The SMSs are sent from the nation identified by the Country code; SMS-out activity: activity proportional to the amount of sent SMSs inside a given Square id during a given Time interval. Proceedings of ICMI, 427434 (2014). Find Open Datasets and Machine Learning Projects | Kaggle Internet Explorer). This information is directly provided by ARPA (Agenzia Regionale per la Protezione dellAmbiente).Temporal aggregation 1 hour. Bruno Lepri. Telecom Italia's board of directors has agreed to the spin-off of its 23 data centers into a separate business. wrote the paper. By. The data is released under ODbL license. MobiHoc '15 Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing, 317326 (2015). Kung, K., Greco, K., Sobolevsky, S. & Ratti, C. Exploring universal patterns in human home-work commuting from mobile phone data. It is expressed as a geojson point and projected in WGS84 (EPSG:4326). To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0 Metadata associated with this Data Descriptor is available at http://www.nature.com/sdata/ and is released under the CC0 waiver to maximize reuse. These metrics were also linked to socio-economical data in order to estimate poverty levels in a region. and now . Eagle, N., Macy, M. & Claxton, R. Network diversity and economic development. The dataset contains millions of records of data covering the period from November to December 2013 extracted from telecommunications records, energy, weather, public and private transport, social networks and events. Interview: Telecom Italia's Big Data Challenge - UN Global Pulse Hence, it is possible to capture the evolution observing permanent hotspots (places that are important all day), intermittent (with a lifespan of only few hours per day) and intermediate (with a lifespan ~ 12h). 3). Telecom Italia SpA fell on Tuesday following a Bloomberg report that Italy's state lender will drop its offer for the carrier's landline network, ending a bidding war with KKR & Co. master 1 branch 0 tags Go to file Code dwhitena Update README.md 398c34c on Apr 15, 2015 3 commits README.md Update README.md 8 years ago call_in_mgrid.png Initial Commit It is a value between 0 and 3; Coverage: percentage value of the quadrant covered by the precipitation; Type: type of the precipitation. MATH 156 Recommendations 0 Learn more about stats on ResearchGate Abstract In this work, we are interested in the applications of big data in the telecommunication domain, analysing two weeks of. Schlpfer, M. et al. The dataset is composed of telecommunications, weather, news, social networks and electricity data from the city of Milan and the Province of Trentino. You can do anything you want, as you remain under the terms and conditions of the ODbL license conditions. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. The network was deployed in Milan and the dataset is provided by Telecom Italia. This dataset contains data derived from an analysis of geolocalized tweets originated from Milan during the months of November and December.Each row corresponds to a tweet. dwhitena/analyze-visualize-model-telecom-italia - GitHub At the beginning of 2014, Telecom Italia, in collaboration with several international partners, launched the Telecom Italia Big Data Challenge. The multi-source nature of the current dataset permits the modeling of multiple dimensions of a given geographical area and to address a variety of problems and scientific issues that range from the classic human mobility and traffic analysis studies to energy consumption and linguistic studies. For example, if you combine the information with your own data, the resultant information must be published under the Attribution Share-Alike ODbL. These datasets are now freely available for anyone to use. Tizzoni, M. et al. Hawelka, B. et al. Multi-Source Dataset - Massachusetts Institute of Technology Pedregosa, F. et al. The possible values are, - 90: address (e.g., Via del Brennero, 52). During the same connection a CDR is generated if the connection lasts for more than 15min or the user transferred more than 5MB. Quantifying the impact of human mobility on malaria. The . Limits of predictability in human mobility. Since they adopt different standards, we organized two sections to describe them. Defined as type 1; Moderate: precipitation quantity equal in [2,10] mm/h. R.L. For example, we observed the stadium area of Milan and we noticed a steep increase in the number of communications in this area compared to other days. Consequently, the Customer site dataset shows the number of customer sites of each power line per grid square, while the Line measurement dataset indicates the amount of flowing energy through the lines at time t. Customer sites provide energy to different types of customers (e.g., houses, condominiums, business activities, industries etc. CAS Proceedings of the 9th Python in Science Conference 445, 5156 (2010). For the latter, each task is performed for predicting service-specific traffic data based on a fully connected network. The data are split into two datasets called