This paper proposes a new method of early change detection for people flow analysis. Some conventional methods often focus on a single location (spot) to demonstrate how the number of people changes over time. In contrast, our proposed method takes into account the links between the spots to grasp a foretaste of congestion of a specific spot as early as possible. The main advantage of the proposed method is that it not only describes the characteristics of each spot, but also the relationships among spots, i.e., whether the connectivities are strong/weak. We introduce an idea of PageRank, which is based on a centrality of graph theory and extend that idea to represent the amount of people flow among spots. We call the extended method “SpotRank”. SpotRank assigns an importance score to each spot. The score of a particular spot is calculated by the number of paths and the amount of people flow from other spots. Therefore, the more paths and people flow, the importance score (ranking) increases. The proposed method begins with the calculation of SpotRank every 10 min, followed by change detection, i.e., how much the ranking changes over time. In our experiments, we measured people flow using Wi-Fi packet sensors for a period of over 16 weeks. We confirmed the effectiveness of the proposed method, which successfully grasped a foretaste of congestion at a restaurant in our university.