1,720,989 research outputs found
Emergence of scaling in ecological communities from tropical forests to human mobility
Full text linkThis work is mainly focused on the study of the interrelationships between transportation networks and the structure of the ecological communities in which the transport takes place.
Transportation is a common need in many and diverse natural systems composed by individuals that interact with each other by competing for the same resources or by cooperation.
For the cases we investigated we found that the features of the transportation system are closely related to the structure of the community. For example in tropical forests we showed that the optimization of the transportation of water and resources to the leaves poses strict restrictions on the distribution of tree sizes. Also, in the context of human mobility we found that the commuting fluxes of people within a country are driven by the spatial distribution of population.
In particular we discovered that in both tropical forests and human settlements the adaptation/evolution of the transportation network responds to simple optimality principles of efficiency, distinctive of each system.
In forests, the transportation network is constituted by trees, whose number, size and shape characterize the efficiency in the transportation of water. The relationship between size and abundance as well as the scaling relationships between diameter, height, crown extension can be determined by assuming that each tree maximizes its metabolic rate for a given mass, and that the entire forest fully utilizes all available resources.
The transportation network describing human mobility is defined as a weighted network where the nodes are the locations (e.g. municipalities or counties) and a link of weight w between two nodes indicates a commuting flux of w people between the two locations. Again, we found that one is able to reproduce the observed mobility patterns with remarkable accuracy assuming that each individual choses her/his destination trying to balance between the attractiveness versus the distance of the locations (i.e. choosing the closest location with higher attractiveness).
Scaling plays a major role in our analysis.
In both systems we were able to identify scaling relationships between the important variables, and thanks to finite-size scaling we succeeded in linking together seemingly unrelated quantities. This provided new insights and helped in discovering universal relationships.Il principale obiettivo di questo lavoro è lo studio delle relazioni tra le reti di trasporto e la struttura delle comunità ecologiche in cui il trasporto ha luogo.
Il trasporto è un'esigenza comune in molti dei sistemi naturali costituiti da individui che interagiscono tra loro attraverso la competizione per le stesse risorse o tramite meccanismi di cooperazione.
Nei casi che abbiamo trattato abbiamo riscontrato che le caratteristiche del sistema di trasporto sono strettamente legate alla struttura della comunità. Per esempio, abbiamo mostrato come nelle foreste tropicali l'ottimizzazione del trasporto dell'acqua e delle sostanze nutritive alle foglie impone dei rigidi vincoli alla distribuzione delle dimensioni degli alberi.
Inoltre, nel contesto della "mobilità umana" abbiamo dimostrato che i flussi dei pendolari all'interno di uno stato sono determinati dalla distribuzione spaziale della popolazione.
In particolare abbiamo scoperto che sia nelle foreste tropicali che negli insediamenti umani l'adattamento e l'evoluzione della rete di trasporto sono determinati da semplici principi di ottimizzazione dell'efficienza, caratteristici del particolare sistema.
Nelle foreste la rete di trasporto è costituita dagli alberi, il cui numero, dimensioni e forma determinano l'efficienza nel trasporto dell'acqua. Il legame tra le dimensioni e la numerosità degli alberi e le relazioni di scala tra diametro, altezza, raggio della chioma, possono essere determinate assumendo che ogni albero massimizza il metabolismo a parità di massa, e che l'intera foresta utilizza tutte le risorse disponibili.
D'altro canto, la rete di trasporto con cui si descrive la mobilità umana è un grafo pesato, in cui i nodi sono i luoghi (le municipalità o le contee) e un legame con peso w tra due nodi indica un flusso di w persone tra i due luoghi. Ancora una volta abbiamo trovato che è possibile riprodurre con sorprendente precisione i flussi osservati assumendo che ogni individuo scelga la sua destinazione cercando di bilanciare l'attrattività con la distanza dal luogo in cui si trova (scegliendo cioè la più vicina località che abbia una attrattiva maggiore del luogo in cui si trova).
Lo scaling ha un ruolo fondamentale nella nostra analisi.
