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2021 Contribution to journal Open Access OPEN

Introduction to the special issue on social mining and big data ecosystem for open, responsible data science
Pappalardo L., Grossi V., Pedreschi D.
Source: International Journal of Data Science and Analytics (Online) (2021). doi:10.1007/s41060-021-00253-5
DOI: 10.1007/s41060-021-00253-5
Project(s): SoBigData via OpenAIRE, SoBigData-PlusPlus via OpenAIRE

See at: link.springer.com Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted


2021 Journal article Open Access OPEN

STS-EPR: Modelling individual mobility considering the spatial, temporal, and social dimensions together
Cornacchia G., Pappalardo L.
Modelling human mobility is crucial in several scientific areas, from urban planning to epidemic modeling, traffic forecasting, and what-if analysis. On the one hand, existing models focus on the spatial and temporal dimensions of mobility only, while the social dimension is often neglected. On other hand, models that embed a social mechanism have trivial or unrealistic spatial and temporal mechanisms. We propose STS-EPR, a mechanistic model that captures the spatial, temporal, and social dimensions of human mobility together. Our results show that STS-EPR generates realistic trajectories, making it better than models that lack either in the social, the spatial, or the temporal mechanisms. STS-EPR is a step towards the design of mechanistic models that can capture all the aspects of human mobility in a comprehensive way.Source: Procedia computer science 184 (2021): 258–265. doi:10.1016/j.procs.2021.03.035
DOI: 10.1016/j.procs.2021.03.035
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: ISTI Repository Open Access | CNR ExploRA Open Access | www.sciencedirect.com Open Access


2021 Journal article Open Access OPEN

Evaluation of home detection algorithms on mobile phone data using individual-level ground truth
Pappalardo L., Ferres L., Sacasa M., Cattuto C., Bravo L.
Inferring mobile phone users' home location, i.e., assigning a location in space to a user based on data generated by the mobile phone network, is a central task in leveraging mobile phone data to study social and urban phenomena. Despite its widespread use, home detection relies on assumptions that are difficult to check without ground truth, i.e., where the individual who owns the device resides. In this paper, we present a dataset that comprises the mobile phone activity of sixty-five participants for whom the geographical coordinates of their residence location are known. The mobile phone activity refers to Call Detail Records (CDRs), eXtended Detail Records (XDRs), and Control Plane Records (CPRs), which vary in their temporal granularity and differ in the data generation mechanism. We provide an unprecedented evaluation of the accuracy of home detection algorithms and quantify the amount of data needed for each stream to carry out successful home detection for each stream. Our work is useful for researchers and practitioners to minimize data requests and maximize the accuracy of the home antenna location.Source: EPJ 10 (2021). doi:10.1140/epjds/s13688-021-00284-9
DOI: 10.1140/epjds/s13688-021-00284-9
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: epjdatascience.springeropen.com Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2021 Journal article Open Access OPEN

Explaining the difference between men's and women's football
Pappalardo L., Rossi A., Natilli M., Cintia P.
Women's football is gaining supporters and practitioners worldwide, raising questions about what the differences are with men's football. While the two sports are often compared based on the players' physical attributes, we analyze the spatio-temporal events during matches in the last World Cups to compare male and female teams based on their technical performance. We train an artificial intelligence model to recognize if a team is male or female based on variables that describe a match's playing intensity, accuracy, and performance quality. Our model accurately distinguishes between men's and women's football, revealing crucial technical differences, which we investigate through the extraction of explanations from the classifier's decisions. The differences between men's and women's football are rooted in play accuracy, the recovery time of ball possession, and the players' performance quality. Our methodology may help journalists and fans understand what makes women's football a distinct sport and coaches design tactics tailored to female teams.Source: PloS one 16 (2021). doi:10.1371/journal.pone.0255407
DOI: 10.1371/journal.pone.0255407
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: journals.plos.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2021 Report Open Access OPEN

Living in a pandemic: adaptation of individual mobility and social activity in the US
Lucchini L., Centellegher S., Pappalardo L., Gallotti R., Privitera F., Lepri B., De Nadai M.
The non-pharmaceutical interventions (NPIs), aimed at reducing the diffusion of the COVID-19 pandemic, has dramatically influenced our behaviour in everyday life. In this work, we study how individuals adapted their daily movements and person-to-person contact patterns over time in response to the COVID-19 pandemic and the NPIs. We leverage longitudinal GPS mobility data of hundreds of thousands of anonymous individuals in four US states and empirically show the dramatic disruption in people's life. We find that local interventions did not just impact the number of visits to different venues but also how people experience them. Individuals spend less time in venues, preferring simpler and more predictable routines and reducing person-to-person contact activities. Moreover, we show that the stringency of interventions alone does explain the number and duration of visits to venues: individual patterns of visits seem to be influenced by the local severity of the pandemic and a risk adaptation factor, which increases the people's mobility regardless of the stringency of interventions.Source: ISTI Research Report, SoBigData++, 2021
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: arxiv.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2021 Report Open Access OPEN

