Investigating spatial non-stationary and scale-dependent relationships between urban surface temperature and environmental factors using geographically weighted regression

2010 ◽  
Vol 25 (12) ◽  
pp. 1789-1800 ◽  
Author(s):  
Shuangcheng Li ◽  
Zhiqiang Zhao ◽  
Xie Miaomiao ◽  
Yanglin Wang
Keyword(s):  
Environmental Factors ◽  
Surface Temperature ◽  
Geographically Weighted Regression ◽  
Urban Surface ◽  
Weighted Regression

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

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 Assessing the impact of land surface temperature on urban net primary productivity increment based on geographically weighted regression model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xue-Yuan Lu ◽  
Xu Chen ◽  
Xue-Li Zhao ◽  
Dan-Jv Lv ◽  
Yan Zhang
Keyword(s):  
Surface Temperature ◽  
Spatial Heterogeneity ◽  
Land Surface Temperature ◽  
Primary Productivity ◽  
Geographically Weighted Regression ◽  
Land Surface ◽  
Net Primary Productivity ◽  
Weighted Regression ◽  
The Impact ◽  
Gwr Model

AbstractUrbanization had a huge impact on the regional ecosystem net primary productivity (NPP). Although the urban heat island (UHI) caused by urbanization has been found to have a certain promoting effect on urban vegetation NPP, the factors on the impact still are not identified. In this study, the impact of urbanization on NPP was divided into direct impact (NPPdir) and indirect impact (NPPind), taking Kunming city as a case study area. Then, the spatial heterogeneity impact of land surface temperature (LST) on NPPind was analyzed based on the geographically weighted regression (GWR) model. The results indicated that NPP, LST, NPPdir and NPPind in 2001, 2009 and 2018 had significant spatial autocorrelation in Kunming based on spatial analytical model. LST had a positive impact on NPPind in the central area of Kunming. The positively correlation areas of LST on NPPind increased by 4.56%, and the NPPind caused by the UHI effect increased by an average of 4.423 gC m−2 from 2009 to 2018. GWR model can reveal significant spatial heterogeneity in the impacts of LST on NPPind. Overall, our findings indicated that LST has a certain role in promoting urban NPP.

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.

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