MobiSenseUs: Inferring Population Well-Being from Mobile Data

Measures of population well-being usually come from surveys and official statistics. Because they are costly to collect and not always updated frequently, MobiSenseUs tested whether aggregated patterns of mobile-phone use could complement them.

The analysis used privacy-preserving, aggregated data on mobility, application use, and communication from more than one million smartphone users in the United Kingdom; it did not include the content of calls or messages.

The models estimated the UK's Index of Multiple Deprivation more accurately than self-reported life satisfaction. That index includes income, employment, health, and education. Mobility patterns were most informative for deprivation, whereas communication patterns were more useful for life satisfaction.

Deprivation left a clearer signal in the mobile data than life satisfaction did. These patterns may complement surveys and public statistics, but they do not measure every aspect of well-being and cannot replace information collected directly from people.

A bar chart compares how accurately mobility, app-use, and communication signals estimated deprivation and life satisfaction.

Combining all three types of mobile behavior worked best for estimating deprivation, while communication signals performed best for life satisfaction.

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Martin Hillebrand, Imran Khan, Filipa Peleja, Nuria Oliver (2020). MobiSenseUs: Inferring Aggregate Objective and Subjective Well-Being from Mobile Data. Proceedings of the European Conference on Artificial Intelligence (ECAI 2020). https://doi.org/10.3233/faia200297

BibTeX
@inproceedings{hillebrand2020mobisenseus,
  author = {Martin Hillebrand and Imran Khan and Filipa Peleja and Nuria Oliver},
  title = {{MobiSenseUs}: Inferring Aggregate Objective and Subjective Well-Being from Mobile Data},
  booktitle = {Proceedings of the European Conference on Artificial Intelligence (ECAI 2020)},
  address = {Santiago de Compostela, Spain},
  year = 2020,
  doi = {10.3233/faia200297},
  file = {/projects/mobisenseus/mobisenseus.pdf},
  cites = 8,
  citesdate = {2026-03-23}
}

MobiSenseUs: Inferring Aggregate Objective and Subjective Well-Being from Mobile Data

Martin Hillebrand, Imran Khan, Filipa Peleja, Nuria Oliver
Proceedings of the European Conference on Artificial Intelligence (ECAI 2020) ยท 2020

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