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

2019 ◽  
Author(s):  
Marlvin Anemey Tewara ◽  
Liu Yunxia ◽  
Prisca Ngetemalah Mbah-Fongkimeh ◽  
Zheng Zhaolei ◽  
Helen Barong Binang ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Spatial Variability ◽  
Geographically Weighted Regression ◽  
Environmental Variables ◽  
Vegetation Index ◽  
Spatial Association ◽  
Ordinary Least Squares ◽  
Weighted Regression ◽  
Bed Nets ◽  
Gwr Model

Abstract Background Cameroon has witnessed a 131,000 increase in malaria cases, according to a recent report addressing the malaria burden and control strategies in endemic regions. Studies have illustrated the association between malaria cases and environmental factors in Cameroon but limited in addressing how these factors vary in space for timely 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) tool 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 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. Spatial maps of mosquito bed net ownership and GWR outputs were also created for public health surveillance. Results The OLS candidate environmental variable coefficients were statistically significant for a properly specified GWR model (adjusted R2 = 22.3% and p < 0.01). The GWR model identified a strong 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 (adjusted R2= 24.3%). The mosquito bed nets analysis maps demonstrated an overall low coverage(<50%) of household ownership. Conclusion The generated GWR maps suggest that for policymakers to eliminate malaria in Cameroon by 2030, there should be the creation of outreach programs that will target malaria hotspots locations, intensify free insecticidal net distribution, allocate specific funding, establish vaccination campaigns and carry out further investigations in areas where the environmental variables showed strong spatial associations with malaria hotspot cases.

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

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.

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

2020 ◽  
Author(s):  
Marlvin Anemey Tewara ◽  
Liu Yunxia ◽  
Weiqiang Lin ◽  
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.

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

2019 ◽  
Author(s):  
Marlvin Anemey Tewara ◽  
Liu Yunxia ◽  
Weiqiang Lin ◽  
Binang Helen Barong ◽  
Prisca Ngetemalah Mbah-Fongkimeh ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Spatial Variability ◽  
Least Squares ◽  
Geographically Weighted Regression ◽  
Environmental Variables ◽  
Spatial Association ◽  
Ordinary Least Squares ◽  
Geographical Information ◽  
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 R 2 = 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 R 2 = 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 . Keywords: Geographically weighted regression, Ordinary least squares, malaria, spatial statistics, mapping, Geographical information systems.

scholarly journals A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters

2016 ◽  
Author(s):  
Abhishek K Kala ◽  
Chetan Tiwari ◽  
Armin R Mikler ◽  
Samuel F Atkinson
Keyword(s):  
West Nile Virus ◽  
Environmental Factors ◽  
Least Squares ◽  
Geographically Weighted Regression ◽  
Environmental Variables ◽  
Environmental Data ◽  
Weighted Regression ◽  
Least Squares Regression ◽  
West Nile ◽  
The Relationship

Background. The primary aim of the study reported here was to determine the effectiveness of utilizing local spatial variations in environmental data to uncover the statistical relationships between West Nile Virus (WNV) risk and environmental factors. Because least squares regression methods do not account for spatial autocorrelation and non-stationarity of the type of spatial data analyzed for studies that explore the relationship between WNV and environmental determinants, we hypothesized that a geographically weighted regression model would help us better understand how environmental factors are related to WNV risk patterns without the confounding effects of spatial non-stationarity. Methods. We examined commonly mapped environmental factors using both ordinary least squares regression (LSR) and geographically weighted regression (GWR). Both types of models were applied to examine the relationship between WNV-infected dead bird counts and various environmental factors for those locations. The goal was to determine which approach yielded a better predictive model. Results. LSR efforts lead to identifying three environmental variables that were statistically significantly related to WNV infected dead birds (adjusted R2=0.61): stream density, road density, and land surface temperature. GWR efforts increased the explanatory value of these three environmental variables with better spatial precision (adjusted R2 = 0.71). Conclusions. The spatial granularity resulting from the geographically weighted approach provides a better understanding of how environmental spatial heterogeneity is related to WNV risk as implied by WNV infected dead birds, which should allow improved planning of public health management strategies.

scholarly journals A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3070 ◽  
Author(s):  
Abhishek K. Kala ◽  
Chetan Tiwari ◽  
Armin R. Mikler ◽  
Samuel F. Atkinson
Keyword(s):  
West Nile Virus ◽  
Environmental Factors ◽  
Least Squares ◽  
Geographically Weighted Regression ◽  
Environmental Variables ◽  
Environmental Data ◽  
Weighted Regression ◽  
Least Squares Regression ◽  
West Nile ◽  
The Relationship

