2022
Conference article
Open Access
Predicting vehicles parking behaviour for EV recharge optimization
Monteiro De Lira V., Pallonetto F., Gabrielli L., Renso C.
The global electric car sales in 2020 continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises with the objective of estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. This predicted duration feeds the energy management system that will allocate the power over the duration reducing the overall peak electricity demand. We experiment different algorithms and features combination for 4 datasets from 2 different campus facilities in Italy and Brazil. Using both contextual and time of the day features, the overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systems.Source: SEBD 2022 - 30th Italian Symposium on Advanced Database Systems, pp. 199–206, Tirrenia, Pisa, Italy, 19-22/06/2022
Monteiro De Lira V., Pallonetto F., Gabrielli L., Renso C.
The global electric car sales in 2020 continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises with the objective of estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. This predicted duration feeds the energy management system that will allocate the power over the duration reducing the overall peak electricity demand. We experiment different algorithms and features combination for 4 datasets from 2 different campus facilities in Italy and Brazil. Using both contextual and time of the day features, the overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systems.Source: SEBD 2022 - 30th Italian Symposium on Advanced Database Systems, pp. 199–206, Tirrenia, Pisa, Italy, 19-22/06/2022
See at:
ceur-ws.org 
| ISTI Repository | CNR ExploRA
2022
Journal article
Open Access
Did exposure to asylum seeking migration affect the electoral outcome of the 'Alternative für Deutschland' in Berlin? Evidence from the 2019 european elections
Pettrachin A., Gabrielli L., Kim J., Ludwig-Dehm S., Potzschke S.
This article analyses the impact of exposure to asylum-seeking migration during the European 'refugee crisis' on votes for the far-right Alternative für Deutschland at the 2019 European elections in Berlin. While other scholars investigated the relationship between locals' exposure to asylum-seekers and far-right voting, we analyse this relationship at a very small scale (electoral district level), adopting an innovative methodological approach, based on geo-localization techniques and high-resolution spatial statistics. Furthermore, we assess the impact on this relationship of some previously neglected variables. Through spatial regression models, we show that exposure to asylum-seeking migration is negatively correlated with AfD vote shares, which provides support for so-called 'contact theory' and that the relationship is stronger in better-off districts. Remarkably, the relationship is weaker in districts containing bigger reception centres, which suggests that the effects of asylum-seeking migration depend on the perceived contact intensity (and, therefore, a moderating effect of reception centre size). Finally, the effects of districts' socio-economic deprivation on the relationship between exposure to asylum-seeking migration and AfD vote shares is different in districts located in former East and West Berlin, which suggests an effect of socio-cultural history on the relationship between exposure to migration and far-right voting.Source: Journal of ethnic and migration studies (2022). doi:10.1080/1369183X.2022.2100543
DOI: 10.1080/1369183x.2022.2100543
Metrics:
Pettrachin A., Gabrielli L., Kim J., Ludwig-Dehm S., Potzschke S.
This article analyses the impact of exposure to asylum-seeking migration during the European 'refugee crisis' on votes for the far-right Alternative für Deutschland at the 2019 European elections in Berlin. While other scholars investigated the relationship between locals' exposure to asylum-seekers and far-right voting, we analyse this relationship at a very small scale (electoral district level), adopting an innovative methodological approach, based on geo-localization techniques and high-resolution spatial statistics. Furthermore, we assess the impact on this relationship of some previously neglected variables. Through spatial regression models, we show that exposure to asylum-seeking migration is negatively correlated with AfD vote shares, which provides support for so-called 'contact theory' and that the relationship is stronger in better-off districts. Remarkably, the relationship is weaker in districts containing bigger reception centres, which suggests that the effects of asylum-seeking migration depend on the perceived contact intensity (and, therefore, a moderating effect of reception centre size). Finally, the effects of districts' socio-economic deprivation on the relationship between exposure to asylum-seeking migration and AfD vote shares is different in districts located in former East and West Berlin, which suggests an effect of socio-cultural history on the relationship between exposure to migration and far-right voting.Source: Journal of ethnic and migration studies (2022). doi:10.1080/1369183X.2022.2100543
DOI: 10.1080/1369183x.2022.2100543
Metrics:
See at:
ISTI Repository | CNR ExploRA | www.tandfonline.com
2021
Report
Open Access
Predicting vehicles parking behaviour in shared premises for aggregated EV electricity demand response programs
Monteiro De Lira V., Pallonetto F., Gabrielli L., Renso C.
