Error <600m. The weather data describe meteorological phenomena type and intensity in Milan and Trentino. The network learns the temporal and spatial dependence of cellular traffic data. 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. 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. Google Scholar. The Orange Telecom's Churn Dataset, which consists of cleaned customer activity data (features), along with a churn label specifying whether a customer canceled the subscription, will be used to develop predictive models. 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 Trento RISE recently organized the Telecom Italia Big Data Challenge (http://www.telecomitalia.com/tit/en/bigdatachallenge/contest.html), providing various geo-referenced and anonymized datasets. To obtain This dataset provides information regarding the level of interaction between the areas of the city of Milan and the Italian provinces. Telecom Italia: As part of the "Big Data Challenge", consists of data about telecommunication activity in the city of Milan and in the province of Trentino. telecommunications street furniture poletop poles pole attachments + 23. These metrics were also linked to socio-economical data in order to estimate poverty levels in a region. Original data sources include ISTAT and Eurostat data. The contest involved the participation of 1,100+ participants (652 teams and 105 universities) from all over the world. It is a rich, open multi-source aggregation of telecommunications, weather, news, social networks and electricity data. Bajardi, P., Delfino, M., Panisson, A., Petri, G. & Tizzoni, M. Unveiling patterns of international communities in a global city using mobile phone data. This dataset provides a description of the primary distribution lines in the Province of Trentino. The Joint Open Lab SKIL of Telecom Italia organizes the contest. The data of the Italian Administrative Regions are provided from ISTAT and were updated in 2011. acheneID: unique identification string of Dandelion; level: the level of this administrative region which can be. In order to ensure the privacy of the original users, their username has been obfuscated and the text of the tweet has been replaced with a list of entities extracted by the dataTXT-NEX tool (https://dandelion.eu/products/datatxt/). Instead, news stories exhibit a strong weekly seasonality which is probably due to work cycles, since Saturdays and Sundays less news are published (on the website) respectively 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. The sensors can measure different meteorological phenomena: Wind Direction, Wind Speed, Temperature, Relative Humidity, Precipitation, Global Radiation, Atmospheric Pressure and Net Radiation. 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. Physica A: statistical mechanics and its applications 392, 14591473 (2013). 25). There are many types of CDRs and Telecom Italia has recorded the following activities: Received SMS a CDR is generated each time a user receives an SMS, Sent SMS a CDR is generated each time a user sends an SMS, Incoming Call a CDR is generated each time a user receives a call, Outgoing Call a CDR is generated each time a user issues a call. CAS Isaacman, S. et al. Telecom Italia made a dataset of its own mobile phone data (millions of anonymized and geo-referenced records of calls from Milan and . 31. A multi-source dataset of urban life in the city of Milan and the Province of Trentino. date: publication date, formatted according to ISO 8601; timestamp: Unix timestamp generated from the publication date; municipality.acheneID: Dandelion achene for the municipality. The current flowing through the distribution lines has been recorded every 10 minutes. It is expressed as a geojson point and projected in WGS84 (EPSG:4326). In each traffic component, the spatial-temporal attention module is designed to capture the dynamic spatial-temporal correlation of cellular traffic; the spatial-temporal convolution module. Journal of Machine Learning Research 12, 28252830 (2011). It is composed by two subsets of data. Recently, the dataset used for the contest was made open to the public via their website. (t) follows the rule: where k is a constant defined by Telecom Italia, which hides the true number of calls, SMS and connections. In the second layer we lose the exact geometries of customer sites and power lines. Botta, F., Moat, H. & Tobias, P. Quantifying crowd size with mobile phone and Twitter data. However, this is only the minimal requirement. Italy State Lender to Drop $21 Billion Telecom Italia Offer Dataset with 6 projects 1 file 1 table. S Making Sense of Microposts (# Microposts2014), 115 (2014). PLoS ONE 10, e0128692 (2015). Data collectors divide Milan into 100100 regions, and all traffic data statistics are based on regions. Google Scholar. T elecom Italia, a telecom company in Italy, organized a Big Data Challenge back in 2014. The Open Database License (ODbL) explicitly covers data and not just creative works like photographs or text. The data of Milan and Trentino are collected by ARPA (http://www.arpa.piemonte.it/rischinaturali) and