{"title":"Capture–Recapture Estimation of Characteristics of U.S. Local Food Farms Using a Web-Scraped List Frame","authors":"Michael Hyman, L. Sartore, L. Young","doi":"10.1093/jssam/smab008","DOIUrl":null,"url":null,"abstract":"\n The emerging sectors of agriculture, such as organics, urban, and local food, tend to be dominated by farms that are smaller, more transient, more diverse, and more dispersed than the traditional farms in the rural areas of the United States. As a consequence, a list frame of all farms within one of these sectors is difficult to construct and, even with the best of efforts, is incomplete. The United States Department of Agriculture’s (USDA’s) National Agricultural Statistics Service (NASS) maintains a list frame of all known and potential U.S. farms and uses this list frame as the sampling frame for most of its surveys. Traditionally, NASS has used its area frame to assess undercoverage. However, getting a good measure of the incompleteness of the NASS list frame using an area frame is cost prohibitive for farms in these emerging sectors that tend to be located within and near urban areas. In 2016, NASS conducted the Local Food Marketing Practices (LFMP) survey. Independent samples were drawn from (1) the NASS list frame and (2) a web-scraped list of local food farms. Using these two samples and capture–recapture methods, the total number and sales of local food operations at the United States, regional, and state levels were estimated. To our knowledge, the LFMP survey is the first survey in which a web-scraped list frame has been used to assess undercoverage in a capture–recapture setting to produce official statistics. In this article, the methods are presented, and the challenges encountered are reviewed. Best practices and open research questions for conducting surveys using web-scraped list frames and capture–recapture methods are discussed.","PeriodicalId":17146,"journal":{"name":"Journal of Survey Statistics and Methodology","volume":" ","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2021-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Survey Statistics and Methodology","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1093/jssam/smab008","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"SOCIAL SCIENCES, MATHEMATICAL METHODS","Score":null,"Total":0}
引用次数: 2
Abstract
The emerging sectors of agriculture, such as organics, urban, and local food, tend to be dominated by farms that are smaller, more transient, more diverse, and more dispersed than the traditional farms in the rural areas of the United States. As a consequence, a list frame of all farms within one of these sectors is difficult to construct and, even with the best of efforts, is incomplete. The United States Department of Agriculture’s (USDA’s) National Agricultural Statistics Service (NASS) maintains a list frame of all known and potential U.S. farms and uses this list frame as the sampling frame for most of its surveys. Traditionally, NASS has used its area frame to assess undercoverage. However, getting a good measure of the incompleteness of the NASS list frame using an area frame is cost prohibitive for farms in these emerging sectors that tend to be located within and near urban areas. In 2016, NASS conducted the Local Food Marketing Practices (LFMP) survey. Independent samples were drawn from (1) the NASS list frame and (2) a web-scraped list of local food farms. Using these two samples and capture–recapture methods, the total number and sales of local food operations at the United States, regional, and state levels were estimated. To our knowledge, the LFMP survey is the first survey in which a web-scraped list frame has been used to assess undercoverage in a capture–recapture setting to produce official statistics. In this article, the methods are presented, and the challenges encountered are reviewed. Best practices and open research questions for conducting surveys using web-scraped list frames and capture–recapture methods are discussed.
期刊介绍:
The Journal of Survey Statistics and Methodology, sponsored by AAPOR and the American Statistical Association, began publishing in 2013. Its objective is to publish cutting edge scholarly articles on statistical and methodological issues for sample surveys, censuses, administrative record systems, and other related data. It aims to be the flagship journal for research on survey statistics and methodology. Topics of interest include survey sample design, statistical inference, nonresponse, measurement error, the effects of modes of data collection, paradata and responsive survey design, combining data from multiple sources, record linkage, disclosure limitation, and other issues in survey statistics and methodology. The journal publishes both theoretical and applied papers, provided the theory is motivated by an important applied problem and the applied papers report on research that contributes generalizable knowledge to the field. Review papers are also welcomed. Papers on a broad range of surveys are encouraged, including (but not limited to) surveys concerning business, economics, marketing research, social science, environment, epidemiology, biostatistics and official statistics. The journal has three sections. The Survey Statistics section presents papers on innovative sampling procedures, imputation, weighting, measures of uncertainty, small area inference, new methods of analysis, and other statistical issues related to surveys. The Survey Methodology section presents papers that focus on methodological research, including methodological experiments, methods of data collection and use of paradata. The Applications section contains papers involving innovative applications of methods and providing practical contributions and guidance, and/or significant new findings.