To weight, or not to weight? Comparing methodological approaches in mapping climate risk and vulnerability for metropolitan regions

Using Geographic Information System (GIS) outputs as a tool to inform policy decision-making is an increasingly popular approach in addressing climate change challenges. Maps and geospatial visualisations are typically well understood across multiple disciplines, including non-experts, thus removing technical barriers to subject matter and transcending the boundaries of organisational structures and working regimes related to climate change adaptation. However, the impacts of climate change at the local scale, particularly across metropolitan regions, are complex and often heterogeneous across different areas. The geospatial variables necessary to compile a holistic climate risk and vulnerability indicator therefore vary from place to place, and research into developing such maps across the world highlights that there is no “one size fits all” approach. Using GIS, this study compares different statistical methodological approaches to mapping climate risk and vulnerability for Birmingham, UK, where a co-created climate risk and vulnerability assessment (CRVA) map is already embedded in local authority decision-making. The same underpinning variables and spatial resolution (100m) across methods are used, considering approaches to aggregating variables in different ways, involving weighting techniques. Comparing the results serves as a validation exercise in utilising the most appropriate approach for the city of Birmingham and has broader implications for the West Midlands region of the UK, where the CRVA is being upscaled and enhanced further in collaboration with the regional authority for targeted climate change adaptation planning. Further research to replicate this study for other metropolitan regions would potentially highlight the strengths and weaknesses of geospatial methodological approaches more robustly. While this study does not intend to provide a single solution to climate risk mapping, it instead aims to draw out key implications from a spatial perspective of selecting one approach over another for decision-making purposes.