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

TL;DR

This paper applies a novel NMF-based method to Wi-Fi user count data to automatically identify human movement patterns in a smart city environment, demonstrating the technique's effectiveness in urban population analysis.

Contribution

It introduces a new matrix embedding approach for NMF to analyze Wi-Fi data for population movement detection in smart city settings.

Findings

Successfully identified human movement patterns from Wi-Fi data

Demonstrated the effectiveness of the novel matrix embedding method

Enhanced understanding of urban population dynamics

Abstract

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.