Determinants of geographical inequalities in domestic water supply across city of Pune, India

Abstract The water supply system in the city of Pune is affected due to the fast and chaotic development in and around the city. The quantity of per capita water supply and hours of supply per day varies substantially across the city. Some central parts of the city are benefited from a large availability of water as compared to peripheral areas. This research employed Ordinary Least Squares (OLS) Regression, Geographically Weighted Regression (GWR), and the new version of GWR termed as Multi-scale Geographically Weighted Regression (MGWR) models to better understand the factors behind observed spatial patterns of water supply distribution and to predict water supply in newly merged and proposed villages in the Pune city's periphery. Results showed statistical significance of slope; distance from service reservoirs; and water supply hour. MGWR and GWR models improved our results (adjusted R2: 0.916 and 0.710 respectively) significantly over those of the OLS model (adjusted R2: 0.252) and proved how local conditions influence variables. The maps of GWR display how a particular variable is highly important in some areas but less important in other parts of the city. The results from the current study can help decision-makers to make appropriate decisions for future planning to achieve Sustainable Development Goal number 6 (SDG #6), which focuses on achieving universal and equitable access to safe and affordable drinking water for all.

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Modeling spatial determinates of teenage pregnancy in Ethiopia; geographically weighted regression

BMC Women s Health ◽

10.1186/s12905-021-01400-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Seblewongel Tigabu ◽

Alemneh Mekuriaw Liyew ◽

Bisrat Misganaw Geremew

Keyword(s):

Spatial Distribution ◽

Geographically Weighted Regression ◽

Teenage Pregnancy ◽

Wealth Quintile ◽

Ordinary Least Squares ◽

Weighted Regression ◽

Contraceptive Methods ◽

Hotspot Analysis ◽

Increased Risk ◽

Risk Areas

Abstract Background In developing countries, 20,000 under 18 children give birth every day. In Ethiopia, teenage pregnancy is high with Afar and Somalia regions having the largest share. Even though teenage pregnancy has bad maternal and child health consequences, to date there is limited evidence on its spatial distribution and driving factors. Therefore, this study is aimed to assess the spatial distribution and spatial determinates of teenage pregnancy in Ethiopia. Methods A secondary data analysis was conducted using 2016 EDHS data. A total weighted sample of 3381 teenagers was included. The spatial clustering of teenage pregnancy was priorly explored by using hotspot analysis and spatial scanning statistics to indicate geographical risk areas of teenage pregnancy. Besides spatial modeling was conducted by applying Ordinary least squares regression and geographically weighted regression to determine factors explaining the geographic variation of teenage pregnancy. Result Based on the findings of exploratory analysis the high-risk areas of teenage pregnancy were observed in the Somali, Afar, Oromia, and Hareri regions. Women with primary education, being in the household with a poorer wealth quintile using none of the contraceptive methods and using traditional contraceptive methods were significant spatial determinates of the spatial variation of teenage pregnancy in Ethiopia. Conclusion geographic areas where a high proportion of women didn’t use any type of contraceptive methods, use traditional contraceptive methods, and from households with poor wealth quintile had increased risk of teenage pregnancy. Whereas, those areas with a higher proportion of women with secondary education had a decreased risk of teenage pregnancy. The detailed maps of hotspots of teenage pregnancy and its predictors had supreme importance to policymakers for the design and implementation of adolescent targeted programs.

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Geographically weighted regression modelling of the spatial association between malaria cases and environmental factors in Cameroon.

10.21203/rs.2.13021/v3 ◽

2020 ◽

Author(s):

Marlvin Anemey Tewara ◽

Liu Yunxia ◽

Weiqiang Ling ◽

Binang Helen Barong ◽

Prisca Ngetemalah Mbah-Fongkimeh ◽

...

Keyword(s):

Environmental Factors ◽

Spatial Variability ◽

Geographically Weighted Regression ◽

Environmental Variables ◽

Vegetation Index ◽

Spatial Association ◽

Ordinary Least Squares ◽

Variable Coefficients ◽

Weighted Regression ◽

Gwr Model

Abstract Background: Studies have illustrated the association of malaria cases with environmental factors in Cameroon but limited in addressing how these factors vary in space for timely public health interventions. Thus, we want to find the spatial variability between malaria hotspot cases and environmental predictors using Geographically weighted regression (GWR) spatial modelling technique.Methods: The global Ordinary least squares (OLS) in the modelling spatial relationships tool in ArcGIS 10.3. was used to select candidate explanatory environmental variables for a properly specified GWR model. The local GWR model used the global OLS candidate variables to examine, predict and explore the spatial variability between environmental factors and malaria hotspot cases generated from Getis-Ord Gi* statistical analysis. Results: The OLS candidate environmental variable coefficients were statistically significant (adjusted R2 = 22.3% and p < 0.01) for a properly specified GWR model. The GWR model identified a strong spatial association between malaria cases and rainfall, vegetation index, population density, and drought episodes in most hotspot areas and a weak correlation with aridity and proximity to water with an overall model performance of 0.243 (adjusted R2= 24.3%).Conclusion: The generated GWR maps suggest that for policymakers to eliminate malaria in Cameroon, there should be the creation of malaria outreach programs and further investigations in areas where the environmental variables showed strong spatial associations with malaria hotspot cases.

