EnGeoData2018: Environmental and Geo-spatial Data Analytics [IEEE DSAA 2018]

EnGeoData'2018: Environmental and Geo-spatial Data Analytics

Oct2

14:00 - 16:00, room “Torino”

14:00Opening remarks
14:08“Convolutional neural networks for disaggregated population mapping using open data”
Luciano Gervasoni, Serge Fenet, Regis Perrier and Peter Sturm
14:36“On non-routine places in urban human mobility”
Christian Quadri, Matteo Zignani, Sabrina Gaito and Gian Paolo Rossi
15:04“Crowdsourcing landforms for open GIS enrichment”
Rocio Torres, Darian Frajberg, Piero Fraternali and Sergio Luis Herrera Gonzalez
15:32“aipred: A Flexible R Package Implementing Methods for Predicting Air Pollution”
M. Benjamin Sabath, Qian Di, Danielle Braun, Francesca Dominici and Christine Choirat

Aims and Scope

Environmental and more generally geo-spatial information is now provided by crowdsourcing but also by public administrations in the context of the open data policies. Analyses of such data are still challenging. Firstly because of their heterogeneity (structural, semantic, spatial and temporal), and secondly because of the difficulty in choosing the “best” knowledge discovery process to apply, according to the needs of the experts in the field. This special issue aims to provide high quality research covering all or part of the challenges mentioned above, from a theoretical or experimental point of view.

Challenge about data science deals with creation, storage, search, sharing, modeling, analysis, and visualization of data, information, and knowledge. In Data Science context, spatio-temporal aspects are crucial in order to manage and mine data, to index and retrieve information, and finally to discover and visualize knowledge. By taking into account these spatio-temporal aspects, original methods have to be proposed for processing real and complex data from different domains, e.g., environment, agriculture, health, urban, and so forth.