Legend dataset and Weather Phenomena. The almost universal adoption of mobile phones and the exponential increase in the use of Internet services is generating an enormous amount of data that can be used to provide new fundamental and quantitative insights on socio-technical systems. designed the dataset and wrote the paper. This dataset provides a description of the primary distribution lines in the Province of Trentino. This dataset contains all the articles published on the website milanotoday.it from 01/11/2013 to 31/12/2013.The values are not spatially aggregated.The temporal aggregation values are discrete. This dataset provides information regarding the directional interaction strength between the city of Milan different areas based on the calls exchanged between Telecom Italia Mobile users. The precipitation intensity values for Trentino are spatial aggregated over the Trentino grid and temporal aggregated every 10min and they follow the standard described as: very slight: precipitation intensity defined [1,3] meaning an amount of [0.20,2.0] mm/hr; slight: precipitation intensity defined [4,6] meaning an amount of [2.0,7.0] mm/hr; moderate: precipitation intensity defined [7,9] meaning an amount of [7.0,16.0] mm/hr; heavy: precipitation intensity defined [10,12] meaning an amount of [16.0,30.0] mm/hr; very heavy: precipitation intensity defined [13,15] meaning an amount of [30.0,70.0] mm/hr; extreme: precipitation intensity defined [16,18] meaning an amount of more than 70mm/hr; The precipitation data collection is not continuous due to some technical issues such as the presence of snow over the sensor radar. Defined as type 3. while the precipitation intensity is characterized as Absent (type: 0), Rain (type: 1) and Snow (type: 2). In this context, research challenges that provide access to a large number of research teams to the same dataset are becoming a truly valuable framework to advance the state of the art in the field. Open Data Institute - node Trento We would like to show you a description here but the site won't allow us. Under the project, a big data package is given to participants, be . name: The name of the administrative region; parentAchenes: A composite object storing the achene IDs of all the administrative regions in which the current entity is placed; localCode: official government code, based on the country the administrative region belongs to (for Italy: ISTAT); cadastralCode: official cadastral code, where available; postCodes: list of post codes in the area; population: data about the population of the administrative region; isProvinceCheflieu: (only for level=50) whether the provice is a cheflieu or not; isMountainMunicipality: (only for level=60) whether the administrative region is mountainous or not. The Line measurement dataset is temporal aggregated in time-slots of 10min. This dataset is a multi-source aggregation of telecommunications, weather, news, social network and electricity data which we believe will stimulate researchers to design algorithms able to exploit an enormous number of behavioral and social indicators. This dataset contains measurements about temperature, precipitation and wind speed/direction taken in 36 Weather Stations.15 minutes time interval. Algorithms | Free Full-Text | Citywide Cellular Traffic Prediction It is then possible to distribute the energy flowing through a powerline p over the grid in order to build a choropleth map of the energy consumption in each grid square (last layer in Fig. This work is licensed under a Creative Commons Attribution 4.0 International License. As you can see, the data was supplied in batch mode, using downloadable compressed files, or through API, if this kind of access is meaningful.API data access allows a specific audience to use data more quickly, easily and efficiently when they are looking to do something specific with the information. Thus, the area of Milan is composed of a grid overlay of 1,000 (squares with size of about 235235meters and Trentino is composed of a grid overlay of 6,575 squares (see Fig. In the third layer we know how the customer sites of a power line are distributed over the grid and the energy flowing through each power-line (from the Line measurement dataset). GitHub - dwhitena/analyze-visualize-model-telecom-italia: Python (pandas, statsmodels, etc.) For instance, given the article http://www.milanotoday.it/eventi/concerti/eventi-capodanno-2014-milano.html, text: Tutti invitati al gran concerto di Capodanno in piazza [], title: Concerto Capodanno in piazza Duomo:, url: http://www.milanotoday.it/eventi/concerti/eventi-capodanno-2014-milano.html. Alex Alley. However, he resolution of the data is not uniform over the national territory. De Domenico, M., Lima, A., Gonzlez, M. & Arenas, A. Personalized routing for multitudes in smart cities. It can also be useful to visualize the data and the distribution of the events inside the geographical areas. Similarly to the physical network where people and goods move, the virtual network determines how information and knowledge moves. Tagged. 