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From mobile phone data to the spatial structure of cities
TLDR
An urban dilatation index is defined which measures how the average distance between individuals evolves during the day, allowing us to highlight different types of city structure, and a parameter free method to detect hotspots, the most crowded places in the city is proposed. Expand
Comparing and modelling land use organization in cities
TLDR
A functional network approach is applied to determine land use patterns from mobile phone records and a model inspired by Schelling's segregation is introduced, able to explain and reproduce these results with simple interaction rules between different land uses. Expand
Cross-Checking Different Sources of Mobility Information
TLDR
A cross-check analysis by comparing results obtained with data collected from three different sources: Twitter, census, and cell phones to assess the correlation between the datasets on different aspects: the spatial distribution of people concentration, the temporal evolution of people density, and the mobility patterns of individuals. Expand
Uncovering the spatial structure of mobility networks
TLDR
A versatile method is proposed, which extracts a coarse-grained signature of mobility networks, under the form of a 2 × 2 matrix that separates the flows into four categories, and allows the determination of categories of networks, and in the mobility case, the classification of cities according to their commuting structure. Expand
Influence of sociodemographics on human mobility
TLDR
Credit-card records from Barcelona and Madrid are analyzed and by examining the geolocated credit-card transactions of individuals living in the two provinces, it is found that the mobility patterns vary according to gender, age and occupation. Expand
Visual Analytics and Machine Learning for Air Traffic Management Performance Modelling
INTUIT is a SESAR 2020 Exploratory Research project which aims to explore the potential of visual analytics and machine learning techniques to improve our understanding of the trade-offs between ATMExpand
Influence of sociodemographic characteristics on human mobility [corrected].
Human mobility has been traditionally studied using surveys that deliver snapshots of population displacement patterns. The growing accessibility to ICT information from portable digital media hasExpand
Combining Visual Analytics and Machine Learning for Route Choice Prediction Application to Pre-Tactical Traffic Forecast
One of the key enablers of ATM Network Management is the forecasting of the volume and complexity of traffic demand at different planning horizons. This paper proposes a visual analytics and machineExpand
Corrigendum: Influence of sociodemographic characteristics on human mobility
TLDR
Credit-card records from Barcelona and Madrid are analyzed and by examining the geolocated credit-card transactions of individuals living in the two provinces, it is found that the mobility patterns vary according to gender, age and occupation. Expand
Visual Analytics in the Aviation and Maritime Domains
TLDR
Four case studies are described in which distinct kinds of knowledge have been derived from trajectories of vessels and airplanes and related spatial and temporal data by human analytical reasoning empowered by interactive visual interfaces combined with computational operations. Expand
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