It was found that external factors do not directly affect the growth in the value of banks in the process of M&A transactions. The question of expanding the system of factors that will influence the M&A processes and, as a consequence, the value of the banks, will be the subject of further research. Keywords: globalization of markets, mergers and acquisitions of banks, consolidation, M&A dynamic, market capitalization, bank value. JEL Classification Е44, Е47, G14 Formulas: 2; fig.: 4; tabl.: 4; bibl.: 14.

scholarly journals Energy Efficiency Of National Economy: Social, Economic And Ecological Indicators

2020 ◽  
Vol 4 (4) ◽  
pp. 160-174
Author(s):  
Yevheniia Ziabina ◽  
Tetyana Pimonenko ◽  
Luidmyla Starchenko
Keyword(s):  
Energy Efficiency ◽  
Economic Development ◽  
Regression Analysis ◽  
National Economy ◽  
Green Energy ◽  
Economic Indicators ◽  
Strong Impact ◽  
Energy Efficiency Indicator ◽  
The Impact ◽  
The Relationship

The paper aimed to formalise the relationship between the level of Ukrainian energy efficiency from 30 indicators of social, ecological and economic development of the country. The main purpose of the study is to identify the impact and dependence of socio-ecological and economic indicators on the level of energy efficiency using multiple correlation-regression analysis. The systematisation of the analysed results allowed identifying the core directions to overcome the issues of the slow pace of energy efficiency improvement and the development of carbon neutrality of the country. The relevance of this scientific solution to the problem is that the level of energy efficiency is influenced by a large number of socio-ecological and economic factors sometimes independent of each other. The authors analysed the relationship between the level of energy efficiency and socio-ecological and economic indicators of country development. The investigation consisted from the following stages: conducting polynomial-regression analysis of energy efficiency development in Ukraine; development of correlation-regression multiple models of relationships between energy efficiency indicator and socio-ecological-economic indicators; explaining the conclusions and providing recommendations considering the findings. The object of the study was the processes of energy efficiency relationships with 30 indicators of socio-ecological and economic development, namely how much they affect the energy development of the country. The conclusions were theoretical and practical in terms of the impact on the level of energy efficiency of interdisciplinary indicators. The conclusions which proved by the empirical findings allowed identifying weaknesses in the development of the national economy, as well as to improve and increase the energy potential of the country through energy efficiency development strategies due to the studied determinants that have a strong impact on the level of energy efficiency. Keywords: energy efficiency, carbon-free economy, sustainable development, energy sector, green energy, energy security.

scholarly journals Estimation model of PM2.5 Concentrations Based on Spatiotemporal Adaptability and Satellite Remote Sensing

2021 ◽  
Author(s):  
Weidong li ◽  
Liye Dong ◽  
Linyan Bai
Keyword(s):  
Remote Sensing ◽  
Regression Model ◽  
Geographically Weighted Regression ◽  
Satellite Remote Sensing ◽  
Dimensional Space ◽  
Meteorological Data ◽  
Weighted Regression ◽  
Observation Data ◽  
Estimation Model ◽  
Geographically Weighted Regression Model

Abstract Based on satellite remote sensing AOD, we can estimate and monitor the continuous changes of PM2.5, which solved the disadvantages of traditional ground station discrete monitoring. Four-dimensional spatiotemporal heterogeneity is not considered in the construction of traditional empirical regression models, such as geographically weighted regression model (GWR) and spatiotemporal geographically weighted regression model (gtwr). To solve this four-dimensional spatiotemporal nonstationarity, this article proposes and constructs a spatiotemporal adaptive fine particulate matter (PM2.5) concentration estimation model: 4D-GTWR by introducing a DEM (Digital elevation model) and time effects into a GWR model. This method solves the heterogeneity between the three-dimensional space and one-dimensional time by constructing a four-dimensional space kernel function and obtaining its weight. Based on PM2.5 ground observation data and meteorological data collected from December 2017 to February 2018 in Zhengzhou City, Henan Province, PM2.5 estimations are obtained from MODIS MYD-3K AOD data using the GWR, TWR, GTWR and 4D-GTWR models. The results showed that the MAE (mean absolute error) of the 4D-GTWR model decreased by 54.13%, 54.06% and 37.90%, compared to those of the GWR, TWR and GTWR models, respectively, and that the PM2.5 concentrations predicted by the 4D-GTWR model were closest to the measured values. The R2 (the correlation coefficient) of the 4D-GTWR model was 0.9496, which was better than those of the GWR (R2 =0.7761), TWR (R2 =0.7763) and GTWR (R2=0.8811) models. The 4D-GTWR model can not only improve the precision of PM2.5 estimations but can also reveal the four-dimensional spatial heterogeneity of PM2.5 concentrations and the differentiation of the DEM's influence on the spatial dimensions.

Sign in / Sign up

Export Citation Format

Share Document