在這個資訊快速擴張的時代，許多種類的資料庫被應用在各式各樣的領域中。空間資料探勘即是一個例子，它在空間資料庫中探勘出頻繁的樣式以及空間關係。空間資料探勘是在空間資料庫中挖掘出有趣的、以前不知道的、但實際上是有用的樣式或空間關係。 在本篇論文中，我們探勘空間序列的問題。我們主要討論兩個主題：空間相關樣式，以及空間相似相關樣式。關於空間相關樣式，我們提出以Apriori為基礎以及深度優先為基礎的解法。在空間相關相似樣式部分，我們提出兩個演算法AP-mine以及AS-mine來解決我們的問題。在AP-mine中，我們提出一個名為AP-tree的資料結構來有效率的挖掘出空間相關相似樣式。最後我們以實驗來驗證我們的演算法。 / With the growth of data, a variety of databases are applied in many applications. Spatial data mining is an example, and it discovers patterns or spatial relations from large spatial databases. Spatial data mining is the process of discovering interesting and previously unknown, but potential useful patterns or spatial relations from large spatial databases. In this thesis, we explore the problem of spatial sequential pattern mining. The two issues spatial co-relation patterns and approximate spatial co-relation patterns will be discussed. We utilize Apriori-based method and depth-first based method to solve the problem of spatial co-relation patterns. About approximate co-relation spatial patterns, we propose two algorithms, named AP-mine and AS-mine. In AP-mine, we propose a data structure, named AP-tree, to efficient mining the approximate spatial co-relation patterns. Lastly, We also perform the experiments to evaluate our spatial co-relation pattern mining algorithms.