Estimation of people’s home locations using location-based services data from smartphones is a common task in human mobility assessment. However, commonly used home detection algorithms (HDAs) are often arbitrary and unexamined. In this study, we review existing HDAs and examine five HDAs using eight high-quality mobile phone geolocation datasets. These include four commonly used HDAs as well as an HDA proposed in this work. To make quantitative comparisons, we propose three novel metrics to assess the quality of detected home locations and test them on eight datasets across four U.S. cities. We find that all three metrics show a consistent rank of HDAs’ performances, with the proposed HDA outperforming the others. We infer that the temporal and spatial continuity of the geolocation data points matters more than the overall size of the data for accurate home detection. We also find that HDAs with high (and similar) performance metrics tend to create results with better consistency and closer to common expectations. Further, the performance deteriorates with decreasing data quality of the devices, though the patterns of relative performance persist. Finally, we show how the differences in home detection can lead to substantial differences in subsequent inferences using two case studies—(i) hurricane evacuation estimation, and (ii) correlation of mobility patterns with socioeconomic status. Our work contributes to improving the transparency of large-scale human mobility assessment applications.

使用智能手机基于位置的服务数据估计人们的家庭位置是人员流动性评估中的一项常见任务。然而，常用的家庭检测算法（HDA）通常是任意的且未经检查的。在本研究中，我们回顾了现有的 HDA，并使用八个高质量手机地理定位数据集检查了五个 HDA。其中包括四种常用的 HDA 以及本工作中提出的 HDA。为了进行定量比较，我们提出了三个新颖的指标来评估检测到的家庭位置的质量，并在美国四个城市的八个数据集上对其进行测试。我们发现所有三个指标都显示 HDA 性能的排名一致，其中所提出的 HDA 优于其他方案。我们推断，对于准确的家庭检测来说，地理位置数据点的时间和空间连续性比数据的总体大小更重要。我们还发现，具有高（和相似）性能指标的 HDA 往往会创建具有更好一致性且更接近常见期望的结果。此外，尽管相对性能的模式仍然存在，但性能会随着设备数据质量的降低而恶化。最后，我们使用两个案例研究（i）飓风疏散估计和（ii）流动模式与社会经济地位的相关性，展示了家庭检测的差异如何导致后续推论的显着差异。我们的工作有助于提高大规模人员流动评估应用的透明度。