# Identifying Population Movements with Non-Negative Matrix Factorization from Wi-Fi User Counts in Smart and Connected Cities

arXiv: https://arxiv.org/abs/2111.10459 · HTML: https://mlsanity.com/paper/2111.10459/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 32 references that are in the map.
- First posted on arXiv 19 Nov 2021.

## Builds on (most cited first, 2 of 2)

- [Population estimation from mobile network traffic metadata](https://mlsanity.com/paper/1610.06947/) (2016)
- [Compact representation of temporal processes in echosounder time series via matrix decomposition](https://mlsanity.com/paper/2007.02906/) (2020)

## Abstract (arXiv)

Non-Negative Matrix Factorization (NMF) is a valuable matrix factorization technique which produces a "parts-based" decomposition of data sets. Wi-Fi user counts are a privacy-preserving indicator of population movements in smart and connected urban environments. In this paper, we apply NMF with a novel matrix embedding to Wi-Fi user count data from the University of Colorado at Boulder Campus for the purpose of automatically identifying patterns of human movement in a Smart and Connected infrastructure environment.