  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.
