01834nas a2200145   4500008004100000245006100041210006100102260001300163520137000176100001401546700001701560700002101577700002301598856006701621       2020                        eng d00aDigital Footprints of International Migration on Twitter0 aDigital Footprints of International Migration on Twitter  bSpringer3 aStudying migration using traditional data has some limitations. To date, there have been several studies proposing innovative methodologies to measure migration stocks and flows from social big data. Nevertheless, a uniform definition of a migrant is difficult to find as it varies from one work to another depending on the purpose of the study and nature of the dataset used. In this work, a generic methodology is developed to identify migrants within the Twitter population. This describes a migrant as a person who has the current residence different from the nationality. The residence is defined as the location where a user spends most of his/her time in a certain year. The nationality is inferred from linguistic and social connections to a migrant’s country of origin. This methodology is validated first with an internal gold standard dataset and second with two official statistics, and shows strong performance scores and correlation coefficients. Our method has the advantage that it can identify both immigrants and emigrants, regardless of the origin/destination countries. The new methodology can be used to study various aspects of migration, including opinions, integration, attachment, stocks and flows, motivations for migration, etc. Here, we exemplify how trending topics across and throughout different migrant communities can be observed.1 aKim, Jisu1 aSirbu, Alina1 aGiannotti, Fosca1 aGabrielli, Lorenzo  uhttps://link.springer.com/chapter/10.1007/978-3-030-44584-3_2201528nas a2200145   4500008004100000245009700041210006900138260000900207520102400216100001901240700002101259700002301280700002101303856005801324       2020                        eng d00aEstimating countries’ peace index through the lens of the world news as monitored by GDELT0 aEstimating countries peace index through the lens of the world n  c20203 aPeacefulness is a principal dimension of well-being, and its measurement has lately drawn the attention of researchers and policy-makers. During the last years, novel digital data streams have drastically changed 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 machine learning techniques, we demonstrate that news media attention, sentiment, and social stability from GDELT can be used as proxies for measuring GPI at a monthly level. Additionally, through the variable importance analysis, we show that each country's socio-economic, political, and military profile emerges. This could bring added value to researchers interested in "Data Science for Social Good", to policy-makers, and peacekeeping organizations since they could monitor peacefulness almost real-time, and therefore facilitate timely and more efficient policy-making.
1 aVoukelatou, V.1 aPappalardo, Luca1 aGabrielli, Lorenzo1 aGiannotti, Fosca  uhttps://ieeexplore.ieee.org/abstract/document/926005201574nas a2200193   4500008004100000020001400041245005500055210005400110260001600164300001100180490000800191520100500199100002301204700002301227700001801250700001901268700002101287856007201308       2020                        eng d  a0169-023X00aPRIMULE: Privacy risk mitigation for user profiles0 aPRIMULE Privacy risk mitigation for user profiles  c2020/01/01/  a1017860 v1253 aThe availability of mobile phone data has encouraged the development of different data-driven tools, supporting social science studies and providing new data sources to the standard official statistics. However, this particular kind of data are subject to privacy concerns because they can enable the inference of personal and private information. In this paper, we address the privacy issues related to the sharing of user profiles, derived from mobile phone data, by proposing PRIMULE, a privacy risk mitigation strategy. Such a method relies on PRUDEnce (Pratesi et al., 2018), a privacy risk assessment framework that provides a methodology for systematically identifying risky-users in a set of data. An extensive experimentation on real-world data shows the effectiveness of PRIMULE strategy in terms of both quality of mobile user profiles and utility of these profiles for analytical services such as the Sociometer (Furletti et al., 2013), a data mining tool for city users classification.1 aPratesi, Francesca1 aGabrielli, Lorenzo1 aCintia, Paolo1 aMonreale, Anna1 aGiannotti, Fosca  uhttps://www.sciencedirect.com/science/article/pii/S0169023X1830534201603nas a2200193   4500008004100000245007700041210006900118260001300187520097300200100002301173700001901196700002101215700001701236700002401253700002001277700002101297700002401318856006701342       2018                        eng d00aDiscovering Mobility Functional Areas: A Mobility Data Analysis Approach0 aDiscovering Mobility Functional Areas A Mobility Data Analysis A  bSpringer3 aHow 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.1 aGabrielli, Lorenzo1 aFadda, Daniele1 aRossetti, Giulio1 aNanni, Mirco1 aPiccinini, Leonardo1 aPedreschi, Dino1 aGiannotti, Fosca1 aLattarulo, Patrizia  uhttps://link.springer.com/chapter/10.1007/978-3-319-73198-8_2702705nas a2200181   4500008004100000245007900041210006900120260001200189300000600201490000600207520211900213100002302332700002302355700001902378700002002397700002102417856008502438       