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Estimation of people's home locations using location-based services data fromsmartphones is a common task in human mobility assessment. However, commonlyused home detection algorithms (HDAs) are often arbitrary and unexamined. Inthis study, we review existing HDAs and examine five HDAs using eighthigh-quality mobile phone geolocation datasets. These include four commonlyused HDAs as well as an HDA proposed in this work. To make quantitativecomparisons, we propose three novel metrics to assess the quality of detectedhome locations and test them on eight datasets across four U.S. cities. We findthat all three metrics show a consistent rank of HDAs' performances, with theproposed HDA outperforming the others. We infer that the temporal and spatialcontinuity of the geolocation data points matters more than the overall size ofthe data for accurate home detection. We also find that HDAs with high (andsimilar) performance metrics tend to create results with better consistency andcloser to common expectations. Further, the performance deteriorates withdecreasing data quality of the devices, though the patterns of relativeperformance persist. Finally, we show how the differences in home detection canlead to substantial differences in subsequent inferences using two case studies- (i) hurricane evacuation estimation, and (ii) correlation of mobilitypatterns with socioeconomic status. Our work contributes to improving thetransparency of large-scale human mobility assessment applications.

Comparison of home detection algorithms using smartphone GPS data