近年來，許多專家學者廣泛探討偵測稀有疾病的發生率或稱為叢集上的空間或空間對時間的統計方法及模型。這些方法大部分都是處理個別資料或是只能偵測接近圓形的叢集。在這篇論文中，根據Choynowski在1959年所探討的方法，我們進一步提出針對整體資料去偵測非圓形叢集的方法，並且會將此方法與Nagarwalla’s Spatial Scan Statistic做比較。同時，我們會呈現模擬結果中的型一、型二誤差來衡量此方法的可行性。另外，我們也會將此方法實際應用到台灣的癌症死亡資料做探討。 / In recent years, many statistical methods have been proposed for detecting excesses of rare diseases, i.e., clusters, in space or in space-time. Most of these methods deal with case-event or individual-level data and can only detect clusters with shape close to circles. In this study, adapting Choynowski's (1959) idea, a simulation-based approach is proposed to detect non-circular clusters with aggregate or group-level data. The proposed cluster detection method will be used to compare with a frequently used method: Nagarwalla’s Spatial Scan Statistic. Computer simulation is used to illustrate the validity, with respect to Type-I and Type-II errors, of the proposed approach. In addition, the cancer mortality data in Taiwan area are also used as a demonstration of the proposed test.