2019
Other
Metadata Only Access
Material for Soccer Data Cup
Pappalardo L, Cintia P
Materiale didattico per i partecipanti alla Soccer Data Cup, una competizione su dati calcistici per gli studenti delle scuole superiori.Project(s): SoBigData
Pappalardo L, Cintia P
Materiale didattico per i partecipanti alla Soccer Data Cup, una competizione su dati calcistici per gli studenti delle scuole superiori.Project(s): SoBigData
See at:
github.com 
| CNR IRIS
2019
Software
Metadata Only Access
PlayeRank
Cintia P, Pappalardo L
PlayeRank is a data-driven algorithm that offers a principled multi-dimensional and role-aware evaluation of the performance of soccer players. Playerank is designed to work with soccer-logs, in which a match consists of a sequence of events encoded as a tuple: (id, type, position, timestamp), where id is the identifer of the player that originated/refers to this event, type is the event type (i.e., passes, shots, goals, tackles, etc.), position and timestamp denote the spatio-temporal coordinates of the event over the soccer field. PlayeRank assumes that soccer-logs are stored into a database, which is updated with new events after each soccer match. An exhaustive description of PlayeRank framework is available in this paper: Pappalardo, Luca, Cintia, Paolo, Ferragina, Paolo, Massucco, Emanuele, Pedreschi, Dino & Giannotti, Fosca (2019) PlayeRank: Data-driven Performance Evaluation and Player Ranking in Soccer via a Machine Learning Approach. ACM Transactions on Intelligent Systems and Technologies 10(5), DOI:https://doi.org/10.1145/3343172Project(s): SoBigData
Cintia P, Pappalardo L
PlayeRank is a data-driven algorithm that offers a principled multi-dimensional and role-aware evaluation of the performance of soccer players. Playerank is designed to work with soccer-logs, in which a match consists of a sequence of events encoded as a tuple: (id, type, position, timestamp), where id is the identifer of the player that originated/refers to this event, type is the event type (i.e., passes, shots, goals, tackles, etc.), position and timestamp denote the spatio-temporal coordinates of the event over the soccer field. PlayeRank assumes that soccer-logs are stored into a database, which is updated with new events after each soccer match. An exhaustive description of PlayeRank framework is available in this paper: Pappalardo, Luca, Cintia, Paolo, Ferragina, Paolo, Massucco, Emanuele, Pedreschi, Dino & Giannotti, Fosca (2019) PlayeRank: Data-driven Performance Evaluation and Player Ranking in Soccer via a Machine Learning Approach. ACM Transactions on Intelligent Systems and Technologies 10(5), DOI:https://doi.org/10.1145/3343172Project(s): SoBigData
See at:
github.com | CNR IRIS
2019
Conference article
Open Access
Human mobility from theory to practice: data, models and applications
Simini F, Pellungrini R, Barlacchi G, Pappalardo L
The inclusion of tracking technologies in personal devices opened the doors to the analysis of large sets of mobility data like GPS traces and call detail records. This tutorial presents an overview of both modeling principles of human mobility and machine learning models applicable to specific problems. We review the state of the art of five main aspects in human mobility: (1) human mobility data landscape; (2) key measures of individual and collective mobility; (3) generative models at the level of individual, population and mixture of the two; (4) next location prediction algorithms; (5) applications for social good. For each aspect, we show experiments and simulations using the Python library "scikit-mobility" developed by the presenters of the tutorial.DOI: 10.1145/3308560.3320099
Project(s): SoBigData
Metrics:
Simini F, Pellungrini R, Barlacchi G, Pappalardo L
The inclusion of tracking technologies in personal devices opened the doors to the analysis of large sets of mobility data like GPS traces and call detail records. This tutorial presents an overview of both modeling principles of human mobility and machine learning models applicable to specific problems. We review the state of the art of five main aspects in human mobility: (1) human mobility data landscape; (2) key measures of individual and collective mobility; (3) generative models at the level of individual, population and mixture of the two; (4) next location prediction algorithms; (5) applications for social good. For each aspect, we show experiments and simulations using the Python library "scikit-mobility" developed by the presenters of the tutorial.DOI: 10.1145/3308560.3320099
Project(s): SoBigData
Metrics:
See at:
arpi.unipi.it 
| Explore Bristol Research | dl.acm.org | doi.org | CNR IRIS | CNR IRIS
2019
Software
Metadata Only Access
scikit-mobility
Pappalardo L, Simini F, Barlacchi G, Pellungrini R
scikit-mobility is a library for human mobility analysis in Python. The library allows to: - represent trajectories and mobility flows with proper data structures, TrajDataFrame and FlowDataFrame. - manage and manipulate mobility data of various formats (call detail records, GPS data, data from social media, survey data, etc.); - extract mobility metrics and patterns from data, both at individual and collective level (e.g., length of displacements, characteristic distance, origin-destination matrix, etc.) - generate synthetic individual trajectories using standard mathematical models (random walk models, exploration and preferential return model, etc.) - generate synthetic mobility flows using standard migration models (gravity model, radiation model, etc.) - assess the privacy risk associated with a mobility data setProject(s): SoBigData
