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
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
开展植被固碳变化的影响机制研究,是实现区域“双碳”目标和高质量发展的科学支撑。然而,现有研究仍欠缺对影响因子时空自相关性的综合考虑,且未能准确反映因子的动态影响过程。基于此,本研究基于31个国家气象站点2001—2020年的气象数...开展植被固碳变化的影响机制研究,是实现区域“双碳”目标和高质量发展的科学支撑。然而,现有研究仍欠缺对影响因子时空自相关性的综合考虑,且未能准确反映因子的动态影响过程。基于此,本研究基于31个国家气象站点2001—2020年的气象数据及其周边10 km范围内植被净初级生产力(NPP)数据,准确识别粤北地区植被固碳量的时空变化特征;通过构建面板数据的空间滞后模型,结合偏相关分析和优势分析方法分析气候因子的动态影响机制,并进一步采用地理加权回归模型剖析影响因素的空间差异特征;采用残差趋势法分别测度气候因素和人类活动因素对植被固碳变化量的贡献度。结果表明:2001—2020年,粤北地区平均固碳量为955.43 g C·m^(-2),区域的植被固碳量呈波动下降趋势,变化的空间分布具有较强的异质性;年均相对湿度、年均日照时数和年均降水量是显著影响区域植被固碳变化的气候因子;上述显著影响因素的空间差异较大,且与海拔存在较强的相关关系;相较于气候因素,人类活动是粤北地区植被固碳变化的主要影响因素,人类因素和气候因素的平均贡献率分别为70.2%和29.8%。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.
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.
文摘开展植被固碳变化的影响机制研究,是实现区域“双碳”目标和高质量发展的科学支撑。然而,现有研究仍欠缺对影响因子时空自相关性的综合考虑,且未能准确反映因子的动态影响过程。基于此,本研究基于31个国家气象站点2001—2020年的气象数据及其周边10 km范围内植被净初级生产力(NPP)数据,准确识别粤北地区植被固碳量的时空变化特征;通过构建面板数据的空间滞后模型,结合偏相关分析和优势分析方法分析气候因子的动态影响机制,并进一步采用地理加权回归模型剖析影响因素的空间差异特征;采用残差趋势法分别测度气候因素和人类活动因素对植被固碳变化量的贡献度。结果表明:2001—2020年,粤北地区平均固碳量为955.43 g C·m^(-2),区域的植被固碳量呈波动下降趋势,变化的空间分布具有较强的异质性;年均相对湿度、年均日照时数和年均降水量是显著影响区域植被固碳变化的气候因子;上述显著影响因素的空间差异较大,且与海拔存在较强的相关关系;相较于气候因素,人类活动是粤北地区植被固碳变化的主要影响因素,人类因素和气候因素的平均贡献率分别为70.2%和29.8%。
