Spatiotemporal Analysis of Urban Mobility Using Aggregate Mobile Phone Derived Presence and Demographic Data: A Case Study in the City of Rome, Italy
1
Department of Occupational & Environmental Medicine, INAIL, Via Fontana Candida 1, 00040 Monteporzio Catone (RM), Italy
2
Department of Technological Innovation, INAIL, Via R. Ferruzzi, 38/40, 00143 Rome, Italy
*
Author to whom correspondence should be addressed.
Data 2019, 4(1), 8; https://doi.org/10.3390/data4010008
Received: 11 October 2018 / Revised: 18 December 2018 / Accepted: 29 December 2018 / Published: 8 January 2019
(This article belongs to the Special Issue Spatial Denominators: Modeling Population and Demographic Distributions)
Urban mobility is known to have a relevant impact on work related car accidents especially during commuting. It is characterized by highly dynamic spatial–temporal variability. There are open questions about the size of this phenomenon; its spatial, temporal, and demographic characteristics; and driving mechanisms. A case study is here presented for the city of Rome, Italy. High-resolution population presence and demographic data, derived from mobile phone traffic, were used. Hourly profiles of a defined mobility factor (NPM) were calculated for a gridded domain during working days and cluster analyzed to obtain mean diurnal NPM mobility patterns. Age distributions of the population were calculated from demographic data to get insight in the type of population involved in mobility, and spatially linked with the mobility patterns. Census data about production units and their employees were related with the classified NPM mobility patterns. Seven different NPM mobility patterns were identified and mapped over the study area. The mobility slightly deviates from the census-based demography (0.15 on average, in a range of 0 to 1). The number of employees per 100 inhabitants was found to be the main driving mechanism of mobility. Finally, contributions of people employed in different economic macrocategories were assigned to each mobility time-pattern. Results provide a deeper knowledge of urban dynamics and their driving mechanisms in Rome.
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MDPI and ACS Style
Gariazzo, C.; Pelliccioni, A.; Bogliolo, M.P. Spatiotemporal Analysis of Urban Mobility Using Aggregate Mobile Phone Derived Presence and Demographic Data: A Case Study in the City of Rome, Italy. Data 2019, 4, 8.
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