Coach2vec: autoencoding the playing style of soccer coaches
Cintia P., Pappalardo L.
Capturing the playing style of professional soccer coaches is a complex, and yet barely explored, task in sports analytics. Nowadays, the availability of digital data describing every relevant spatio-temporal aspect of soccer matches, allows for capturing and analyzing the playing style of players, teams, and coaches in an automatic way. In this paper, we present coach2vec, a workflow to capture the playing style of professional coaches using match event streams and artificial intelligence. Coach2vec extracts ball possessions from each match, clusters them based on their similarity, and reconstructs the typical ball possessions of coaches. Then, it uses an autoencoder, a type of artificial neural network, to obtain a concise representation (encoding) of the playing style of each coach. Our experiments, conducted on soccer-logs describing the last four seasons of the Italian first division, reveal interesting similarities between prominent coaches, paving the road to the simulation of playing styles and the quantitative comparison of professional coaches.Source: ISTI Research Report, SoBigData++, 2021
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: arxiv.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2021 Report Open Access OPEN

Understanding peacefulness through the world news
Voukelatou V., Miliou I., Giannotti F., Pappalardo L.
Peacefulness is a principal dimension of well-being for all humankind and is the way out of inequity and every single form of violence. Thus, its measurement has lately drawn the attention of researchers and policy-makers. During the last years, novel digital data streams have drastically changed the research in this field. In the current study, we exploit information extracted from Global Data on Events, Location, and Tone (GDELT) digital news database, to capture peacefulness through the Global Peace Index (GPI). Applying predictive machine learning models, we demonstrate that news media attention from GDELT can be used as a proxy for measuring GPI at a monthly level. Additionally, we use the SHAP methodology to obtain the most important variables that drive the predictions. This analysis highlights each country's profile and provides explanations for the predictions overall, and particularly for the errors and the events that drive these errors. We believe that digital data exploited by Social Good researchers, policy-makers, and peace-builders, with data science tools as powerful as machine learning, could contribute to maximize the societal benefits and minimize the risks to peacefulness.Source: ISTI Research Report, SoBigData++, 2021
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: arxiv.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2021 Report Open Access OPEN

Improving vehicles' emissions reduction policies by targeting gross polluters
Böhm M., Nanni M., Pappalardo L.
Vehicles' emissions produce a significant share of cities' air pollution, with a substantial impact on the environment and human health. Traditional emission estimation methods use remote sensing stations, missing vehicles' full driving cycle, or focus on a few vehicles. This study uses GPS traces and a microscopic model to analyse the emissions of four air pollutants from thousands of vehicles in three European cities. We discover the existence of gross polluters, vehicles responsible for the greatest quantity of emissions, and grossly polluted roads, which suffer the greatest amount of emissions. Our simulations show that emissions reduction policies targeting gross polluters are way more effective than those limiting circulation based on a non-informed choice of vehicles. Our study applies to any city and may contribute to shaping the discussion on how to measure emissions with digital data.Source: ISTI Research Report, SoBigData++, 2021
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: arxiv.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2021 Conference article Open Access OPEN

Quantifying the presence of air pollutants over a road network in high spatio-temporal resolution
Böhm M., Nanni M., Pappalardo L.
Monitoring air pollution plays a key role when trying to reduce its impact on the environment and on human health. Traditionally, two main sources of information about the quantity of pollutants over a city are used: monitoring stations at ground level (when available), and satellites' remote sensing. In addition to these two, other methods have been developed in the last years that aim at understanding how traffic emissions behave in space and time at a finer scale, taking into account the human mobility patterns. We present a simple and versatile framework for estimating the quantity of four air pollutants (CO2, NOx, PM, VOC) emitted by private vehicles moving on a road network, starting from raw GPS traces and information about vehicles' fuel type, and use this framework for analyses on how such pollutants distribute over the road networks of different cities.Source: NeurIPS 2020 Workshop - Tackling Climate Change with Machine Learning, Online conference, 11/12/2020
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: ISTI Repository Open Access | CNR ExploRA Open Access | www.climatechange.ai Open Access


