The adjustment of administrative divisions is one of the important factors guiding China's urbanization, which has profound economic and social effects for regional development. In this paper, we comprehensively d...The adjustment of administrative divisions is one of the important factors guiding China's urbanization, which has profound economic and social effects for regional development. In this paper, we comprehensively describe the process of the adjustment of administrative divisions at provincial and municipal levels in China and summarize the effects on the basic structure and patterns of the spatial development. We quantitatively assess the effects on fields such as urbanization and social economy through the use of multidimensional scaling. The results show that: 1) Upgrading county to municipality(or city-governed district) is the main way of adjusting the administrative divisions. It is also an important factor in the spatial differentiation of interprovincial urbanization. China's population urbanization can be divided into four patterns including interprovincial migration, provincial migration, natural growth, and growth caused by the adjustment of administrative divisions, which is also the main reason for the increased Chinese urbanization rate at the provincial level. 2) Taking the city of Beijing as an example, we generalize five adjustment patterns made to administrative divisions: the set-up of sub-districts, the set-up of regional offices, the upgrading of townships to sub-districts, the upgrading of townships to towns, and the set-up of towns and the addition of new regional offices. We summarize the municipal urban spatial structure, including the sub-district office area in the central urban area, the regional office area in the new urban area, the mixed area of villages, towns, and sub-district offices in the suburb area, and the township area in the outer suburb area. 3) The adjustment of administrative divisions triggers a significant circulative accumulation effect, resulting in the spatial locking of population and industrial agglomeration. It affects the evolution of the urban spatial form and plays an important role in shaping the urban spatial structure to move to the characteristic of multicenter. In general, the adjustment of administrative divisions was an important factor affecting the inflated statistical level of urbanization and also an important driving force for the evolution of Chinese urban spatial organization structure.展开更多
When it comes to evaluating the effectiveness of interventions, the random experiment is considered the ＂gold standard＂. Randomization is considered the gold standard because it provides a way of decreasing the chan...When it comes to evaluating the effectiveness of interventions, the random experiment is considered the ＂gold standard＂. Randomization is considered the gold standard because it provides a way of decreasing the chance that systematic differences, other than type of intervention, will be obtained between treatment and control groups. What has received little attention in the literature, however, is the fact that even with random assignment researchers may end up facing problems similar to those faced with data from a study that did not use randomization. This is because attrition may result in the values of potentially confounding variables no longer being ＂balanced＂ between （or among） the groups under investigation. This means that in order to estimate the effect of the treatment, one must find some way of adjusting for these potential confounders. Although multiple regression modeling is the way social science researchers typically control for the effects of potentially confounding variables, this paper argues that a modification of multiple regression modeling that uses propensity scores, under some conditions, may provide more parsimonious and better fitting models.展开更多
This paper considers the estimation problem of distribution functions and quantiles with nonignorable missing response data. Three approaches are developed to estimate distribution functions and quantiles, i.e., the H...This paper considers the estimation problem of distribution functions and quantiles with nonignorable missing response data. Three approaches are developed to estimate distribution functions and quantiles, i.e., the Horvtiz-Thompson-type method, regression imputation method and augmented inverse probability weighted approach. The propensity score is specified by a semiparametric expo- nential tilting model. To estimate the tilting parameter in the propensity score, the authors propose an adjusted empirical likelihood method to deal with the over-identified system. Under some regular conditions, the authors investigate the asymptotic properties of the proposed three estimators for distri- bution functions and quantiles, and find that these estimators have the same asymptotic variance. The jackknife method is employed to consistently estimate the asymptotic variances. Simulation studies are conducted to investigate the finite sample performance of the proposed methodologies.展开更多
Using Ensemble Adjustment Kalman Filter(EAKF), two types of ocean satellite datasets were assimilated into the First Institute of Oceanography Earth System Model(FIO-ESM), v1.0. One control experiment without data ass...Using Ensemble Adjustment Kalman Filter(EAKF), two types of ocean satellite datasets were assimilated into the First Institute of Oceanography Earth System Model(FIO-ESM), v1.0. One control experiment without data assimilation and four assimilation experiments were conducted. All the experiments were ensemble runs for 1-year period and each ensemble started from different initial conditions. One assimilation experiment was designed to assimilate sea level anomaly(SLA); another, to assimilate sea surface temperature(SST); and the other two assimilation experiments were designed to assimilate both SLA and SST but in different orders. To examine the effects of data assimilation, all the results were compared with an objective analysis dataset of EN3. Different from the ocean model without coupling, the momentum and heat fluxes were calculated via air-sea coupling in FIO-ESM, which makes the relations among variables closer to the reality. The outputs after the assimilation of satellite data were improved on the whole, especially at depth shallower than 1000 m. The effects due to the assimilation of different kinds of satellite datasets were somewhat different. The improvement due to SST assimilation was greater near the surface, while the improvement due to SLA assimilation was relatively great in the subsurface. The results after the assimilation of both SLA and SST were much better than those only assimilated one kind of dataset, but the difference due to the assimilation order of the two kinds of datasets was not significant.展开更多