In entrambi i contesti siamo stati in grado di identificare le relazioni di scala tra le principali variabili e, grazie alle tecniche delle leggi di scala finite, siamo riusciti a trovare un legame tra quantità apparentemente indipendenti. Questo ci ha portato a una comprensione più profonda dei sistemi analizzati e ci ha permesso di scoprire l'esistenza di relazioni di universalità
scikit-mobility: A Python Library for the Analysis, Generation, and Risk Assessment of Mobility Data
Full text linkThe last decade has witnessed the emergence of massive mobility datasets, such as tracks generated by GPS devices, call detail records, and geo-tagged posts from social media platforms. These datasets have fostered a vast scientific production on various applications of mobility analysis, ranging from computational epidemiology to urban planning and transportation engineering. A strand of literature addresses data cleaning issues related to raw spatiotemporal trajectories, while the second line of research focuses on discovering the statistical "laws" that govern human movements. A significant effort has also been put on designing algorithms to generate synthetic trajectories able to reproduce, realistically, the laws of human mobility. Last but not least, a line of research addresses the crucial problem of privacy, proposing techniques to perform the re-identification of individuals in a database. A view on state-of-the-art cannot avoid noticing that there is no statistical software that can support scientists and practitioners with all the aspects mentioned above of mobility data analysis. In this paper, we propose scikit-mobility, a Python library that has the ambition of providing an environment to reproduce existing research, analyze mobility data, and simulate human mobility habits. scikit-mobility is efficient and easy to use as it extends pandas, a popular Python library for data analysis. Moreover, scikit-mobility provides the user with many functionalities, from visualizing trajectories to generating synthetic data, from analyzing statistical patterns to assessing the privacy risk related to the analysis of mobility datasets
A Deep Gravity model for mobility flows generation
No full textThe movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment. Here, the authors use deep neural networks to discover non-linear relationships between geographical variables and mobility flows
Zipf's and Taylor's laws
Full text linkZipf's law states that the frequency of an observation with a given value is inversely proportional to the square of that value; Taylor's law, instead, describes the scaling between fluctuations in the size of a population and its mean. Empirical evidence of the validity of these laws has been found in many and diverse domains. Despite the numerous models proposed to explain the presence of Zipf's law, there is no consensus on how it originates from a microscopic process of individual dynamics without fine-tuning. Here we show that Zipf's law and Taylor's law can emerge from a general class of stochastic processes at the individual level, which incorporate one of two features: environmental variability, i.e., fluctuations of parameters, or correlations, i.e., dependence between individuals. Under these assumptions, we show numerically and with theoretical arguments that the conditional variance of the population increments scales as the square of the population, and that the corresponding stationary distribution of the processes follows Zipf's law
Comparing general mobility and mobility by car
No full textIn the last years, the emergence of big data led scientists from diverse disciplines toward the study of the laws underlying human mobility. Although these recent discoveries have shed light on very interesting and fascinating aspects about people movements, they are generally focused on global and general mobility patterns. For this reason, they do not necessarily capture phenomena related to specific types of mobility, such as mobility by car, by public transportations means, by foot and so on. In this work, we aim to compare general human mobility with mobility expressed by a specific conveyance, trying to address the following question: What are the differences between general mobility and mobility by car? To answer this question, we present the results of an analysis performed on a big mobile phone dataset and on a GPS dataset storing information about car travels in Italy. © 2013 IEEE
Modeling Adversarial Behavior Against Mobility Data Privacy
Full text linkPrivacy risk assessment is a crucial issue in any privacy-aware analysis process. Traditional frameworks for privacy risk assessment systematically generate the assumed knowledge for a potential adversary, evaluating the risk without realistically modelling the collection of the background knowledge used by the adversary when performing the attack. In this work, we propose Simulated Privacy Annealing (SPA), a new adversarial behavior model for privacy risk assessment in mobility data. We model the behavior of an adversary as a mobility trajectory and introduce an optimization approach to find the most effective adversary trajectory in terms of privacy risk produced for the individuals represented in a mobility data set. We use simulated annealing to optimize the movement of the adversary and simulate a possible attack on mobility data. We finally test the effectiveness of our approach on real human mobility data, showing that it can simulate the knowledge gathering process for an adversary in a more realistic way
The agglomeration and dispersion dichotomy of human settlements on Earth
No full textHuman settlements on Earth are scattered in a multitude of shapes, sizes and spatial arrangements. These patterns are often not random but a result of complex geographical, cultural, economic and historical processes that have profound human and ecological impacts. However, little is known about the global distribution of these patterns and the spatial forces that creates them. This study analyses human settlements from high-resolution satellite imagery and provides a global classification of spatial patterns. We find two emerging classes, namely agglomeration and dispersion. In the former, settlements are fewer than expected based on the predictions of scaling theory, while an unexpectedly high number of settlements characterizes the latter. To explain the observed spatial patterns, we propose a model that combines two agglomeration forces and simulates human settlements’ historical growth. Our results show that our model accurately matches the observed global classification (F1: 0.73), helps to understand and estimate the growth of human settlements and, in turn, the distribution and physical dynamics of all human settlements on Earth, from small villages to cities
Returners and explorers dichotomy in human mobility
Full text linkThe availability of massive digital traces of human whereabouts has offered a series of novel insights on the quantitative patterns characterizing human mobility. In particular, numerous recent studies have lead to an unexpected consensus: the considerable variability in the characteristic travelled distance of individuals coexists with a high degree of predictability of their future locations. Here we shed light on this surprising coexistence by systematically investigating the impact of recurrent mobility on the characteristic distance travelled by individuals. Using both mobile phone and GPS data, we discover the existence of two distinct classes of individuals: returners and explorers. As existing models of human mobility cannot explain the existence of these two classes, we develop more realistic models able to capture the empirical findings. Finally, we show that returners and explorers play a distinct quantifiable role in spreading phenomena and that a correlation exists between their mobility patterns and social interactions.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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