Due to the booming industry of location-based services, the analysis of human location histories is increasingly important. Next location prediction is essential to many location-based services. Predicting user's next location usually involves obtaining significant places from the history trajectories and predicting location with a certain statistic model. This paper presents new approaches to deal with both of above problems. For the former problem, a hierarchical clustering algorithm is proposed. We first identify specific features of stay points and then group the GPS points satisfying the identified features to form stay points by a new algorithm which is a variant of DBSCAN clustering algorithm. After that these stay points can be clustered to form significant places. For the later problem, taking the drawbacks like high space complexity and zero frequency problem in N-order Markov Model into consideration, we train a variable order Markov Model to predict next location. The variable order Markov Model uses escape mechanism to address the zero frequency problem and uses a tree structure to decrease the amount of memory needed in N-order Markov Model. An extensive set of experiments have been conducted to demonstrate the performance of proposed methods based on a real-world dataset, GeoLife.