As considerable amounts of mobility data have been accumulated, next point-of-interest (POI) recommendation has become one of the important tasks in location-based social networks. Existing approaches for next POI recommendation mainly focus on capturing local dynamic preferences from user's recent check-in records, but ignore global static information in historical mobility data. As a result, it prevents further mining of user's preferences and limits the recommendation accuracy. To this end, a global and local feature fusion based approach is proposed for next POI recommendation (GLNR). GLNR can model user dynamic behavior by taking advantage of the sequential dependencies between check-ins and the underlying relationships between entities contained in global static information. Two types of global static information are firstly introduced, i.e., user-POI association paths and POI-POI association paths, to learn user's global static preferences and the global dependency between successive check-ins. Specifically, a heterogeneous information network is constructed based on interactive data and geographical information. To capture global static features, a relevance-guided path sampling strategy and a hierarchical attention based representation learning method are designed. Moreover, the representations of POIs in the user's check-in sequence are updated based on the two types of global static features. Position and time interval aware self-attention mechanism are further utilized to model the sequential dependency between multiple check-ins. Then, the check-in probability is predicted and a set of next POIs is recommended for the target user. Finally, the extensive experiments are conducted on two real-world datasets to evaluate the performance of the proposed model GLNR. Experimental results validate the superiority of GLNR for improving recommendation accuracy. Besides, the case study indicates that the explicit paths in the global static information help GLNR to provide interpretable recommendations.