A new approach to estimating flood-affected populations by combining mobility patterns with multi-source data: A case study of Wuhan, China

In this paper, the authors develop a new approach to estimating flood-affected populations, including their quantity, structure, and spatial distribution, by integrating commuter mobility patterns and road inundation records using multi-source data. Mobility patterns were built by the Gravity model with point-of-interest (POI) data and then downscaled to individual routes by Monte Carlo simulation. Inundation locations and impassable roads were retrieved by combining flood hazard maps with social media data.

This approach was applied to Wuhan, a large city in Central China, to estimate the affected automobile commuters under a typical flood event (July 6, 2016) and three flood scenarios (10-year, 20-year, and 50-year). The researchers' approach produces an estimate of the affected population that is more accurate and detailed than existing approaches. It also provides high-resolution impact information for urban studies and can be applied easily to other cities and disasters that include road closures. It will help governments to estimate affected populations rapidly and assess indirect economic losses caused by labor disruptions and work delays.