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Modelling Housing Rents Using Spatial Autoregressive Geographically Weighted Regression: A Case Study in Cracow, Poland

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9060346 ◽

2020 ◽

Vol 9 (6) ◽

pp. 346

Author(s):

Mateusz Tomal

Keyword(s):

Geographically Weighted Regression ◽

Rental Housing ◽

Ordinary Least Squares ◽

Weighted Regression ◽

Spatial Econometric ◽

Econometric Methods ◽

Depth Analysis ◽

Sar Model ◽

Internet Platform ◽

Spatial Autoregressive

The proportion of tenants will undoubtedly rise in Poland, where at present, the ownership housing model is very dominant. As a result, the rental housing market in Poland is currently under-researched in comparison with owner-occupancy. In order to narrow this research gap, this study attempts to identify the determinants affecting rental prices in Cracow. The latter were obtained from the internet platform otodom.pl using the web scraping technique. To identify rent determinants, ordinary least squares (OLS) regression and spatial econometric methods were used. In particular, traditional spatial autoregressive model (SAR) and spatial autoregressive geographically weighted regression (GWR-SAR) were employed, which made it possible to take into account the spatial heterogeneity of the parameters of determinants and the spatially changing spatial autocorrelation of housing rents. In-depth analysis of rent determinants using the GWR-SAR model exposed the complexity of the rental market in Cracow. Estimates of the above model revealed that many local markets can be identified in Cracow, with different factors shaping housing rents. However, one can identify some determinants that are ubiquitous for almost the entire city. This concerns mainly the variables describing the area of the flat and the age of the building. Moreover, the Monte Carlo test indicated that the spatial autoregressive parameter also changes significantly over space.

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

Land ◽

10.3390/land8100147 ◽

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.

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Spatial Variations in Fertility of South Korea: A Geographically Weighted Regression Approach

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8060262 ◽

2019 ◽

Vol 8 (6) ◽

pp. 262 ◽

Cited By ~ 2

Author(s):

Myunggu Jung ◽

Woorim Ko ◽

Yeohee Choi ◽

Youngtae Cho

Keyword(s):

South Korea ◽

Geographically Weighted Regression ◽

Population Policy ◽

Local Population ◽

Sociodemographic Factors ◽

Ordinary Least Squares ◽

Spatial Variations ◽

Low Fertility ◽

Weighted Regression

South Korea has witnessed a remarkable decline in birth rates in the last few decades. Although there has been a large volume of literature exploring the determinants of low fertility in South Korea, studies on spatial variations in fertility are scarce. This study compares the Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models to investigate the potential role of the spatially heterogeneous response of the total fertility rate (TFR) to sociodemographic factors. The study finds that the relationships between sociodemographic factors and TFRs in South Korea vary across 252 sub-administrative areas in terms of both magnitude and direction. This study therefore demonstrates the value of using spatial analysis for providing evidence-based local-population policy options in pursuit of a fertility rebound in South Korea.

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Spatial Heterogeneity, Broadband, and New Firm Formation

Quality Innovation Prosperity ◽

10.12776/qip.v20i3.791 ◽

2017 ◽

Vol 21 (1) ◽

pp. 165

Author(s):

Jitendra Parajuli ◽

Kingsley Haynes

Keyword(s):

Spatial Heterogeneity ◽

Least Squares ◽

Geographically Weighted Regression ◽

Ordinary Least Squares ◽

Weighted Regression ◽

Least Squares Regression ◽

Firm Formation ◽

Broadband Internet ◽

New Firm Formation ◽

New Firm

Purpose: This paper examines the spatial heterogeneity associated with broadband Internet and new firm formation in a number of U.S. states.Methodology/Approach: Both ordinary least-squares regression and Geographically Weighted Regression are used for the estimation purpose.Findings: The global coefficient estimates of ordinary least-squares regression account for the marginal change in a phenomenon, but such a global measure cannot reveal the locally-varying dynamics. Using Geographically Weighted Regression, it was found that at the aggregate and economic sector levels, the association between single-unit firm births and the provision of broadband Internet varies across counties in Florida and Ohio.Originality/Value of paper: There are numerous studies on broadband Internet in the U.S., but this is the first that explicitly examines broadband provision and new firm formation by taking into account spatial heterogeneity across countries.