2018                        eng d00aDiscovering temporal regularities in retail customers’ shopping behavior0 aDiscovering temporal regularities in retail customers shopping b  c01/2018  a60 v73 aIn this paper we investigate the regularities characterizing the temporal purchasing behavior of the customers of a retail market chain. Most of the literature studying purchasing behavior focuses on what customers buy while giving few importance to the temporal dimension. As a consequence, the state of the art does not allow capturing which are the temporal purchasing patterns of each customers. These patterns should describe the customer’s temporal habits highlighting when she typically makes a purchase in correlation with information about the amount of expenditure, number of purchased items and other similar aggregates. This knowledge could be exploited for different scopes: set temporal discounts for making the purchases of customers more regular with respect the time, set personalized discounts in the day and time window preferred by the customer, provide recommendations for shopping time schedule, etc. To this aim, we introduce a framework for extracting from personal retail data a temporal purchasing profile able to summarize whether and when a customer makes her distinctive purchases. The individual profile describes a set of regular and characterizing shopping behavioral patterns, and the sequences in which these patterns take place. We show how to compare different customers by providing a collective perspective to their individual profiles, and how to group the customers with respect to these comparable profiles. By analyzing real datasets containing millions of shopping sessions we found that there is a limited number of patterns summarizing the temporal purchasing behavior of all the customers, and that they are sequentially followed in a finite number of ways. Moreover, we recognized regular customers characterized by a small number of temporal purchasing behaviors, and changing customers characterized by various types of temporal purchasing behaviors. Finally, we discuss on how the profiles can be exploited both by customers to enable personalized services, and by the retail market chain for providing tailored discounts based on temporal purchasing regularity.1 aGuidotti, Riccardo1 aGabrielli, Lorenzo1 aMonreale, Anna1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-018-0133-001833nas a2200169   4500008004100000245005500041210005500096260000700151300000900158490000700167520134100174100002701515700002101542700002301563700002801586856004901614       2018                        eng d00aGravity and scaling laws of city to city migration0 aGravity and scaling laws of city to city migration  c07  a1-190 v133 aModels of human migration provide powerful tools to forecast the flow of migrants, measure the impact of a policy, determine the cost of physical and political frictions and more. Here, we analyse the migration of individuals from and to cities in the US, finding that city to city migration follows scaling laws, so that the city size is a significant factor in determining whether, or not, an individual decides to migrate and the city size of both the origin and destination play key roles in the selection of the destination. We observe that individuals from small cities tend to migrate more frequently, tending to move to similar-sized cities, whereas individuals from large cities do not migrate so often, but when they do, they tend to move to other large cities. Building upon these findings we develop a scaling model which describes internal migration as a two-step decision process, demonstrating that it can partially explain migration fluxes based solely on city size. We then consider the impact of distance and construct a gravity-scaling model by combining the observed scaling patterns with the gravity law of migration. Results show that the scaling laws are a significant feature of human migration and that the inclusion of scaling can overcome the limits of the gravity and the radiation models of human migration.1 aCuriel, Rafael, Prieto1 aPappalardo, Luca1 aGabrielli, Lorenzo1 aBishop, Steven, Richard  uhttps://doi.org/10.1371/journal.pone.019989201594nas a2200169   4500008004100000245007800041210006900119260001200188300000700200490000600207520108600213100002201299700002201321700001801343700002301361856004001384       2017                        eng d00aDiscovering and Understanding City Events with Big Data: The Case of Rome0 aDiscovering and Understanding City Events with Big Data The Case  c06/2017  a740 v83 aThe increasing availability of large amounts of data and digital footprints has given rise
to ambitious research challenges in many fields, which spans from medical research, financial and
commercial world, to people and environmental monitoring. Whereas traditional data sources and
census fail in capturing actual and up-to-date behaviors, Big Data integrate the missing knowledge
providing useful and hidden information to analysts and decision makers. With this paper, we focus
on the identification of city events by analyzing mobile phone data (Call Detail Record), and we study
and evaluate the impact of these events over the typical city dynamics. We present an analytical
process able to discover, understand and characterize city events from Call Detail Record, designing
a distributed computation to implement Sociometer, that is a profiling tool to categorize phone users.
