Projecting high resolution population distribution using Local Climate Zones and multi-source big data

High precision and easily updatable data on population estimates are critical for urban planning, disaster risk assessment, and public health campaigns. However, traditional administrative cen-sus data often faces limitations in spatial resolution, making it difficult for further applications. In this study, we construct a fast and efficient population estimation model using Local Climate Zones (LCZ) products at a fine spatial scale. The population totals are estimated by LCZ units, based on a robust linkage between built LCZ types and population density. Validated by city-and county-level census data in 21 cities in China, the new model exhibits a very good fit with R2 val-ues of 0.77 and 0.73 respectively, which confirms the effectiveness of LCZ-based population esti-mates. To our knowledge, this is the first study that directly estimates the population via LCZ maps. In addition, it was proven that population density can be used as a new property for LCZ type definition. As LCZ products are convenient to obtain, this work provides a simple, economi-cal, and reliable population estimation method that can complement the traditional census.