This thesis introduces a new algorithm to search for optimal spatial sampling design. It is found in previous studies of Zhu and Stein (2006) that the optimal sampling design for spatial prediction with estimated parameters is nearly regular with a few clustering points. The pattern is similar to the generalized Neyman-Scott (GNS) process introduced by Yau and Loh (2012), which allows for regularity in the parent process. This motivates the use of a realization of the GNS process as a spatial sampling design. This method translates the high dimensional optimization problem of selecting sampling sites into a low dimensional optimization problem of searching for the optimal parameter sets in the GNS process. Simulation studies indicate that the proposed sampling design algorithm is more computationally efficient while the result of criterion minimization is comparable to traditional methods. / 本文介紹了一種新的算法來搜索最優空間採樣設計。先前Zhu和Stein（2006）的研究發現，按被估計參數的空間預測的最優採樣設計是近乎有規律的，同時伴隋一些聚類點。該圖案與Yau和Loh（2012）介紹的廣義Neyman-Scott（GNS）過程相似，其中的父過程擁有規律性。這驅使我們使用GNS過程的實現作為空間採樣設計。這種方法把選擇採樣點的高維優化問題轉化為搜索最優GNS過程參數集的低維優化問題。模擬實驗顯示，該採樣設計的算法是計算效率更高，同時其最小化判別函數的結果是可以媲美傳統的方法。 / Lai, Sai Yu. / Thesis M.Phil. Chinese University of Hong Kong 2015. / Includes bibliographical references (leaves 31-34). / Abstracts also in Chinese. / Title from PDF title page (viewed on 18, October, 2016). / Detailed summary in vernacular field only.