关键词: 人口, 空间化, 格网, 模型, 尺度

Abstract:

The research purpose of the spatialization of population data is to capture the size and the distribution location of population in the geographical space. It plays an important role in presenting the geographical meaning of demographic data. Spatializing the statistical population data has increasingly become a research hotspot in the fields of demography, geography and GIS. Population distribution dataset is a key achievement in the spatialization study. At present, there are a few widely-used population distribution datasets and influential population spatialization projects, including GPW/GRUMP, LandScan and UNEP/GRID & China km grid population datasets. Population distribution dataset has practical application values and the scientific significance for relevant researches, such as government planning and decision making at all levels, disaster assessment and resource allocation. After nearly 30 years' development, the spatialization researches are evolving into the maturity stage. They have obtained many achievements and produce a rich variety of spatialization models of population data. Based on the purpose of spatializing census data and the differences between modeling concept and model principle, this paper reviews the spatialization methodologies in three major aspects: (1) the method and characteristic of the selection of grid size (scale); (2) 3 types of common adopted modeling ideas and a comparative analysis between 6 types of basic models; and (3) the proper strategies used for improving the simulation accuracy and their application background and advantages. Finally, according to the research contents of population data spatialization at present stage, this article discusses the further study direction through four perspectives: (1) the suitability of grid size; (2) the simulation of spatial distribution of population at high spatial and temporal resolution; (3) the adoption of new type of data source; and (4) the comprehensive application of multi-thought and multi-model. It is significant to grasp the current status of spatialization research and promote the further development of spatialization methodologies.

Key words: population, spatialization, grid, model, scale