Ecological space data integration method

Abstract

The invention discloses an ecological spatial data integration method, which belongs to the technical field of geographic information, and is characterized by comprising the following steps of: 1, collecting and acquiring data, namely acquiring spatial data including raster data, vector data and document data; 2, performing data fusion, conversion and management: performing data conversion througha spatial data conversion model by utilizing the obtained data to complete data standardization management; 3, making data analysis and starting application: analyzing the relationship between the element types and attributes of the original data and the target data, and checking the analysis result from the aspects of position precision, attribute precision, element integrity, element relationship and the like; 4, designing and realizing a data management and application platform, performing cloud storage scheme design on the analysis data, and establishing an experiment cluster of the system. The spatial data is effectively converted, uniformity of the spatial data is ensured, and finally, the effective application of the spatial data is obtained.

Description

technical field [0001] The invention belongs to the technical field of geographic information, and specifically relates to a method for effectively integrating spatial data and other information to achieve effective combination and application of data integration, processing and application, and to achieve an ecological space data integration method. Background technique [0002] Spatial data refers to the data used to represent the position, shape, size and distribution characteristics of spatial entities. It can be used to describe objects from the real world. It has characteristics such as positioning, qualitative, time and spatial relations. Data is a kind of data that uses basic spatial data structures such as points, lines, surfaces, and entities to represent the natural world on which people live. [0003] At present, the characteristics of spatial big data are as follows: large-scale data, complex information, and a lot of implicit knowledge; based on the above three...

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Problems solved by technology

[0003] At present, the characteristics of spatial big data are as follows: large-scale data, complex information, and a lot of implicit knowledge; based on the above three main characteristics, the traditional single physical machine can no longer meet the computing needs; the traditional ArcGis platform as the main research tool, in Without optimization, efficient correlation analysis cannot be achieved, and the discovery of data implicit knowledge, the removal of noise points in big data, and the visualization of data analysis results are all ineffective.

[0004] Spatial data is widely used in many fields. Spatial data has the characteristics of massiveness, diversity, timeliness, spatiality, sensitivity, and heterogeneity. However, due to different spatial data collection methods or organizational systems, there are differences in spatial data Due to the structural situation, it cannot be directly reused, the re-production method takes a long time, and the data is not current, resulting in high cost and long time for obtaining spatial data; due to different data systems, the existing spatial data cannot be directly used. In order to make full use of the existing results, it is necessary to convert these data. There are few softwares that can be used to convert data at home and abroad, and the existing software also has the disadvantages of poor flexibility and low degree of automation; therefore, it is necessary to effectively convert It is of great significance to acquire, transform and analyze data, and finally build a data management and application platform

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