This study proposes a heuristic approach for constructing time-scaled topology maps of indoor WiFi networks using only WiFi log data, without relying on access point (AP) coordinates or signal strength measurements. The method extracts time differences (TD) between consecutive AP transitions from remote authentication dial in user service (RADIUS) server logs. It uses the interquartile range (IQR) method to remove outliers caused by delayed handoffs or inconsistent logging intervals. Descriptive statistics – including the mean and the 25th, 50th, 75th, and 95th percentiles – are then applied to summarize travel behavior and construct time-based distance matrices. A modified Dijkstra algorithm limited to two-hop paths is employed to address missing TD values using both shortest-path and average-path estimation strategies. These completed matrices are projected into two-dimensional space using multidimensional scaling (MDS) to generate topology maps that reflect temporal proximities among APs. The proposed approach is validated through experiments conducted on two floors of a university library, with ground truth walking-time data collected through controlled experiments. Evaluation using Procrustes analysis and normalized Frobenius norm reveal that the 95th percentile provides the most accurate and robust representation of spatial structure, effectively capturing delays and transitional lags commonly observed in indoor mobility. Moreover, average-path Dijkstra estimation outperforms the shortest-path approach in maintaining spatial continuity when direct TD data are missing. This framework enables scalable, low-cost indoor mobility analysis. It offers practical applications in indoor navigation, smart facility management, and spatio-temporal behavior modeling in environments where physical layout information is unavailable or impractical.