A novel multivariate spectral regression model for learning relationships between communication activity and urban ecology

Abstract

In this paper we demonstrate a novel approach to the use of spatio-temporally aggregated cell phone data to learn features of urban ecology (i.e., spatial distributions of distinct social and economic entities and their associated activities). Specifically, our technique involves four stages: (i) decomposing the aggregated cell phone activity within local areal units using spectral methods; (ii) learning spectral characteristics associated with ecological features using a training set; (iii) predicting local ecology composition for out-of-sample areas; and (iv) predicting activity time series for out-of-sample areas. The core of our approach is the projection of spectral features in cell phone activity series into an ecology-associated basis, allowing both identification of communication patterns arising from particular types of local activities and/or institutions and leveraging of those patterns for classification and activity prediction. We apply our methodology to aggregated communication and Internet traffic data from the cities of Milan and Trento to show the effectiveness of our method.