基金Under the auspices of National Natural Science Foundation of China(No.41701164,71433008)Programme of Excellent Young Scientists of the Institute of Geographic Science and Natural Resources Research,Chinese Academy of Science
文摘The adjustment of administrative divisions is one of the important factors guiding China's urbanization, which has profound economic and social effects for regional development. In this paper, we comprehensively describe the process of the adjustment of administrative divisions at provincial and municipal levels in China and summarize the effects on the basic structure and patterns of the spatial development. We quantitatively assess the effects on fields such as urbanization and social economy through the use of multidimensional scaling. The results show that: 1) Upgrading county to municipality(or city-governed district) is the main way of adjusting the administrative divisions. It is also an important factor in the spatial differentiation of interprovincial urbanization. China's population urbanization can be divided into four patterns including interprovincial migration, provincial migration, natural growth, and growth caused by the adjustment of administrative divisions, which is also the main reason for the increased Chinese urbanization rate at the provincial level. 2) Taking the city of Beijing as an example, we generalize five adjustment patterns made to administrative divisions: the set-up of sub-districts, the set-up of regional offices, the upgrading of townships to sub-districts, the upgrading of townships to towns, and the set-up of towns and the addition of new regional offices. We summarize the municipal urban spatial structure, including the sub-district office area in the central urban area, the regional office area in the new urban area, the mixed area of villages, towns, and sub-district offices in the suburb area, and the township area in the outer suburb area. 3) The adjustment of administrative divisions triggers a significant circulative accumulation effect, resulting in the spatial locking of population and industrial agglomeration. It affects the evolution of the urban spatial form and plays an important role in shaping the urban spatial structure to move to the characteristic of multicenter. In general, the adjustment of administrative divisions was an important factor affecting the inflated statistical level of urbanization and also an important driving force for the evolution of Chinese urban spatial organization structure.
文摘When it comes to evaluating the effectiveness of interventions, the random experiment is considered the ＂gold standard＂. Randomization is considered the gold standard because it provides a way of decreasing the chance that systematic differences, other than type of intervention, will be obtained between treatment and control groups. What has received little attention in the literature, however, is the fact that even with random assignment researchers may end up facing problems similar to those faced with data from a study that did not use randomization. This is because attrition may result in the values of potentially confounding variables no longer being ＂balanced＂ between （or among） the groups under investigation. This means that in order to estimate the effect of the treatment, one must find some way of adjusting for these potential confounders. Although multiple regression modeling is the way social science researchers typically control for the effects of potentially confounding variables, this paper argues that a modification of multiple regression modeling that uses propensity scores, under some conditions, may provide more parsimonious and better fitting models.
基金supported by the National Natural Science Foundation of China under Grant Nos.11671349 and 11601195the Scientific Research Innovation Team of Yunnan Province under Grant No.2015HC028the Natural Science Foundation of Jiangsu Province of China under Grant No.BK20160289
文摘This paper considers the estimation problem of distribution functions and quantiles with nonignorable missing response data. Three approaches are developed to estimate distribution functions and quantiles, i.e., the Horvtiz-Thompson-type method, regression imputation method and augmented inverse probability weighted approach. The propensity score is specified by a semiparametric expo- nential tilting model. To estimate the tilting parameter in the propensity score, the authors propose an adjusted empirical likelihood method to deal with the over-identified system. Under some regular conditions, the authors investigate the asymptotic properties of the proposed three estimators for distri- bution functions and quantiles, and find that these estimators have the same asymptotic variance. The jackknife method is employed to consistently estimate the asymptotic variances. Simulation studies are conducted to investigate the finite sample performance of the proposed methodologies.
基金the National Natural Science Foundation of China-Shandong Joint Fund for Marine Science Research Centers (Grant No. U1406404)the Public Science and Technology Research Funds Projects of Ocean (Grant No. 201505013)Scientific Research Foundation of the First Institute of Oceanography, State Oceanic Administration (Grant No. 2012G24)
文摘Using Ensemble Adjustment Kalman Filter(EAKF), two types of ocean satellite datasets were assimilated into the First Institute of Oceanography Earth System Model(FIO-ESM), v1.0. One control experiment without data assimilation and four assimilation experiments were conducted. All the experiments were ensemble runs for 1-year period and each ensemble started from different initial conditions. One assimilation experiment was designed to assimilate sea level anomaly(SLA); another, to assimilate sea surface temperature(SST); and the other two assimilation experiments were designed to assimilate both SLA and SST but in different orders. To examine the effects of data assimilation, all the results were compared with an objective analysis dataset of EN3. Different from the ocean model without coupling, the momentum and heat fluxes were calculated via air-sea coupling in FIO-ESM, which makes the relations among variables closer to the reality. The outputs after the assimilation of satellite data were improved on the whole, especially at depth shallower than 1000 m. The effects due to the assimilation of different kinds of satellite datasets were somewhat different. The improvement due to SST assimilation was greater near the surface, while the improvement due to SLA assimilation was relatively great in the subsurface. The results after the assimilation of both SLA and SST were much better than those only assimilated one kind of dataset, but the difference due to the assimilation order of the two kinds of datasets was not significant.