2021 Journal article Open Access OPEN

A mechanistic data-driven approach to synthesize human mobility considering the spatial, temporal, and social dimensions together
Cornacchia G., Pappalardo L.
Modelling human mobility is crucial in several areas, from urban planning to epidemic modelling, traffic forecasting, and what-if analysis. Existing generative models focus mainly on reproducing the spatial and temporal dimensions of human mobility, while the social aspect, though it influences human movements significantly, is often neglected. Those models that capture some social perspectives of human mobility utilize trivial and unrealistic spatial and temporal mechanisms. In this paper, we propose the Spatial, Temporal and Social Exploration and Preferential Return model (STS-EPR), which embeds mechanisms to capture the spatial, temporal, and social aspects together. We compare the trajectories produced by STS-EPR with respect to real-world trajectories and synthetic trajectories generated by two state-of-the-art generative models on a set of standard mobility measures. Our experiments conducted on an open dataset show that STS-EPR, overall, outperforms existing spatial-temporal or social models demonstrating the importance of modelling adequately the sociality to capture precisely all the other dimensions of human mobility. We further investigate the impact of the tile shape of the spatial tessellation on the performance of our model. STS-EPR, which is open-source and tested on open data, represents a step towards the design of a mechanistic data-driven model that captures all the aspects of human mobility comprehensively.Source: ISPRS international journal of geo-information 10 (2021). doi:10.3390/ijgi10090599
DOI: 10.3390/ijgi10090599
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: ISTI Repository Open Access | CNR ExploRA Open Access | www.mdpi.com Open Access


2020 Report Open Access OPEN

Mobile phone data analytics against the COVID-19 epidemics in Italy: flow diversity and local job markets during the national lockdown
Bonato P., Cintia P., Fabbri F., Fadda D., Giannotti F., Lopalco P. L., Mazzilli S., Nanni M., Pappalardo L., Pedreschi D., Penone F., Rinzivillo S., Rossetti G., Savarese M., Tavoschi L.
Understanding human mobility patterns is crucial to plan the restart of production and economic activities, which are currently put in "stand-by" to fight the diffusion of the epidemics. A recent analysis shows that, following the national lockdown of March 9th, the mobility fluxes have decreased by 50% or more, everywhere in the country. To this purpose, we use mobile phone data to compute the movements of people between Italian provinces, and we analyze the incoming, outcoming and internal mobility flows before and during the national lockdown (March 9th, 2020) and after the closure of non-necessary productive and economic activities (March 23th, 2020). The population flow across provinces and municipalities enable for the modeling of a risk index tailored for the mobility of each municipality or province. Such an index would be a useful indicator to drive counter-measures in reaction to a sudden reactivation of the epidemics. Mobile phone data, even when aggregated to preserve the privacy of individuals, are a useful data source to track the evolution in time of human mobility, hence allowing for monitoring the effectiveness of control measures such as physical distancing. In this report, we address the following analytical questions: How does the mobility structure of a territory change? Do incoming and outcoming flows become more predictable during the lockdown, and what are the differences between weekdays and weekends? Can we detect proper local job markets based on human mobility flows, to eventually shape the borders of a local outbreak?Source: ISTI Technical Reports 005/2020, 2020, 2020
DOI: 10.32079/isti-tr-2020/005

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2020 Journal article Open Access OPEN

(So) Big Data and the transformation of the city
Andrienko G., Andrienko N., Boldrini C., Caldarelli G., Cintia P., Cresci S., Facchini A., Giannotti F., Gionis A., Guidotti R., Mathioudakis M., Muntean C. I., Pappalardo L., Pedreschi D., Pournaras E., Pratesi F., Tesconi M., Trasarti R.
The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the "City of Citizens" thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality.Source: International Journal of Data Science and Analytics (Print) 1 (2020). doi:10.1007/s41060-020-00207-3
DOI: 10.1007/s41060-020-00207-3
Project(s): SoBigData via OpenAIRE, SoBigData-PlusPlus via OpenAIRE

See at: Aaltodoc Publication Archive Open Access | HELDA - Digital Repository of the University of Helsinki Open Access | Archivio istituzionale della ricerca - Università degli Studi di Venezia Ca' Foscari Open Access | link.springer.com Open Access | International Journal of Data Science and Analytics Open Access | City Research Online Open Access | ISTI Repository Open Access | Fraunhofer-ePrints Open Access | CNR ExploRA Open Access | International Journal of Data Science and Analytics Restricted | Archivio della Ricerca - Università di Pisa Restricted | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted


