Authors
Cesare Furlanello, Markus Neteler, Stefano Merler, Stefano Menegon, Steno Fontanari, Angela Donini, Annapaola Rizzoli, C Chemini
Publication date
2003
Journal
Proceedings of DSC
Pages
2
Description
We discuss how sophisticated machine learning methods may be rapidly integrated within a GIS for the development of new approaches in landscape epidemiology. A multitemporal predictive map is obtained by modeling in R, analyzing geodata and digital maps in GRASS, and managing biodata samples and weather data in PostgreSQL. In particular, we present a risk mapping system for tick-borne diseases, applied to model the risk of exposure to Lyme borreliosis and tick-borne encephalitis (TBE) in Trentino, Italian Alps.
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Scholar articles
C Furlanello, M Neteler, S Merler, S Menegon… - Proceedings of DSC, 2003