BackgroundThe primary aim of the study reported here was to determine the effectiveness of utilizing local spatial variations in environmental data to uncover the statistical relationships between West Nile Virus (WNV) risk and environmental factors. Because least squares regression methods do not account for spatial autocorrelation and non-stationarity of the type of spatial data analyzed for studies that explore the relationship between WNV and environmental determinants, we hypothesized that a geographically weighted regression model would help us better understand how environmental factors are related to WNV risk patterns without the confounding effects of spatial non-stationarity.MethodsWe examined commonly mapped environmental factors using both ordinary least squares regression (LSR) and geographically weighted regression (GWR). Both types of models were applied to examine the relationship between WNV-infected dead bird counts and various environmental factors for those locations. The goal was to determine which approach yielded a better predictive model.ResultsLSR efforts lead to identifying three environmental variables that were statistically significantly related to WNV infected dead birds (adjustedR2 = 0.61): stream density, road density, and land surface temperature. GWR efforts increased the explanatory value of these three environmental variables with better spatial precision (adjustedR2 = 0.71).ConclusionsThe spatial granularity resulting from the geographically weighted approach provides a better understanding of how environmental spatial heterogeneity is related to WNV risk as implied by WNV infected dead birds, which should allow improved planning of public health management strategies.

scholarly journals Geographically Weighted Regression to Explore Spatially Varying Relationships of Recreation Resource Impacts: A Case Study from Adirondack Park, New York, USA

2020 ◽  
Author(s):  
Min-Kook Kim ◽  
David Graefe
Keyword(s):  
Geographically Weighted Regression ◽  
Critical Role ◽  
Ordinary Least Squares ◽  
Global Scale ◽  
Spatial Variations ◽  
Weighted Regression ◽  
Adirondack Park ◽  
Recreation Resource Impacts ◽  
Spatially Varying ◽  
Gwr Model

Empirical studies based on spatial explorations have played a critical role in understanding dynamics of recreation resource impact and recovery at multiple scales. However, little research has been done to examine spatially varying relationships between resource conditions and associated geospatial variables, especially using a predictive modeling approach. The primary purpose of this study was to explore spatially varying relationships of recreation resource impacts by using a geographically weighted regression (GWR) model. Specifically, the study was designed to compare the GWR with an ordinary least squares (OLS) multiple linear regression model to better understand localized spatial variations with roadside campsite conditions in Adirondack Park, NY, USA. Geospatial variables contained in the OLS model explained approximately 22% of the variance in campsite conditions (adjusted R2 = 0.220, p < 0.001). Statistically significant predictors of the campsite condition at the global scale included site circumference, distance from water resource, distance from major road, distance from hosting forest road, and slope. Non-significant variables included site designation, distance from recreational trail, and elevation. The subsequent analysis using the GWR model resulted in adjusted R2 values ranging from 0.198 to 0.271 (mean = 0.221). Roadside campsites located in the northern region of the park exhibited relatively higher R2 values, and roadside campsites located in the southern region exhibited relatively lower R2 values. All of the statistically significant global variables showed spatially varying relationships with the campsite condition. Additionally, elevation and site designation factors in the GWR model, which were non-significant variables at the global scale, suggested localized spatial variations with the campsite condition. Overall, the GWR model provided a more robust examination of campsite condition by accounting for localized spatial variations and by improving the model performance. This paper provides a discussion of the methodological and resource management implications of these findings.

scholarly journals A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters

2016 ◽  
Author(s):  
Abhishek K Kala ◽  
Chetan Tiwari ◽  
Armin R Mikler ◽  
Samuel F Atkinson
Keyword(s):  
West Nile Virus ◽  
Environmental Factors ◽  
Least Squares ◽  
Geographically Weighted Regression ◽  
Environmental Variables ◽  
Environmental Data ◽  
Weighted Regression ◽  
Least Squares Regression ◽  
West Nile ◽  
The Relationship

