论文标题
迈向实时人口估计：在县级引入居民和非居民的Twitter每日估算
Towards real-time population estimates: introducing Twitter daily estimates of residents and non-residents at the county level
论文作者
论文摘要
历史上，对迁移和流动性的研究一直受到可靠数据或可用数据的时间稀疏性的严重限制。使用地理空间数字痕量数据，可以更精确，动态地测量人口运动的研究。我们的研究旨在开发几乎实时的（为期一天的滞后）Twitter人口普查，从而为县级的本地和非本地人口提供了更详细的图片。利用地理标记的推文来确定所有活跃的Twitter用户的家庭位置，我们通过获得准确的每日Twitter人口库存（居民和非居民）来为数字和计算人口统计学领域做出贡献。与用户自我报告的家居位置相比，内部验证揭示了80％以上的准确性。外部验证结果表明，这些股票与县级居民/非居民的可用统计数据相关，并且可以准确反映常规（季节性旅游）和非规范事件，例如2017年美国大太阳日食。这些发现表明，Twitter具有在人群估计中通常缺乏动态成分的潜力。
The study of migrations and mobility has historically been severely limited by the absence of reliable data or the temporal sparsity of the available data. Using geospatial digital trace data, the study of population movements can be much more precisely and dynamically measured. Our research seeks to develop a near real-time (one-day lag) Twitter census that gives a more temporally granular picture of local and non-local population at the county level. Leveraging geotagged tweets to determine the home location of all active Twitter users, we contribute to the field of digital and computational demography by obtaining accurate daily Twitter population stocks (residents and non-residents). Internal validation reveals over 80% of accuracy when compared with users self-reported home location. External validation results suggest these stocks correlate with available statistics of residents/non-residents at the county level and can accurately reflect regular (seasonal tourism) and non-regular events such as the Great American Solar Eclipse of 2017. The findings demonstrate that Twitter holds potential to introduce the dynamic component often lacking in population estimates.
