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There exists a sort of dynamic geographic phenomena with a property from production through development to dissipation in a real world， and the integrated Earth observation technology and the crowd sources technology promote the capabilities of obtaining these dynamics. The traditional spatiotemporal methods take a point， a line， a polygon or a voxel as an analyzing unit， and a scale of data acquisition as the analyzing scale， which splits a continuity of temporal evolutions， and limits their dynamic analysis. This paper abstracts the property from production through development to dissipation into a geographical process， takes it as a scale of analysis， and proposes a novel approach of geographical dynamic analysis. Firstly， a process semantics with a hierarchical abstraction of "geographical process-evolution sequence- instantaneous state" is proposed. And a graph-based model with a "node-edge" is used to represent and store geographical objects and their evolution behaviors. Secondly， a geographical process is used as a unit to design a process-oriented object extracting method with an "extracting of instantaneous state-tracking of evolution sequence- reconstructing of geographical process"， and on the basis of in-degree and out-degree of a graph， four evolution behaviors are identified. Then， series of new concepts about spatiotemporal mining are redefined on the basis of a scale of process， and some process-oriented mining methods are designed， e.g. spatiotemporal clustering， association rule mining. Finally， a real dataset of monthly Sea Surface Temperature Anomaly （SSTA） during the period of January. 1950 to December. 2019 is to explore their evolving structures in Pacific Ocean， and the association patterns between the evolving structures of SSTA and the types of ENSO are addressed. Results demonstrate the effectiveness and the advantages of process-oriented dynamic analyzing method.