Background. The primary aim of the study reported here was to determine the effectiveness of utilizing local spatial variations in environmental data to uncover the statistical relationships between West Nile Virus (WNV) risk and environmental factors. Because least squares regression methods do not account for spatial autocorrelation and non-stationarity of the type of spatial data analyzed for studies that explore the relationship between WNV and environmental determinants, we hypothesized that a geographically weighted regression model would help us better understand how environmental factors are related to WNV risk patterns without the confounding effects of spatial non-stationarity. Methods. We examined commonly mapped environmental factors using both ordinary least squares regression (LSR) and geographically weighted regression (GWR). Both types of models were applied to examine the relationship between WNV-infected dead bird counts and various environmental factors for those locations. The goal was to determine which approach yielded a better predictive model. Results. LSR efforts lead to identifying three environmental variables that were statistically significantly related to WNV infected dead birds (adjusted R2=0.61): stream density, road density, and land surface temperature. GWR efforts increased the explanatory value of these three environmental variables with better spatial precision (adjusted R2 = 0.71). Conclusions. The spatial granularity resulting from the geographically weighted approach provides a better understanding of how environmental spatial heterogeneity is related to WNV risk as implied by WNV infected dead birds, which should allow improved planning of public health management strategies.

Analysis of Landslide Surface Deformation Using Geographically Weighted Regression

2012 ◽  
Vol 594-597 ◽  
pp. 2406-2409
Author(s):  
Hai Feng Huang ◽  
Wu Yi ◽  
Qing Lin Yi ◽  
Guo Dong Zhang
Keyword(s):  
Geographically Weighted Regression ◽  
Surface Deformation ◽  
Information Criterion ◽  
Ordinary Least Squares ◽  
Weighted Regression ◽  
Three Gorges Reservoir Area ◽  
Trigger Factors ◽  
Surface Displacements ◽  
Reservoir Type ◽  
Gwr Model

Traditional regression analysis methods such as Ordinary Least Squares (OLS) are usually used to explore data relations, but they cannot reflect the spatial non-stationarity of the data. Geographically Weighted Regression (GWR) is an effective tool for dealing with this situation, whereas there has not any related studies about using GWR to analyze the landslide surface deformation. This paper tries to base on a typical reservoir-type landslide in Three Gorges Reservoir area of Yangtze River, China, and uses monitoring data, to build OLS and GWR model between landslide surface displacements and trigger factors by ArcGIS. Analysis showed that the GWR model has greater R2 and smaller Akaike information criterion (AIC) value, and the residuals spatial autocorrelation degree can be significantly reduced then the OLS model, what means the GWR model can capture the spatial non-stationarity of independent variables and is more reliable in analysis of landslide surface deformation.

scholarly journals Modeling and Predictive Mapping of Soil Organic Carbon Density in a Small-Scale Area Using Geographically Weighted Regression Kriging Approach

2020 ◽  
Vol 12 (22) ◽  
pp. 9330
Author(s):  
Tao Liu ◽  
Huan Zhang ◽  
Tiezhu Shi
Keyword(s):  
Environmental Factors ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Geographically Weighted Regression ◽  
Environmental Variables ◽  
Spatial Interpolation ◽  
Weighted Regression ◽  
Regression Kriging ◽  
Carbon Density ◽  
Soil Organic Carbon Density

Different natural environmental variables affect the spatial distribution of soil organic carbon (SOC), which has strong spatial heterogeneity and non-stationarity. Additionally, the soil organic carbon density (SOCD) has strong spatial varying relationships with the environmental factors, and the residuals should keep independent. This is one hard and challenging study in digital soil mapping. This study was designed to explore the different impacts of natural environmental factors and construct spatial prediction models of SOC in the junction region (with an area of 2130.37 km2) between Enshi City and Yidu City, Hubei Province, China. Multiple spatial interpolation models, such as stepwise linear regression (STR), geographically weighted regression (GWR), regression kriging (RK), and geographically weighted regression kriging (GWRK), were built using different natural environmental variables (e.g., terrain, environmental, and human factors) as auxiliary variables. The goodness of fit (R2), root mean square error, and improving accuracy were used to evaluate the predicted results of the spatial interpolation models. Results from Pearson correlation coefficient analysis and STR showed that SOCD was strongly correlated with elevation, topographic position index (TPI), topographic wetness index (TWI), slope, and normalized difference vegetation index (NDVI). GWRK had the highest simulation accuracy, followed by RK, whereas STR was the weakest. A theoretical scientific basis is, therefore, provided for exploring the relationship between SOCD and the corresponding environmental variables as well as for modeling and estimating the regional soil carbon pool.

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

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.

Sign in / Sign up

Export Citation Format

Share Document