{"title":"将职业就业数据从州降级到人口普查区一级","authors":"","doi":"10.1016/j.apgeog.2024.103349","DOIUrl":null,"url":null,"abstract":"<div><p>The lack of detailed occupational employment data at more granular geographic levels presents significant challenges in forecasting and analyzing local and regional employment changes in the era of the new technological revolution. This study aims to develop detailed occupational employment data by downscaling state-level employment information to the Census tract level. We introduce two downscaling algorithms that leverage employment, population, and sociodemographic composition data sourced from the American Community Survey, the Current Population Survey, and the Occupational Employment and Wage Statistics. This approach allows us to create a tract-level employment dataset covering 808 occupations. Such data are crucial for examining the effects of expected technological and demographic shifts on employment at this scale, which is critical for understanding tax base implications and job mobility opportunities. We demonstrate the value of these datasets by examining employment projections for two occupations anticipated to decline due to technological advancements in the near future.</p></div>","PeriodicalId":48396,"journal":{"name":"Applied Geography","volume":null,"pages":null},"PeriodicalIF":4.0000,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Downscaling occupational employment data from the state to the Census tract level\",\"authors\":\"\",\"doi\":\"10.1016/j.apgeog.2024.103349\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The lack of detailed occupational employment data at more granular geographic levels presents significant challenges in forecasting and analyzing local and regional employment changes in the era of the new technological revolution. This study aims to develop detailed occupational employment data by downscaling state-level employment information to the Census tract level. We introduce two downscaling algorithms that leverage employment, population, and sociodemographic composition data sourced from the American Community Survey, the Current Population Survey, and the Occupational Employment and Wage Statistics. This approach allows us to create a tract-level employment dataset covering 808 occupations. Such data are crucial for examining the effects of expected technological and demographic shifts on employment at this scale, which is critical for understanding tax base implications and job mobility opportunities. We demonstrate the value of these datasets by examining employment projections for two occupations anticipated to decline due to technological advancements in the near future.</p></div>\",\"PeriodicalId\":48396,\"journal\":{\"name\":\"Applied Geography\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2024-07-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Geography\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0143622824001541\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOGRAPHY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Geography","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0143622824001541","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOGRAPHY","Score":null,"Total":0}
Downscaling occupational employment data from the state to the Census tract level
The lack of detailed occupational employment data at more granular geographic levels presents significant challenges in forecasting and analyzing local and regional employment changes in the era of the new technological revolution. This study aims to develop detailed occupational employment data by downscaling state-level employment information to the Census tract level. We introduce two downscaling algorithms that leverage employment, population, and sociodemographic composition data sourced from the American Community Survey, the Current Population Survey, and the Occupational Employment and Wage Statistics. This approach allows us to create a tract-level employment dataset covering 808 occupations. Such data are crucial for examining the effects of expected technological and demographic shifts on employment at this scale, which is critical for understanding tax base implications and job mobility opportunities. We demonstrate the value of these datasets by examining employment projections for two occupations anticipated to decline due to technological advancements in the near future.
期刊介绍:
Applied Geography is a journal devoted to the publication of research which utilizes geographic approaches (human, physical, nature-society and GIScience) to resolve human problems that have a spatial dimension. These problems may be related to the assessment, management and allocation of the world physical and/or human resources. The underlying rationale of the journal is that only through a clear understanding of the relevant societal, physical, and coupled natural-humans systems can we resolve such problems. Papers are invited on any theme involving the application of geographical theory and methodology in the resolution of human problems.