The methodology provides an useful tool for city mobility manager to manage the events and taking
future decisions on specific classes of users, i.e., residents, commuters and tourists.1 aFurletti, Barbara1 aTrasarti, Roberto1 aCintia, Paolo1 aGabrielli, Lorenzo  uhttps://doi.org/10.3390/info803007401370nas a2200121   4500008004100000245008000041210006900121260001300190520093200203100002301135700002301158856006701181       2017                        eng d00aRecognizing Residents and Tourists with Retail Data Using Shopping Profiles0 aRecognizing Residents and Tourists with Retail Data Using Shoppi  bSpringer3 aThe huge quantity of personal data stored by service providers registering customers daily life enables the analysis of individual fingerprints characterizing the customers’ behavioral profiles. We propose
a framework for recognizing residents, tourists and occasional shoppers
among the customers of a retail market chain. We employ our recognition framework on a real massive dataset containing the shopping transactions of more than one million of customers, and we identify representative temporal shopping profiles for residents, tourists and occasional
customers. Our experiments show that even though residents are about
33% of the customers they are responsible for more than 90% of the expenditure. We statistically validate the number of residents and tourists
with national official statistics enabling in this way the adoption of our
recognition framework for the development of novel services and analysis.1 aGuidotti, Riccardo1 aGabrielli, Lorenzo  uhttps://link.springer.com/chapter/10.1007/978-3-319-76111-4_3500594nas a2200181   4500008004100000245009200041210006900133300001400202490000600216100002200222700002300244700002000267700002300287700002200310700001700332700001700349856004600366       2017                        eng d00aScalable and flexible clustering solutions for mobile phone-based population indicators0 aScalable and flexible clustering solutions for mobile phonebased  a285–2990 v41 aLulli, Alessandro1 aGabrielli, Lorenzo1 aDazzi, Patrizio1 aDell'Amico, Matteo1 aMichiardi, Pietro1 aNanni, Mirco1 aRicci, Laura  uhttps://doi.org/10.1007/s41060-017-0065-y01717nas a2200181   4500008004100000245007400041210006900115300001200184490000600196520110000202100002101302700002101323700002301344700002201367700002001389700002101409856010501430       2016                        eng d00aAn analytical framework to nowcast well-being using mobile phone data0 aanalytical framework to nowcast wellbeing using mobile phone dat  a75–920 v23 aAn intriguing open question is whether measurements derived from Big Data recording human activities can yield high-fidelity proxies of socio-economic development and well-being. Can we monitor and predict the socio-economic development of a territory just by observing the behavior of its inhabitants through the lens of Big Data? In this paper, we design a data-driven analytical framework that uses mobility measures and social measures extracted from mobile phone data to estimate indicators for socio-economic development and well-being. We discover that the diversity of mobility, defined in terms of entropy of the individual users’ trajectories, exhibits (i) significant correlation with two different socio-economic indicators and (ii) the highest importance in predictive models built to predict the socio-economic indicators. Our analytical framework opens an interesting perspective to study human behavior through the lens of Big Data by means of new statistical indicators that quantify and possibly “nowcast” the well-being and the socio-economic development of a territory.1 aPappalardo, Luca1 aVanhoof, Maarten1 aGabrielli, Lorenzo1 aSmoreda, Zbigniew1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/analytical-framework-nowcast-well-being-using-mobile-phone-data01050nas a2200157   4500008004100000245004700041210004700088260003800135300001400173520054400187100002100731700002300752700002000775700001800795856007900813       2016                        eng d00aUnderstanding human mobility with big data0 aUnderstanding human mobility with big data  bSpringer International Publishing  a208–2203 aThe paper illustrates basic methods of mobility data mining, designed to extract from the big mobility data the patterns of collective movement behavior, i.e., discover the subgroups of travelers characterized by a