It is of great significance to optimize the spatial matching of basic education facilities in rural areas and promote the equalization of urban and rural basic public services and the overall revitalization of rural a...It is of great significance to optimize the spatial matching of basic education facilities in rural areas and promote the equalization of urban and rural basic public services and the overall revitalization of rural areas.Based on the theory of spatial accessibility and spatial balance of supply and demand,this paper constructs the evaluation system of rural basic education facilities and layout rationality.In this study,we took 42 primary and secondary schools and 2089 rural areas with concentrated population in six township areas of Hancheng City as the research subjects.We used ArcGIS network analysis,population coupling model,and other methods,from the administrative scale and rural population concentration area scale for evaluation.The accessibility of basic education facilities and its coupling relationship with the spatial distribution of student population describe the spatial and geographical pattern of basic education facilities in rural areas.The results show that the accessibility of rural administrative scale and the spatial balance of supply and demand of population concentration area scale are associated with great problems,the differences between towns are obvious,and the overall level of the best performing towns is still low.On the scale of population concentration area,the overall coupling degree is too low,the"dislocation"distribution of educational facilities and student population is obvious,and the matching degree of the student population and school in different towns is significantly different,showing a more obvious"core-periphery"spatial structure characteristics centered on the eastern and southeast regions.The terrain characteristics of rural areas in Shaanxi province are prominent,and the accessibility of the road network is particularly significant.Therefore,optimizing the construction of rural roads and optimizing school layout according to local conditions will become an important solution to promote the balanced allocation of basic education resources in Shaanxi province.展开更多
When sampling from a finite population there is often auxiliary information available on unit level. Such information can be used to improve the estimation of the target parameter. We show that probability samples tha...When sampling from a finite population there is often auxiliary information available on unit level. Such information can be used to improve the estimation of the target parameter. We show that probability samples that are well spread in the auxiliary space are balanced, or approximately balanced, on the auxiliary variables. A consequence of this balancing effect is that the Horvitz-Thompson estimator will be a very good estimator for any target variable that can be well approximated by a Lipschitz continuous function of the auxiliary variables. Hence we give a theoretical motivation for use of well spread probability samples. Our conclusions imply that well spread samples, combined with the Horvitz- Thompson estimator, is a good strategy in a varsity of situations.展开更多
Background:The local pivotal method(LPM)utilizing auxiliary data in sample selection has recently been proposed as a sampling method for national forest inventories(NFIs).Its performance compared to simple random samp...Background:The local pivotal method(LPM)utilizing auxiliary data in sample selection has recently been proposed as a sampling method for national forest inventories(NFIs).Its performance compared to simple random sampling(SRS)and LPM with geographical coordinates has produced promising results in simulation studies.In this simulation study we compared all these sampling methods to systematic sampling.The LPM samples were selected solely using the coordinates(LPMxy)or,in addition to that,auxiliary remote sensing-based forest variables(RS variables).We utilized field measurement data(NFI-field)and Multi-Source NFI(MS-NFI)maps as target data,and independent MS-NFI maps as auxiliary data.The designs were compared using relative efficiency(RE);a ratio of mean squared errors of the reference sampling design against the studied design.Applying a method in NFI also requires a proven estimator for the variance.Therefore,three different variance estimators were evaluated against the empirical variance of replications:1)an estimator corresponding to SRS;2)a Grafström-Schelin estimator repurposed for LPM;and 3)a Matérn estimator applied in the Finnish NFI for systematic sampling design.Results:The LPMxy was nearly comparable with the systematic design for the most target variables.The REs of the LPM designs utilizing auxiliary data compared to the systematic design varied between 0.74–1.18,according to the studied target variable.The SRS estimator for variance was expectedly the most biased and conservative estimator.Similarly,the Grafström-Schelin estimator gave overestimates in the case of LPMxy.When the RS variables were utilized as auxiliary data,the Grafström-Schelin estimates tended to underestimate the empirical variance.In systematic sampling the Matérn and Grafström-Schelin estimators performed for practical purposes equally.Conclusions:LPM optimized for a specific variable tended to be more efficient than systematic sampling,but all of the considered LPM designs were less efficient than the systematic sampling design for some target variables.The Grafström-Schelin estimator could be used as such with LPMxy or instead of the Matérn estimator in systematic sampling.Further studies of the variance estimators are needed if other auxiliary variables are to be used in LPM.展开更多