2020 Journal article Open Access OPEN

Measuring objective and subjective well-being: Dimensions and data sources
Voukelatou V., Gabrielli L., Miliou I., Cresci S., Sharma R., Tesconi M., Pappalardo L.
Well-being is an important value for people's lives, and it could be considered as an index of societal progress. Researchers have suggested two main approaches for the overall measurement of well-being, the objective and the subjective well-being. Both approaches, as well as their relevant dimensions, have been traditionally captured with surveys. During the last decades, new data sources have been suggested as an alternative or complement to traditional data. This paper aims to present the theoretical background of well-being, by distinguishing between objective and subjective approaches, their relevant dimensions, the new data sources used for their measurement and relevant studies. We also intend to shed light on still barely unexplored dimensions and data sources that could potentially contribute as a key for public policing and social development.Source: International Journal of Data Science and Analytics (Print) (2020). doi:10.1007/s41060-020-00224-2
DOI: 10.1007/s41060-020-00224-2
Project(s): SoBigData via OpenAIRE

See at: link.springer.com Open Access | International Journal of Data Science and Analytics Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted


2020 Journal article Restricted

Human migration: the big data perspective
Sîrbu A., Andrienko G., Andrienko N., Boldrini C., Conti M., Giannotti F., Guidotti R., Bertoli S., Kim J., Muntean C. I., Pappalardo L., Passarella A., Pedreschi D., Pollacci L., Pratesi F., Sharma R.
How can big data help to understand the migration phenomenon? In this paper, we try to answer this question through an analysis of various phases of migration, comparing traditional and novel data sources and models at each phase. We concentrate on three phases of migration, at each phase describing the state of the art and recent developments and ideas. The first phase includes the journey, and we study migration flows and stocks, providing examples where big data can have an impact. The second phase discusses the stay, i.e. migrant integration in the destination country. We explore various data sets and models that can be used to quantify and understand migrant integration, with the final aim of providing the basis for the construction of a novel multi-level integration index. The last phase is related to the effects of migration on the source countries and the return of migrants.Source: International Journal of Data Science and Analytics (Online) (2020). doi:10.1007/s41060-020-00213-5
DOI: 10.1007/s41060-020-00213-5
Project(s): SoBigData via OpenAIRE

See at: International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted | International Journal of Data Science and Analytics Restricted | link.springer.com | CNR ExploRA


2020 Report Open Access OPEN

The relationship between human mobility and viral transmissibility during the COVID-19 epidemics in Italy
Cintia P., Fadda D., Giannotti F., Pappalardo L., Rossetti G., Pedreschi D., Rinzivillo S., Bonato P., Fabbri F., Penone F., Bavarese M., Checchi D., Chiaromonte F., Vineis P., Gazzetta G., Riccardo F., Marziano V., Poletti P., Trentini F., Bella A., Xanthi A., Del Manso M., Fabiani M., Bellino S., Boros S., Urdiales A. M., Vescia M. F., Brusaferro S., Rezza G., Pezzotti P., Ajelli M., Merler S.
We describe in this report our studies to understand the relationship between human mobility and the spreading of COVID-19, as an aid to manage the restart of the social and economic activities after the lockdown and monitor the epidemics in the coming weeks and months. We compare the evolution (from January to May 2020) of the daily mobility flows in Italy, measured by means of nation-wide mobile phone data, and the evolution of transmissibility, measured by the net reproduction number, i.e., the mean number of secondary infections generated by one primary infector in the presence of control interventions and human behavioural adaptations. We find a striking relationship between the negative variation of mobility flows and the net reproduction number, in all Italian regions, between March 11th and March 18th, when the country entered the lockdown. This observation allows us to quantify the time needed to "switch off" the country mobility (one week) and the time required to bring the net reproduction number below 1 (one week). A reasonably simple regression model provides evidence that the net reproduction number is correlated with a region's incoming, outgoing and internal mobility. We also find a strong relationship between the number of days above the epidemic threshold before the mobility flows reduce significantly as an effect of lockdowns, and the total number of confirmed SARS-CoV-2 infections per 100k inhabitants, thus indirectly showing the effectiveness of the lockdown and the other non-pharmaceutical interventions in the containment of the contagion. Our study demonstrates the value of "big" mobility data to the monitoring of key epidemic indicators to inform choices as the epidemics unfolds in the coming months.Project(s): SoBigData via OpenAIRE

See at: arxiv.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2020 Software Unknown

Exploring spatio-temporal soccer events using public event data
Pappalardo L., Rossi A., Cintia P.
Software for the exploration of an open collection of soccer-logs described in the following paper: (PCR2019) Pappalardo, L., Cintia, P., Rossi, A. et al. A public data set of spatio-temporal match events in soccer competitions. Nature Scientific Data 6, 236 (2019). https://doi.org/10.1038/s41597-019-0247-7Project(s): SoBigData via OpenAIRE

See at: github.com | CNR ExploRA


2020 Other Unknown

Teaching material for course Mobility Data Analysis
Pappalardo L.
Material (Jupyter notebooks, Python scripts, data, documentation) for the practical lessons of the course "Mobility Data Analysis" at the Master in Big Data Analytics & Social Mining of the University of Pisa.