common purpose, profiles of individual movement activity, i.e., characterize the routine mobility of each traveler. We illustrate a number of concrete case studies where mobility data mining is put at work to create powerful analytical services for policy makers, businesses, public administrations, and individual citizens.1 aGiannotti, Fosca1 aGabrielli, Lorenzo1 aPedreschi, Dino1 aRinzivillo, S  uhttps://kdd.isti.cnr.it/publications/understanding-human-mobility-big-data01157nas a2200157   4500008004100000245005600041210005300097260003600150520061100186100002300797700002200820700002200842700002100864700002000885856009400905       2015                        eng d00aCity users’ classification with mobile phone data0 aCity users classification with mobile phone data  aSanta Clara (CA) - USAc11/20153 aNowadays mobile phone data are an actual proxy for studying the users’ social life and urban dynamics. In this paper we present the Sociometer, and analytical framework aimed at classifying mobile phone users into behavioral categories by means of their call habits. The analytical process starts from spatio-temporal profiles, learns the different behaviors, and returns annotated profiles. After the description of the methodology and its evaluation, we present an application of the Sociometer for studying city users of one small and one big city, evaluating the impact of big events in these cities.1 aGabrielli, Lorenzo1 aFurletti, Barbara1 aTrasarti, Roberto1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/city-users%E2%80%99-classification-mobile-phone-data01393nas a2200169   4500008004100000245005700041210005700098260002000155520084000175100002201015700002301037700002201060700002201082700002101104700002201125856007601147       2015                        eng d00aDetecting and understanding big events in big cities0 aDetecting and understanding big events in big cities  aBostonc04/20153 aRecent studies have shown the great potential of big data such as mobile phone location data to model human behavior. Big data allow to analyze people presence in a territory in a fast and effective way with respect to the classical surveys (diaries or questionnaires). One of the drawbacks of these collection systems is incompleteness of the users' traces; people are localized only when they are using their phones. In this work we define a data mining method for identifying people presence and understanding the impact of big events in big cities. We exploit the ability of the Sociometer for classifying mobile phone users in mobility categories through their presence profile. The experiment in cooperation with Orange Telecom has been conduced in Paris during the event F^ete de la Musique using a
privacy preserving protocol.1 aFurletti, Barbara1 aGabrielli, Lorenzo1 aTrasarti, Roberto1 aSmoreda, Zbigniew1 aVanhoof, Maarten1 aZiemlicki, Cezary  uhttp://www.netmob.org/assets/img/netmob15_book_of_abstracts_posters.pdf00674nas a2200205   4500008004100000245006100041210006000102300001400162490000700176100002300183700002100206700002000227700002000247700002100267700002000288700001800308700002100326700002300347856009800370       2015                        eng d00aSmall Area Model-Based Estimators Using Big Data Sources0 aSmall Area ModelBased Estimators Using Big Data Sources  a263–2810 v311 aMarchetti, Stefano1 aGiusti, Caterina1 aPratesi, Monica1 aSalvati, Nicola1 aGiannotti, Fosca1 aPedreschi, Dino1 aRinzivillo, S1 aPappalardo, Luca1 aGabrielli, Lorenzo  uhttps://kdd.isti.cnr.it/publications/small-area-model-based-estimators-using-big-data-sources01297nas a2200193   4500008004100000022002200041245006500063210006500128260003800193300001200231490000900243520068200252100002300934700002200957700002100979700001701000700001801017856006801035       2015                        eng d  a978-3-319-15200-400aUse of Mobile Phone Data to Estimate Visitors Mobility Flows0 aUse of Mobile Phone Data to Estimate Visitors Mobility Flows  bSpringer International Publishing  a214-2260 v89383 aBig Data originating from the digital breadcrumbs of human activities, sensed as by-product of the technologies that we use for our daily activities, allows us to observe the individual and collective behavior of people at an unprecedented detail. Many dimensions of our social life have big data “proxies”, such as the mobile calls data for mobility. In this paper we investigate to what extent