The use of new sources of big data collected at a high-frequency rate in conjunction with administrative data is critical to developing indicators of the exposure to risks of small urban areas. Correctly accounting for the crowding of people and for their movements is crucial to mitigate the effect of natural disasters, while guaranteeing the quality of life in a “smart city” approach. We use two different types of mobile phone data to estimate people crowding and traffic intensity. We analyze the temporal dynamics of crowding and traffic using a Model-Based Functional Cluster Analysis, and their spatial dynamics using the T-mode Principal Component Analysis. Then, we propose five indicators useful for risk management in small urban areas: two composite indicators based on cutting-edge mobile phone dynamic data and three indicators based on open-source street map static data. A case study for the flood-prone area of the Mandolossa (the western outskirts of the city of Brescia, Italy) is presented. We present a multi-dimensional description of the territory based on the proposed indicators at the level of small areas defined by the Italian National Statistical Institute as “Sezioni di Censimento” and “Aree di Censimento”.
Statistical indicators based on mobile phone and street maps data for risk management in small urban areas
Perazzini S.;Metulini R.;Carpita M.
2023-01-01
Abstract
The use of new sources of big data collected at a high-frequency rate in conjunction with administrative data is critical to developing indicators of the exposure to risks of small urban areas. Correctly accounting for the crowding of people and for their movements is crucial to mitigate the effect of natural disasters, while guaranteeing the quality of life in a “smart city” approach. We use two different types of mobile phone data to estimate people crowding and traffic intensity. We analyze the temporal dynamics of crowding and traffic using a Model-Based Functional Cluster Analysis, and their spatial dynamics using the T-mode Principal Component Analysis. Then, we propose five indicators useful for risk management in small urban areas: two composite indicators based on cutting-edge mobile phone dynamic data and three indicators based on open-source street map static data. A case study for the flood-prone area of the Mandolossa (the western outskirts of the city of Brescia, Italy) is presented. We present a multi-dimensional description of the territory based on the proposed indicators at the level of small areas defined by the Italian National Statistical Institute as “Sezioni di Censimento” and “Aree di Censimento”.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