by Meteotrentino (http://www.meteotrentino.it) respectively. It uses around 180 primary distribution lines (medium voltage lines) to bring energy from the national grid to Trentino's consumers. Unfortunately the availability of communications and social media data is usually restricted to a few research teams that sign non-disclosure agreements (NDAs) and research contracts with telecommunication and other private companies. Urban areas have a resolution of 1:50.000, while areas with low population density have a resolution of 1:25.000. This dataset [Data citations 4,5] serves as measure of the level of interaction between the users and the mobile phone network. Our goal is to use these datasets in order to identify hotspots (areas of high communication strength) and analyse their interactions. 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. Thank you for visiting nature.com. This quantity is positive if the direction of the current goes from the national grid into the local line, negative otherwise. Metropolitan Cellular Traffic Prediction Using Deep Learning Techniques Cite this article. Cellular traffic prediction with machine learning: A survey 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. This grid is projected with the WGS84 (EPSG:4326) standard. processed the data and wrote the paper. 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). 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. Proceedings of CHI, 511520 (2014). G.B. 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. The datasets are released under the Open Database License (ODbL) and are publicly available in the Harvard Dataverse. At the Big Data Jam, hosted in Trento, the three winning teams, one for each track (Data Analytics, Data Visualization and App Development) were announced: Easystats Ltd, the University of Trento and LocaliData, from London, Trento and Milano. R.L. In the Telecommunications and Social pulse datasets, we provided record level data which are not algorithmically aggregated on purpose. Get the most important science stories of the day, free in your inbox. There is also code to generate the box-plots in this paper; Box-plots showing the calls, SMS, and Internet CDRs distributions per weekday and per cell in Milan. Article Kung, K., Greco, K., Sobolevsky, S. & Ratti, C. Exploring universal patterns in human home-work commuting from mobile phone data. Each record (or feature) describes a square providing the following information, This dataset provides information about the telecommunication activity over the Province of Trento.The dataset is the result of a computation over the Call Detail Records (CDRs) generated by the Telecom Italia cellular network over the city of Milano. 6 (2015). 1 10 0.2724 0.1127 0.0035 0.0807. Wesolowski, A. et al. Original data sources include ISTAT and Eurostat data. Its main role was to provide an affordable way to access to all the data related to the challenge and Dandelion is the original platform where all of this data was published.Its not the first time that some large datasets are made available to the public, through a controlled access: we can cite the public data sets published on Amazon S3, for example.But its the first time that there is an official Open Data release starting from some Big Data sets: we know that its an hot topic.Using your account on dandelion.eu to access the data, let us to collect some useful insights on the real demand side of the Open Data value chain.Well publish these statistics of usage as Open Data, to make all the community involved more aware about the data value chain.Its also useful to give some real perceptions on the Smart Cities and Smart Communities visions. The plot confirms our expectations. First Online: 20 June 2020 Part of the Contributions to Statistics book series (CONTRIB.STAT.) A pair of decimal numbers is given as the level of interaction. Date in the format YYYY-MM-DD HH24 : MI; Value: the ampere value of the current passing through a given powerline (Line id) at a given Timestamp. To get some useful insight about the data we further describe and visualize activity and connectivity maps from Telecom Italia data sets and mobility from Telekom Srbija data set. For each information point I referring to a geographical area v (contained in the shapefile), we can calculate the proportion of data which belongs to each GRID's square g: where Ap is the area of a polygon p. After this process, the ISTAT data is correctly linked to the GRID. The Telecommunication activity dataset for the city of Milan (i.e., data citation 5 in the paper), which contains mobile network traffic. From this definition, it is possible to study several behavioural aspects and cities' characteristics. It is proportional to the number of calls exchanged between callers, which are located in the Square id, and receivers located in the Province; Province to Square Inter: Value representing the interaction between the Square id and the Province. & Ratti, C. Towards a comparative science of cities: using mobile traffic records in new york, london, and hong kong. CDRs log the user activity for billing purposes and network management.