Developing and implementing a workflow for a new global dataset for high resolution travel time
There is no fieldwork in this topic.
The candidate must have good programming skills for this topic, preferably competence in data extraction and geospatial computing in Python.
ITC research staff from the GIP, PGM and NRS departments, in collaboration with CRIB have developed global maps that estimate land-based travel time from any location in the world to the nearest city. These travel time estimates are an important factor for assessing inequalities in access to resources, economic opportunities, health care and education. They also play a role in assessing how resilient a transport network is to disasters such as floods or landslides.
The travel time estimates depend on a suite of spatial data layers that characterise the transport network (roads, railways, rivers/canals) and areas not served by the transport network (landcover, slope, elevation) as well as international borders.
Until now, all global travel time maps have been at 1km spatial resolution. The aim of this MSc is to
- develop and implement a workflow to increase the spatial resolution of the global travel time map to 100m. This will require new workflows to extract information from OpenStreetMap vectors and (where appropriate) combine them with slope.
- a workflow to integrate all the spatial layers into a single layer that represents the time required to cross each 100m pixel of the Earth’s surface.
- produce a new 100m resolution global travel time map and validate it against travel time estimates from Google Maps.