Anomalies of dwellers' collective geotagged behaviors in response to rainstorms: a case study of eight cities in China using smartphone location data

Understanding city residents' collective geotagged behaviors (CGTBs) in response to hazards and emergency events is important in disaster mitigation and emergency response. It is a challenge, if not impossible, to directly observe CGTBs during a real-time matter. This study used the number of location requests (NLR) data generated by smartphone users for a variety of purposes such as map navigation, car hailing, and food delivery to infer the dynamics of CGTBs in response to rainstorms in eight Chinese cities. We examined rainstorms, flooding, and NLR anomalies, as well as the associations among them, in eight selected cities across mainland China. The time series NLR clearly reflects cities' general diurnal rhythm, and the total NLR is moderately correlated with the total city population. Anomalies of the NLR were identified at both the city and grid scale using the Seasonal Hybrid Extreme Studentized Deviate (S-H-ESD) method. Analysis results demonstrated that the NLR anomalies at the city and grid levels are well associated with rainstorms, indicating that city residents request more location-based services (e.g., map navigation, car hailing, food delivery, etc.) when there is a rainstorm. However, the sensitivity of the city residents' collective geotagged behaviors in response to rainstorms varies in different cities as shown by different peak rainfall intensity thresholds. Significant high peak rainfall intensity tends to trigger city flooding, which leads to increased location-based requests as shown by positive anomalies in the time series NLR.