Predictive modeling of spatiotemporal phenomena in Geographic Information Systems using Machine Learning

01 October 2013 → 30 September 2017
Regional and community funding: Special Research Fund, Research Foundation - Flanders (FWO)
Research disciplines
  • Natural sciences
    • Applied mathematics in specific fields
    • Computer architecture and networks
    • Distributed computing
    • Information sciences
    • Information systems
    • Programming languages
    • Scientific computing
    • Theoretical computer science
    • Visual computing
    • Other information and computing sciences
  • Social sciences
    • Economic geography
    • Human geography
    • Recreation, leisure and tourism geography
    • Urban and regional geography
    • Other social and economic geography
  • Engineering and technology
    • Geomatic engineering
GIS machine learning predictive models Big Data spatiotemporal phenomena mass-­‐events
Project description

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 --‐ a data--‐driven approach for predictive modeling. The usefull techniques for this challenge will be identified, ported to the GIS--‐framework and tested using data from a mass--‐event.