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Project
Predictive modeling of spatiotemporal phenomena in Geographic Information Systems using Machine Learning
The growing wealth of data on spatiotemporal phenomena allows for new modeling approaches in Geographical
Information Science. In this context, this doctoral mandate will examine the usefulness of machine learning techniques --U+2010 a data--U+2010driven approach for predictive modeling. The usefull techniques for this challenge will be identified, ported to the GIS--U+2010framework and tested using data from a mass--U+2010event.
Date:1 Oct 2013 → 30 Sep 2017
Keywords:GIS, machine learning, predictive models, Big Data, spatiotemporal phenomena, mass-U+2010events
Disciplines:Geomatic engineering, Theoretical computer science, Programming languages, Computer architecture and networks, Urban and regional geography, Distributed computing, Visual computing, Applied mathematics in specific fields, Economic geography, Human geography, Recreation, leisure and tourism geography, Other social and economic geography, Scientific computing, Other information and computing sciences, Information sciences, Information systems