关键词: 社会经济数据, 人口, 空间化, 多源数据融合, 大数据, 栅格数据

Abstract:

There is a spatial unit mismatch between statistical socio-economic data that based on administrative division statistics and geographic elements expressed in spatial grid units. It requires spatial processing technique to solve this mismatch. Research on the disaggregation of socio-economic data currently focuses on the indicators of population and gross domestic product. There is relatively few disaggregation of other socio-economic indicators, such as capital stock and housing which are essential input data for risk analysis. Dozens of spatial disaggregation models exist for different research objects. According to the differences in disaggregation ideas and methods of disaggregation, disaggregation models can be classified into three categories: area weighting method, statistical model method, and multi-source data fusion method. Area weighting method is simple but was criticized by its low resolution when applied on small scale studies. Statistical model method is widely used in disaggregation of large-scale socioeconomic statistical data, but needs sufficient spatial data for spatial statistics. While these methods can produce acceptable results, their actual resolution cannot be considered ideal. With the updating and appearing of new data sources for the disaggregation of socio-economic data, multi-source data fusion method has become the main disaggregation method recently. Moreover, the data and methods needed for disaggregation are continuously improved. Comparing principles, advantages and disadvantages of different disaggregation methods, we can see that diversification of socio-economic research indicators and spatial precision requirements, and wide application of big data are the development trend of disaggregation of socio-economic data. Meanwhile, the appearance of new data source is an important opportunity for improving spatial accuracy of the disaggregation. Overall, disaggregation of the socioeconomic data will be a hot subject for future study, one of the reasons for which is the increase of the research needs for high resolution grid data. Another reason is that research institutes and publishers have paid more attention to the scientific data which is reflected by new emerging scientific data journals.

Key words: socio-economic data, population, disaggregation, research progress, big data, raster dataset