100+ projects submitted. The lack of open datasets limits the number of potential studies and creates issues in the process of validation and reproducibility needed by the scientific community. to share: to copy, distribute and use the database; to create: to produce works from the database; to adapt: to modify, transform and build upon the database. Dataset Telecom Italia organized the 'Telecom Italia Big Data Challenge' in 2014, they provided data of two Italian areas: the city of Milan and the Province of Trentino. volume2, Articlenumber:150055 (2015) Gianni Barlacchi and Marco De Nadai: These authors contributed equally to this work. We refer to this grid as the Trentino Grid. The output is written in the same directory where the script resides. Science 327, 10181021 (2010). This helps researchers to observe and understand the spatial distribution of the various datasets. arunasubbiah/milan-telecom-data-modeling - GitHub Each sensor has a unique ID, a type and a location. processed the data and wrote the paper. geometry: geometry of the Weather Station as a GeoJSON projected in WGS84 (EPSG:4326); elevation: elevation of the Weather Station in metres; date: date in the following format: YYYY-MM-dd; timestamp: date in Unix timestamp format; minTemperature: min temperature during the day in Celsius degrees; maxTemperature: max temperature during the day in Celsius degrees; temperatures: a map of temperature measurements where the key is the instant expressed as HHmm, and the value is the temperature at that time (Celsius); precipitation: a boolean set to true if any precipitation measurement is greater than 0; precipitations: a map of precipitation measurements where the key is the instant expressed as HHmm, and the value is the precipitation in that time interval (mm); minWind: min wind speed during the day (m/s); maxWind: max wind speed during the day (m/s); winds: a map of wind measurements where the key is the instant expressed as HHmm, and the value is the string speed@direction. The company is now looking for external investors for the new venture when it begins operations in 2021. Specifically, we are releasing three different datasets, one for telecommunication activities and two for telecommunication interactions. A multi-source dataset of urban life in the city of Milan and the Province of Trentino. Square id1: identification string of the square of Milan/Trentino GRID that represents the origin of the interaction; Square id2: identification string of the square of Milan or Trentino GRID that represents the destination of the interaction; Directional Inter. Telecom Data | Kaggle The first number is proportional to the number of calls issued from the area B to the province A, the second one is proportional to the number of calls from the province A to the area B.The spatial aggregation values are provided for the squares of the Milano GRID.The temporal values are aggregated in timeslots of ten minutes, This dataset provides information regarding the directional interaction strength between the city of Milan different areas based on the calls exchanged between Telecom Italia Mobile users.The directional interaction strength between the area A and the area B is proportional to the number of calls issued from the area A to the area B.The spatial aggregation values are provided for the squares of the Milano GRID.The temporal values are aggregated in timeslots of ten minutes, The dataset describes various meteorological phenomena type and intensity of Milan city using sensors located within the city limits.This dataset provides information about meteorological phenomena intensity and type for Milan city. This dataset contains all the articles published on the website trentotoday.it from 01/11/2013 to 31/12/2013. There is no spatial aggregation for this dataset. Telecom Italia dataset elds. Since the datasets come from various companies which have adopted different standards, their spatial distribution irregularity is aggregated in a grid with square cells. SpazioDati is the technological partner hosting the data distribution platform. wrote the paper. G.T. Understanding individual human mobility patterns. PLoS ONE 9, 6 (2014). The data of Milan [Data citation 12] are split into two datasets called Legend dataset and Weather Phenomena. The contest made available to developers, designers and scientists a large dataset of 30+ kinds of data (mobile, weather, energy, etc.) Aleix Bassolas, Hugo Barbosa-Filho, Jos J. Ramasco, Hugo Barbosa, Surendra Hazarie, Gourab Ghoshal, Jan Priesmann, Lars Nolting, Aaron Praktiknjo, Carmen Cabrera-Arnau, Chen Zhong, Soong Moon Kang, Scientific Data Article The second set is composed of the administrative boundaries used in the last three censuses. All the news referring to the general area (the whole city of Milan or the whole Province of Trentino) are geo-tagged to its administrative centre. https://doi.org/10.1038/sdata.2015.55, DOI: https://doi.org/10.1038/sdata.2015.55. Csji, B. et al. However, this is only the minimal requirement. Timestamp: