山东省地理国情信息综合统计分析技术与实现

Realization of Comprehensive Statistical Analysis Technology of National Geographical Conditions in Shandong Province

摘要:

地理国情统计分析工作是地理国情数据向地理国情信息、地理国情知识乃至决策智慧转化与提升的重要方法与技术手段。鉴于地理国情综合统计是一项计算密集型、知识密集型、学科交叉的复合型工作，难度大、牵涉面广，目前省级尺度工作开展需进行多项关键技术研究与攻关。该研究从基于指标模型自由组合及统计分析范围自由选择的地理国情信息统计分析计算、基于一致性哈希高容错性算法实现地理数据的高效运算、基于统计知识库的统计分析专题地图快速精细化制图模式、基于归一化方法实现统计分析主题指标分级评估体系等4个方面关键技术入手，创新性的完成山东省地理国情信息综合统计分析工作，取得了软件系统、指标模型、报告、专题图等多项成果。成果在多个政府部门与科研机构得到应用，为部门、科研机构相关处室业务整合与统筹规划，宏观数据对比分析，量化决策向大数据决策转变等提供有力技术支撑和可靠依据。

Abstract:

Statistical analysis technology of national geographical conditions is an important technical means to transform and enhance geographic national conditions data to geographic information, geographic knowledge and decisionmaking intelligence. Comprehensive statistics is a computationally intensive, knowledge intensive and cross disciplinary work. It is difficult and involves a wide range of knowledge. At present, many key technologies need to be researched and tackled at the provincial level. From 4 aspects, such as statistical analysis and calculation based on free combination of the index model and the range of statistical analysis of geographical conditions information, the efficient operation of geographic data based on Hash high fault tolerance algorith, rapid fine drawing pattern of statistical analysis thematic maps based on statistical knowledge base, and the realization, the classification and evaluation system of the statistical analysis of thematic indicators based on the normalization method, comprehensive statistical analysis of national geographical conditons in Shandong province has been innovatively completed, and many achievements have been obtained, such as the software system, the index model, the report, the thematic map and so on. The achievements have been applied in many government departments and scientific research institutions, providing strong technical support and reliable basis for the integration and overall planning of relevant departments and scientific research institutions, the comparative analysis of macro data, the change of quantitative decision making to large data decisionmaking and so on.