Pappalardo L, Simini F, Barlacchi G, Pellungrini R
scikit-mobility is a library for human mobility analysis in Python. The library allows to: - represent trajectories and mobility flows with proper data structures, TrajDataFrame and FlowDataFrame. - manage and manipulate mobility data of various formats (call detail records, GPS data, data from social media, survey data, etc.); - extract mobility metrics and patterns from data, both at individual and collective level (e.g., length of displacements, characteristic distance, origin-destination matrix, etc.) - generate synthetic individual trajectories using standard mathematical models (random walk models, exploration and preferential return model, etc.) - generate synthetic mobility flows using standard migration models (gravity model, radiation model, etc.) - assess the privacy risk associated with a mobility data setProject(s): SoBigData
See at:
github.com | CNR IRIS
2019
Other
Metadata Only Access
Injury forecasting in soccer utilizing machine learning and multivariate time series
Guerrini L Laureando Relatori Paolo Ferragina, Luca Pappalardo, Paolo Cintia
Injuries have a great impact on professional soccer due to their influence on team performance and considerable costs of rehabilitation for players. In this thesis, we use injury records and workload data describing the training sessions of players in a professional soccer club, spanning two entire seasons, to train and compare three classes of approaches to injury forecasting, i.e., predicting whether or not a player will get injured in next matches or training sessions. The first class of approaches is based on traditional techniques used in sports science and industry, such as the Acute Chronic Workload Ratio. The second class is based on machine learning tools such as decision tree and k-nearest neighbor classifier. The third class of approaches extends the second class by fully exploiting the temporal information present in the data through the usage of a multivariate time series representation of a player's workload history. We demonstrate that machine learning approaches significantly outperform traditional techniques still used in sports industry, moving accuracy prediction from 4% up to 50%, paving the way to a more accurate monitoring of the health status of soccer players.Project(s): SoBigData
Guerrini L Laureando Relatori Paolo Ferragina, Luca Pappalardo, Paolo Cintia
Injuries have a great impact on professional soccer due to their influence on team performance and considerable costs of rehabilitation for players. In this thesis, we use injury records and workload data describing the training sessions of players in a professional soccer club, spanning two entire seasons, to train and compare three classes of approaches to injury forecasting, i.e., predicting whether or not a player will get injured in next matches or training sessions. The first class of approaches is based on traditional techniques used in sports science and industry, such as the Acute Chronic Workload Ratio. The second class is based on machine learning tools such as decision tree and k-nearest neighbor classifier. The third class of approaches extends the second class by fully exploiting the temporal information present in the data through the usage of a multivariate time series representation of a player's workload history. We demonstrate that machine learning approaches significantly outperform traditional techniques still used in sports industry, moving accuracy prediction from 4% up to 50%, paving the way to a more accurate monitoring of the health status of soccer players.Project(s): SoBigData
See at:
etd.adm.unipi.it | CNR IRIS
2019
Journal article
Open Access
Personalized market basket prediction with temporal annotated recurring sequences
Guidotti R, Rossetti G, Pappalardo L, Giannotti F, Pedreschi D
Nowadays, a hot challenge for supermarket chains is to offer personalized services to their customers. Market basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services. Current approaches are not capable of capturing at the same time the different factors influencing the customer's decision process: co-occurrence, sequentuality, periodicity and recurrency of the purchased items. To this aim, we define a pattern Temporal Annotated Recurring Sequence (TARS) able to capture simultaneously and adaptively all these factors. We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to understand the level of the customer's stocks and recommend the set of most necessary items. By adopting the TBP the supermarket chains could crop tailored suggestions for each individual customer which in turn could effectively speed up their shopping sessions. A deep experimentation shows that TARS are able to explain the customer purchase behavior, and that TBP outperforms the state-of-the-art competitors.Source: IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (PRINT), vol. 31 (issue 11), pp. 2151-2163
DOI: 10.1109/tkde.2018.2872587
Project(s): SoBigData
