Uncovering the spatial structure of mobility networks

The extraction of a clear and simple footprint of the structure of large, weighted and directed networks is a general problem that has many applications. An important example is given by origin-destination matrices which contain the complete information on commuting flows, but are difficult to analyze and compare. We propose here a versatile method which extracts a coarse-grained signature of mobility networks, under the form of a $2\times 2$ matrix that separates the flows into four categories. We apply this method to origin-destination matrices extracted from mobile phone data recorded in thirty-one Spanish cities. We show that these cities essentially differ by their proportion of two types of flows: integrated (between residential and employment hotspots) and random flows, whose importance increases with city size. Finally the method allows to determine categories of networks, and in the mobility case to classify cities according to their commuting structure.Comment: 10 pages, 5 figures +Supplementary informatio

From mobile phone data to the spatial structure of cities

Pervasive infrastructures, such as cell phone networks, enable to capture large amounts of human behavioral data but also provide information about the structure of cities and their dynamical properties. In this article, we focus on these last aspects by studying phone data recorded during 55 days in 31 Spanish metropolitan areas. We first define an urban dilatation index which measures how the average distance between individuals evolves during the day, allowing us to highlight different types of city structure. We then focus on hotspots, the most crowded places in the city. We propose a parameter free method to detect them and to test the robustness of our results. The number of these hotspots scales sublinearly with the population size, a result in agreement with previous theoretical arguments and measures on employment datasets. We study the lifetime of these hotspots and show in particular that the hierarchy of permanent ones, which constitute the "heart" of the city, is very stable whatever the size of the city. The spatial structure of these hotspots is also of interest and allows us to distinguish different categories of cities, from monocentric and "segregated" where the spatial distribution is very dependent on land use, to polycentric where the spatial mixing between land uses is much more important. These results point towards the possibility of a new, quantitative classification of cities using high resolution spatio-temporal data.Comment: 14 pages, 15 figure

Risk profiles and one-year outcomes of patients with newly diagnosed atrial fibrillation in India: Insights from the GARFIELD-AF Registry.

BACKGROUND: The Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF) is an ongoing prospective noninterventional registry, which is providing important information on the baseline characteristics, treatment patterns, and 1-year outcomes in patients with newly diagnosed non-valvular atrial fibrillation (NVAF). This report describes data from Indian patients recruited in this registry. METHODS AND RESULTS: A total of 52,014 patients with newly diagnosed AF were enrolled globally; of these, 1388 patients were recruited from 26 sites within India (2012-2016). In India, the mean age was 65.8 years at diagnosis of NVAF. Hypertension was the most prevalent risk factor for AF, present in 68.5% of patients from India and in 76.3% of patients globally (P < 0.001). Diabetes and coronary artery disease (CAD) were prevalent in 36.2% and 28.1% of patients as compared with global prevalence of 22.2% and 21.6%, respectively (P < 0.001 for both). Antiplatelet therapy was the most common antithrombotic treatment in India. With increasing stroke risk, however, patients were more likely to receive oral anticoagulant therapy [mainly vitamin K antagonist (VKA)], but average international normalized ratio (INR) was lower among Indian patients [median INR value 1.6 (interquartile range {IQR}: 1.3-2.3) versus 2.3 (IQR 1.8-2.8) (P < 0.001)]. Compared with other countries, patients from India had markedly higher rates of all-cause mortality [7.68 per 100 person-years (95% confidence interval 6.32-9.35) vs 4.34 (4.16-4.53), P < 0.0001], while rates of stroke/systemic embolism and major bleeding were lower after 1 year of follow-up. CONCLUSION: Compared to previously published registries from India, the GARFIELD-AF registry describes clinical profiles and outcomes in Indian patients with AF of a different etiology. The registry data show that compared to the rest of the world, Indian AF patients are younger in age and have more diabetes and CAD. Patients with a higher stroke risk are more likely to receive anticoagulation therapy with VKA but are underdosed compared with the global average in the GARFIELD-AF. CLINICAL TRIAL REGISTRATION-URL: http://www.clinicaltrials.gov. Unique identifier: NCT01090362

