Measuring Spatial Subdivisions in Urban Mobility with Mobile Phone Data

Abstract

Urban population grows constantly. By 2050 two thirds of the world population will reside in urban areas. This growth is faster and more complex than the ability of cities to measure and plan for their sustainability. To understand what makes a city inclusive for all, we define a methodology to identify and characterize spatial subdivisions: areas with over-and under-representation of specific population groups, named hot and cold spots respectively. Using aggregated mobile phone data, we apply this methodology to the city of Barcelona to assess the mobility of three groups of people: women, elders, and tourists. We find that, within the three groups, cold spots have a lower diversity of amenities and services than hot spots. Also, cold spots of women and tourists tend to have lower population income. These insights apply to the floating population of Barcelona, thus augmenting the scope of how inclusiveness can be analyzed in the city. CCS CONCEPTS• Human-centered computing → Empirical studies in collaborative and social computing.

Modalflow: Cross-Origin Flow Data Visualization for Urban Mobility

“…These improvements will be addressed in future versions of the system. Still, the use of three categories seems to be enough to already tackle on-going problems in mobility, for instance, there are several gaps in mobility that can be explored with two or three variables [52]; thus, Modalflow would allow for urban planners to uncover not only biases in methodological workflows, but also gaps in behaviour that are commonly hidden in traditional analyses and visualizations [31,32].…”

The Camp Nou Stadium as a Testbed for City Physiology: A Modular Framework for Urban Digital Twins

“…Foursquare allowed its users to broadcast their positions to the social network in the form of a check-in, that is, a record such as "user A was in place B at date C." is includes check-ins in Barcelona and at Camp Nou [23]. e private data consist of aggregated Mobile Phone Data, obtained from an agreement between the Barcelona City Council and Vodafone [24].…”

“…To infer the demographic and origin of visitors, the information about gender, age, and residence (either local or a tourist) is analyzed. To identify the demographic attributes of visitors to Camp Nou, the results of a previous study [24] are improved using a mobile phone dataset from 2018 created by the mobile phone operator Vodafone for the Barcelona City Hall. e dataset contains aggregated visitor counts in several areas of the city, based on the information gathered from cellular antennas across the city.…”

“…e dataset already includes a measure of the number of local and tourist visitors at each grid cell, which is adapted to the venue [24]. However, the tourist category is severely biased by the availability of the mobile operator in foreign countries.…”

Estimating crime in place: Moving beyond residence location

“…However, estimates of crimes reported to the police may be affected by the uncertainty arising from the small samples of crimes recorded in small areas (Rosenbaum and Lavrakas 1995). Previous research has shown that crime in Barcelona is highly concentrated in the 'old quarter' (González Murciano and Murrià Sangenís 2020;Maldonado-Guzmán et al 2021), which is characterised by very large concentrations of tourist, hotels, restaurants, museums, bars and clubs, but not necessarily a larger population density (Graells-Garrido et al 2020;Gutiérrez et al 2017). As a result, we would expect survey-based estimates of offence locations to show large concentrations of crime in the city centre, while this may remain hidden when we analyse survey-based estimates of area victimizations.…”