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

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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Prediction of soil properties by using geographically weighted regression at a regional scale

Soil Research ◽

10.1071/sr16177 ◽

2017 ◽

Vol 55 (4) ◽

pp. 318 ◽

Cited By ~ 5

Author(s):

Xing Tan ◽

Peng-Tao Guo ◽

Wei Wu ◽

Mao-Fen Li ◽

Hong-Bin Liu

Keyword(s):

Spatial Distribution ◽

Soil Properties ◽

Precision Agriculture ◽

Geographically Weighted Regression ◽

Regional Scale ◽

Ordinary Least Squares ◽

Ecological Modelling ◽

Weighted Regression ◽

Coefficient Of Determination ◽

Ols Regression

Detailed information about spatial distribution of soil properties is important in ecological modelling, environmental prediction, precision agriculture, and natural resources management, as well as land-use planning. In the present study, a recently developed method called geographically weighted regression (GWR) is applied to predict spatial distribution of soil properties (pH, soil organic matter, available nitrogen, available potassium) based on topographical indicators, climate factors, and geological stratum at a regional scale. In total, 1914 soil samples collected from a depth of 0–20cm were used to calibrate and validate the models. Performances of the GWR models were compared with the traditional, ordinary least-squares (OLS) regression. The results indicated that the GWR models made significant improvements to model performances over OLS regression, based on F-test, coefficient of determination, and corrected Akaike information criterion. GWR models also improved the reliability of the soil–environment relationships by reducing the spatial autocorrelations in model residuals. Meanwhile, the use of GWR models disclosed that the relationships between soil properties and environmental variables were not invariant over space but exhibited significant spatial non-stationarity. Accordingly, the GWR models remarkably improved the prediction accuracies over the corresponding OLS models. The results demonstrated that GWR could serve as a useful tool for digital soil mapping in areas with complex terrain.

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Spatial distribution and source identification of heavy metals (As, Cr, Cu and Ni) at sub-watershed scale using geographically weighted regression

International Soil and Water Conservation Research ◽

10.1016/j.iswcr.2019.01.005 ◽

2019 ◽

Vol 7 (3) ◽

pp. 308-315 ◽

Cited By ~ 1

Author(s):

Maziar Mohammadi ◽

Abdulvahed Khaledi Darvishan ◽

Nader Bahramifar

Keyword(s):

Heavy Metals ◽

Spatial Distribution ◽

Geographically Weighted Regression ◽

Source Identification ◽

Weighted Regression ◽

Watershed Scale

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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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Identifying Vulnerable Populations in Subtropical Brisbane, Australia: A Guide for Heatwave Preparedness and Health Promotion

ISRN Epidemiology ◽

10.1155/2014/821759 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 10

Author(s):

Margaret Loughnan ◽

Nigel Tapper ◽

Thu Phan

Keyword(s):

Public Health ◽

Health Promotion ◽

Spatial Distribution ◽

High Risk ◽

Urban Areas ◽

Spatiotemporal Analysis ◽

Prevention Policy ◽

Increased Risk ◽

Risk Areas ◽

Heat Events

Building healthy societies is a key step towards climate resilient communities. Ill health is related to increased risk during heat events and is disproportionally distributed within and between communities. To understand the differences in the spatial distribution of climate related health risks and how this will change in the future we have undertaken a spatiotemporal analysis of heatwave risks in urban populations in Brisbane, Australia. The aim of this was to advise emergency managers and public health authorities of high-risk areas during extreme heat events (EHEs). The spatial distribution of heat related morbidity identified areas of high healthcare service demand during EHEs. An index of risk was developed based on social and environmental determinants of vulnerability. Regression analysis was used to determine the key drivers of heat related morbidity from the index. A weighted map of population vulnerability was produced which identified the high risk areas and provided key information to target public health interventions and heat stress prevention policy. The predicted changes in high risk populations such as the proportion of elderly people living in urban areas were also mapped to support longer term adaptation and develop health care infrastructure and health promotion strategies.

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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 Distribution and Health Risk Assessment of Potentially Toxic Elements in Surface Soils of Bosten Lake Basin, Central Asia

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16193741 ◽

2019 ◽

Vol 16 (19) ◽

pp. 3741 ◽

Cited By ~ 2

Author(s):

Long Ma ◽

Jilili Abuduwaili ◽

Wen Liu

Keyword(s):

Organic Matter ◽

Spatial Distribution ◽

Linear Model ◽

Geographically Weighted Regression ◽

Toxic Elements ◽

Potentially Toxic Elements ◽

Weighted Regression ◽

Lake Basin ◽

Total Organic Matter ◽

Bosten Lake

A geographically weighted regression and classical linear model were applied to quantitatively reveal the factors influencing the spatial distribution of potentially toxic elements of forty-eight surface soils from Bosten Lake basin in Central Asia. At the basin scale, the spatial distribution of the majority of potentially toxic elements, including: cobalt (Co), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), thallium (Tl), vanadium (V), and zinc (Zn), had been significantly influenced by the geochemical characteristics of the soil parent material. However, the arsenic (As), cadmium (Cd), antimony (Sb), and mercury (Hg) have been influenced by the total organic matter in soils. Compared with the results of the classical linear model, the geographically weighted regression can significantly increase the level of simulation at the basin spatial scale. The fitting coefficients of the predicted values and the actual measured values significantly increased from the classical linear model (Hg: r2 = 0.31; Sb: r2 = 0.64; Cd: r2 = 0.81; and As: r2 = 0.68) to the geographically weighted regression (Hg: r2 = 0.56; Sb: r2 = 0.74; Cd: r2 = 0.89; and As: r2 = 0.85). Based on the results of the geographically weighted regression, the average values of the total organic matter for As (28.7%), Cd (39.2%), Hg (46.5%), and Sb (26.6%) were higher than those for the other potentially toxic elements: Cr (0.1%), Co (4.0%), Ni (5.3%), V (0.7%), Cu (18.0%), Pb (7.8%), Tl (14.4%), and Zn (21.4%). There were no significant non-carcinogenic risks to human health, however, the results suggested that the spatial distribution of potentially toxic elements had significant differences.

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Comparison of artificial neural networks, geographically weighted regression and Cokriging methods for predicting the spatial distribution of soil macronutrients (N, P, and K)

Chinese Geographical Science ◽

10.1007/s11769-017-0906-6 ◽

2017 ◽

Vol 27 (5) ◽

pp. 747-759 ◽

Cited By ~ 5

Author(s):

Samad Emamgholizadeh ◽

Shahin Shahsavani ◽

Mohamad Amin Eslami

Keyword(s):

Neural Networks ◽

Spatial Distribution ◽

Artificial Neural Networks ◽

Geographically Weighted Regression ◽

Weighted Regression ◽

Artificial Neural

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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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