Technology Adoption in Air Traffic Management: A Combination of Agent-Based Modeling with Behavioral Economics

The European Air Traffic Management (ATM) system is responsible for the safe and timely transportation of more than a billion passengers annually. It is a system that depends heavily on technology and is expected to stay on top of the technological advancements and be an early adopter of technologies. Nevertheless, technological change in ATM has historically developed at a slow pace. In this paper, an agent-based model (ABM) of the ATM technology deployment cycle is proposed. The proposed ABM is part of a larger project, which intends to recommend new policy measures for overcoming any barriers associated with technology adoption in ATM. It is a novel and one of the first approaches aiming at simulating the adoption of technology in ATM that combines the organizational point of view, i.e. stakeholders’ level, the focus on policy testing and the inclusion of behavioral economics aspects.QC 20210618</p

Urban population dynamics during the COVID- 19 pandemic based on mobile phone data

Trabajo presentado en: R-Evolucionando el transporte, XIV Congreso de Ingeniería del Transporte (CIT 2021), realizado en modalidad online los días 6, 7 y 8 de julio de 2021, organizado por la Universidad de BurgosBecause of the fast expansion of the COVID-19 pandemic in 2020, many countries established lockdowns, implementing different restrictions on people’s mobility. Analysing the effectiveness of these measures is crucial to better react to similar future scenarios. This research uses anonymous mobile phone data to study the impact of the Spanish lockdown on the daily dynamics of the Madrid metropolitan area. The analysis is focused on a reference week prior to the lockdown and on several weeks of the lockdown in which different restrictions were in place. For this timeframe, 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 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 maintain some activity during lockdown.This research was financed by the Spanish The Ministry of Science, Innovation and Universities through the project “DynMobility - Análisis dinámico de los patrones de movilidad a partir del Big Data” (code RTI2018-098402-B-I00), and also supported by the Madrid Region authority through the Research Network “INNJOBMAD-CM - Atracción de actividades económicas innovadoras y creadoras de trabajo en Madrid.

New urban mobility options: Alternative futures and their impact in transport planning techniques

Trabajo presentado en: R-Evolucionando el transporte, XIV Congreso de Ingeniería del Transporte (CIT 2021), realizado en modalidad online los días 6, 7 y 8 de julio de 2021, organizado por la Universidad de BurgosThe acceleration of technology evolution is changing urban mobility at a much faster pace than we have seen in previous decades, leading to an increasingly uncertain future within this field. It is very likely that current transport planning tools and techniques will have to be adapted to the increasing number of innovative mobility forms in order to maintain their usefulness in the urban policy cycle. In this paper, we present a series of explorative scenarios for European urban mobility and the consequent challenges that they imply for such tools and techniques. Two groups of scenarios have been developed for assessing two different uncertain relations. First, a set of exogeneous scenarios has been defined for studying how different urban mobility socioeconomic contexts could affect the evolution of emerging mobility solutions. These scenarios are adaptations of the IPCC’s Shared Socioeconomic Pathways. Second, a set of pathways that these mobility innovations may follow has been shaped in order to determine to what extent each innovation will potentially pose new requirements on transport data sources, models and decision support tools. The methodology used for developing the scenarios started by a literature review covering the most prominent urban mobility trends. Then, policy-makers and modellers were engaged in the process through a series of workshops and a Delphi poll. This served to gather inputs from a wide range of end-users and practitioners. The paper covers the results from these methodologies, unveils the resultant scenarios, and outlines the conclusions in terms of future plausible requirements for transport planning tools and techniques.This research is supported by the European project MOMENTUM-Modelling Emerging Transport Solutions for Urban Mobility, funded from the European Union’s Horizon 2020 research and innovation programme, under Grant Agreement No 815069

The city turned off: Urban dynamics during the COVID-19 pandemic based on mobile phone data

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