Sadhana Nirandjan’s PhD research is part of the RECEIPT project, which is funded by the European Union’s Horizon 2020 Research and Innovation Programme. The results of her work have been published in the scientific journal Nature Scientific Data.
Overview of risk areas
Natural disasters such as floods, landslides, earthquakes and cyclones can damage all kinds of critical infrastructure. They can lead to power outages or cause clean drinking water to become unavailable. Critical infrastructure also includes healthcare, education, waste disposal and telecommunications. Damage to this infrastructure network can lead to socioeconomic disruption, which means there is much to gain from investing in its robustness. To ensure a resilient global network, it is important to first understand where critical infrastructure is located.
To develop her dataset, Nirandjan used open-source data to develop an index that represents the spatial density of critical infrastructure around the world: the Critical Infrastructure Spatial Index(CISI). For her research, Nirandjan extracted high-resolution data from OpenStreetMap, demonstrating that this data can be successfully used to create an index for critical infrastructure. The index represents the spatial density of global critical infrastructure on a scale of 0 to 1. Areas without critical infrastructure were assigned a 0, and areas with the highest density of critical infrastructure were given a 1.
The CISI can be used to investigate where in the world critical infrastructure is exposed to potential natural disasters, and what the financial consequences would be if a disaster were to occur. But the dataset could also be used for other purposes, such as evaluating various Sustainable Development Goals (SDGs), for instance by identifying locations where certain types of critical infrastructure – such as energy infrastructure – are missing. Combined with population data, this kind of information could be used for SDG 7: ‘Ensure access to affordable, reliable, sustainable and modern energy for all’. Nirandjan’s spatial dataset is freely accessible at Zenodo.
The dataset’s code is also open source, meaning it can be freely accessed. This allows users to create their own datasets for different infrastructure types, spatial scales and resolutions, while also ensuring that the model can be further developed. The code is available on GitHub.