Marine And Fishery GIS Spatial_Temporal Data Organizing And Analyzing

With the developing of ocean monitoring survey technology, the mankind gets much large of marine data. For the characters that ocean inheres, such as the dynamics, the multidimensional, the multi-resolution, the periodicity, the fuzziness, and so on, the current spatial-temporal data organizing models which are mainly used for land can not be suitable fully for the organizing of marine data. Based on other people' s research about the organizing of marine spatial-temporal data, this paper brings forward a new frame model. This frame gives the fire-new summarizing and designs for the marine firsthand data defining, the spatial-temporal data warehouse, the data organizing layer, the algorithm applying layer and so on. The frame is the base for the next analyzing and research. Based on the frame, the paper brings forward the grid-based sequent snapshots amendment data model (GSSADM). And in the format changing form firsthand data to grid-based data, the paper also gives a vector isoline cell points interpolating algorithm which is based on grid spatial index.The'marine fishery multidimensional spatial-temporal data warehouse is the core of the whole frame model. The paper first gives the formalized description and definition for the marine fishery multidimensional database, this is the base concept for the next data warehouse construction. And in the next recognizing of facts, dimensions, dimension layers and dimension layers relationship, especially for the organizing and retrieving of spatial and temporal dimensions, the paper makes a deep discussion, and constructs the data warehouse by using the mixed multidimensional data model. In order to enhance the query efficiency of data warehouse, the paper studies out the selecting strategy of top view and materialized views according to the query dependent relationship between the multidimensional views.The aim of data organizing is to make things convenient for the fishery subject-oriented analyzing. As an example, the paper analyzed the North Pacific fishery harvest data that are saved in data warehouse and extracted some useful information. In order to study the cluster feature of the North Pacific sleeve-fish, the paper firstly brought forward a new influence power-based hierarchical-tree neighbor function clustering algorithm(IPHTNFCA), and used this algorithm to analyze the relationship between center fishing area and sea surface temperature.As a example of the data organizing frame model, the paper also developed the North Pacific producing aided decision making system.