As one of the primary tasks in data mining, outlierdetection serves a significant role in data quality enhancementfor the scientific model prediction and revealing the abnormalhidden patterns from large scale trajectory datasets. In this paper,we introduce a versatile framework for detecting local trajectoryoutliers using spatial and temporal features of moving objects.Our local outlier detection consists of three phases. In the firstphase, we divide the raw trajectory into trajectory segments byusing a time-based partition strategy and extracting trajectoryfeatures from spatial attributes for each trajectory segment. Inthe second phase, we create template trajectory segments basedon a clustering schema. In the final phase, we compute theabnormal score, which measures the dissimilarity among thequery and template trajectory segments, and thus determine theoutlying trajectory segments according to the overall distributionof abnormal score. To show the effectiveness of our approach, weconduct two case studies on the real-life solar active region andCoronal Mass Ejection (CME) trajectory datasets. Our resultsshow that our local outlier detection method can successfullydetect the reporting errors and anomalous phenomenon in bothof our case studies.