To know the number of city users is essential since it provides a big amount of useful information in the context of Smart City evaluations that traditional static measures—represented by the number of residents from census data—are not able to provide. In this paper we use spatiotemporal mobile phone data along with administrative data to develop a dynamic indicator for the number of city users. In doing so, we propose a multi-stage approach for high-dimensional data, that, in the first part, it permits to estimate the number of phone company users for different reference days by means of an approach based on Histogram of Oriented Gradients for data dimensionality reduction, and by means of a mix of k-means and Functional Data Analysis Model-Based Clustering methods for clustering days. The second part is aimed at employing a method—based on matching mobile phone and administrative data—to estimate the phone company market share at small area level, which is used to derive city users. Applying the method to the case study of the Municipality of Brescia, we find that our estimated market share outperforms the national level counterpart. Moreover, we find that the number of city users reaches a peak of 270–280 thousand during the central hours of autumn to spring weekdays.

A Spatio-Temporal Indicator for City Users Based on Mobile Phone Signals and Administrative Data

Rodolfo Metulini
;
2020

Abstract

To know the number of city users is essential since it provides a big amount of useful information in the context of Smart City evaluations that traditional static measures—represented by the number of residents from census data—are not able to provide. In this paper we use spatiotemporal mobile phone data along with administrative data to develop a dynamic indicator for the number of city users. In doing so, we propose a multi-stage approach for high-dimensional data, that, in the first part, it permits to estimate the number of phone company users for different reference days by means of an approach based on Histogram of Oriented Gradients for data dimensionality reduction, and by means of a mix of k-means and Functional Data Analysis Model-Based Clustering methods for clustering days. The second part is aimed at employing a method—based on matching mobile phone and administrative data—to estimate the phone company market share at small area level, which is used to derive city users. Applying the method to the case study of the Municipality of Brescia, we find that our estimated market share outperforms the national level counterpart. Moreover, we find that the number of city users reaches a peak of 270–280 thousand during the central hours of autumn to spring weekdays.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4739683
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