Analyzing Personal Happiness from Global Survey and Weather Data: A Geospatial Approach.

Past studies have shown that personal subjective happiness is associated with various macro- and micro-level background factors, including environmental conditions, such as weather and the economic situation, and personal health behaviors, such as smoking and exercise. We contribute to this literature of happiness studies by using a geospatial approach to examine both macro and micro links to personal happiness. Our geospatial approach incorporates two major global datasets: representative national survey data from the International Social Survey Program (ISSP) and corresponding world weather data from the National Oceanic and Atmospheric Administration (NOAA). After processing and filtering 55,081 records of ISSP 2011 survey data from 32 countries, we extracted 5,420 records from China and 25,441 records from 28 other countries. Sensitivity analyses of different intervals for average weather variables showed that macro-level conditions, including temperature, wind speed, elevation, and GDP, are positively correlated with happiness. To distinguish the effects of weather conditions on happiness in different seasons, we also adopted climate zone and seasonal variables. The micro-level analysis indicated that better health status and eating more vegetables or fruits are highly associated with happiness. Never engaging in physical activity appears to make people less happy. The findings suggest that weather conditions, economic situations, and personal health behaviors are all correlated with levels of happiness.