Metrics:
Guidotti R, Rossetti G, Pappalardo L, Giannotti F, Pedreschi D
Nowadays, a hot challenge for supermarket chains is to offer personalized services to their customers. Market basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services. Current approaches are not capable of capturing at the same time the different factors influencing the customer's decision process: co-occurrence, sequentuality, periodicity and recurrency of the purchased items. To this aim, we define a pattern Temporal Annotated Recurring Sequence (TARS) able to capture simultaneously and adaptively all these factors. We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to understand the level of the customer's stocks and recommend the set of most necessary items. By adopting the TBP the supermarket chains could crop tailored suggestions for each individual customer which in turn could effectively speed up their shopping sessions. A deep experimentation shows that TARS are able to explain the customer purchase behavior, and that TBP outperforms the state-of-the-art competitors.Source: IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (PRINT), vol. 31 (issue 11), pp. 2151-2163
DOI: 10.1109/tkde.2018.2872587
Project(s): SoBigData
Metrics:
See at:
Archivio della Ricerca - Università di Pisa | CNR IRIS | ieeexplore.ieee.org | IEEE Transactions on Knowledge and Data Engineering | ISTI Repository | IEEE Transactions on Knowledge and Data Engineering | CNR IRIS | CNR IRIS
2019
Other
Open Access
ISTI Young Researcher Award "Matteo Dellepiane" - Edition 2019
Barsocchi P, Candela L, Crivello A, Esuli A, Ferrari A, Girardi M, Guidotti R, Lonetti F, Malomo L, Moroni D, Nardini Fm, Pappalardo L, Rinzivillo S, Rossetti G, Robol L
The ISTI Young Researcher Award (YRA) selects yearly the best young staff members working at Institute of Information Science and Technologies (ISTI). This award focuses on quality and quantity of the scientific production. In particular, the award is granted to the best young staff members (less than 35 years old) by assessing their scientific production in the year preceding the award. This report documents the selection procedure and the results of the 2019 YRA edition. From the 2019 edition on the award is named as "Matteo Dellepiane", being dedicated to a bright ISTI researcher who prematurely left us and who contributed a lot to the YRA initiative from its early start.
Barsocchi P, Candela L, Crivello A, Esuli A, Ferrari A, Girardi M, Guidotti R, Lonetti F, Malomo L, Moroni D, Nardini Fm, Pappalardo L, Rinzivillo S, Rossetti G, Robol L
The ISTI Young Researcher Award (YRA) selects yearly the best young staff members working at Institute of Information Science and Technologies (ISTI). This award focuses on quality and quantity of the scientific production. In particular, the award is granted to the best young staff members (less than 35 years old) by assessing their scientific production in the year preceding the award. This report documents the selection procedure and the results of the 2019 YRA edition. From the 2019 edition on the award is named as "Matteo Dellepiane", being dedicated to a bright ISTI researcher who prematurely left us and who contributed a lot to the YRA initiative from its early start.
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2019
Journal article
Open Access
A public data set of spatio-temporal match events in soccer competitions
Pappalardo L, Cintia P, Rossi A, Massucco E, Ferragina P, Pedreschi D, Giannotti F
Soccer analytics is attracting increasing interest in academia and industry, thanks to the availability of sensing technologies that provide high-fidelity data streams for every match. Unfortunately, these detailed data are owned by specialized companies and hence are rarely publicly available for scientific research. To fill this gap, this paper describes the largest open collection of soccer-logs ever released, containing all the spatio-temporal events (passes, shots, fouls, etc.) that occured during each match for an entire season of seven prominent soccer competitions. Each match event contains information about its position, time, outcome, player and characteristics. The nature of team sports like soccer, halfway between the abstraction of a game and the reality of complex social systems, combined with the unique size and composition of this dataset, provide an ideal ground for tackling a wide range of data science problems, including the measurement and evaluation of performance, both at individual and at collective level, and the determinants of success and failure.Source: SCIENTIFIC DATA, vol. 6 (issue 1), p. 236
DOI: 10.1038/s41597-019-0247-7
Project(s): SoBigData
Metrics:
Pappalardo L, Cintia P, Rossi A, Massucco E, Ferragina P, Pedreschi D, Giannotti F
