An Emergency Mapping and Route Planning Method Driven by Social Media Data

ABSTRACT

During disasters, social media platforms serve as valuable sources of real‐time spatial information, capturing the evolving needs of affected individuals and the locations of emergent incidents. However, the unstructured nature of such data, pervasive noise, and the absence of a computational mechanism for translating textual semantics into spatial weights and road network traversal costs leave social media data effectively “visible yet unusable” in disaster analysis and emergency route planning. To address these challenges, this study proposes a social media‐driven framework for emergency mapping and risk‐aware route planning, using urban flooding as a case study. In the information processing stage, a BERT‐TextCNN model is employed for topic classification, filtering approximately 69% of irrelevant content; a BERT‐BiLSTM‐CRF model is subsequently applied to extract disaster and rescue locations, achieving an F1‐score of 95.8% and outperforming the baseline by approximately 4%. In the route planning stage, a multiplicative smooth‐decay impedance model transforms discrete disaster events into a continuous spatial risk field, which is integrated with Sort‐Tile‐Recursive spatial indexing and the Single‐Source Shortest Path algorithm to enable real‐time, adaptive, hazard‐aware navigation across large‐scale road networks. Experimental results demonstrate that the proposed framework effectively balances route safety and travel efficiency, offering robust decision support for emergency rescue operations in complex urban environments.