Code
3F004113
Duration
01 October 2013 → 30 September 2017
Funding
Regional and community funding: Special Research Fund, Research Foundation - Flanders (FWO)
Promotor
Fellow
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
Keywords
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.