Soccer analytics is attracting increasing interest in academia and industry, thanks to the availability of sensing technologies that provide high-fidelity data streams for every match. Unfortunately, these detailed data are owned by specialized companies and hence are rarely publicly available for scientific research. To fill this gap, this paper describes the largest open collection of soccer-logs ever released, containing all the spatio-temporal events (passes, shots, fouls, etc.) that occured during each match for an entire season of seven prominent soccer competitions. Each match event contains information about its position, time, outcome, player and characteristics. The nature of team sports like soccer, halfway between the abstraction of a game and the reality of complex social systems, combined with the unique size and composition of this dataset, provide an ideal ground for tackling a wide range of data science problems, including the measurement and evaluation of performance, both at individual and at collective level, and the determinants of success and failure.Source: SCIENTIFIC DATA, vol. 6 (issue 1), p. 236
DOI: 10.1038/s41597-019-0247-7
Project(s): SoBigData
Metrics:
See at:
Scientific Data | Scientific Data | Archivio della Ricerca - Università di Pisa | CNR IRIS | Scientific Data | ISTI Repository | Scientific Data | www.nature.com | CNR IRIS
2019
Journal article
Open Access
Relationship between external and internal workloads in elite soccer players: Comparison between rate of perceived exertion and training load
Rossi A, Perri E, Pappalardo L, Cintia P, Iaia Fm
The use of machine learning (ML) in soccer allows for the management of a large amount of data deriving from the monitoring of sessions and matches. Although the rate of perceived exertion (RPE), training load (S-RPE), and global position system (GPS) are standard methodologies used in team sports to assess the internal and external workload; how the external workload affects RPE and S-RPE remains still unclear. This study explores the relationship between both RPE and SRPE and the training workload through ML. Data were recorded from 22 elite soccer players, in 160 training sessions and 35 matches during the 2015/2016 season, by using GPS tracking technology. A feature selection process was applied to understand which workload features influence RPE and SRPE the most. Our results show that the training workloads performed in the previous week have a strong effect on perceived exertion and training load. On the other hand, the analysis of our predictions shows higher accuracy for medium RPE and S-RPE values compared with the extremes. These results provide further evidence of the usefulness of ML as a support to athletic trainers and coaches in understanding the relationship between training load and individual-response in team sports.Source: APPLIED SCIENCES, vol. 9 (issue 23)
DOI: 10.3390/app9235174
Project(s): SoBigData
Metrics:
Rossi A, Perri E, Pappalardo L, Cintia P, Iaia Fm
The use of machine learning (ML) in soccer allows for the management of a large amount of data deriving from the monitoring of sessions and matches. Although the rate of perceived exertion (RPE), training load (S-RPE), and global position system (GPS) are standard methodologies used in team sports to assess the internal and external workload; how the external workload affects RPE and S-RPE remains still unclear. This study explores the relationship between both RPE and SRPE and the training workload through ML. Data were recorded from 22 elite soccer players, in 160 training sessions and 35 matches during the 2015/2016 season, by using GPS tracking technology. A feature selection process was applied to understand which workload features influence RPE and SRPE the most. Our results show that the training workloads performed in the previous week have a strong effect on perceived exertion and training load. On the other hand, the analysis of our predictions shows higher accuracy for medium RPE and S-RPE values compared with the extremes. These results provide further evidence of the usefulness of ML as a support to athletic trainers and coaches in understanding the relationship between training load and individual-response in team sports.Source: APPLIED SCIENCES, vol. 9 (issue 23)
DOI: 10.3390/app9235174
Project(s): SoBigData
Metrics:
See at:
Applied Sciences | CNR IRIS | ISTI Repository | www.mdpi.com | Applied Sciences | CNR IRIS
2019
Conference article
Restricted
Exploring students eating habits through individual profiling and clustering analysis
Natilli M, Monreale A, Guidotti R, Pappalardo L
Individual well-being strongly depends on food habits, therefore it is important to educate the general population, and especially young people, to the importance of a healthy and balanced diet. To this end, understanding the real eating habits of people becomes fundamental for a better and more effective intervention to improve the students' diet. In this paper we present two exploratory analyses based on centroid-based clustering that have the goal of understanding the food habits of university students. The first clustering analysis simply exploits the information about the students' food consumption of specific food categories, while the second exploratory analysis includes the temporal dimension in order to capture the information about when the students consume specific foods. The second approach enables the study of the impact of the time of consumption on the choice of the food.DOI: 10.1007/978-3-030-13463-1_12
Project(s): SoBigData
Metrics:
Natilli M, Monreale A, Guidotti R, Pappalardo L