data coming from mobile operators could be a support in producing reliable and timely estimates of intra-city mobility flows. The idea is to define an estimation method based on calling data to characterize the mobility habits of visitors at the level of a single municipality.1 aGabrielli, Lorenzo1 aFurletti, Barbara1 aGiannotti, Fosca1 aNanni, Mirco1 aRinzivillo, S  uhttp://link.springer.com/chapter/10.1007%2F978-3-319-15201-1_1400498nas a2200157   4500008003900000024001300039245005600052210005500108260002800163100002200191700002200213700002300235700001700258700002000275856004500295       2014                          d  aVol-113300aBig data analytics for smart mobility: a case study0 aBig data analytics for smart mobility a case study  aAthens, Greecec03/20141 aFurletti, Barbara1 aTrasarti, Roberto1 aGabrielli, Lorenzo1 aNanni, Mirco1 aPedreschi, Dino  uhttp://ceur-ws.org/Vol-1133/paper-57.pdf01818nas a2200205   4500008003900000245002300039210002300062260001500085300000900100520128200109100001701391700002201408700001801430700002201448700001801470700002301488700001801511700002101529856006201550       2014                          d00aMobility Profiling0 aMobility Profiling  bIGI Global  a1-293 aThe ability to understand the dynamics of human mobility is crucial for tasks like urban planning and transportation management. The recent rapidly growing availability of large spatio-temporal datasets gives us the possibility to develop sophisticated and accurate analysis methods and algorithms that can enable us to explore several relevant mobility phenomena: the distinct access paths to a territory, the groups of persons that move together in space and time, the regions of a territory that contains a high density of traffic demand, etc. All these paradigmatic perspectives focus on a collective view of the mobility where the interesting phenomenon is the result of the contribution of several moving objects. In this chapter, the authors explore a different approach to the topic and focus on the analysis and understanding of relevant individual mobility habits in order to assign a profile to an individual on the basis of his/her mobility. This process adds a semantic level to the raw mobility data, enabling further analyses that require a deeper understanding of the data itself. The studies described in this chapter are based on two large datasets of spatio-temporal data, originated, respectively, from GPS-equipped devices and from a mobile phone network. 1 aNanni, Mirco1 aTrasarti, Roberto1 aCintia, Paolo1 aFurletti, Barbara1 aRenso, Chiara1 aGabrielli, Lorenzo1 aRinzivillo, S1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/mobility-profiling-000505nas a2200145   4500008004100000245008100041210006900122100001800191700002300209700001700232700002100249700002000270700002100290856004800311       2014                        eng d00aThe purpose of motion: Learning activities from Individual Mobility Networks0 apurpose of motion Learning activities from Individual Mobility N1 aRinzivillo, S1 aGabrielli, Lorenzo1 aNanni, Mirco1 aPappalardo, Luca1 aPedreschi, Dino1 aGiannotti, Fosca  uhttp://dx.doi.org/10.1109/DSAA.2014.705809002456nas a2200205   4500008003900000020002200039245014900061210006900210260002300279520172700302100002202029700002302051700002102074700001902095700001702114700002002131700001902151700002302170856005702193       2014                          d  a978-88-8467-874-400aUse of mobile phone data to estimate mobility flows. Measuring urban population and inter-city mobility using big data in an integrated approach0 aUse of mobile phone data to estimate mobility flows Measuring ur  aCagliari c06/20143 aThe Big Data, originating from the digital breadcrumbs of human activi-
ties, sensed as a by-product of the technologies that we use for our daily activities, let
us to observe the individual and collective behavior of people at an unprecedented
detail. Many dimensions of our social life have big data “proxies”, as the mobile
calls data for mobility. In this paper we investigate to what extent such ”big data”,
in integration with administrative ones, could be a support in producing reliable and
timely estimates of inter-city mobility. The study has been jointly developed by Is-
tat, CNR, University of Pisa in the range of interest of the “Commssione di studio
avente il compito di orientare le scelte dellIstat sul tema dei Big Data ”. In an on-
