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Mobility functional areas are tools based on human mobility that can be useful for spatial and transport planning in delicate situations such as the COVID-19 pandemic. In this work, we aim to map functional areas in Spain from four days corresponding to different phases of the disease. For that goal, mobile phone data provided by Spanish Statistical National Institute (INE) has been used due to its value and potential to provide constantly updated information of mobility at almost-real time. The methodology consists of a network analysis over an origin-destination matrix to obtain modularity values for 3214 population cells provided by the INE. These values were then used to cluster the cells into functional areas. The results show how different confinement and mobility restriction policies influence the amount, size and shape of the functional areas, and therefore, they affect access to services or jobs. [ABSTRACT FROM AUTHOR]

People's daily movements, as aggregated in spatial units, shape mobility flows that can be modelled by geospatial networks. The structure of these networks both reflects and influences how cities are lived in, perceived and planned. In this study, location tracking data from mobile phones were used to investigate the functional and spatial structures of daily mobility networks and how these networks change as they grow in size. A case study was performed on 81 Spanish functional Urban Areas (FUAs). The results of this study show that the friction of the space and the average length of the aggregate daily mobility are constant and do not depend on the area and demographic size of the FUAs. The average length of the aggregate daily mobility is associated with the increase in complexity that occurs as mobility networks grow, which is reflected in an increase in the number of levels of interaction and the proportion of local flows that integrate adjacent spaces across different scales. The method used provides quantitative indicators that reveal the similarities and differences of daily mobility, as well as the interactions that occur in FUAs according to their size. This information is very useful in urban and mobility planning. • The average length of aggregate daily mobility is identical in all Spanish FUAs. • The mobility networks of the largest FUAs are composed of functional modules. • Modularity is a mechanism for networks to grow without increasing the average length. • Long-distance movements are less numerous and weaker than short-distance movements. • These weak ties make that daily mobility networks function as a spatial unit. [ABSTRACT FROM AUTHOR]

Due to the rapid expansion of the COVID-19 pandemic, many countries ordained lockdowns, establishing different restrictions on people's mobility. Exploring to what extent these measures have been effective is critical in order to better respond to similar future scenarios. This article uses anonymous mobile phone data to study the impact of the Spanish lockdown on the daily dynamics of the Madrid metropolitan area (Spain). The analysis has been carried out for a reference week prior to the lockdown and during several weeks of the lockdown in which different restrictions were in place. During these weeks, population distribution is compared during the day and at night and presence profiles are obtained throughout the day for each type of land use. In addition, a spatial multiple regression analysis is carried out to determine the impact of the different land uses on the local population. The results in the reference week, pre-COVID-19, show how the population in activity areas increases in each time slot on a specific day and how in residential areas it decreases. However, during the lockdown, activity areas cease to attract population during the day and the residential areas therefore no longer show a decrease. Only basic essential commercial activities, or others that require the presence of workers (industrial or logistics) maintain some activity during lockdown. • Mobile phone data have a great potential for analysing the effectiveness of the restrictions on mobility, implemented to contain COVID-19 spread. • Mobile phone data analysis brings the possibility of evaluating the impact of different measures with high spatio-temporal resolution. • There is a close link between the presence of people and the spatial distribution of the different land uses. • It is possible to identify and monitor areas with more or less activity during the COVID-19 lockdown than in a normal week. [ABSTRACT FROM AUTHOR]

Air pollution is one of the greatest challenges cities are facing today and improving air quality is a pressing need to reduce negative health impacts. In order to efficiently evaluate which are the most appropriate policies to reduce the impact of urban pollution sources (such as road traffic), it is essential to conduct rigorous population exposure assessments. One of the main limitations associated with those studies is the lack of information about population distribution in the city along the day (population dynamics). The pervasive use of mobile devices in our daily lives opens new opportunities to gather large amounts of anonymized and passively collected geolocation data allowing the analysis of population activity and mobility patterns. This study presents a novel methodology to estimate population dynamics from mobile phone data based on a user-centric mobility model approach. The methodology was tested in the city of Madrid (Spain) to evaluate population exposure to NO 2 . A comparison with traditional census-based methods shows relevant discrepancies at disaggregated levels and highlights the need to incorporate mobility patterns into population exposure assessments.