Disasters, Statistics, and the Humanitarian Sector

Abstract

This article examines the role of statistics in the humanitarian sector, with a particular focus on disasters caused by natural hazards. It begins by outlining current applications, including primary data collection, anticipatory action frameworks, Earth observation, mobile positioning data, and artificial intelligence. It then highlights key challenges such as gaps and biases in disaster impact and response data, difficulties in communicating statistical findings clearly, inequities in aid allocation, and the widespread outsourcing of statistics-related work. In exploring future applications, the article discusses the potential of impact-based early warning models, dynamic population data, and artificial intelligence to enhance communication and decision-making. Throughout, emphasis is placed on the need for interoperable systems as well as ethical and inclusive data practices. In doing so, the article presents statistics as both a diagnostic and strategic tool for strengthening the effectiveness, fairness, and responsiveness of humanitarian action in disaster contexts.