期刊
PROCEEDINGS OF THE 2017 ACM WEB SCIENCE CONFERENCE (WEBSCI '17)
卷 -, 期 -, 页码 103-110出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3091478.3091496
关键词
Twitter; Migration; Mobility; Demographic research
资金
- Eunice Kennedy Shriver National Institute of Child Health and Human Development research infrastructure grant [R24 HD042828]
- Shanahan Endowment Fellowship
- Eunice Kennedy Shriver National Institute of Child Health and Human Development training grant [T32 HD007543]
Migration estimates are sensitive to definitions of time interval and duration. For example, when does a tourist become a migrant? As a result, harmonizing across different kinds of estimates or data sources can be difficult. Moreover in countries like the United States, that do not have a national registry system, estimates of internal migration typically rely on survey data that can require over a year from data collection to publication. In addition, each survey can ask only a limited set questions about migration (e.g., where did you live a year ago? where did you live five years ago?). We leverage a sample of geo-referenced Twitter tweets for about 62,000 users, spanning the period between 2010 and 2016, to estimate a series of US internal migration flows under varying time intervals and durations. Our findings, expressed in terms of 'migration curves', document, for the first time, the relationships between short-term mobility and long-term migration. The results open new avenues for demographic research. More specifically, future directions include the use of migration curves to produce probabilistic estimates of long-term migration from short-term (and vice versa) and to nowcast mobility rates at different levels of spatial and temporal granularity using a combination of previously published American Community Survey data and up-to-date data from a panel of Twitter users.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据