GIP Magazine | Geoinformation Processing Department @ ITC

Developing and implementing a workflow for a new global dataset for high resolution travel time

Travel time to the nearest human settlement from anywhere on the planet in the year 2015

Objective:

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 project 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 obtained from Google Maps.

Keywords:

travel time, global grid, OpenStreetMap

See above. This illustration will eventually be replaced.

Description:

Research Theme *

STAMP / FORAGES / CRIB (see below)

Additional remarks (optional)

This project will be carried out in collaboration with Andy Nelson (NRS) and Serkan Girgin (CRIB). They would have featured in the list of staff above if this web page would allow.

Upscaling a derived data set is an ambitious, challenging and non-trivial task. With this project, we aim to improve the spatial resolution of this fundamental global dataset by a factor ten. This means that associated datasets typically increase by a factor 100. Handling such data wisely is one concern. But, at different resolutions also different spatial features come into play: a motorway no longer is a stretch of concrete from A to B, but it has exits with on and off ramps, and distinctions between 2-, 3- and 4-lane carriageways may become important. Thus, also the data model itself requires a review. Finally, the computations from the base data to produce the travel friction grid will require careful scrutiny: with so much more base data, the computational complexity of the chosen algorithms will be an important factor of success.

If time allows, we may also want to address another characteristic of current models. They do not perform so well in hilly and mountainous areas. (In a 1km2 grid cell a mountain road may have four hairpins, for instance.) These models apply terrain slope in a rather crude way, which causes higher systematic error in travel time forecasts over undulating terrain. The current models are independent of direction of travel. While slope is an important determining factor of cruising speed, regardless of travel mode, more sensible models shall account for the interplay between slope angle, aspect and travel direction.

In short, there are numerous challenges in this exciting project. The product that we aim to develop has many applications, and holds promise to become a flagship dataset.

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. We are keen to discuss the options with any serious candidate.