Background: Remote sensing-based inventories are essential in estimating forest cover in tropical and subtropical countries, where ground inventories cannot be performed periodically at a large scale owing to high cos...Background: Remote sensing-based inventories are essential in estimating forest cover in tropical and subtropical countries, where ground inventories cannot be performed periodically at a large scale owing to high costs and forest inaccessibility(e.g. REDD projects) and are mandatory for constructing historical records that can be used as forest cover baselines. Given the conditions of such inventories, the survey area is partitioned into a grid of imagery segments of pre-fixed size where the proportion of forest cover can be measured within segments using a combination of unsupervised(automated or semi-automated) classification of satellite imagery and manual(i.e. visual on-screen)enhancements. Because visual on-screen operations are time expensive procedures, manual classification can be performed only for a sample of imagery segments selected at a first stage, while forest cover within each selected segment is estimated at a second stage from a sample of pixels selected within the segment. Because forest cover data arising from unsupervised satellite imagery classification may be freely available(e.g. Landsat imagery)over the entire survey area(wall-to-wall data) and are likely to be good proxies of manually classified cover data(sample data), they can be adopted as suitable auxiliary information.Methods: The question is how to choose the sample areas where manual classification is carried out. We have investigated the efficiency of one-per-stratum stratified sampling for selecting segments and pixels, where to carry out manual classification and to determine the efficiency of the difference estimator for exploiting auxiliary information at the estimation level. The performance of this strategy is compared with simple random sampling without replacement.Results: Our results were obtained theoretically from three artificial populations constructed from the Landsat classification(forest/non forest) available at pixel level for a study area located in central Italy, assuming three levels of error rates of the unsupervised classification of satellite imagery. The exploitation of map data as auxiliary information in the difference estimator proves to be highly effective with respect to the Horvitz-Thompson estimator,in which no auxiliary information is exploited. The use of one-per-stratum stratified sampling provides relevant improvement with respect to the use of simple random sampling without replacement.Conclusions: The use of one-per-stratum stratified sampling with many imagery segments selected at the first stage and few pixels within at the second stage- jointly with a difference estimator- proves to be a suitable strategy to estimate forest cover by remote sensing-based inventories.展开更多
文摘It is of great significance to optimize the spatial matching of basic education facilities in rural areas and promote the equalization of urban and rural basic public services and the overall revitalization of rural areas.Based on the theory of spatial accessibility and spatial balance of supply and demand,this paper constructs the evaluation system of rural basic education facilities and layout rationality.In this study,we took 42 primary and secondary schools and 2089 rural areas with concentrated population in six township areas of Hancheng City as the research subjects.We used ArcGIS network analysis,population coupling model,and other methods,from the administrative scale and rural population concentration area scale for evaluation.The accessibility of basic education facilities and its coupling relationship with the spatial distribution of student population describe the spatial and geographical pattern of basic education facilities in rural areas.The results show that the accessibility of rural administrative scale and the spatial balance of supply and demand of population concentration area scale are associated with great problems,the differences between towns are obvious,and the overall level of the best performing towns is still low.On the scale of population concentration area,the overall coupling degree is too low,the"dislocation"distribution of educational facilities and student population is obvious,and the matching degree of the student population and school in different towns is significantly different,showing a more obvious"core-periphery"spatial structure characteristics centered on the eastern and southeast regions.The terrain characteristics of rural areas in Shaanxi province are prominent,and the accessibility of the road network is particularly significant.Therefore,optimizing the construction of rural roads and optimizing school layout according to local conditions will become an important solution to promote the balanced allocation of basic education resources in Shaanxi province.
文摘When sampling from a finite population there is often auxiliary information available on unit level. Such information can be used to improve the estimation of the target parameter. We show that probability samples that are well spread in the auxiliary space are balanced, or approximately balanced, on the auxiliary variables. A consequence of this balancing effect is that the Horvitz-Thompson estimator will be a very good estimator for any target variable that can be well approximated by a Lipschitz continuous function of the auxiliary variables. Hence we give a theoretical motivation for use of well spread probability samples. Our conclusions imply that well spread samples, combined with the Horvitz- Thompson estimator, is a good strategy in a varsity of situations.
