主体功能区监测评价指标体系及典型空间数据集建设 英文标题:The Index System for Evaluation of Monitoring the Major Function Oriented Zone and Typical Spatial Data Sets Construction

主体功能区在国土资源分配与规划方面发挥巨大作用。近10年来，研究者逐渐意识到主体功能区规划、监测与评价对区域规划的重要性，但是大多数研究主要集中于规划，对监测与评价研究甚少。本文针对主体功能区监测评价内容、目标不明确的现象，从监测评价指标体系的构建和指标空间数据集生产方面开展研究工作，主要内容和结论如下：(1)以主体功能区监测评价需求为基础，充分考虑全面性、科学性、空间性、区域性、可操作性原则，建立了涵盖资源、环境、生态、自然灾害、经济、人口社会、政策、交通等内容的指标体系，实现监测评价目标与内容的具体化。(2)依据指标数据的不同数据源，将指标数据划分为通用基础类地理空间数据、遥感类数据、社会经济统计类数据，分别分析三者的获取与处理方法，并建立多源数据整合方法，实现指标空间数据集生产可操作性。(3)选取人口作为关键指标，采用城乡分区建模思想，分别建立城镇、农村和总人口空间离散模型，其中，在农村人口空间离散模型参数界定过程中，通过地统计学分析方法，得出农村人口分布受农村居民点、公路、河流、土地利用影响的决定系数R2分别为0.4145、0.3955、0.7878、0.9578。而后，采用基础地理数据和2000年全国第五次人口普查数据，应用模型计算得到2000年黄河流域500m城镇、农村、总人口空间分布栅格图，并统计黄河流域二级流域的城镇、农村和总人口数，实现人口典型空间数据集的建设。结果显示龙门至三门峡流域人口数量占黄河流域人口总数的38%，实现不以行政区划而以地理区划进行人口数量的统计。(4)选取GDP作为关键指标，分别建立各产业GDP与总GDP空间离散模型，并基于Arc Workstation下环境的AML编程实现模型运算，得到2000年黄河流域500m各产业GDP空间分布栅格图，结合总人口空间分布栅格图模拟黄河流域500m人均GDP空间分布，实现GDP典型空间数据集的建设，体现经济数据和人口数据的空间融合。

Major Functional Zones (MFZs) play a crucial role in land resource allocation and planning. Over the past decade, researchers have gradually recognized the importance of MFZ planning, monitoring and evaluation for regional planning, but most studies have mainly focused on planning, with very limited research on monitoring and evaluation. Aiming at the issue of unclear content and objectives of MFZ monitoring and evaluation, this paper conducts research from two aspects: the construction of a monitoring and evaluation indicator system and the production of indicator spatial datasets. The main contents and conclusions are as follows:
(1) Based on the monitoring and evaluation needs of major functional zones, and fully adhering to the principles of comprehensiveness, scientificity, spatiality, regionality and operability, an indicator system covering resources, environment, ecology, natural disasters, economy, population and society, policies, transportation and other related contents is established, which concretizes the objectives and content of monitoring and evaluation.
(2) According to different data sources of indicator data, indicator data is classified into three categories: general basic geospatial data, remote sensing data, and socio-economic statistical data. The acquisition and processing methods of the three types of data are analyzed respectively, and a multi-source data integration method is established, which ensures the operability of indicator spatial dataset production.
(3) Taking population as the key indicator, the urban-rural partitioned modeling framework is adopted to establish spatial disaggregation models for urban, rural and total population respectively. In the process of defining the parameters of the rural population spatial disaggregation model, through geostatistical analysis, it is concluded that the coefficients of determination (R²) of the impacts of rural residential areas, highways, rivers and land use on rural population distribution are 0.4145, 0.3955, 0.7878 and 0.9578 respectively. Then, using basic geographic data and the 2000 Fifth National Population Census data of China, the model is applied to calculate the 500m-resolution spatial distribution raster maps of urban, rural and total population in the Yellow River Basin in 2000, and the urban, rural and total population numbers of the secondary watersheds of the Yellow River Basin are counted, thereby completing the construction of a typical population spatial dataset. The results show that the population in the Longmen-Sanmenxia reach accounts for 38% of the total population of the Yellow River Basin, realizing population statistics based on geographic divisions rather than administrative divisions.
(4) Taking GDP as the key indicator, spatial disaggregation models for the GDP of each industry and the total GDP are established respectively. The model operation is implemented via AML programming under the Arc Workstation environment, and the 500m-resolution spatial distribution raster maps of industrial GDP in the Yellow River Basin in 2000 are obtained. Combined with the total population spatial distribution raster map, the 500m-resolution per capita GDP spatial distribution of the Yellow River Basin is simulated, thereby completing the construction of a typical GDP spatial dataset, which reflects the spatial fusion of economic data and population data.