Non-sampling errors can generally be divided into three types:sampling frame errors,non-response errors and measurement errors.Missing target units in the sam-pling frame,improper handling of non-responses,and misrepo...Non-sampling errors can generally be divided into three types:sampling frame errors,non-response errors and measurement errors.Missing target units in the sam-pling frame,improper handling of non-responses,and misreporting or underreport-ing of key variables in the questionnaire can all cause deviations in a survey’s results.The widespread application of Computer-Assisted Personal Interviewing(CAPI)systems and the inclusion of administrative records from government sources in sur-veys has strengthened the ability to control non-sampling errors.Taking a national fertility sampling survey as an example,this study summarizes the sources of var-ious non-sampling errors and explains how to harness big data resources such as administrative records to control non-sampling errors throughout the survey.The study analyzes the impact of three types of non-sampling errors on the results of the fertility survey and examines the strategies used to address the problems caused by these non-sampling errors.The findings indicate that non-sampling errors were the main source of total error in the survey,and that the errors found came mainly from sampling frame errors;non-response errors and measurement errors were controlled and had little impact on the survey results.展开更多
Leadership in Energy and Environmental Design Commercial Interiors (LEED-CI) is more relevant to interior design, which, according to the sharing layer concept, differs from exterior design (which is usually evaluated...Leadership in Energy and Environmental Design Commercial Interiors (LEED-CI) is more relevant to interior design, which, according to the sharing layer concept, differs from exterior design (which is usually evaluated with the LEED New Construction sub-scheme). LEED-CI requires separate empirical analyses of LEED-CI certified buildings to further improve this sub-scheme. Therefore, in this study, Silver and Gold projects certified under LEED-CI-2009 in 14 US states were considered. Three project performance analyses, (i) certification, (ii) category, and (iii) cross-certification, were studied. The following results were revealed: (i) the range of the medians for Silver- and Gold-certified projects were 51-57 pts and 62-71 pts, respectively;(ii) in both Silver- and Gold-certified projects, Sustainable Sites (SS), Water Efficiency (WE), and Innovation in Design (ID) were the best-performing;Energy and Atmosphere (EA) and Indoor Environmental Quality (EQ) were intermediate-performing;and Material and Resources (MR) was the worst-performing categories;and (iii) in Silver-Gold cross-certification, category-focused (in 10 of 14 states) and category-unfocused (in four of 14 states) strategies were determined;in the category-focused strategy, the highest popular category was EA;the intermediate popular categories were WE, MR, and ID;and the lowest popular category was SS. Pooling all projects and all states into one frame can lead to the obscurement of the actual LEED-CI-2009 strategy(ies) in the transition from Silver to Gold certification in the US.展开更多
An optimal validation of a thematic map would ideally require in-situ observations of a large sample of units specifically conceived for the map under validation.This is often not possible due to budget limitations.Th...An optimal validation of a thematic map would ideally require in-situ observations of a large sample of units specifically conceived for the map under validation.This is often not possible due to budget limitations.The alternative can be using photo-interpretation of high or very high resolution images instead of in-situ observations or using available data sets that do not fully comply with the ideal characteristics:unit size,reference date or sampling plan.This paper illustrates some examples of use of available data in the European Union.For land cover maps,the best existing data set is probably Land Use/Cover Areaframe Survey(LUCAS)that has been conducted by Eurostat on four occasions since 2001.Because LUCAS is based on systematic sampling,advantages and limitations of systematic sampling are discussed.A fine-scale population density map is presented as an example of a situation in which reference data on a statistical sample cannot be collected.展开更多
Nowadays,fast and accurate novelty detection is crucial for public safety and security in surveillance videos.Given the high accuracy of deep learning technique,deep learning based novel detection is a trend.With the ...Nowadays,fast and accurate novelty detection is crucial for public safety and security in surveillance videos.Given the high accuracy of deep learning technique,deep learning based novel detection is a trend.With the huge amount of surveillance videos being generated by surveillance cameras at any time,it is challenging to make novelty detection in surveillance videos efficiently while guaranteeing the accuracy.To address it,we propose a dynamic frame sampling method called ORLNet with both the frame similarity and the intensity of the object movement considered.It is based on the two observations as follows:firstly,there is a high similarity between adjacent frames in a video data.Secondly,in practice,since novel behaviors are always generated by moving targets,we only need to focus on a small number of frames that contain key information which we call key frames.Specifically,ORLNet speeds up surveillance video by setting a reinforcement learning agent to dynamically determine the indexes of key frames at run-time and replace end-to-end inference at non-key frame positions by reusing the last key frame’s calculation.Typically,it defines frame similarity as novelty energy,which is the combina-tion of novel semantic and motion features.On the premise of calculating the distance of novel energy between frames,the calculation of key frames can be reused for other frames corresponding to similar novelty energies,which can thus acceler-ate novelty detection while maintain accuracy.Finally,we evaluate ORLNet experimentally with two surveillance video datasets by comparing with existing methods.Experimental results show that ORLNet reduces processing time by 42%while guaranteeing the accuracy.展开更多
基金sponsored by the Follow-up Research on Fertility Level and Fertility Intentions with the Help of Big Data(No.21BRK001)a research project funded by the National Social Science Fund of China.