The global electric car sales in 2020 continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. Demand response aggregation and load control will enable greater grid stability and greater penetration of renewable energies into the grid. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises with the objective of estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. This predicted duration feeds the energy management system that will allocate the power over the duration reducing the overall peak electricity demand. We structure our experiments inspired by two research questions aiming to discover the accuracy of the proposed machine learning approach and the most relevant features for the prediction models. We experiment different algorithms and features combination for 4 datasets from 2 different campus facilities in Italy and Brazil. Using both contextual and time of the day features, the overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systemsSource: ISTI Research reports, 2021
Monteiro De Lira V., Pallonetto F., Gabrielli L., Renso C.
The global electric car sales in 2020 continued to exceed the expectations climbing to over 3 millions and reaching a market share of over 4%. However, uncertainty of generation caused by higher penetration of renewable energies and the advent of Electrical Vehicles (EV) with their additional electricity demand could cause strains to the power system, both at distribution and transmission levels. Demand response aggregation and load control will enable greater grid stability and greater penetration of renewable energies into the grid. The present work fits this context in supporting charging optimization for EV in parking premises assuming a incumbent high penetration of EVs in the system. We propose a methodology to predict an estimation of the parking duration in shared parking premises with the objective of estimating the energy requirement of a specific parking lot, evaluate optimal EVs charging schedule and integrate the scheduling into a smart controller. We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot. This predicted duration feeds the energy management system that will allocate the power over the duration reducing the overall peak electricity demand. We structure our experiments inspired by two research questions aiming to discover the accuracy of the proposed machine learning approach and the most relevant features for the prediction models. We experiment different algorithms and features combination for 4 datasets from 2 different campus facilities in Italy and Brazil. Using both contextual and time of the day features, the overall results of the models shows an higher accuracy compared to a statistical analysis based on frequency, indicating a viable route for the development of accurate predictors for sharing parking premises energy management systemsSource: ISTI Research reports, 2021
See at:
arxiv.org | ISTI Repository | CNR ExploRA
2020
Conference article
Open Access
Digital footprints of international migration on twitter
Kim J., Sirbu A., Giannotti F., Gabrielli L.
Studying 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.Source: IDA 2020 - 18th International Conference on Intelligent Data Analysis, pp. 274–286, Konstanz, Germany, 27-29 April, 2020
DOI: 10.1007/978-3-030-44584-3_22
Project(s): HumMingBird
, SoBigData
Metrics:
Kim J., Sirbu A., Giannotti F., Gabrielli L.
Studying 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.Source: IDA 2020 - 18th International Conference on Intelligent Data Analysis, pp. 274–286, Konstanz, Germany, 27-29 April, 2020
DOI: 10.1007/978-3-030-44584-3_22
Project(s): HumMingBird
, SoBigData Metrics:
See at:
Lecture Notes in Computer Science | link.springer.com | link.springer.com | ISTI Repository | CNR ExploRA
2020
Journal article
Open Access
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
Metrics:
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
Metrics:
See at:
International Journal of Data Science and Analytics | link.springer.com | International Journal of Data Science and Analytics | ISTI Repository | CNR ExploRA
2020
Conference article
Embargo
Estimating countries' peace index through the lens of the world news as monitored by GDELT
Voukelatou V., Pappalardo L., Miliou I., Gabrielli L., Giannotti F.
Peacefulness 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.Source: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pp. 216–225, 06/10/2020, 09/10/2020
DOI: 10.1109/dsaa49011.2020.00034
Project(s): SoBigData-PlusPlus
Metrics:
Voukelatou V., Pappalardo L., Miliou I., Gabrielli L., Giannotti F.
Peacefulness 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.Source: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pp. 216–225, 06/10/2020, 09/10/2020
DOI: 10.1109/dsaa49011.2020.00034
Project(s): SoBigData-PlusPlus
Metrics:
See at:
doi.org 
| ieeexplore.ieee.org | CNR ExploRA
2019
Journal article
Open Access
Dissecting global air traffic data to discern different types and trends of transnational human mobility
Gabrielli L., Deutschmann E., Natale F., Recchi E., Vespe M.