Individual well-being strongly depends on food habits, therefore it is important to educate the general population, and especially young people, to the importance of a healthy and balanced diet. To this end, understanding the real eating habits of people becomes fundamental for a better and more effective intervention to improve the students' diet. In this paper we present two exploratory analyses based on centroid-based clustering that have the goal of understanding the food habits of university students. The first clustering analysis simply exploits the information about the students' food consumption of specific food categories, while the second exploratory analysis includes the temporal dimension in order to capture the information about when the students consume specific foods. The second approach enables the study of the impact of the time of consumption on the choice of the food.DOI: 10.1007/978-3-030-13463-1_12
Project(s): SoBigData
Metrics:
See at:
doi.org | CNR IRIS | CNR IRIS | link.springer.com
2019
Dataset
Metadata Only Access
Soccer match event dataset
Pappalardo L, Massucco E
Soccer analytics is attracting an increasing interest of academia and industry, thanks to the availability of sensing technologies that provide high-fidelity data streams extracted from every match. Unfortunately, these detailed data are owned by specialized companies and hence are rarely publicly available for scientific research. To fill this gap, we provide to the public the largest open collection of soccer-logs ever released, collected by Wyscout (https://wyscout.com/) containing all the spatio-temporal events (passes, shots, fouls, etc.) that occur during all matches of an entire season of seven competitions (La Liga, Serie A, Bundesliga, Premier League, Ligue 1, FIFA World Cup 2018, UEFA Euro Cup 2016). A match event contains information about its position, time, outcome, player and characteristics. This dataset has been used recently during the Soccer Data Challenge (https://sobigdata-soccerchallenge.it/) and, to the best of our knowledge, it is the largest public collection of soccer-logs.Project(s): SoBigData
Pappalardo L, Massucco E
Soccer analytics is attracting an increasing interest of academia and industry, thanks to the availability of sensing technologies that provide high-fidelity data streams extracted from every match. Unfortunately, these detailed data are owned by specialized companies and hence are rarely publicly available for scientific research. To fill this gap, we provide to the public the largest open collection of soccer-logs ever released, collected by Wyscout (https://wyscout.com/) containing all the spatio-temporal events (passes, shots, fouls, etc.) that occur during all matches of an entire season of seven competitions (La Liga, Serie A, Bundesliga, Premier League, Ligue 1, FIFA World Cup 2018, UEFA Euro Cup 2016). A match event contains information about its position, time, outcome, player and characteristics. This dataset has been used recently during the Soccer Data Challenge (https://sobigdata-soccerchallenge.it/) and, to the best of our knowledge, it is the largest public collection of soccer-logs.Project(s): SoBigData
See at:
figshare.com | CNR IRIS
2019
Journal article
Open Access
PlayeRank: data-driven performance evaluation and player ranking in Soccer via a machine learning approach
Pappalardo L, Cintia P, Ferragina P, Massucco E, Pedreschi D, Giannotti F
The problem of evaluating the performance of soccer players is attracting the interest of many companies and the scientific community, thanks to the availability of massive data capturing all the events generated during a match (e.g., tackles, passes, shots, etc.). Unfortunately, there is no consolidated and widely accepted metric for measuring performance quality in all of its facets. In this article, we design and implement PlayeRank, a data-driven framework that offers a principled multi-dimensional and role-aware evaluation of the performance of soccer players. We build our framework by deploying a massive dataset of soccer-logs and consisting of millions of match events pertaining to four seasons of 18 prominent soccer competitions. By comparing PlayeRank to known algorithms for performance evaluation in soccer, and by exploiting a dataset of players' evaluations made by professional soccer scouts, we show that PlayeRank significantly outperforms the competitors. We also explore the ratings produced by PlayeRank and discover interesting patterns about the nature of excellent performances and what distinguishes the top players from the others. At the end, we explore some applications of PlayeRank-i.e. searching players and player versatility-showing its flexibility and efficiency, which makes it worth to be used in the design of a scalable platform for soccer analytics.Source: ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY (PRINT), vol. 10 (issue 5)
DOI: 10.1145/3343172
DOI: 10.48550/arxiv.1802.04987
Project(s): SoBigData
Metrics:
Pappalardo L, Cintia P, Ferragina P, Massucco E, Pedreschi D, Giannotti F