going project at ISTAT, called “Persons and Places” – based on an integration of
administrative data sources, it has been produced a first release of Origin Destina-
tion matrix – at municipality level – assuming that the places of residence and that
of work (or study) be the terminal points of usual individual mobility for work or
study. The coincidence between the city of residence and that of work (or study) –
is considered as a proxy of the absence of intercity mobility for a person (we define
him a static resident). The opposite case is considered as a proxy of presence of mo-
bility (the person is a dynamic resident: commuter or embedded). As administrative
data do not contain information on frequency of the mobility, the idea is to specify
an estimate method, using calling data as support, to define for each municipality the
stock of standing residents, embedded city users and daily city users (commuters)1 aFurletti, Barbara1 aGabrielli, Lorenzo1 aGiannotti, Fosca1 aMilli, Letizia1 aNanni, Mirco1 aPedreschi, Dino1 aVivio, Roberta1 aGarofalo, Giuseppe  uhttp://www.sis2014.it/proceedings/allpapers/3026.pdf01202nas a2200145   4500008003900000245006500039210006500104520069100169100002300860700002200883700002100905700001700926700001800943856009500961       2014                          d00aUse of mobile phone data to estimate visitors mobility flows0 aUse of mobile phone data to estimate visitors mobility flows3 aBig Data originating from the digital breadcrumbs of human activities,
sensed as by-product of the technologies that we use for our daily activities, allows
us to observe the individual and collective behavior of people at an unprecedented
detail. Many dimensions of our social life have big data “proxies”, such as the mo-
bile calls data for mobility. In this paper we investigate to what extent data coming
from mobile operators could be a support in producing reliable and timely estimates
of intra-city mobility flows. The idea is to define an estimation method based on
calling data to characterize the mobility habits of visitors at the level of a single
municipality1 aGabrielli, Lorenzo1 aFurletti, Barbara1 aGiannotti, Fosca1 aNanni, Mirco1 aRinzivillo, S  uhttp://www.di.unipi.it/mokmasd/symposium-2014/preproceedings/GabrielliEtAl-mokmasd2014.pdf00525nas a2200133   4500008003900000245007200039210006900111260002800180100002200208700002300230700001800253700001800271856010200289       2013                          d00aAnalysis of GSM Calls Data for Understanding User Mobility Behavior0 aAnalysis of GSM Calls Data for Understanding User Mobility Behav  aSanta Clara, California1 aFurletti, Barbara1 aGabrielli, Lorenzo1 aRenso, Chiara1 aRinzivillo, S  uhttps://kdd.isti.cnr.it/publications/analysis-gsm-calls-data-understanding-user-mobility-behavior02277nas a2200193   4500008004100000245004800041210004600089260001900135520169200154100001701846700002201863700002201885700002301907700002401930700002101954700001901975700001801994856007102012       2013                        eng d00aMP4-A Project: Mobility Planning For Africa0 aMP4A Project Mobility Planning For Africa  aCambridge, USA3 aThis project aims to create a tool that uses mobile phone transaction (trajectory) data that will be able to address transportation related challenges, thus allowing promotion and facilitation of sustainable urban mobility planning in Third World countries. The proposed tool is a transport demand model for Ivory Coast, with emphasis on its major urbanization Abidjan. The consortium will bring together available data from the internet, and integrate these with the mobility data obtained from the mobile phones in order to build the best possible transport model. A transport model allows an understanding of current and future infrastructure requirements in Ivory Coast. As such, this project will provide the first proof of concept. In this context, long-term analysis of individual call traces will be performed to reconstruct systematic movements, and to infer an origin-destination matrix. A similar process will be performed using the locations of caller and recipient of phone calls, enabling the comparison of socio-economic ties vs. mobility. The emerging links between different areas will be used to build an effective map to optimize regional border definitions and road infrastructure from a mobility perspective. Finally, we will try to build specialized