timestamp value with the following format: YYYYMMDDHHmm; Square id: id of a given square of Milan/Trentino GRID; Intensity: intensity value of the precipitation. Because the 10 min interval dataset was quite sparse, it was not conducive to extracting spatiotemporal characteristics. The precipitation types are described as: Absent: precipitation quantity equal to 0mm/h. This dataset contains data derived from an analysis of geolocalized tweets originated from Milan during the months of November and December. provenance: list of strings, representing the original source of information. converter.py It converts the raw CDRs to the grid overlay as explained previously. Telecom Italia Big Data Challenge Aug. 13, 2016 0 likes 1,164 views Download Now Download to read offline Data & Analytics Analysis and presentation of the Telecom Italia Big data Challenge and discuss how the Big Data open challenges help to get the new insights of the collected data. A.P. Wesolowski, A. et al. Bogomolov, A. et al. The data of Milan are collected by Agenzia Regionale per la Protezione dell'Ambiente (ARPA) (http://www2.arpalombardia.it/siti/arpalombardia/meteo/richiesta-dati-misurati/Pagine/RichiestaDatiMisurati.aspx) while Trentino's data are collected by Meteotrentino (http://www.meteotrentino.it). This dataset contains data derived from an analysis of geolocalized tweets originated from the Province of Trento during the months of November and December. Province: the name of the Italian province. This dataset provides information regarding the level of interaction between the Province of Trento and the Italian provinces. There is no spatial aggregation and the data is aggregated in 60min time-slots. 4 SETlayers. In this paper we described the richest open multi-source dataset ever released on two geographical areas. Barlacchi, G., De Nadai, M., Larcher, R. et al. Geo-located twitter as proxy for global mobility patterns. Proceedings of CHI, 511520 (2014). The dataset contains measurements about temperature, precipitation and wind speed/direction taken in 36 Weather Stations placed around the Province of Trentino. arXiv preprint arXiv: 1407.4885 (2014). Social-media data for urban sustainability, Inter-urban mobility via cellular position tracking in the southeast Songliao Basin, Northeast China, Hierarchical organization of urban mobility and its connection with city livability, Uncovering the socioeconomic facets of human mobility, The temporal network of mobile phone users in Changchun Municipality, Northeast China, Multiscale dynamic human mobility flow dataset in the U.S. during the COVID-19 epidemic, Time series of useful energy consumption patterns for energy system modeling, Inferring urban polycentricity from the variability in human mobility patterns, Machine-accessible metadata file describing the reported data, http://www.itu.int/en/ITU-D/Statistics/Pages/facts/default.aspx, http://www.telecomitalia.com/tit/en/bigdatachallenge/contest.html, http://www.agcom.it/documents/10179/1734740/Studio-Ricerca+24-07-2014/5541e017-3c7a-42ff-b82f-66b460175f68?version=1.0, https://dev.twitter.com/docs/streaming-apis, http://www2.arpalombardia.it/siti/arpalombardia/meteo/richiesta-dati-misurati/Pagine/RichiestaDatiMisurati.aspx, http://www.arpa.piemonte.it/rischinaturali, http://www.milanotoday.it/eventi/concerti/eventi-capodanno-2014-milano.html, http://www.telecomitalia.com/tit/en/innovazione/big-data-challenge-2015.html, http://creativecommons.org/licenses/by/4.0, Selected Aspects of Non orthogonal Multiple Access forFuture Wireless Communications, Identify spatio-temporal properties of network traffic by model checking, Predicting dynamic spectrum allocation: a review covering simulation, modelling, and prediction, A comparative study of cellular traffic prediction mechanisms, A novel network traffic prediction method based on a Bayesian network model for establishing the relationship between traffic and population. Blondel, V. et al. This dataset is temporally aggregated every 10min and spatially aggregated in four quadrants of equal size of 11.7511.75km, corresponding to 50 squares of the grid used for the aggregation. The challenge was organized by Telecom Italia, in association with EIT ICT Labs, SpazioDati, MIT Media Lab, Polytechnic University of Milan, Fondazione Bruno Kessler,University of Trento and TrentoRISE.The data provided in the dataset of the Big Data Challenge is geo-referenced (areas: Milan and the Autonomous Province of Trento Italy) and anonymized. This dataset [Data citations 8,9] provides the directional interaction strengths between different areas of Milan and the Province of Trento. It is a rich, open multi-source aggregation of telecommunications, weather, news, social networks and electricity data. July 21, 2014. Dataverse The sensors can measure different meteorological phenomena: Wind Direction, Wind Speed, Temperature, Relative Humidity, Precipitation, Global Radiation, Atmospheric Pressure and Net Radiation. This dataset provides information about the telecommunication activity over the city