基金the Ministry of Agriculture and Forestry key project“Puuta liikkeelle ja uusia tuotteita metsästä”(“Wood on the move and new products from forest”)Academy of Finland(project numbers 295100 , 306875).
文摘Background:The local pivotal method(LPM)utilizing auxiliary data in sample selection has recently been proposed as a sampling method for national forest inventories(NFIs).Its performance compared to simple random sampling(SRS)and LPM with geographical coordinates has produced promising results in simulation studies.In this simulation study we compared all these sampling methods to systematic sampling.The LPM samples were selected solely using the coordinates(LPMxy)or,in addition to that,auxiliary remote sensing-based forest variables(RS variables).We utilized field measurement data(NFI-field)and Multi-Source NFI(MS-NFI)maps as target data,and independent MS-NFI maps as auxiliary data.The designs were compared using relative efficiency(RE);a ratio of mean squared errors of the reference sampling design against the studied design.Applying a method in NFI also requires a proven estimator for the variance.Therefore,three different variance estimators were evaluated against the empirical variance of replications:1)an estimator corresponding to SRS;2)a Grafström-Schelin estimator repurposed for LPM;and 3)a Matérn estimator applied in the Finnish NFI for systematic sampling design.Results:The LPMxy was nearly comparable with the systematic design for the most target variables.The REs of the LPM designs utilizing auxiliary data compared to the systematic design varied between 0.74–1.18,according to the studied target variable.The SRS estimator for variance was expectedly the most biased and conservative estimator.Similarly,the Grafström-Schelin estimator gave overestimates in the case of LPMxy.When the RS variables were utilized as auxiliary data,the Grafström-Schelin estimates tended to underestimate the empirical variance.In systematic sampling the Matérn and Grafström-Schelin estimators performed for practical purposes equally.Conclusions:LPM optimized for a specific variable tended to be more efficient than systematic sampling,but all of the considered LPM designs were less efficient than the systematic sampling design for some target variables.The Grafström-Schelin estimator could be used as such with LPMxy or instead of the Matérn estimator in systematic sampling.Further studies of the variance estimators are needed if other auxiliary variables are to be used in LPM.
文摘Background: Remote sensing-based inventories are essential in estimating forest cover in tropical and subtropical countries, where ground inventories cannot be performed periodically at a large scale owing to high costs and forest inaccessibility(e.g. REDD projects) and are mandatory for constructing historical records that can be used as forest cover baselines. Given the conditions of such inventories, the survey area is partitioned into a grid of imagery segments of pre-fixed size where the proportion of forest cover can be measured within segments using a combination of unsupervised(automated or semi-automated) classification of satellite imagery and manual(i.e. visual on-screen)enhancements. Because visual on-screen operations are time expensive procedures, manual classification can be performed only for a sample of imagery segments selected at a first stage, while forest cover within each selected segment is estimated at a second stage from a sample of pixels selected within the segment. Because forest cover data arising from unsupervised satellite imagery classification may be freely available(e.g. Landsat imagery)over the entire survey area(wall-to-wall data) and are likely to be good proxies of manually classified cover data(sample data), they can be adopted as suitable auxiliary information.Methods: The question is how to choose the sample areas where manual classification is carried out. We have investigated the efficiency of one-per-stratum stratified sampling for selecting segments and pixels, where to carry out manual classification and to determine the efficiency of the difference estimator for exploiting auxiliary information at the estimation level. The performance of this strategy is compared with simple random sampling without replacement.Results: Our results were obtained theoretically from three artificial populations constructed from the Landsat classification(forest/non forest) available at pixel level for a study area located in central Italy, assuming three levels of error rates of the unsupervised classification of satellite imagery. The exploitation of map data as auxiliary information in the difference estimator proves to be highly effective with respect to the Horvitz-Thompson estimator,in which no auxiliary information is exploited. The use of one-per-stratum stratified sampling provides relevant improvement with respect to the use of simple random sampling without replacement.Conclusions: The use of one-per-stratum stratified sampling with many imagery segments selected at the first stage and few pixels within at the second stage- jointly with a difference estimator- proves to be a suitable strategy to estimate forest cover by remote sensing-based inventories.
