Feature Importance of Climate Vulnerability Indicators with Gradient Boosting across Five Global Cities

TL;DR

This study uses gradient boosting to analyze the importance of traditional and non-traditional climate vulnerability indicators across five global cities, revealing that context-specific variables better predict exposure to extreme weather events.

Contribution

It introduces a novel application of feature importance analysis with gradient-boosted trees to evaluate climate vulnerability indicators across diverse urban contexts.

Findings

Non-traditional variables are more relevant than traditional ones for predicting weather exposure.

Variable importance varies across hazard types and cities, indicating the need for context-specific indicators.

Traditional socioeconomic factors like income and age are less predictive than social identity and perception variables.

Abstract

Efforts are needed to identify and measure both communities' exposure to climate hazards and the social vulnerabilities that interact with these hazards, but the science of validating hazard vulnerability indicators is still in its infancy. Progress is needed to improve: 1) the selection of variables that are used as proxies to represent hazard vulnerability; 2) the applicability and scale for which these indicators are intended, including their transnational applicability. We administered an international urban survey in Buenos Aires, Argentina; Johannesburg, South Africa; London, United Kingdom; New York City, United States; and Seoul, South Korea in order to collect data on exposure to various types of extreme weather events, socioeconomic characteristics commonly used as proxies for vulnerability (i.e., income, education level, gender, and age), and additional characteristics not often included in existing composite indices (i.e., queer identity, disability identity, non-dominant primary language, and self-perceptions of both discrimination and vulnerability to flood risk). We then use feature importance analysis with gradient-boosted decision trees to measure the importance that these variables have in predicting exposure to various types of extreme weather events. Our results show that non-traditional variables were more relevant to self-reported exposure to extreme weather events than traditionally employed variables such as income or age. Furthermore, differences in variable relevance across different types of hazards and across urban contexts suggest that vulnerability indicators need to be fit to context and should not be used in a one-size-fits-all fashion.