Predicting Air Conditioner Ownership in Low- and Middle-Income Countries using Geospatial Machine Learning Methods - Mapping Cooling Inequality

Abstract Background: Rising global temperatures are intensifying exposure to extreme heat, yet access to air conditioning (AC) remains unequal which leaves vulnerable populations without protection against the heat. Household survey data on AC ownership is scarce in low- and middle-income countries limiting the ability to identify areas of cooling gaps. Methods: In this study, we develop a geospatial machine learning framework that integrates household data from the Demographic and Health Surveys (DHS) program with high resolution climatic, socioeconomic and demographic covariates to generate

This content is for informational purposes only and is not medical advice. It is not intended to diagnose, treat, cure, or prevent any disease. Consult a healthcare professional before starting any supplement, treatment, or program — especially if you are pregnant, nursing, taking medication, or managing a health condition.