Note: Home Location Detection from Mobile Phone Data: Evidence from Togo
4th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies, COMPASS 2022
; Par F180472:685-692, 2022.
Article
in English
| Scopus | ID: covidwho-1950301
ABSTRACT
Algorithms for home location inference from mobile phone data are frequently used to make high-stakes policy decisions, particularly when traditional sources of location data are unreliable or out of date. This paper documents analysis we performed in support of the government of Togo during the COVID-19 pandemic, using location information from mobile phone data to direct emergency humanitarian aid to individuals in specific geographic regions. This analysis, based on mobile phone records from millions of Togolese subscribers, highlights three main results. First, we show that a simple algorithm based on call frequencies performs reasonably well in identifying home locations, and may be suitable in contexts where machine learning methods are not feasible. Second, when machine learning algorithms can be trained with reliable and representative data, we find that they generally out-perform simpler frequency-based approaches. Third, we document considerable heterogeneity in the accuracy of home location inference algorithms across population subgroups, and discuss strategies to ensure that vulnerable mobile phone subscribers are not disadvantaged by home location inference algorithms. © 2022 Owner/Author.
geospatial data; mobile phone data; mobility; Cellular telephones; COVID-19; Inference engines; Learning algorithms; Machine learning; Documents analysis; Geo-spatial data; Inference algorithm; Location data; Location detection; Location information; Mobile phone datum; Paper documents; Policy decisions; Location
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
SIGCHI Conference on Computing and Sustainable Societies, COMPASS 2022
Year:
2022
Document Type:
Article
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