See at: github.com | CNR ExploRA


2020 Other Unknown

Human mobility analysis and simulation with Python
Pappalardo L., Simini F., Barlacchi G., Pellungrini R.
The availability of geo-spatial mobility data (e.g., GPS traces, call detail and social media records) is a trend that will grow in the near future. For this reason, understanding human mobility is of paramount importance for many present and future applications, such as traffic forecasting, urban planning, and epidemic modeling, and hence for many actors, from urban planners to decision-makers and advertising companies. In this hands-on tutorial at the Applied Machine Learning Days 2020 (AMLD2020) we present, with a strong focus on code implementation, an overview of the fundamental principles underlying the analysis of big mobility data. Starting from mobility data describing the whereabouts of individuals on a territory for a large-enough observation window, we drive the audience through the extraction of mobility patterns and measures by using scikit-mobility, a specific Python library designed by the tutorial presenters. The library allows the user to: filter and clean raw mobility data by using standard techniques proposed in the mobility data mining literature; analyze mobility data by using the main measures characterizing human mobility patterns (e.g., radius of gyration, daily motifs, mobility entropy); simulate individual and collective mobility by executing the most common human mobility models (e.g., gravity and radiation models, exploration and preferential return model); assess the privacy risk related to the analysis of a real-world mobility data set. Since it is supposed to be a practical hands-on tutorial, for every concept presented during the training we show a practical code example presented through the Jupyter notebook. scikit-mobility is a starting point for the development of urban simulation and what-if analysis, e.g., simulating changes in urban mobility after the construction of a new infrastructure or when traumatic events occur like epidemic diffusion, terrorist attacks or international events.Project(s): SoBigData via OpenAIRE

See at: github.com | CNR ExploRA


2020 Master thesis Unknown

Modeling Human Mobility considering Spatial, Temporal and Social Dimensions
Cornacchia G.
The analysis of human mobility is crucial in several areas, from urban planning to epidemic modeling, estimation of migratory flows and traffic forecasting.However, mobility data (e.g., Call Detail Records and GPS traces from vehicles or smartphones) are sensitive since it is possible to infer personal information even from anonymized datasets.A solution to dealing with this privacy issue is to use synthetic and realistic trajectories generated by proper generative models.Existing mechanistic generative models usually consider the spatial and temporal dimensions only. In this thesis, we select as a baseline model GeoSim, which considers the social dimension together with spatial and temporal dimensions during the generation of the synthetic trajectories.Our contribution in the field of the human mobility consists of including, incrementally, three mobility mechanisms, specifically the introduction of the distance and the use of a gravity-model in the location selection phase, finally, we include a diary generator, an algorithm capable to capture the tendency of humans to follow or break their routine, improving the modeling capability of the GeoSim model.
We show that the three implemented models, obtained from GeoSim with the introduction of the mobility mechanisms, can reproduce the statistical proprieties of real trajectories, in all the three dimensions, more accurately than GeoSim.
Project(s): SoBigData via OpenAIRE

See at: etd.adm.unipi.it | CNR ExploRA


2020 Master thesis Unknown

Generative Models of Human Mobility based on Deep Learning
Briganti S.
Goal of the thesis is the generation of synthetic human mobility based on Deep Learning. Three different generative recurrent models have been implemented: a Seq2Seq Variational Autoencoder (VAE), a Generative Adversarial Network (GAN) and a Wasserstein GAN. The aim of this study is the generation of a synthetic dataset of GPS trajectories having characteristics and typical measures proper of the real human mobility. Scopo della tesi è la generazione di mobilità umana sintetica basata suDeep Learning. Sono stati implementati tre modelli generativi: un Seq2Seq Variational Autoencoder (VAE), una Generative Adversarial Network (GAN) e una Wasserstein GAN. Obiettivo finale dello studio è lagenerazione di un dataset sintetico di traiettorie GPS, avente caratteristiche e misure proprie della mobilità umana.Project(s): SoBigData via OpenAIRE

See at: etd.adm.unipi.it | CNR ExploRA