origin-destination matrices for specific categories of population. Such categories will be inferred from data through analysis of calling behaviours, and will also be used to characterize the population of different cities. The project also includes a study of data compliance with distributions of standard measures observed in literature, including distribution of calls, call durations and call network features.1 aNanni, Mirco1 aTrasarti, Roberto1 aFurletti, Barbara1 aGabrielli, Lorenzo1 aVan Der Mede, Peter1 aDe Bruijn, Joost1 ade Romph, Erik1 aBruil, Gerard  uhttp://perso.uclouvain.be/vincent.blondel/netmob/2013/D4D-book.pdf00516nas a2200121   4500008003900000245008700039210006900126100002200195700002300217700001800240700001800258856011800276       2013                          d00aPisa Tourism fluxes Observatory: deriving mobility indicators from GSM call habits0 aPisa Tourism fluxes Observatory deriving mobility indicators fro1 aFurletti, Barbara1 aGabrielli, Lorenzo1 aRenso, Chiara1 aRinzivillo, S  uhttps://kdd.isti.cnr.it/publications/pisa-tourism-fluxes-observatory-deriving-mobility-indicators-gsm-call-habits00577nas a2200169   4500008004100000245008100041210006900122100001700191700002200208700002200230700002300252700002400275700002100299700001900320700001800339856005000357       2013                        eng d00aTransportation Planning Based on {GSM} Traces: {A} Case Study on Ivory Coast0 aTransportation Planning Based on GSM Traces A Case Study on Ivor1 aNanni, Mirco1 aTrasarti, Roberto1 aFurletti, Barbara1 aGabrielli, Lorenzo1 aVan Der Mede, Peter1 aDe Bruijn, Joost1 ade Romph, Erik1 aBruil, Gerard  uhttp://dx.doi.org/10.1007/978-3-319-04178-0_200535nas a2200145   4500008003900000245006700039210006600106300001200172100001800184700002900202700002300231700001800254700001900272856009800291       2013                          d00aWhere Have You Been Today? Annotating Trajectories with DayTag0 aWhere Have You Been Today Annotating Trajectories with DayTag  a467-4711 aRinzivillo, S1 aSiqueira, Fernando Lucca1 aGabrielli, Lorenzo1 aRenso, Chiara1 aBogorny, Vania  uhttps://kdd.isti.cnr.it/publications/where-have-you-been-today-annotating-trajectories-daytag00515nas a2200157   4500008003900000245004500039210004400084260002100128100002200149700002200171700002300193700001800216700002100234700002100255856008100276       2012                          d00aAnalisi di Mobilita' con dati eterogenei0 aAnalisi di Mobilita con dati eterogenei  aPisabISTI - CNR1 aFurletti, Barbara1 aTrasarti, Roberto1 aGabrielli, Lorenzo1 aRinzivillo, S1 aPappalardo, Luca1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/analisi-di-mobilita-con-dati-eterogenei01756nas a2200157   4500008003900000020002200039245005600061210005600117260004900173520110400222100002201326700002301348700001801371700001801389856019101407       2012                          d  a978-1-4503-1542-500aIdentifying users profiles from mobile calls habits0 aIdentifying users profiles from mobile calls habits  aBeijing, ChinabACM New York, NY, USA ©20123 aThe huge quantity of positioning data registered by our mobile phones stimulates several research questions, mainly originating from the combination of this huge quantity of data with the extreme heterogeneity of the tracked user and the low granularity of the data. We propose a methodology to partition the users tracked by GSM phone calls into profiles like resident, commuters, in transit and tourists. The methodology analyses the phone calls with a combination of top-down and bottom up techniques where the top-down phase is based on a sequence of queries that identify some behaviors. The bottom-up is a machine learning phase to find groups of similar call behavior, thus refining the previous step. The integration of the two steps results in the partitioning of mobile traces into these four user categories that can be deeper analyzed, for example to understand the tourist movements in city or the traffic effects of commuters. An experiment on the identification of user profiles on a real dataset collecting call records from one month in the city of Pisa illustrates the methodology.1 aFurletti, Barbara1 aGabrielli, Lorenzo1 aRenso, Chiara1 aRinzivillo, S  uhttp://delivery.acm.org/10.1145/2350000/2346500/p17-furletti.pdf?ip=146.48.83.121&acc=ACTIVE%20SERVICE&CFID=166768290&CFTOKEN=58719386&__acm__=1357648050_e23771c2f6bd8feb96bd66b39294175d