The problem of evaluating the performance of soccer players is attracting the interest of many companies and the scientific community, thanks to the availability of massive data capturing all the events generated during a match (e.g., tackles, passes, shots, etc.). Unfortunately, there is no consolidated and widely accepted metric for measuring performance quality in all of its facets. In this article, we design and implement PlayeRank, a data-driven framework that offers a principled multi-dimensional and role-aware evaluation of the performance of soccer players. We build our framework by deploying a massive dataset of soccer-logs and consisting of millions of match events pertaining to four seasons of 18 prominent soccer competitions. By comparing PlayeRank to known algorithms for performance evaluation in soccer, and by exploiting a dataset of players' evaluations made by professional soccer scouts, we show that PlayeRank significantly outperforms the competitors. We also explore the ratings produced by PlayeRank and discover interesting patterns about the nature of excellent performances and what distinguishes the top players from the others. At the end, we explore some applications of PlayeRank-i.e. searching players and player versatility-showing its flexibility and efficiency, which makes it worth to be used in the design of a scalable platform for soccer analytics.Source: ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY (PRINT), vol. 10 (issue 5)
DOI: 10.1145/3343172
DOI: 10.48550/arxiv.1802.04987
Project(s): SoBigData
Metrics:
See at:
arXiv.org e-Print Archive | dl.acm.org | ACM Transactions on Intelligent Systems and Technology | Archivio della Ricerca - Università di Pisa | CNR IRIS | ISTI Repository | ACM Transactions on Intelligent Systems and Technology | doi.org | CNR IRIS
2019
Other
Open Access
scikit-mobility: a Python library for the analysis, generation and risk assessment of mobility data
Pappalardo L, Simini F, Barlacchi G, Pellungrini R
The last decade has witnessed the emergence of massive mobility data sets, such as tracks generated by GPS devices, call detail records, and geo-tagged posts from social media platforms. These data sets have fostered a vast scientific production on various applications of human mobility analysis, ranging from computational epidemiology to urban planning and transportation engineering. A strand of literature addresses data cleaning issues related to raw spatiotemporal trajectories, while the second line of research focuses on discovering the statistical "laws" that govern human movements. A significant effort has also been put on designing algorithms to generate synthetic trajectories able to reproduce, realistically, the laws of human mobility. Last but not least, a line of research addresses the crucial problem of privacy, proposing techniques to perform the re-identification of individuals in a database. Despite the increasing importance of human mobility analysis for many scientific and industrial domains, a view on state of the art cannot avoid noticing that there is no statistical software that can support scientists and practitioners with all the aspects mentioned above of mobility data analysis. To fill this gap, we propose scikit-mobility, a Python library that has the ambition of providing an environment to reproduce existing research, analyze mobility data, and simulate human mobility habits. scikit-mobility is efficient and easy to use as it extends the well-known standard pandas, a popular Python library for data analysis. Moreover, scikit-mobility provides the user with many functionalities, from visualizing trajectories to generating synthetic data, from analyzing the statistical patterns of trajectories to assessing the privacy risk related to the analysis of mobility data sets.Project(s): SoBigData
Pappalardo L, Simini F, Barlacchi G, Pellungrini R
The last decade has witnessed the emergence of massive mobility data sets, such as tracks generated by GPS devices, call detail records, and geo-tagged posts from social media platforms. These data sets have fostered a vast scientific production on various applications of human mobility analysis, ranging from computational epidemiology to urban planning and transportation engineering. A strand of literature addresses data cleaning issues related to raw spatiotemporal trajectories, while the second line of research focuses on discovering the statistical "laws" that govern human movements. A significant effort has also been put on designing algorithms to generate synthetic trajectories able to reproduce, realistically, the laws of human mobility. Last but not least, a line of research addresses the crucial problem of privacy, proposing techniques to perform the re-identification of individuals in a database. Despite the increasing importance of human mobility analysis for many scientific and industrial domains, a view on state of the art cannot avoid noticing that there is no statistical software that can support scientists and practitioners with all the aspects mentioned above of mobility data analysis. To fill this gap, we propose scikit-mobility, a Python library that has the ambition of providing an environment to reproduce existing research, analyze mobility data, and simulate human mobility habits. scikit-mobility is efficient and easy to use as it extends the well-known standard pandas, a popular Python library for data analysis. Moreover, scikit-mobility provides the user with many functionalities, from visualizing trajectories to generating synthetic data, from analyzing the statistical patterns of trajectories to assessing the privacy risk related to the analysis of mobility data sets.Project(s): SoBigData
See at:
arxiv.org | CNR IRIS | ISTI Repository | CNR IRIS