Please cite our paper if you publish material based on those datasets G. Khodabandelou, V. Gauthier, M. El-Yacoubi, M. Fiore, "Estimation of Static and Dynamic Urban Populations with Mobile Network Metadata", in IEEE Trans. on Mobile Computing, 2018 (in Press). 10.1109/TMC.2018.2871156 Abstract Communication-enabled devices that are physically carried by individuals are today pervasive, which opens unprecedented opportunities for collecting digital metadata about the mobility of large populations. In this paper, we propose a novel methodology for the estimation of people density at metropolitan scales, using subscriber presence metadata collected by a mobile operator. We show that our approach suits the estimation of static population densities, i.e., of the distribution of dwelling units per urban area contained in traditional censuses. Specifically, it achieves higher accuracy than that granted by previous equivalent solutions. In addition, our approach enables the estimation of dynamic population densities, i.e., the time-varying distributions of people in a conurbation. Our results build on significant real-world mobile network metadata and relevant ground-truth information in multiple urban scenarios. Dataset Columns This dataset cover one month of data taken during the month of April 2015 for three Italian cities: Rome, Milan, Turin. The raw data has been provided during the Telecom Italia Big Data Challenge (http://www.telecomitalia.com/tit/en/innovazione/archivio/big-data-challenge-2015.html) 1. grid_id: the coordinate of the grid can be retrieved with the shapefile of a given city 2. date: format Y-M-D H:M:S 4. landuse_label: the land use label has been computed by through method described in [2] 5. population: Census population of a given grid block as defined by the Istituto nazionale di statistica (ISTAT https://www.istat.it/en/censuses) in 2011 6. estimation: Dynamics density population estimation (in person) as the result of the method described in [1] 7. area: surface of the "grid id" considered in km^2 8. geometry: the shape of the area considered with the EPSG:3003 coordinate system (only with quilt) Note Due to legal constraints, we cannot share directly the original data from the Telecom Italia Big Data Challenge we used to build this dataset. Easy access to this dataset with quilt Install the dataset repository: $ quilt install vgauthier/DynamicPopEstimate Use the dataset with a Panda Dataframe >>> from quilt.data.vgauthier import DynamicPopEstimate >>> import pandas as pd >>> df = pd.DataFrame(DynamicPopEstimate.rome()) Use the dataset with a GeoPanda Dataframe >>> from quilt.data.vgauthier import DynamicPopEstimate >>> import geopandas as gpd >>> df = gpd.DataFrame(DynamicPopEstimate.rome()) References [1] G. Khodabandelou, V. Gauthier, M. El-Yacoubi, M. Fiore, "Population estimation from mobile network traffic metadata", in proc of the 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 1 - 9, 2016. [2] A. Furno, M. Fiore, R. Stanica, C. Ziemlicki, and Z. Smoreda, "A tale of ten cities: Characterizing signatures of mobile traffic in urban areas," IEEE Transactions on Mobile Computing, Volume: 16, Issue: 10, 2017.

{"references": ["A. Furno, M. Fiore, R. Stanica, C. Ziemlicki, and Z. Smoreda, \"A tale of ten cities: Characterizing signatures of mobile traffic in urban areas,\" IEEE Transactions on Mobile Computing, Volume: 16, Issue: 10, 201"]}