Guest editorial: special issue on Environmental and Geospatial Data Analytics

摘要

Environmental and more generally geospatial 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, because of their heterogeneity (structural, semantic, spatial, and temporal) and because of the difficulty in choosing the "best" knowledge discovery process to apply, according to the needs of the experts in the field. Challenges about data science deal with creation, storage, search, sharing, modeling, analysis, and visualization of data, information, and knowledge. In the data science context, spatiotemporal 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 spatiotemporal 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.