An Integrated Method for Assessing the Text Content Quality of Volunteered Geographic Information in Disaster Management

Abstract

Volunteered geographic information (VGI) has the potential to provide much-needed information for emergency management stakeholders. However, stakeholders often lack scalability to identify useful and high-quality text content from the often-overwhelming amount of information. To solve this problem, most studies have concentrated on using text-related features in supervised learning models to classify text contents. This article proposes an assumption that the geographic attributes of VGI can be integrated into the model as features for enhancing the model's performance. To evaluate this assumption, the authors developed a case study based on VGI collected from two flooding events in Brisbane. They validated the accuracy of associated geographic coordinates and defined the geographic features relevant to the flood phenomenon. From their experiments, model based on this integrated method can have better performance in comparison with the model trained from the text-related features. The results suggest great potential for using the integrated method to harvest useful VGI for the needs of disaster management.