Spatialization Research of Statistic Population based on Multi-Source Geospatial Data

Population spatial distribution is of great significance to the study on various fields related to human activities. Compared with the traditional demographic data that uses administrative regions as the statistical unit, the fine-scale spatialized population data is better at displaying details and easier to update dynamically. This study proposes a spatialization method of statistic population. It separates urban and rural areas firstly, and applies land use, residential buildings, point of interest (POI) to develop an algorithm to obtain the population distributed at each independent building and 100m grid. Night lights intensity and life circle of Qingdao were performed to evaluate the applicability of population distribution data. Results show that the algorithm used in this study can obtain fine-scale population distribution. At the same time, it could be provided as a basis for applications in territorial spatial planning, regional economic development, ecological environmental protection, public health and emergency response.