Human mobility across national borders is a key phenomenon of our time. At the global scale, however, we still know relatively little about the structure and nature of such transnational movements. This study uses a large dataset on monthly air passenger traffic between 239 countries worldwide from 2010 to 2018 to gain new insights into (a) mobility trends over time and (b) types of mobility. A time series decomposition is used to extract a trend and a seasonal component. The trend component permits--at a higher level of granularity than previous sources--to examine the development of mobility between countries and to test how it is affected by policy and infrastructural changes, economic developments, and violent conflict. The seasonal component allows, by measuring the lag between initial and return motion, to discern different types of mobility, from tourism to seasonal work migration. Moreover, the exact shape of seasonal mobility patterns is extracted, allowing to identify regular mobility peaks and nadirs throughout the year. The result is a unique classification of trends and types of mobility for a global set of country pairs. A range of implications and possible applications are discussed.Source: EPJ 8 (2019): 1–24. doi:10.1140/epjds/s13688-019-0204-x
DOI: 10.1140/epjds/s13688-019-0204-x
Metrics:
Gabrielli L., Deutschmann E., Natale F., Recchi E., Vespe M.
Human mobility across national borders is a key phenomenon of our time. At the global scale, however, we still know relatively little about the structure and nature of such transnational movements. This study uses a large dataset on monthly air passenger traffic between 239 countries worldwide from 2010 to 2018 to gain new insights into (a) mobility trends over time and (b) types of mobility. A time series decomposition is used to extract a trend and a seasonal component. The trend component permits--at a higher level of granularity than previous sources--to examine the development of mobility between countries and to test how it is affected by policy and infrastructural changes, economic developments, and violent conflict. The seasonal component allows, by measuring the lag between initial and return motion, to discern different types of mobility, from tourism to seasonal work migration. Moreover, the exact shape of seasonal mobility patterns is extracted, allowing to identify regular mobility peaks and nadirs throughout the year. The result is a unique classification of trends and types of mobility for a global set of country pairs. A range of implications and possible applications are discussed.Source: EPJ 8 (2019): 1–24. doi:10.1140/epjds/s13688-019-0204-x
DOI: 10.1140/epjds/s13688-019-0204-x
Metrics:
See at:
EPJ Data Science | epjdatascience.springeropen.com | EPJ Data Science | ISTI Repository | CNR ExploRA
2019
Journal article
Open Access
PRIMULE: Privacy risk mitigation for user profiles
Pratesi F., Gabrielli L., Cintia P., Monreale A., Giannotti F.
The 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.Source: Data & knowledge engineering 125 (2019). doi:10.1016/j.datak.2019.101786
DOI: 10.1016/j.datak.2019.101786
Project(s): SoBigData
Metrics:
Pratesi F., Gabrielli L., Cintia P., Monreale A., Giannotti F.
The 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.Source: Data & knowledge engineering 125 (2019). doi:10.1016/j.datak.2019.101786
DOI: 10.1016/j.datak.2019.101786
Project(s): SoBigData
Metrics:
See at:
ISTI Repository | Archivio istituzionale della Ricerca - Scuola Normale Superiore | Data & Knowledge Engineering | CNR ExploRA | www.sciencedirect.com
2018
Conference article
Open Access
Discovering Mobility Functional Areas: A Mobility Data Analysis Approach
Gabrielli L., Fadda D., Rossetti G., Nanni M., Piccinini L., Pedreschi D., Giannotti F., Lattarulo P.
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.Source: 9th Conference on Complex Networks, CompleNet, pp. 311–322, Boston, 6/03/2018
DOI: 10.1007/978-3-319-73198-8_27
Project(s): SoBigData
Metrics:
Gabrielli L., Fadda D., Rossetti G., Nanni M., Piccinini L., Pedreschi D., Giannotti F., Lattarulo P.
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.Source: 9th Conference on Complex Networks, CompleNet, pp. 311–322, Boston, 6/03/2018
DOI: 10.1007/978-3-319-73198-8_27
Project(s): SoBigData
Metrics:
See at:
ISTI Repository | ISTI Repository | Springer Proceedings in Complexity | link.springer.com | CNR ExploRA
2018
Journal article
Open Access
Discovering temporal regularities in retail customers' shopping behavior
Guidotti R., Gabrielli L., Monreale A., Pedreschi D., Giannotti F.
