用学校作为稳定锚点，校准手机数据，实现全美街区级小时级人口动态估算。
Nationwide Hourly Population Estimating at the Neighborhood Scale in the United States Using Stable-Attendance Anchor Calibration
- 以学校为稳定锚点，通过设备事件推算人流进出，校正数据偏差。
- 在全美普查街区层级实现每小时人口估计，结果与已有研究吻合。
- 适合城市规划、公共健康等需高精度动态人口数据的领域使用。
传统人口数据多为静态，难以捕捉由日常移动带来的强时序变化。近期基于智能手机的移动数据提供了前所未有的时空覆盖，但将其转化为准确的人口估计仍具挑战，受限于感知不全、设备覆盖率空间异质性以及观测过程不稳定等问题。本文提出一种稳定出席锚点校准（SAAC）框架，用于重建美国全境普查街区层级的小时级人口分布。该方法将人口估计建模为基于平衡的人口核算问题，结合常住人口与从设备事件中推断的动态流入流出。为解决观测偏差和可识别性限制，框架利用具有高度规律性出席行为的地点作为校准锚点（本研究采用中学）。这些锚点帮助估算观测缩放因子，修正被低估的流动事件。通过整合锚点校准与显式采样模型，SAAC实现了从观测设备事件到精细时间分辨率下人口存在的稳定转换。推断出的人口模式与既往移动性及城市人口研究的实证发现一致。该框架为将大规模、有偏的数字轨迹数据转化为可解释的动态人口产品提供通用路径，对城市科学、公共卫生与人类移动性研究具有重要意义。小时级人口估计数据可访问：https://gladcolor.github.io/hourly_population。
原文摘要 · Abstract (English)
Traditional population datasets are largely static and therefore unable to capture the strong temporal dynamics of human presence driven by daily mobility. Recent smartphone-based mobility data offer unprecedented spatiotemporal coverage, yet translating these opportunistic observations into accurate population estimates remains challenging due to incomplete sensing, spatially heterogeneous device penetration, and unstable observation processes. We propose a Stable-Attendance Anchor Calibration (SAAC) framework to reconstruct hourly population presence at the Census block group level across the United States. SAAC formulates population estimation as a balance-based population accounting problem, combining residential population with time-varying inbound and outbound mobility inferred from device-event observations. To address observation bias and identifiability limitations, the framework leverages locations with highly regular attendance as calibration anchors, using high schools in this study. These anchors enable estimation of observation scaling factors that correct for under-recorded mobility events. By integrating anchor-based calibration with an explicit sampling model, SAAC enables consistent conversion from observed device events to population presence at fine temporal resolution. The inferred population patterns are consistent with established empirical findings in prior mobility and urban population studies. SAAC provides a generalizable framework for transforming large-scale, biased digital trace data into interpretable dynamic population products, with implications for urban science, public health, and human mobility research. The hourly population estimates can be accessed at: https://gladcolor.github.io/hourly_population.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。