A multi-source dataset of urban life in the city of Milan and the Province of Trentino
By Gianni Barlacchi, Marco De Nadai, Roberto Larcher, A. Casella, Cristiana Chitic, Giovanni Torrisi, F. Antonelli, Alessandro Vespignani, Alex Pentland, Bruno Lepri. Scientific Data, 2015
562 citations recorded by the source corpus.
The study of socio-technical systems has been revolutionized by the unprecedented amount of digital records that are constantly being produced by human activities such as accessing Internet services, using mobile devices, and consuming energy and knowledge. In this paper, we describe the richest open multi-source dataset ever released on two geographical areas. The dataset is composed of telecommunications, weather, news, social networks and electricity data from the city of Milan and the Province of Trentino. The unique multi-source composition of the dataset makes it an ideal testbed for methodologies and approaches aimed at tackling a wide range of problems including energy consumption, mobility planning, tourist and migrant flows, urban structures and interactions, event detection, urban well-being and many others. Design Type(s) data integration objective • observation design Measurement Type(s) surface layer precipitation • Media • electrical energy consumption • administrative region • Telecommunications Technology Type(s) weather stations • Internet Electronic Communication • network analysis • Geographic Information System • Telecommunication Device Factor Type(s) Sample Characteristic(s) Milan • Trentino-South Tyrol • anthropogenic habitat Design Type(s) data integration objective • observation design Measurement Type(s) surface layer precipitation • Media • electrical energy consumption • administrative region • Telecommunications Technology Type(s) weather stations • Internet Electronic Communication • network analysis • Geographic Information System • Telecommunication Device Factor Type(s) Sample Characteristic(s) Milan • Trentino-South Tyrol • anthropogenic habitat Machine-accessible metadata file describing the reported data (ISA-Tab format)
Subfields: Epidemiology, Global and Planetary Change, Transportation
View Paper: https://www.semanticscholar.org/paper/cd3b315857e6a6a9d93e46cd54b402486c939ab9
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In Global and Planetary Change
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Citations in the corpus
- Deep Learning in Mobile and Wireless Networking: A Survey (2018, methodology): “Figures adapted from [193] using data from [458].”
- Human mobility: Models and applications (2017, background): “Alongside anonymized call records, additional metadata related to weather, news, Twitter activity, and electricity data from two areas in Italy (Milan and Trentino) was provided [52].”
- Deep Transfer Learning for Intelligent Cellular Traffic Prediction Based on Cross-Domain Big Data (2019, methodology): “The cellular traffic dataset analyzed in this paper comes from a large telephony services provider in Europe, Telecom Italia, as part of the “Big Data Challenge” [38].”