In 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.Source: EPJ 7 (2018): 6. doi:10.1140/epjds/s13688-018-0133-0
DOI: 10.1140/epjds/s13688-018-0133-0
Project(s): SoBigData
Metrics:
Guidotti R., Gabrielli L., Monreale A., Pedreschi D., Giannotti F.
In 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.Source: EPJ 7 (2018): 6. doi:10.1140/epjds/s13688-018-0133-0
DOI: 10.1140/epjds/s13688-018-0133-0
Project(s): SoBigData
Metrics:
See at:
EPJ Data Science | epjdatascience.springeropen.com | EPJ Data Science | Archivio della Ricerca - Università di Pisa | EPJ Data Science | ISTI Repository | CNR ExploRA
2018
Journal article
Open Access
Gravity and scaling laws of city to city migration
Prieto Curiel R., Pappalardo L., Gabrielli L., Bishop S. R.
Models 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.Source: PloS one 13 (2018): 1–19. doi:10.1371/journal.pone.0199892
DOI: 10.1371/journal.pone.0199892
Project(s): CIMPLEX, SoBigData
Metrics:
Prieto Curiel R., Pappalardo L., Gabrielli L., Bishop S. R.
Models 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.Source: PloS one 13 (2018): 1–19. doi:10.1371/journal.pone.0199892
DOI: 10.1371/journal.pone.0199892
Project(s): CIMPLEX
, SoBigData Metrics:
See at:
PLoS ONE | PLoS ONE | ISTI Repository | CNR ExploRA
2018
Conference article
Open Access
Recognizing Residents and Tourists with Retail Data Using Shopping Profiles
Guidotti R., Gabrielli L.
The huge quantity of personal data stored by service providers registering customers daily life enables the analysis of individual findgerprints 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.Source: 3rd EAI International Conference on Smart Objects and Technologies for Social Good, pp. 353–363, Pisa, Italy, 29-30/11/2017
DOI: 10.1007/978-3-319-76111-4_35
Project(s): SoBigData
Metrics:
Guidotti R., Gabrielli L.
The huge quantity of personal data stored by service providers registering customers daily life enables the analysis of individual findgerprints 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.Source: 3rd EAI International Conference on Smart Objects and Technologies for Social Good, pp. 353–363, Pisa, Italy, 29-30/11/2017
DOI: 10.1007/978-3-319-76111-4_35
Project(s): SoBigData
Metrics:
See at:
link.springer.com | ISTI Repository | CNR ExploRA | Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
2018
Conference article
Open Access
MOBILITY ATLAS BOOKLET: AN URBAN DASHBOARD DESIGN and IMPLEMENTATION
Gabrielli L., Rossi M., Giannotti F., Fadda D., Rinzivillo S.
The new data sources give the possibility to answer analytically the questions that arise from mobility manager. The process of transforming raw data into knowledge is very complex, and it is necessary to provide metaphors of visualizations that are understandable to decision makers. Here, we propose an analytical platform that extracts information on the mobility of individuals from mobile phone by applying Data Mining methodologies. The main results highlighted here are both technical and methodological. First, communicating information through visual analytics techniques facilitates understanding of information to those who have no specific technical or domain knowledge. Secondly, the API system guarantees the ability to export aggregates according to the granularity required, enabling other actors to produce new services based on the extracted models. For the future, we expect to extend the platform by inserting other layers. For example, a layer for measuring the sustainability index of a territory, such as the ability of public transport to attract private mobility or the index that measures how many private vehicle trips can be converted into electrical mobility.Source: 3rd International Conference on Smart Data and Smart Cities, SDSC 2018, pp. 51–58, Delft, Netherlands, 04-05/10/2018
DOI: 10.5194/isprs-annals-iv-4-w7-51-2018
Metrics:
Gabrielli L., Rossi M., Giannotti F., Fadda D., Rinzivillo S.