of Milano. Intuitively, the former provides the locations of the sensors and the unit of measurements, while the latter contains the measurement files for each sensor. The network learns the temporal and spatial dependence of cellular traffic data. The dataset used for analysis is for Telecom Italia for the city of Milano and is made public in 2014 after the contest of Big data challenge . The Call Detail Records (CDRs) of the 6.8 billion mobile phone subscribers worldwide (http://www.itu.int/en/ITU-D/Statistics/Pages/facts/default.aspx, date of access 06/08/2014) potentially represent the most invaluable proxy for people's communication and mobility habits at a global scale. Telecom Italia Confirms Exploring Strategic Options for Network 6 (2015). A plain language summary of the ODbL is available on the Open Data Commons website. This dataset provides information regarding the level of interaction between the Province of Trento and the Italian provinces. The dataset describes the pollution type and intensity of Milan city using various types of sensors located within the city limits. The Big data challenge initiative triggered a long tail of follow on research work based on its data, and thus Telecom Italia is currently running a second edition of the challenge (http://www.telecomitalia.com/tit/en/innovazione/big-data-challenge-2015.html, date of access 06/08/2015). Article Google Scholar. This allows comparisons between different areas and eases the geographical management of the data. Moreover, Bocconi has less mobile phone activity than Duomo, which is the centre of the city and the most important tourist attraction. The possible values are, - 0: the geometry comes directly from the original source, and has not been edited by SpazioDati or anyone, - 1: the geometry has been inferred by SpazioDati from other fields, such as the locality/municipality, - 2: the geometry has been geocoded from an address, geomComplex.accuracy: quality of the geometry. Scaiella, U. et al. The dataset has been released to the whole research community and here we provide a detailed description of the data records' structure, and present the methodology used in the data collection/aggregation process. The quadrants are referred with IDs 1, 2, 3 and 4 and the corresponding grid squares IDs are computed by the formula y100+x, where x and y follow the following rules:. Users can get more data about the municipality (e.g., boundaries, population) using the acheneID as a primary key in the Administrative Regions; created: Tweet time in ISO format YYYY-MM-DDTHH: mm: SS, Europe/Rome timezone; geometry: approximate position of the tweet, in geoJSON format. This information is derived from the images provided by ARPA (Agenzia Regionale per la Protezione dellAmbiente) at the following websites:- [Precipitation intensity](http://www.arpa.piemonte.it/rischinaturali/tematismi/meteo/osservazioni/radar/intensita-precipitazione.html?delta=0)- [Precipitation type](http://www.arpa.piemonte.it/rischinaturali/tematismi/meteo/osservazioni/radar/tipo-precipitazione.html?delta=0)Temporal AggregationPrecipitation intensity and type values are provided every ten minutes. A paid subscription is required for full access.. The number of records in the datasets The Census dataset represents an interesting source of information that can be linked to the data described in this paper to, for example, understand and predict the socio-economic well-being of a given territorial area. The dataset describes various meteorological phenomena type and intensity of Milan city using sensors located within the city limits, The dataset describes precipitation intensity and type over the city of Milan, The dataset describes the pollution type and intensity of Milan city using various types of sensors located within the city limits. Analogously, Telecom Italia in association with EIT ICT Labs, SpazioDati, MIT Media Lab, Northeastern University, Polytechnic University of Milan, Fondazione Bruno Kessler, University of Trento. and JavaScript. The obfuscation of the username has been done using the hash function SHA-1, and two random generated strings (SALT1 and SALT2): The dataTXT is a tool to identify meaningful sequences of one or more terms, and then to link them to the most appropriate Wikipedia page. First Online: 20 June 2020 Part of the Contributions to Statistics book series (CONTRIB.STAT.) The data are released on 7 Italian cities: Bari, Milan, Naples, Rome, Turin, Venice and Palermo. The first set contains the geographical shapefile data of all the Italian regional areas. Then, a new CDR is created recording the time of the interaction and the RBS which handled it. It uses around 180 primary distribution lines (medium voltage lines) to bring energy from the national grid and distribute it among Trentino users. This dataset contains all the articles published on the website trentotoday.it from 01/11/2013 to 31/12/2013.The values are not spatially aggregated.The temporal aggregation values are discrete.
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