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时空大数据分类体系研究
引用本文:田立征,李成名,刘晓丽,印洁.时空大数据分类体系研究[J].测绘通报,2021,0(5):1-4.
作者姓名:田立征  李成名  刘晓丽  印洁
作者单位:1. 聊城大学, 山东 聊城 252000;2. 中国测绘科学研究院, 北京 100089
基金项目:国家重点研发计划(2018YFB2100702);中国测绘科学研究院基本科研业务费(AR1909)
摘    要:“统一行使全民所有自然资源资产所有者职责,统一行使所有国土空间用途管制和生态保护修复职责”是自然资源部组建后对时空大数据组织分类提出的新需求。本文在梳理总结国内大数据分类体系的演化特点和解析中国现行时空大数据分类体系存在的问题的基础上,结合自然资源部对大数据分类的需求,通过历史继承、合并和添加,提出了一套既能服务于自然资源部“两统一”职责,又能服务于全社会的时空大数据分类体系。研究建立的时空大数据分类体系为3级分类,共6种类型,分为17个一级类型,68个二级类型,24个三级类型,该分类较之前最明显的变化是将自然资源数据单独分为一大类,该分类对于大数据的组织管理具有重要意义。

关 键 词:时空大数据  自然资源  分类体系  智慧城市  组织管理  
收稿时间:2020-06-30
修稿时间:2020-08-12

Study on classification of spatio-temporal big data
TIAN Lizheng,LI Chengming,LIU Xiaoli,YIN Jie.Study on classification of spatio-temporal big data[J].Bulletin of Surveying and Mapping,2021,0(5):1-4.
Authors:TIAN Lizheng  LI Chengming  LIU Xiaoli  YIN Jie
Institution:1. Liaocheng University, Liaocheng 252000, China;2. Chinese Academy of Surveying and Mapping, Beijing 100089, China
Abstract:“Unified exercise of the responsibility of the owner of all natural resources assets owned by the whole people, unified exercise of the responsibility of all land and space use control and ecological protection and restoration” is a new demand for the organization and classification of spatio-temporal big data after the establishment of the Ministry of Natural Resources. On the basis of summarizing the evolution characteristics of domestic big data classification system and analyzing the existing problems of China’s current spatio-temporal big data classification system, combined with the demand of the Ministry of natural resources for big data classification, and through historical inheritance, merger and addition, a set of spatio-temporal big data classification system that can serve both the “two unifications” responsibilities of the Ministry of Natural Resources and the whole society is proposed. The classification system of spatio-temporal big data established in this study is a three-level classification with six types, which can be divided into seventeen first-level types, sixty-eight second-level types and twenty-four third-level types. Compared with the previous classification, the most obvious change of this classification is to separate the natural resource data into a large category, which is of great significance for the organization and management of big data.
Keywords:spatio-temporal big data  natural resources  classification system  smart city  organization and management  
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