Understanding requirements and issues in disaster area using geotemporal visualization of Twitter analysis

Abstract

During disasters, requirements and situations on the ground change very rapidly. Moreover, they depend on timing and location; thus, it is very hard to understand them in a timely manner. Social media may contain such information with the posted time and the location information. However, it is difficult to extract situational requirements from numbers of conflicting sources. In this article, we propose a system that enables us to find out such useful information from social media and visualize it to understand the data easily. The system is divided into two steps. The first step is to extract requirements and issues from textual data, such as “We cannot buy gas here” or “We are short of batteries,” using natural language processing (NLP) technologies. The system also uses NLP to extract geolocation information, such as city names and location landmarks. The second step is to visualize the results in a timely and geolocated manner. We show the system results with using real Twitter data from the Kumamoto Earthquake in 2016. By visualizing the information, the personnel in the disaster area, such as the local governments and/or volunteer organizations, can utilize this information very effectively. For instance, they can decide how to distribute food and water in the disaster area and also how to implement and responsed to their logistics.