This study utilized census data from Henan Province for the years 2000,2010,and 2020 to investigate the spatiotemporal evolution of population aging,defined by the proportion of the population aged 65 and above.Employ...This study utilized census data from Henan Province for the years 2000,2010,and 2020 to investigate the spatiotemporal evolution of population aging,defined by the proportion of the population aged 65 and above.Employing spatial analysis techniques such as spatial autocorrelation and the standard deviation ellipse,the research mapped out the progression and distribution of aging demographics.Furthermore,the study delved into the influencing factors of aging using an optimal parameters-based geographical detector.Results indicate a deepening degree of population aging in Henan Province,transitioning from an adult type to an old type structure.There is a marked positive spatial correlation among counties,with high-value aging areas initially decreasing,then increasing,and notably spreading from the central to the central and southern regions of the province.The center of gravity for population aging,specifically around Changge City and Xuchang City,exhibits a trajectory moving southeast before shifting northwest.Factor detection reveals that in 2000,2010,and 2020,the elderly dependency ratio predominantly influences the aging trend,with explanatory powers of 88.4%,87.9%,and 90.9%,respectively.Interaction analysis indicates that the interaction between the old-child ratio and the elderly dependency ratio has a strong explanatory power for the aging patterns in Henan Province,reaching 97.3%,97.0%,and 97.4%,respectively.展开更多
Urban flooding is caused by multiple factors,which seriously restricts the sustainable development of society.Understanding the driving factors of urban flooding is pivotal to alleviating flood disasters.Although the ...Urban flooding is caused by multiple factors,which seriously restricts the sustainable development of society.Understanding the driving factors of urban flooding is pivotal to alleviating flood disasters.Although the effects of various factors on urban flooding have been extensively evaluated,few studies consider both interregional flood connection and interactions between driving factors.In this study,driving factors of urban flooding were analyzed based on the water tracer method and the optimal parameters-based geographical detector(OPGD).An urban flood simulation model coupled with the water tracer method was constructed to simulate flooding.Furthermore,interregional flood volume connection was analyzed based on simulation results.Subsequently,driving force of urban flooding factors and interactions between them were quantified using the OPGD model.Taking Haidian Island in Hainan Province,China as an example,the coupled model simulation results show that sub-catchment H6 is the region experiencing the most severe flooding and sub-catchment H9 contributes the most to overall flooding in the study area.The results of subsequent driving effect analysis show that elevation is the factor with the maximum single-factor driving force(0.772) and elevation ∩ percentage of building area is the pair of factors with the maximum two-factor driving force(0.968).In addition,the interactions between driving factors have bivariable or nonlinear enhancement effects.The interactions between two factors strengthen the influence of each factor on urban flooding.This study contributes to understanding the cause of urban flooding and provides a reference for urban flood risk mitigation.展开更多
基金Humanities and Social Science Project of the Ministry of Education(NO.17YJCZH041)。
文摘This study utilized census data from Henan Province for the years 2000,2010,and 2020 to investigate the spatiotemporal evolution of population aging,defined by the proportion of the population aged 65 and above.Employing spatial analysis techniques such as spatial autocorrelation and the standard deviation ellipse,the research mapped out the progression and distribution of aging demographics.Furthermore,the study delved into the influencing factors of aging using an optimal parameters-based geographical detector.Results indicate a deepening degree of population aging in Henan Province,transitioning from an adult type to an old type structure.There is a marked positive spatial correlation among counties,with high-value aging areas initially decreasing,then increasing,and notably spreading from the central to the central and southern regions of the province.The center of gravity for population aging,specifically around Changge City and Xuchang City,exhibits a trajectory moving southeast before shifting northwest.Factor detection reveals that in 2000,2010,and 2020,the elderly dependency ratio predominantly influences the aging trend,with explanatory powers of 88.4%,87.9%,and 90.9%,respectively.Interaction analysis indicates that the interaction between the old-child ratio and the elderly dependency ratio has a strong explanatory power for the aging patterns in Henan Province,reaching 97.3%,97.0%,and 97.4%,respectively.
基金supported by the National Natural Science Foundation of China(Grant No.52379019,42477501)the Key Research and Development Program of Ningxia Hui Autonomous Region(Grant No.2022BEG02020).
文摘Urban flooding is caused by multiple factors,which seriously restricts the sustainable development of society.Understanding the driving factors of urban flooding is pivotal to alleviating flood disasters.Although the effects of various factors on urban flooding have been extensively evaluated,few studies consider both interregional flood connection and interactions between driving factors.In this study,driving factors of urban flooding were analyzed based on the water tracer method and the optimal parameters-based geographical detector(OPGD).An urban flood simulation model coupled with the water tracer method was constructed to simulate flooding.Furthermore,interregional flood volume connection was analyzed based on simulation results.Subsequently,driving force of urban flooding factors and interactions between them were quantified using the OPGD model.Taking Haidian Island in Hainan Province,China as an example,the coupled model simulation results show that sub-catchment H6 is the region experiencing the most severe flooding and sub-catchment H9 contributes the most to overall flooding in the study area.The results of subsequent driving effect analysis show that elevation is the factor with the maximum single-factor driving force(0.772) and elevation ∩ percentage of building area is the pair of factors with the maximum two-factor driving force(0.968).In addition,the interactions between driving factors have bivariable or nonlinear enhancement effects.The interactions between two factors strengthen the influence of each factor on urban flooding.This study contributes to understanding the cause of urban flooding and provides a reference for urban flood risk mitigation.
