Georeferenced information on road infrastructure is essential for spatial planning, socio-economic assessments and environmental impact analyses. However, current publicly available global road maps are outdated and biased. In the Global Roads Inventory Project (GRIP) we gathered, harmonized and integrated nearly 60 geospatial datasets on road infrastructure into a new global roads dataset.
The GRIP database
To compile the GRIP dataset, we searched for publicly available national and supra-national vector datasets from governments, research institutes, NGOs and crowd-sourcing initiatives. We integrated these into one coherent global dataset by harmonizing the road attribute information and checking and correcting road connections across country borders. The resulting dataset covers 222 countries and includes over 21 million km of roads, which is 2–3 times the total length in the currently best available country-based global roads datasets.
Projecting future road length
In order to obtain estimates of future road length, we developed a regression model relating the total road length per country to the country area, population density, GDP and OECD membership. We applied this model to future population densities and GDP estimates from the Shared Socioeconomic Pathway (SSP) scenarios and found an estimate of 3.0–4.7 million km additional road length for the year 2050. Large increases in road length were projected for developing nations in some of the world’s last remaining wilderness areas, such as the Amazon, the Congo basin and New Guinea. This highlights the need for accurate spatial road datasets to underpin strategic spatial planning in order to reduce the impacts of roads in remaining pristine ecosystems