The study aims to investigate county-level variations of the COVID-19 disease and vaccination rate. The COVID-19 data was acquired from usafact.org, and the vaccination records were acquired from the Ohio vaccination ...The study aims to investigate county-level variations of the COVID-19 disease and vaccination rate. The COVID-19 data was acquired from usafact.org, and the vaccination records were acquired from the Ohio vaccination tracker dashboard. GIS-based exploratory analysis was conducted to select four variables (poverty, black race, population density, and vaccination) to explain COVID-19 occurrence during the study period. Consequently, spatial statistical techniques such as Moran’s I, Hot Spot Analysis, Spatial Lag Model (SLM), and Spatial Error Model (SEM) were used to explain the COVID-19 occurrence and vaccination rate across the 88 counties in Ohio. The result of the Local Moran’s I analysis reveals that the epicenters of COVID-19 and vaccination followed the same patterns. Indeed, counties like Summit, Franklin, Fairfield, Hamilton, and Medina were categorized as epicenters for both COVID-19 occurrence and vaccination rate. The SEM seems to be the best model for both COVID-19 and vaccination rates, with R2 values of 0.68 and 0.70, respectively. The GWR analysis proves to be better than Ordinary Least Squares (OLS), and the distribution of R2 in the GWR is uneven throughout the study area for both COVID-19 cases and vaccinations. Some counties have a high R2 of up to 0.70 for both COVID-19 cases and vaccinations. The outcomes of the regression analyses show that the SEM models can explain 68% - 70% of COVID-19 cases and vaccination across the entire counties within the study period. COVID-19 cases and vaccination rates exhibited significant positive associations with black race and poverty throughout the study area.展开更多
Albeit with the notable benefits associated with Dirichlet crash frequency models and spatial ones,there is little research dedicated to exploring their combined advantages.Such ensemble approach could be a viable alt...Albeit with the notable benefits associated with Dirichlet crash frequency models and spatial ones,there is little research dedicated to exploring their combined advantages.Such ensemble approach could be a viable alternative to existing models as it accounts for the unobserved heterogeneity by relaxing the constraints of specific distribution placed on the intercept while addressing the spatial correlations among roadway entities.To fill this gap,the authors aimed to develop Dirichlet semi-parametric models over the overdispersed generalized linear model framework while also incorporating spatially structured random effects using a distance-based weight matrix.Five models were developed which include four semi-parametric with flexible intercept and one parametric base model for comparison purposes.The four semi-parametric models entailed two models with a popular specification of stick-breaking Dirichlet process(DP)and two models with an alternative approach of Dirichlet distribution(DD),which are first applied in the field of traffic safety.All four models were estimated for mixture of points(discrete)and mixture of normals(continuous).The posterior density plots for the precision parameter justified the employment of the flexible Dirichlet approach to fit the crash data and supported the assumed prior for the precision parameter.All four Dirichlet models demonstrated the presence of distinct subpopulations suggesting that the intercepts of the models were not generated from a common distribution.The DP model based on mixture of normals illustrated better performance indicating its potential superiority to fit both insample and out-of-sample crash data.This finding indicated that the approach of continuous densities,unlike discrete points,may lend more flexibility to fit the data.展开更多
This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 199...This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity.Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased.SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices(i.e.,farmland density and temporal change).The GWR model has smooth process when constructing the farmland spatial model.The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations.The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious.The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.展开更多
This paper proposes a mechanism theory on regional development by using a modified Logistic model. It reveals regional evolution is an integration of fluctuation in temporal dimension and disparity in spatial dimensio...This paper proposes a mechanism theory on regional development by using a modified Logistic model. It reveals regional evolution is an integration of fluctuation in temporal dimension and disparity in spatial dimension. T = S model is established by using Logistic model to simulate the growth of per capita GDP in China from 1990 to 1999. The result shows that T=S model accurately simulates the tracks of economic growth.展开更多
文摘The study aims to investigate county-level variations of the COVID-19 disease and vaccination rate. The COVID-19 data was acquired from usafact.org, and the vaccination records were acquired from the Ohio vaccination tracker dashboard. GIS-based exploratory analysis was conducted to select four variables (poverty, black race, population density, and vaccination) to explain COVID-19 occurrence during the study period. Consequently, spatial statistical techniques such as Moran’s I, Hot Spot Analysis, Spatial Lag Model (SLM), and Spatial Error Model (SEM) were used to explain the COVID-19 occurrence and vaccination rate across the 88 counties in Ohio. The result of the Local Moran’s I analysis reveals that the epicenters of COVID-19 and vaccination followed the same patterns. Indeed, counties like Summit, Franklin, Fairfield, Hamilton, and Medina were categorized as epicenters for both COVID-19 occurrence and vaccination rate. The SEM seems to be the best model for both COVID-19 and vaccination rates, with R2 values of 0.68 and 0.70, respectively. The GWR analysis proves to be better than Ordinary Least Squares (OLS), and the distribution of R2 in the GWR is uneven throughout the study area for both COVID-19 cases and vaccinations. Some counties have a high R2 of up to 0.70 for both COVID-19 cases and vaccinations. The outcomes of the regression analyses show that the SEM models can explain 68% - 70% of COVID-19 cases and vaccination across the entire counties within the study period. COVID-19 cases and vaccination rates exhibited significant positive associations with black race and poverty throughout the study area.
文摘Albeit with the notable benefits associated with Dirichlet crash frequency models and spatial ones,there is little research dedicated to exploring their combined advantages.Such ensemble approach could be a viable alternative to existing models as it accounts for the unobserved heterogeneity by relaxing the constraints of specific distribution placed on the intercept while addressing the spatial correlations among roadway entities.To fill this gap,the authors aimed to develop Dirichlet semi-parametric models over the overdispersed generalized linear model framework while also incorporating spatially structured random effects using a distance-based weight matrix.Five models were developed which include four semi-parametric with flexible intercept and one parametric base model for comparison purposes.The four semi-parametric models entailed two models with a popular specification of stick-breaking Dirichlet process(DP)and two models with an alternative approach of Dirichlet distribution(DD),which are first applied in the field of traffic safety.All four models were estimated for mixture of points(discrete)and mixture of normals(continuous).The posterior density plots for the precision parameter justified the employment of the flexible Dirichlet approach to fit the crash data and supported the assumed prior for the precision parameter.All four Dirichlet models demonstrated the presence of distinct subpopulations suggesting that the intercepts of the models were not generated from a common distribution.The DP model based on mixture of normals illustrated better performance indicating its potential superiority to fit both insample and out-of-sample crash data.This finding indicated that the approach of continuous densities,unlike discrete points,may lend more flexibility to fit the data.
基金Under the auspices of National Natural Science Foundation of China(No.40601073,41101192,41201571)Fundamental Research Funds for the Central Universities(No.2011PY112,2011QC041,2011QC091)Huazhong Agricultural University Scientific&Technological Self-innovation Foundation(No.2011SC21)
文摘This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity.Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased.SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices(i.e.,farmland density and temporal change).The GWR model has smooth process when constructing the farmland spatial model.The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations.The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious.The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.
基金Supported by the Knowledge Innovation Program of Chinese Academy of Sciences and National Key Technologies R &D Program in the 10th Five-Ycar Plan of china(2001BA901A40)
文摘This paper proposes a mechanism theory on regional development by using a modified Logistic model. It reveals regional evolution is an integration of fluctuation in temporal dimension and disparity in spatial dimension. T = S model is established by using Logistic model to simulate the growth of per capita GDP in China from 1990 to 1999. The result shows that T=S model accurately simulates the tracks of economic growth.
