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

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.

Derivative Extrapolation Using Least Squares

Here, we present three methods for differentiating discrete sets from streaming processes, e.g. WIFI. One approach is based on optimization of the well-known Savitzky-Golay algorithm. These methods are tested on synthetic data sets and will be implemented on subsets of a real university campus WIFI data set. The applicability of all methods are discussed, where we provide insights on both some of their benefits and pitfalls. This article ends with our conclusion on which method is better for our WIFI data.