The new data sources give the possibility to answer analytically the questions that arise from mobility manager. The process of transforming raw data into knowledge is very complex, and it is necessary to provide metaphors of visualizations that are understandable to decision makers. Here, we propose an analytical platform that extracts information on the mobility of individuals from mobile phone by applying Data Mining methodologies. The main results highlighted here are both technical and methodological. First, communicating information through visual analytics techniques facilitates understanding of information to those who have no specific technical or domain knowledge. Secondly, the API system guarantees the ability to export aggregates according to the granularity required, enabling other actors to produce new services based on the extracted models. For the future, we expect to extend the platform by inserting other layers. For example, a layer for measuring the sustainability index of a territory, such as the ability of public transport to attract private mobility or the index that measures how many private vehicle trips can be converted into electrical mobility.Source: 3rd International Conference on Smart Data and Smart Cities, SDSC 2018, pp. 51–58, Delft, Netherlands, 04-05/10/2018
DOI: 10.5194/isprs-annals-iv-4-w7-51-2018
Metrics:
See at:
ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences | ISTI Repository | CNR ExploRA | ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
2017
Journal article
Open Access
Discovering and understanding city events with big data: the case of Rome
Furletti B., Trasarti R., Cintia P., Gabrielli L.
The 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.Source: Information (Basel) 8 (2017). doi:10.3390/info8030074
DOI: 10.3390/info8030074
Metrics:
Furletti B., Trasarti R., Cintia P., Gabrielli L.
The 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.Source: Information (Basel) 8 (2017). doi:10.3390/info8030074
DOI: 10.3390/info8030074
Metrics:
See at:
Information | ISTI Repository | CNR ExploRA | www.mdpi.com | Information
2017
Journal article
Open Access
Scalable and flexible clustering solutions for mobile phone-based population indicators
Lulli A., Gabrielli L., Dazzi P., Dell'Amico M., Michiardi P., Nanni M., Ricci L.
Mobile phones have an unprecedented rate of penetration across the world. Such devices produce a large amount of data that have been used on different domains. In this work, we make use of mobile calls to monitor the presence of individuals region by region. Traditionally, this activity has been conducted by means of censuses and surveys. Nowadays, technologies open new possibilities to analyse the individual calling behaviour to determine the amount of residents, commuters and visitors moving in an area. To this end, in this paper we provide a clustering technique completely unsupervised able to cluster data by exploring an arbitrary similarity metric. We make use of such technique, and we define metric to analyse mobile calls and individual profiles. The approach provides better population estimation with respect to state of the art when results are compared with real census data and greatly improves the execution time of a previous work of some of the authors of this paper. The scalability and flexibility of the proposed framework enables novel scenarios for the characterization of people by means of data derived from mobile users, ranging from the nearly real-time estimation of presences to the definition of complex, uncommon user archetypes.Source: International Journal of Data Science and Analytics (Print) 4 (2017): 285–299. doi:10.1007/s41060-017-0065-y
DOI: 10.1007/s41060-017-0065-y
Metrics:
Lulli A., Gabrielli L., Dazzi P., Dell'Amico M., Michiardi P., Nanni M., Ricci L.
Mobile phones have an unprecedented rate of penetration across the world. Such devices produce a large amount of data that have been used on different domains. In this work, we make use of mobile calls to monitor the presence of individuals region by region. Traditionally, this activity has been conducted by means of censuses and surveys. Nowadays, technologies open new possibilities to analyse the individual calling behaviour to determine the amount of residents, commuters and visitors moving in an area. To this end, in this paper we provide a clustering technique completely unsupervised able to cluster data by exploring an arbitrary similarity metric. We make use of such technique, and we define metric to analyse mobile calls and individual profiles. The approach provides better population estimation with respect to state of the art when results are compared with real census data and greatly improves the execution time of a previous work of some of the authors of this paper. The scalability and flexibility of the proposed framework enables novel scenarios for the characterization of people by means of data derived from mobile users, ranging from the nearly real-time estimation of presences to the definition of complex, uncommon user archetypes.Source: International Journal of Data Science and Analytics (Print) 4 (2017): 285–299. doi:10.1007/s41060-017-0065-y
DOI: 10.1007/s41060-017-0065-y
Metrics:
See at:
link.springer.com | International Journal of Data Science and Analytics | ISTI Repository | CNR ExploRA | International Journal of Data Science and Analytics
2016
Report
Restricted
PETRA - The framework for individual mobility pattern discovery and mobility diaries/activity model
Nanni M., Trasarti R., Gabrielli L., Romano V.