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Domestic Water Quality in Jeddah, Saudi Arabia

Journal of King Abdulaziz University-Engineering Sciences ◽

10.4197/eng.23-1.9 ◽

2012 ◽

Vol 23 (1) ◽

pp. 207-223 ◽

Cited By ~ 1

Author(s):

Maged H. Hussein Maged H. Hussein

Keyword(s):

Drinking Water ◽

Saudi Arabia ◽

Water Supply ◽

Water Table ◽

Water Purification ◽

Underground Storage ◽

Domestic Water ◽

Intermittent Water Supply ◽

Water Tanks ◽

The City

Due to water resources shortage in Saudi Arabia, the water authority relies on intermittent water supply system. In this system, the city is divided into a number of small sectors, and water is pumped rotationally between the sectors according to an operational schedule. The frequency of water pumping to a given sector varies from several days to weeks. Between the intermittent water supply, pipes remain empty and dry for long periods. Consumers construct the underground storage water tanks to meet their demands. Lack of sewerage systems in many areas with high water table in the city lead to the presence of cesspools near the underground storage water tanks. Potential leakage of polluted water into the distribution network is very high and the pipe system is exposed to the pressure of contaminated surrounding water table. These factors enhance the risk of drinking water contamination. The first objective of this study is to assess the quality of domestic water supply in Jeddah. One hundred and thirteen water samples were collected in a five-month period between January and June 2009. Thirteen parameters were evaluated and compared to the maximum level of the WHO and Gulf standards for un-bottled drinking water. The results showed a compliance with the drinking water standards regarding the physical and chemical parameters, except five samples which have exceeded the maximum allowable limit for iron. The bacteriological results showed that up to 60% of the samples were contaminated with total coliform. The second objective is to evaluate the suitability and reliability of the most purchased domestic water purification units (point-of-use) POU, and to select the suitable water purification unit for the residential water in Jeddah. Unit III, which consists of fiber filters, activated carbon filter and the ultraviolet UV- Sterilizer of ultraviolet (UV), was found to be suitable for domestic use in Jeddah

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

10.32920/ryerson.14637051 ◽

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.

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A Semi-Parametric Geographically Weighted Regression Approach to Exploring Driving Factors of Fractional Vegetation Cover: A Case Study of Guangdong

Sustainability ◽

10.3390/su12187512 ◽

2020 ◽

Vol 12 (18) ◽

pp. 7512

Author(s):

Yuhao Jin ◽

Han Zhang ◽

Yuchao Yan ◽

Peitong Cong

Keyword(s):

Geographically Weighted Regression ◽

Vegetation Cover ◽

Southern China ◽

Ordinary Least Squares ◽

Driving Factors ◽

Weighted Regression ◽

Impact Factors ◽

Negative Effects ◽

Vegetation Growth ◽

Fractional Vegetation Cover

Ecological degradation caused by rapid urbanisation has presented great challenges in southern China. Fractional vegetation cover (FVC) has long been the most common and sensitive index to describe vegetation growth and to monitor vegetation degradation. However, most of the studies have failed to adequately explore the complexity of the relationship between fractional vegetation cover (FVC) and impact factors. In this research, we first constructed a Semi-parametric Geographically Weighted Regression (SGWR) model to analyse both the stationary and nonstationary spatial relationships between FVC and driving factors in Guangdong province in southern China on a county level. Then, climate, topographic, land cover, and socio-economic factors were introduced into the model to distinguish impacts on FVC from 2000–2015. Results suggest that the positive and negative effects of rainfall and elevation coefficients alternated, and local urban land and population estimates indicated a negative association between FVC and the modelled factors in each period. The SGWR FVC make significantly improves performance of the geographically weighted regression and ordinary least squares models, with adjusted R2 higher than 0.78. The findings of this research demonstrated that, although urbanisation in the Pearl River Delta in Guangdong has encroached on the regional vegetation cover, the total vegetation area remained unchanged with the implementation of protection policies and regulations.

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Spatiotemporal Differences in Determinants of City Shrinkage Based on Semiparametric Geographically Weighted Regression

Sustainability ◽

10.3390/su11246891 ◽

2019 ◽

Vol 11 (24) ◽

pp. 6891 ◽

Cited By ~ 1

Author(s):

Wangchongyu Peng ◽

Weijun Gao ◽

Xin Yuan ◽

Rui Wang ◽

Jinming Jiang

Keyword(s):

Urban Planning ◽

Geographically Weighted Regression ◽

Spatial Dependence ◽

Ordinary Least Squares ◽

Low Fertility ◽

Weighted Regression ◽

Ageing Population ◽

Foreign Population ◽

Global Impacts ◽

Global And Local

City shrinkage, as an ongoing worldwide phenomenon, is an issue for urban planning and regional development. City shrinkage is remarkable in Japan, with over 85% of municipalities experiencing population loss from 2005 to 2015. As Japan’s society ages and with its low fertility rate, city shrinkage has had a tremendous negative effect on economic development and urban planning. Understanding the spatial dependence and spatial heterogeneity of city shrinkage and its determinants is essential for ensuring the sustainable development of a city or region. In this study, a semiparametric geographically weighted regression (SGWR) model was adopted to explore the spatiotemporal differences in determinants of city shrinkage. The results reveal that the SGWR model incorporating the global and local variables is more interpretive compared to ordinary least squares and geographically weighted regression models in exploring the correlates of city shrinkage. We found the spatial dependence and heterogeneity of shrinking cities resulted from demographic, economy, and social factors, and revealed low fertility, the ageing population, and enterprise change ratio influenced city shrinkage in different regions at different times in Japan, whereas foreign population ratio, industry structure, and social welfare had global impacts. The findings provide useful information for understanding city shrinkage at global and local scales.

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