Rough-Set-Based Spatiotemporal Assignment Mining Model with Its Application for Marine Fishery
基于粗集的环境机制发现模型及其渔业应用

Geo-event is controlled by the environment factors with nonlinear relationship. That means it is important to discover the spatiotemporal assignment of environmental factors. Vector-based association rule discovery models have been provided to look for environmental pattern successfully in terrestrial applications recently. But they just consider the topological relationship in static state between parcels or objects with complex algorithm. And the data type of the attribution has to be limited as category. Except the topologic relationship, it is difficult to take other relationships into account. In the environment research, the continue field also is important research content, especially in meteorology and oceanography. And the continue field always includes the temporal property as geographic phenomenon or geographic progress. This work is to deal with the process. It disperses the space with time. A neighborhood is defined to extract the spatiotemporal variables to make a decision table. A reduction algorithm based on rough set will mine the spatiotemporal assignment with the environmental factors. Finally, the model was applied in fishery geography to find the environmental pattern, which determines the form of fishing ground.