# An analytical framework to nowcast well-being using mobile phone data

arXiv: https://arxiv.org/abs/1606.06279 · HTML: https://mlsanity.com/paper/1606.06279/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2016.

## Built on by (most cited first, 8 of 8)

- [Deep Activity Model: A Generative Approach for Human Mobility Pattern Synthesis](https://mlsanity.com/paper/2405.17468/) (2024)
- [Trajectory Test-Train Overlap in Next-Location Prediction Datasets](https://mlsanity.com/paper/2203.03208/) (2022)
- [Predicting Next Useful Location With Context-Awareness: The State-Of-The-Art](https://mlsanity.com/paper/2401.08081/) (2024)
- [Text2Traj2Text: Learning-by-Synthesis Framework for Contextual Captioning of Human Movement Trajectories](https://mlsanity.com/paper/2409.12670/) (2024)
- [Reconstructing Human Mobility Pattern: A Semi-Supervised Approach for Cross-Dataset Transfer Learning](https://mlsanity.com/paper/2410.03788/) (2024)
- [Next-Generation Travel Demand Modeling with a Generative Framework for Household Activity Coordination](https://mlsanity.com/paper/2507.08871/) (2025)
- [Understanding peacefulness through the world news](https://mlsanity.com/paper/2106.00306/) (2021)
- [Predicting Socio-Economic Well-being Using Mobile Apps Data: A Case Study of France](https://mlsanity.com/paper/2301.09986/) (2023)

## Abstract (arXiv)

An intriguing open question is whether measurements made on Big Data recording human activities can yield us high-fidelity proxies of socio-economic development and well-being. Can we monitor and predict the socio-economic development of a territory just by observing the behavior of its inhabitants through the lens of Big Data? In this paper, we design a data-driven analytical framework that uses mobility measures and social measures extracted from mobile phone data to estimate indicators for socio-economic development and well-being. We discover that the diversity of mobility, defined in terms of entropy of the individual users' trajectories, exhibits (i) significant correlation with two different socio-economic indicators and (ii) the highest importance in predictive models built to predict the socio-economic indicators. Our analytical framework opens an interesting perspective to study human behavior through the lens of Big Data by means of new statistical indicators that quantify and possibly "nowcast" the well-being and the socio-economic development of a territory.