文摘Non-sampling errors can generally be divided into three types:sampling frame errors,non-response errors and measurement errors.Missing target units in the sam-pling frame,improper handling of non-responses,and misreporting or underreport-ing of key variables in the questionnaire can all cause deviations in a survey’s results.The widespread application of Computer-Assisted Personal Interviewing(CAPI)systems and the inclusion of administrative records from government sources in sur-veys has strengthened the ability to control non-sampling errors.Taking a national fertility sampling survey as an example,this study summarizes the sources of var-ious non-sampling errors and explains how to harness big data resources such as administrative records to control non-sampling errors throughout the survey.The study analyzes the impact of three types of non-sampling errors on the results of the fertility survey and examines the strategies used to address the problems caused by these non-sampling errors.The findings indicate that non-sampling errors were the main source of total error in the survey,and that the errors found came mainly from sampling frame errors;non-response errors and measurement errors were controlled and had little impact on the survey results.
文摘Leadership in Energy and Environmental Design Commercial Interiors (LEED-CI) is more relevant to interior design, which, according to the sharing layer concept, differs from exterior design (which is usually evaluated with the LEED New Construction sub-scheme). LEED-CI requires separate empirical analyses of LEED-CI certified buildings to further improve this sub-scheme. Therefore, in this study, Silver and Gold projects certified under LEED-CI-2009 in 14 US states were considered. Three project performance analyses, (i) certification, (ii) category, and (iii) cross-certification, were studied. The following results were revealed: (i) the range of the medians for Silver- and Gold-certified projects were 51-57 pts and 62-71 pts, respectively;(ii) in both Silver- and Gold-certified projects, Sustainable Sites (SS), Water Efficiency (WE), and Innovation in Design (ID) were the best-performing;Energy and Atmosphere (EA) and Indoor Environmental Quality (EQ) were intermediate-performing;and Material and Resources (MR) was the worst-performing categories;and (iii) in Silver-Gold cross-certification, category-focused (in 10 of 14 states) and category-unfocused (in four of 14 states) strategies were determined;in the category-focused strategy, the highest popular category was EA;the intermediate popular categories were WE, MR, and ID;and the lowest popular category was SS. Pooling all projects and all states into one frame can lead to the obscurement of the actual LEED-CI-2009 strategy(ies) in the transition from Silver to Gold certification in the US.
文摘An optimal validation of a thematic map would ideally require in-situ observations of a large sample of units specifically conceived for the map under validation.This is often not possible due to budget limitations.The alternative can be using photo-interpretation of high or very high resolution images instead of in-situ observations or using available data sets that do not fully comply with the ideal characteristics:unit size,reference date or sampling plan.This paper illustrates some examples of use of available data in the European Union.For land cover maps,the best existing data set is probably Land Use/Cover Areaframe Survey(LUCAS)that has been conducted by Eurostat on four occasions since 2001.Because LUCAS is based on systematic sampling,advantages and limitations of systematic sampling are discussed.A fine-scale population density map is presented as an example of a situation in which reference data on a statistical sample cannot be collected.
文摘Nowadays,fast and accurate novelty detection is crucial for public safety and security in surveillance videos.Given the high accuracy of deep learning technique,deep learning based novel detection is a trend.With the huge amount of surveillance videos being generated by surveillance cameras at any time,it is challenging to make novelty detection in surveillance videos efficiently while guaranteeing the accuracy.To address it,we propose a dynamic frame sampling method called ORLNet with both the frame similarity and the intensity of the object movement considered.It is based on the two observations as follows:firstly,there is a high similarity between adjacent frames in a video data.Secondly,in practice,since novel behaviors are always generated by moving targets,we only need to focus on a small number of frames that contain key information which we call key frames.Specifically,ORLNet speeds up surveillance video by setting a reinforcement learning agent to dynamically determine the indexes of key frames at run-time and replace end-to-end inference at non-key frame positions by reusing the last key frame’s calculation.Typically,it defines frame similarity as novelty energy,which is the combina-tion of novel semantic and motion features.On the premise of calculating the distance of novel energy between frames,the calculation of key frames can be reused for other frames corresponding to similar novelty energies,which can thus acceler-ate novelty detection while maintain accuracy.Finally,we evaluate ORLNet experimentally with two surveillance video datasets by comparing with existing methods.Experimental results show that ORLNet reduces processing time by 42%while guaranteeing the accuracy.
