TY - CONF T1 - EXPHLOT: EXplainable Privacy assessment for Human LOcation Trajectories T2 - Discovery Science Y1 - 2023 A1 - Francesca Naretto A1 - Roberto Pellungrini A1 - Daniele Fadda A1 - Salvo Rinzivillo JF - Discovery Science ER - TY - CONF T1 - Analysis and Visualization of Performance Indicators in University Admission Tests T2 - Formal Methods. FM 2019 International Workshops Y1 - 2020 A1 - Michela Natilli A1 - Daniele Fadda A1 - S Rinzivillo A1 - Dino Pedreschi A1 - Licari, Federica ED - Sekerinski, Emil ED - Moreira, Nelma ED - Oliveira, José N. ED - Ratiu, Daniel ED - Riccardo Guidotti ED - Farrell, Marie ED - Luckcuck, Matt ED - Marmsoler, Diego ED - Campos, José ED - Astarte, Troy ED - Gonnord, Laure ED - Cerone, Antonio ED - Couto, Luis ED - Dongol, Brijesh ED - Kutrib, Martin ED - Monteiro, Pedro ED - Delmas, David AB - This paper presents an analytical platform for evaluation of the performance and anomaly detection of tests for admission to public universities in Italy. Each test is personalized for each student and is composed of a series of questions, classified on different domains (e.g. maths, science, logic, etc.). Since each test is unique for composition, it is crucial to guarantee a similar level of difficulty for all the tests in a session. For this reason, to each question, it is assigned a level of difficulty from a domain expert. Thus, the general difficultness of a test depends on the correct classification of each item. We propose two approaches to detect outliers. A visualization-based approach using dynamic filter and responsive visual widgets. A data mining approach to evaluate the performance of the different questions for five years. We used clustering to group the questions according to a set of performance indicators to provide labeling of the data-driven level of difficulty. The measured level is compared with the a priori assigned by experts. The misclassifications are then highlighted to the expert, who will be able to refine the question or the classification. Sequential pattern mining is used to check if biases are present in the composition of the tests and their performance. This analysis is meant to exclude overlaps or direct dependencies among questions. Analyzing co-occurrences we are able to state that the composition of each test is fair and uniform for all the students, even on several sessions. The analytical results are presented to the expert through a visual web application that loads the analytical data and indicators and composes an interactive dashboard. The user may explore the patterns and models extracted by filtering and changing thresholds and analytical parameters. JF - Formal Methods. FM 2019 International Workshops PB - Springer International Publishing CY - Cham SN - 978-3-030-54994-7 UR - https://link.springer.com/chapter/10.1007/978-3-030-54994-7_14 ER - TY - ABST T1 - Mobile phone data analytics against the COVID-19 epidemics in Italy: flow diversity and local job markets during the national lockdown Y1 - 2020 A1 - Pietro Bonato A1 - Paolo Cintia A1 - Francesco Fabbri A1 - Daniele Fadda A1 - Fosca Giannotti A1 - Pier Luigi Lopalco A1 - Sara Mazzilli A1 - Mirco Nanni A1 - Luca Pappalardo A1 - Dino Pedreschi A1 - Francesco Penone A1 - S Rinzivillo A1 - Giulio Rossetti A1 - Marcello Savarese A1 - Lara Tavoschi AB - Understanding collective 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. In this report, we use mobile phone data to infer the movements of people between Italian provinces and municipalities, 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 modelling 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. 