地理信息系统空间多维数据自动监测方法
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摘要
为了解决传统监测方法有很大不确定性,监测结果不准确的问题,通过正交变换处理研究地理信息系统空间多维数据自动监测方法。对地理信息系统地形空间多维数据和道路空间多维数据进行可视化处理,利用人工神经网络对其进行降维映射。在保证数据项在低维空间中相对距离不变的情况下,通过正交变换处理,令空间数据在相邻时间的坐标偏移达到最小,帮助用户发现相邻时刻监测区域的不同之处,实现地理信息系统空间多维数据自动监测。结果表明:所提方法监测结果和人工统计结果最相符;所提方法监测F值一直高于0. 9。可见所提方法监测精度高,有很强的可行性与可靠性。
In order to solve the problem that the traditional monitoring have great uncertainty and the monitoring results are not accurate,the automatic monitoring method of spatial multi-dimensional data in geographic information system is studied by orthogonal transformation processing method. The terrain spatial multidimensional data and road spatial multidimensional data of geographic information system were visualized,and used artificial neural network to map them. Under the condition of keeping the relative distance of data items unchanged in low-dimensional space,the coordinate offset of spatial data in adjacent time is minimized by orthogonal transformation,which helps users to find the differences of monitoring areas at adjacent time,and realizes the automatic monitoring of spatial multi-dimensional data in geographic information system. The results show that the monitoring results of the proposed method are most consistent with those of manual statistics,and the F value of the proposed method is always higher than 0. 9. It is obvious that the proposed method has high monitoring accuracy and strong feasibility and reliability.
In order to solve the problem that the traditional monitoring have great uncertainty and the monitoring results are not accurate,the automatic monitoring method of spatial multi-dimensional data in geographic information system is studied by orthogonal transformation processing method. The terrain spatial multidimensional data and road spatial multidimensional data of geographic information system were visualized,and used artificial neural network to map them. Under the condition of keeping the relative distance of data items unchanged in low-dimensional space,the coordinate offset of spatial data in adjacent time is minimized by orthogonal transformation,which helps users to find the differences of monitoring areas at adjacent time,and realizes the automatic monitoring of spatial multi-dimensional data in geographic information system. The results show that the monitoring results of the proposed method are most consistent with those of manual statistics,and the F value of the proposed method is always higher than 0. 9. It is obvious that the proposed method has high monitoring accuracy and strong feasibility and reliability.
引文
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15 王广钰,李英玉.基于Hadoop的空间科学大数据的区域检索算法[J].电子设计工程,2018,26(5):48-51Wang Guangyu,Li Yingyu. Region search algorithm of spatial scientific big data based on hadoop[J]. Electronic Design Engineering,2018,26(5):48-51
2 张衡,徐青,古林玉,等.城市地理空间数据集成应用探讨[J].测绘科学,2016,41(9):176-180Zhang Heng,Xu Qing,Gu Linyu,et al. Urban geospatial data analysis,integration and application[J]. Science of Surveying and Mapping,2016,41(9):176-180
3 华一新,周成虎.面向全空间信息系统的多粒度时空对象数据模型描述框架[J].地球信息科学学报,2017,19(9):1142-1149Hua Yixin, Zhou Chenghu. Description frame of data model of multi-granularity spatio-temporal object for pan-spatial information system[J]. Journal of Geo-Information Science,2017,19(9):1142-1149
4 冯健,柴宏博.定性地理信息系统在城市社会空间研究中的应用[J].地理科学进展,2016,35(12):1447-1458Feng Jian,Chai Hongbo. The application of qualitative GIS method in urban socio-spatial structure research[J]. Progress in Geography,2016,35(12):1447-1458
5 苗放,姚丹丹,杨文晖.基于G/S模式的空间信息云服务架构研究[J].计算机测量与控制,2015,23(5):1728-1730Miao Fang,Yao Dandan,Yang Wenhui. Research on spatial information cloud service architecture based on G/S model[J]. Computer Measurement&Control,2015,23(5):1728-1730
6 吕雪锋,程承旗,席福彪.地理空间大数据存储管理的地理网络地址研究[J].地理与地理信息科学,2015,31(1):1-5LüXuefeng,Cheng Chengqi,Xi Fubiao. Study on geographic network address of geospatial big data storage management[J]. Geography and Geo-Information Science,2015,31(1):1-5
7 Barrett B,Nitze I,Green S,et al. Assessment of multi-temporal,multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches[J]. Remote Sensing of Environment,2014,152(152):109-124
8 陆钊.嵌入式数据库中多维离散数据检测仿真[J].计算机仿真,2017,34(3):400-403Lu Zhao. Simulation of multi dimension discrete data detection in embedded database[J]. Computer Simulation,2017,34(3):400-403
9 张凯.大数据网络入侵过程的痕迹数据监测方法研究[J].科学技术与工程,2016,16(14):254-258Zhang Kai. Big data network intrusion traces of process data monitoring method research[J]. Science Technology and Engineering,2016,16(14):254-258
10 Marcos B,Alonso J,Castro P. Evaluating the fitness for use of spatial data sets to promote quality in ecological assessment and monitoring[J]. International Journal of Geographical Information Science,2014,28(11):2356-2371
11 周志光,胡迪欣,刘亚楠,等.面向空气质量监测数据时空多维属性的可视分析方法[J].计算机辅助设计与图形学学报,2017,29(8):1477-1487Zhou Zhiguang,Hu Dixin,Liu Yanan,et al. Visual analytics of the spatio-temporal multidimensional air monitoring data[J]. Journal of Computer-Aided Design&Computer Graphics,2017,29(8):1477-1487
12 Wang H. Progressive measurement and monitoring for multi-resolution data in surface manufacturing considering spatial and cross correlations[J]. IIE Transactions,2015,47(10):1-20
13 Androsov V A,Malynkin K V,Sukhomlinov D V,et al. Situational adaptation of spatial data in on-board geographic information systems[J]. Journal of the Indian Society of Remote Sensing,2016,44(3):1-7
14 胡春梓,袁金国.基于多源卫星遥感数据的河北省NO2柱浓度时空变化[J].科技通报,2016,32(11):30-36Hu Chunzi,Yuan Jinguo. Spatio-temporal change of NO2column density in Hebei Province based on multi-source satellite remote sensing data[J]. Bulletin of Science and Technology,2016,32(11):30-36
15 王广钰,李英玉.基于Hadoop的空间科学大数据的区域检索算法[J].电子设计工程,2018,26(5):48-51Wang Guangyu,Li Yingyu. Region search algorithm of spatial scientific big data based on hadoop[J]. Electronic Design Engineering,2018,26(5):48-51
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