This document accompanies deliverable D3.4, which contains the software modules implementing the methods that form the core of the Mobility Pattern Mining module within the PETRA architecture, as presented in D2.2, devoted to deal with GPS and mobile phone (GSM) individual data. The rationale, motivations and some possible applications of such methods have been described in D3.3. The algorithms learn to identify the role or purpose of each trip or location within the history of a user, in terms of activity to be performed, whether it is a systematic trip or location, etc., and exploit such derived information for prediction purposes. The document briefly summarizes the interfaces and the functionalities provided.Source: Project report, PETRA, Deliverable D3.4, 2016
Project(s): PETRA
Nanni M., Trasarti R., Gabrielli L., Romano V.
This document accompanies deliverable D3.4, which contains the software modules implementing the methods that form the core of the Mobility Pattern Mining module within the PETRA architecture, as presented in D2.2, devoted to deal with GPS and mobile phone (GSM) individual data. The rationale, motivations and some possible applications of such methods have been described in D3.3. The algorithms learn to identify the role or purpose of each trip or location within the history of a user, in terms of activity to be performed, whether it is a systematic trip or location, etc., and exploit such derived information for prediction purposes. The document briefly summarizes the interfaces and the functionalities provided.Source: Project report, PETRA, Deliverable D3.4, 2016
Project(s): PETRA
See at:
CNR ExploRA
2016
Report
Open Access
ProgettISTI 2016
Banterle F., Barsocchi P., Candela L., Carlini E., Carrara F., Cassarà P., Ciancia V., Cintia P., Dellepiane M., Esuli A., Gabrielli L., Germanese D., Girardi M., Girolami M., Kavalionak H., Lonetti F., Lulli A., Moreo Fernandez A., Moroni D., Nardini F. M., Monteiro De Lira V. C., Palumbo F., Pappalardo L., Pascali M. A., Reggianini M., Righi M., Rinzivillo S., Russo D., Siotto E., Villa A.
ProgettISTI research project grant is an award for members of the Institute of Information Science and Technologies (ISTI) to provide support for innovative, original and multidisciplinary projects of high quality and potential. The choice of theme and the design of the research are entirely up to the applicants yet (i) the theme must fall under the ISTI research topics, (ii) the proposers of each project must be of diverse laboratories of the Institute and must contribute different expertise to the project idea, and (iii) project proposals should have a duration of 12 months. This report documents the procedure, the proposals and the results of the 2016 edition of the award. In this edition, ten project proposals have been submitted and three of them have been awarded.Source: ISTI Technical reports, 2016
Banterle F., Barsocchi P., Candela L., Carlini E., Carrara F., Cassarà P., Ciancia V., Cintia P., Dellepiane M., Esuli A., Gabrielli L., Germanese D., Girardi M., Girolami M., Kavalionak H., Lonetti F., Lulli A., Moreo Fernandez A., Moroni D., Nardini F. M., Monteiro De Lira V. C., Palumbo F., Pappalardo L., Pascali M. A., Reggianini M., Righi M., Rinzivillo S., Russo D., Siotto E., Villa A.
ProgettISTI research project grant is an award for members of the Institute of Information Science and Technologies (ISTI) to provide support for innovative, original and multidisciplinary projects of high quality and potential. The choice of theme and the design of the research are entirely up to the applicants yet (i) the theme must fall under the ISTI research topics, (ii) the proposers of each project must be of diverse laboratories of the Institute and must contribute different expertise to the project idea, and (iii) project proposals should have a duration of 12 months. This report documents the procedure, the proposals and the results of the 2016 edition of the award. In this edition, ten project proposals have been submitted and three of them have been awarded.Source: ISTI Technical reports, 2016
See at:
ISTI Repository | CNR ExploRA
2016
Conference article
Restricted
Improving population estimation from mobile calls: A clustering approach
Lulli A., Gabrielli L., Dazzi P., Dell'Amico M., Michiardi P., Nanni M., Ricci L.