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? UR - https://arxiv.org/abs/2004.11278 ER - TY - JOUR T1 - The relationship between human mobility and viral transmissibility during the COVID-19 epidemics in Italy JF - arXiv preprint arXiv:2006.03141 Y1 - 2020 A1 - Paolo Cintia A1 - Daniele Fadda A1 - Fosca Giannotti A1 - Luca Pappalardo A1 - Giulio Rossetti A1 - Dino Pedreschi A1 - S Rinzivillo A1 - Bonato, Pietro A1 - Fabbri, Francesco A1 - Penone, Francesco A1 - Savarese, Marcello A1 - Checchi, Daniele A1 - Chiaromonte, Francesca A1 - Vineis , Paolo A1 - Guzzetta, Giorgio A1 - Riccardo, Flavia A1 - Marziano, Valentina A1 - Poletti, Piero A1 - Trentini, Filippo A1 - Bella, Antonio A1 - Andrianou, Xanthi A1 - Del Manso, Martina A1 - Fabiani, Massimo A1 - Bellino, Stefania A1 - Boros, Stefano A1 - Mateo Urdiales, Alberto A1 - Vescio, Maria Fenicia A1 - Brusaferro, Silvio A1 - Rezza, Giovanni A1 - Pezzotti, Patrizio A1 - Ajelli, Marco A1 - Merler, Stefano AB - 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. UR - https://arxiv.org/abs/2006.03141 ER - TY - JOUR T1 - A Visual Analytics Platform to Measure Performance on University Entrance Tests (Discussion Paper) Y1 - 2019 A1 - Boncoraglio, Daniele A1 - Deri, Francesca A1 - Distefano, Francesco A1 - Daniele Fadda A1 - Filippi, Giorgio A1 - Forte, Giuseppe A1 - Licari, Federica A1 - Michela Natilli A1 - Dino Pedreschi A1 - S Rinzivillo ER - TY - CONF T1 - Discovering Mobility Functional Areas: A Mobility Data Analysis Approach T2 - International Workshop on Complex Networks Y1 - 2018 A1 - Lorenzo Gabrielli A1 - Daniele Fadda A1 - Giulio Rossetti A1 - Mirco Nanni A1 - Piccinini, Leonardo A1 - Dino Pedreschi A1 - Fosca Giannotti A1 - Patrizia Lattarulo AB - How do we measure the borders of urban areas and therefore decide which are the functional units of the territory? Nowadays, we typically do that just looking at census data, while in this work we aim to identify functional areas for mobility in a completely data-driven way. Our solution makes use of human mobility data (vehicle trajectories) and consists in an agglomerative process which gradually groups together those municipalities that maximize internal vehicular traffic while minimizing external one. The approach is tested against a dataset of trips involving individuals of an Italian Region, obtaining a new territorial division which allows us to identify mobility attractors. Leveraging such partitioning and external knowledge, we show that our method outperforms the state-of-the-art algorithms. Indeed, the outcome of our approach is of great value to public administrations for creating synergies within the aggregations of the territories obtained. JF - International Workshop on Complex Networks PB - Springer UR - https://link.springer.com/chapter/10.1007/978-3-319-73198-8_27 ER - TY - CONF T1 - Big Data and Public Administration: a case study for Tuscany Airports T2 - SEBD - Italian Symposium on Advanced Database Systems Y1 - 2016 A1 - Barbara Furletti A1 - Daniele Fadda A1 - Leonardo Piccini A1 - Mirco Nanni A1 - Patrizia Lattarulo AB - In the last decade, the fast development of Information and Communication Technologies led to the wide diffusion of sensors able to track various aspects of human activity, as well as the storage and computational capabilities needed to record and analyze them. The so-called Big Data promise to improve the effectiveness of businesses, the quality of urban life, as well as many other fields, including the functioning of public administrations. Yet, translating the wealth of potential information hidden in Big Data to consumable intelligence seems to be still a difficult task, with a limited basis of success stories. This paper reports a project activity centered on a public administration - IRPET, the Regional Institute for Economic Planning of Tuscany (Italy). The paper deals, among other topics, with human mobility and public transportation at a regional scale, summarizing the open questions posed by the Public Administration (PA), the envisioned role that Big Data might have in answering them, the actual challenges that emerged in trying to implement them, and finally the results we obtained, the limitations that emerged and the lessons learned. JF - SEBD - Italian Symposium on Advanced Database Systems PB - Matematicamente.it CY - Ugento, Lecce (Italy) SN - 9788896354889 UR - http://sebd2016.unisalento.it/grid/SEBD2016-proceedings.pdf ER -