Spatiotemporal Analysis of Urban Mobility Using Aggregate Mobile Phone Derived Presence and Demographic Data: A Case Study in the City of Rome, Italy

Abstract

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.

Estimating real-time high-street footfall from Wi-Fi probe requests

“…In the UK, for example, this is of immediate practical importance in evaluating business rates on properties, and has still wider implications for the setting of retail unit rental values. There are obvious extensions to understanding the ebbs and flows of activities in the 24-hour smart city such as understanding urban mobility (Gariazzo et al 2019) and conceptualising them with a people dimension (Nam and Pardo 2011).…”

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“…The first one is the definition of the physical city boundary. While some researchers define cities by their metropolitan area (Gariazzo et al., 2019; Louail et al., 2014), others only consider the urban core (Chen et al., 2018; Kubíček et al., 2019; Ratti et al., 2006). Metropolitan areas are often defined as an aggregation of municipalities that share industry, infrastructure, and housing and that represent the economic city with a densely populated urban core area—that might span across multiple municipalities—and its surrounding rural, less-populated areas.…”

“…On the other hand, municipalities are generally smaller spatial units embedded within a metropolitan area, with its urban core representing the physical boundary of the city and the region that has emerged historically as the most prominent in the metropolitan area (Demographia, 2020). Therefore, when the term city is used in current CDR-based hotspot analyses, it is important to understand whether it refers only to the densely populated areas within a metropolitan area (Chen et al., 2018; Ratti et al., 2006); or to the metropolitan area as a whole, including both the densely populated urban area and its less-populated, rural surrounding territories (Gariazzo et al., 2019; Le Néchet, 2012; Louail et al., 2014). See Urban and UrbanRural columns in Figure 1.…”

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