社会经济数据空间化现状与发展趋势

行政单元的社会经济统计数据与地理单元的要素数据之间存在空间不匹配的问题,很难满足自然与人文交叉学科研究的需要。本文首先对社会经济数据空间化指标和方法进行了总结,发现空间化研究主要集中在人口与国内生产总值指标,对资本存量、房屋等其他社会经济指标的空间化研究相对较少;根据空间化的思路和方法差异,可将空间化方法归纳为面积权重法、统计模型法和多源数据融合法三类。最后通过对比分析不同空间化方法的原理和优缺点可知：社会经济研究指标多样化、空间化精度要求的多元化和大数据应用的广泛化是社会经济数据空间化的发展趋势。同时,大数据等新的辅助数据源的出现为空间化精度的提高带来了契机,在社会管理精细化要求不断提高的背景下,社会经济数据空间化也越来越成为研究热点。

The Status and Development Trend of Disaggregation of Socio-economic Data

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.