空间信息获取和处理技术,可将传统关系型社会经济数据转变为空间化的矢量或者格网数据。首先,分析了传统关系型社会经济数据库的不足,并对社会经济数据格网化理论研究及格网数据库建设的历史做了回顾;在明确社会经济数据格网化基本概念的基础上,提出了社会经济数据格网化的3个基本要求,即时间可比、空间一致和逻辑自洽;同时提出了一个包括24个关键指标的国家尺度社会经济格网数据库的指标体系,认为社会经济格网数据库生产过程的主要步骤为逻辑检查、空间匹配、代码匹配、空间离散和检查校验;研究对国家尺度社会经济指标的空间离散过程和离散模型、不同层级社会经济数据的整合和离散策略进行了重点分析。研究最后就社会经济数据格网化过程中存在的主要问题进行了总结。
With the developments of modern space information sciences, specifically relying on the quickly updated and large scale remote sensing observation technology, the derivative LUC (land use and land cover) data generations, the strong spatial analysis functions and the visual information representation functions supported by geographic information system (GIS) technology, the traditional social and economic statistics data with Relational format can be transferred to the spatial data (Vector or Grid format). This paper firstly analyzed the shortcomings of traditional statistical data in the new-era applications, which characterized by long update-period, low spatial-resolution, poor visualization effects and non-support for the spatial computing and spatial analysis. We briefly reviewed the history of theoretical studies and database constructions of society and economy grid database. Based on the clarification about the concepts of grid process of society and economy statistics data, three basic requirements, i.e. temporal compatibility, space reference system compatibility and self-consistent logic, were put forward as the main principles in the transferring process, and a series of key index including 24 items to build a national scale society and economy grid database were posted. Then, based on the above data transferring principles and index system, we discussed the 5-step transferring processes which include (1) logical checking, (2) spatial matching, (3) code matching, (4) spatial discretization, and (5) checking and calibration. We also analyzed the general models to transfer the rational datasets into grid dataset in detail, and discussed the corresponding strategies when people integrate different datasets. Finally, we summarized the common questions and suggested that the spatial discrete model construction and the quick update routine works to prepare the background resources/environment datasets are the main focuses in the future studies.
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