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 contains data derived from an analysis of geolocalized tweets originated from the Province of Trento during the months of November and December.Each row corresponds to a tweet. Each record (or feature) describes a square providing the following information * Id of the square * Geometry of the square * Reference system WGS 84 - EPSG4326. Louail, T. et al. 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. As depicted in the mobile phone usage plot (see Fig. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. Defined as type 0; Slight: precipitation quantity equal in [0,2] mm/h. ), We have now opened the data of the contest to everyone, to let anybody understand, study and generate new ideas. In Milan, the type and the intensity of the phenomena are continuously measured by different sensors located within the city limit. 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. Article This helps researchers to observe and understand the spatial distribution of the various datasets. This dataset provides information about the current administrative regions in Europe. Geo-located twitter as proxy for global mobility patterns. In addition, the data pertaining to the challenge have been released to the research teams under the Open Database License (ODbL), thus triggering a long tail of follow on research work based on these data2630. designed the dataset and wrote the paper. Comparing and modeling land use organization in cities. Bogomolov, A. et al. Science 328, 1029 (2010). Square id: identification string of a given square of the Trentino GRID; Line id: identification string of the distribution power line, which is grouped with the Trentino GRID square; Number of customer sites: number of customer sites present in a given square of the Trentino GRID, connected to the grid powerline (Line id). Moreover, Bocconi has less mobile phone activity than Duomo, which is the centre of the city and the most important tourist attraction. Telecom Italia Data Analysis - GitHub Pages Science 338, 267270 (2012). 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. By. Blondel, V., Decuyper, A. Since it is not possible to have a well-established ground truth for the data, some important events with expected high importance for Milan were selected to validate it. 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. Aujasvi-Moudgil/Forecasting-Mobile-Network-Traffic - GitHub On the decomposition of cell phone activity patterns and their connection with urban ecology. MATH The Trentino Grid is provided in GeoJSON format. From mobile phone data to the spatial structure of cities. Cici, B., Gjoka, M., Markopoulou, A., Butts, C. & Irvine, U. A plain language summary of the ODbL is available on the Open Data Commons website. The Line measurement dataset is temporal aggregated in time-slots of 10min. Additionally, a similar dataset is also available for Trentino city, Italy. 2). 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. Barlacchi, G., De Nadai, M., Larcher, R. et al. Telecom Italia's board of directors has agreed to the spin-off of its 23 data centers into a separate business. The images or other third party material in this article are included in the articles Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. We would like to show you a description here but the site won't allow us. This dataset contains measurements about temperature, precipitation and wind speed/direction taken in 36 Weather Stations.15 minutes time interval. The SMSs are received in the nation identified by the Country code; Call-in activity: activity proportional to the amount of received calls inside the Square id during a given Time interval. The Telecom Italia Big Data Challenge dataset is unique in that, since it is a rich, open multi-source aggregation of telecommunications, weather, news, social networks and electricity data from the city of Milan and the Province of Trentino (see Table 1 and Fig. Telecom Italia, in association with EIT ICT Labs, MIT Media Lab, Milan Polytechnic and Trento RISE, has launched the Big Data Challenge, an online call for developers, researchers and designers from all over the world to come up with brand-new big data services and applications. Provided by the Springer Nature SharedIt content-sharing initiative, Scientific Data (Sci Data) 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. Deville, P. et al. The authors declare no competing financial interests. Uncovering the spatial structure of mobility networks. More than 650 teams from more than 100 universities have participated in this Challenge. In this paper, we describe the richest open multi-source dataset ever released on two geographical areas. Big Data Processing, Analysis and Applications in Mobile Cellular Since the text of the news articles is not provided, a service like diffbot (http://www.diffbot.com) or any other similar service (e.g., Apache Tika) could be used to extract the text from a given url. Internet a CDR is generated each time a user