Statistical authorities promote and safeguard the production and publication of official statistics that serve the public good. One of their duties is to monitor the presence of individuals region by region. Traditionally this activity has been conducted by means of censuses and surveys. Nowadays technologies open new possibilities such as a continuous sensing of the presences by leveraging the data associated to mobile devices, e.g., the behaviour of users on doing calls. In this paper first we propose a specifically conceived similarity function able to capture similarity between individuals call behaviours. Second we make use of a clustering algorithm able to handle arbitrary metric leading to a good internal and external consistency of clusters. The approach provides better population estimation with respect to state of the art comparing with real census data. The scalability and flexibility that characterises the proposed framework enables novel scenarios for the characterization of people by means of data derived from mobile users, ranging from the nearly-realtime estimation of presences to the definition of complex, uncommon user archetypes.Source: IEEE Symposium on Computers and Communication, pp. 1097–1102, Messina, Italy, 27-30 June 2016
DOI: 10.1109/iscc.2016.7543882
Metrics:
Lulli A., Gabrielli L., Dazzi P., Dell'Amico M., Michiardi P., Nanni M., Ricci L.
Statistical authorities promote and safeguard the production and publication of official statistics that serve the public good. One of their duties is to monitor the presence of individuals region by region. Traditionally this activity has been conducted by means of censuses and surveys. Nowadays technologies open new possibilities such as a continuous sensing of the presences by leveraging the data associated to mobile devices, e.g., the behaviour of users on doing calls. In this paper first we propose a specifically conceived similarity function able to capture similarity between individuals call behaviours. Second we make use of a clustering algorithm able to handle arbitrary metric leading to a good internal and external consistency of clusters. The approach provides better population estimation with respect to state of the art comparing with real census data. The scalability and flexibility that characterises the proposed framework enables novel scenarios for the characterization of people by means of data derived from mobile users, ranging from the nearly-realtime estimation of presences to the definition of complex, uncommon user archetypes.Source: IEEE Symposium on Computers and Communication, pp. 1097–1102, Messina, Italy, 27-30 June 2016
DOI: 10.1109/iscc.2016.7543882
Metrics:
See at:
doi.org | ieeexplore.ieee.org | CNR ExploRA
2016
Journal article
Open Access
An analytical framework to nowcast well-being using mobile phone data
Pappalardo L., Vanhoof M., Gabrielli L., Smoreda Z., Pedreschi D., Giannotti F.
An 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.Source: International Journal of Data Science and Analytics (Print) 2 (2016): 75–92. doi:10.1007/s41060-016-0013-2
DOI: 10.1007/s41060-016-0013-2
DOI: 10.48550/arxiv.1606.06279
Project(s): CIMPLEX, PETRA , SoBigData
Metrics:
Pappalardo L., Vanhoof M., Gabrielli L., Smoreda Z., Pedreschi D., Giannotti F.
An 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.Source: International Journal of Data Science and Analytics (Print) 2 (2016): 75–92. doi:10.1007/s41060-016-0013-2
DOI: 10.1007/s41060-016-0013-2
DOI: 10.48550/arxiv.1606.06279
Project(s): CIMPLEX
, PETRA , SoBigData Metrics:
See at:
arXiv.org e-Print Archive | International Journal of Data Science and Analytics | International Journal of Data Science and Analytics | ISTI Repository | doi.org | link.springer.com | CNR ExploRA
2016
Contribution to book
Closed Access
Understanding human mobility with big data
Giannotti F., Gabrielli L., Pedreschi D., Rinzivillo S.
The 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.Source: Solving Large Scale Learning Tasks. Challenges and Algorithms. Essays Dedicated to Katharina Morik on the Occasion of Her 60th Birthday, edited by Stefan Michaelis, Nico Piatkowski, Marco Stolpe, pp. 208–220, 2016
DOI: 10.1007/978-3-319-41706-6_10
Metrics:
Giannotti F., Gabrielli L., Pedreschi D., Rinzivillo S.
The 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.Source: Solving Large Scale Learning Tasks. Challenges and Algorithms. Essays Dedicated to Katharina Morik on the Occasion of Her 60th Birthday, edited by Stefan Michaelis, Nico Piatkowski, Marco Stolpe, pp. 208–220, 2016
DOI: 10.1007/978-3-319-41706-6_10
Metrics:
See at:
doi.org | link.springer.com | CNR ExploRA