# A survey of results on mobile phone datasets analysis

arXiv: https://arxiv.org/abs/1502.03406 · HTML: https://mlsanity.com/paper/1502.03406/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2015.

## Built on by (most cited first, 6 of 6)

- [Differentially Private Multivariate Time Series Forecasting of Aggregated Human Mobility With Deep Learning: Input or Gradient Perturbation?](https://mlsanity.com/paper/2205.00436/) (2022)
- [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)
- [Temporal social network modeling of mobile connectivity data with graph neural networks](https://mlsanity.com/paper/2509.03319/) (2025)
- [Cellular Predictions on the Move: What about Data?](https://mlsanity.com/paper/2606.25709/) (2026)
- [ABC: Numerical Data Collection under Local Differential Privacy without Prior Knowledge](https://mlsanity.com/paper/2608.05737/) (2026)

## Abstract (arXiv)

In this paper, we review some advances made recently in the study of mobile phone datasets. This area of research has emerged a decade ago, with the increasing availability of large-scale anonymized datasets, and has grown into a stand-alone topic. We will survey the contributions made so far on the social networks that can be constructed with such data, the study of personal mobility, geographical partitioning, urban planning, and help towards development as well as security and privacy issues.