starts an Internet connection or ends an Internet connection. Moreover, the emergence of new geo-located Information and Communications Technology (ICT) services like Twitter and Foursquare introduces further opportunities for researchers to inspect quantitatively different aspects of human behaviour such as the social well-being of individuals and communities19, socio-economic status of geographical regions20, and people's mobility21. Lenormand, M. et al. Each sensor has a unique ID, a type and a location. Google Scholar. Each state in Europe is composed by administrative regions that may be divided into sub-regions. Google Scholar. arXiv preprint arXiv:1210.0137 (2012). This data was released under the Open Database License (ODbL) available in its raw form or through an API. 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. 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. This work is licensed under a Creative Commons Attribution 4.0 International License. A paid subscription is required for full access.. Slider with three articles shown per slide. Royal Society Open Science 2 (2015). Generally, people perform different activities during the day. 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. to read, aggregate, store, analyze, and model Telecom Italia data from CDRs. There is no spatial aggregation for this dataset. It is proportional to the number of calls exchanged between callers, which are located in the Province, and receivers located in the Square id. B.L. 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. Telecom Italia agrees on data center spin off for 2021 - DCD We select the following areas: Bocconi, one of the most famous Universities in Milan (Square id: 4259); Navigli district, one of the most famous nightlife places in Milan (Square id: 4456); Duomo, the city centre of Milan (Square id: 5060); Duomo, the city centre of Trento (Square id: 5200); Mesiano, the department of Engineering of the University of Trento (Square id: 5085); Bosco della citt, a forest near Trento (Square id: 4703). The dataset is composed of telecommunications, weather, news, social networks and electricity data from the city of Milan and the Province of Trentino. ADS 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. The data has been collected over two months, from November 1st, 2013 to January 1st, 2014 and the information is geo-referenced to the city of Milan and to the Province of Trentino. The end interval time can be obtained by adding 600,000milliseconds (10min) to this value; SMS-in activity: activity proportional to the amount of received SMSs inside a given Square id and during a given Time interval. MobiHoc '15 Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing, 317326 (2015). Correspondence to A.C. processed the dataset. PNAS 111, 1588815893 (2014). You are using a browser version with limited support for CSS. Scientific Reports 3 (2013). This allows comparisons between different areas and eases the geographical management of the data. Proceedings of Mobile Data Management (MDM) 1, 167176 (2013). Weekly spatial behaviour of the six selected areas in Milan and Trentino. The lender . Strength: Value representing the directional interaction strength between Square id1 and Square id2. As a follow up to the second challenge, we will work on releasing that data under the Open Database License (ODbL) as we did for the data described in this paper. This dataset provides information about the pollution intensity and type for Milan city. This test suggests that the data correctly reflects the temporal human behavioural patterns for the two areas considered. SpazioDati is one of the original technical partners of the Big Data Challenge contest. The shared datasets were created combining all this anonymous information, with a temporal aggregation of time slots of ten minutes. The Electricity dataset [Data citation 16], available only for the Province of Trentino, contains information about the energy consumption and how the electrical energy is supplied over the region. 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. Similarly, Twitter data (see Fig. Defined as type 3. while the precipitation intensity is characterized as Absent (type: 0), Rain (type: 1) and Snow (type: 2). Node Centrality Metrics for Hotspots Analysis in Telecom Big Data Some of the datasets referring to the Milan urban area are spatially aggregated using a grid. Introduction Cellular network is an important communication network, which provides call, message, and data services to the end users in the range covered by the base stations. This dataset contains data derived from an analysis of geolocalized tweets originated from the Province of Trento during the months of November and December. 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. Mobile phone activity in a city | Kaggle The third contains census variables, divided into eight different groups: residential population, foreign population, families, education level, work status, commuting, accommodations info and building composition.
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