This research proposes a flood area estimation method in urban areas using personal location data. Many studies have investigated the estimation of flood levels; however, the majority of these previous works are based on the flood monitoring data. The main cause of flood disaster death is drowning due to evacuation delay in the area where observation equipment is not installed, so it is difficult to estimate flood occurrence by the previous method based only on monitoring data. In this research, we propose an estimation method that does not rely on monitoring data and instead estimates flood areas using GPS data collected from smartphones owned by the affected people. This method detects anomaly areas to analyze temporal and spatial changes of an area where a personal movement during a flooding event differs from that during regular times from personal location data. For anomaly detection, we use a dynamic time warping method with fixed window size and inequality metrics to estimate the area where an anomaly event occurred in 2 km grids. We applied this method to Kurashiki city, Okayama Prefecture, where there 52 people died during a flood that occurred in Japan in 2018. Our method found that, at the flooding time, the anomaly occurrence was estimated correctly in the area where inundation actually occurred.