A Geo-Event-Based Geospatial Information Service: A Case Study of Typhoon Hazard

Abstract

Social media is valuable in propagating information during disasters for its timely and available characteristics nowadays, and assists in making decisions when tagged with locations. Considering the ambiguity and inaccuracy in some social data, additional authoritative data are needed for important verification. However, current works often fail to leverage both social and authoritative data and, on most occasions, the data are used in disaster analysis after the fact. Moreover, current works organize the data from the perspective of the spatial location, but not from the perspective of the disaster, making it difficult to dynamically analyze the disaster. All of the disaster-related data around the affected locations need to be retrieved. To solve these limitations, this study develops a geo-event-based geospatial information service (GEGIS) framework and proceeded as follows: (1) a geo-event-related ontology was constructed to provide a uniform semantic basis for the system; (2) geo-events and attributes were extracted from the web using a natural language process (NLP) and used in the semantic similarity match of the geospatial resources; and (3) a geospatial information service prototype system was designed and implemented for automatically retrieving and organizing geo-event-related geospatial resources. A case study of a typhoon hazard is analyzed here within the GEGIS and shows that the system would be effective when typhoons occur.

Investigations of disaster information representation from a geospatial perspective: Progress, challenges and recommendations

“…Some of the most active social networking sites, such as Twitter, WhatsApp, WeChat, Instagram, Youku, and YouTube, provide people free access to personal information, including text messages, location, and social relationships. All of these features have resulted in the inclusion of Twitter/WeChat and other social media in crisis management studies (Zhang, Wu, Wang, & Su, 2017). Therefore, creating visualization and representation platforms for partnering people and various sectors to support the disaster management process can improve the understandability of rescue and relief assessment plans.…”

A cross‐linguistic study of spatial location descriptions in New Zealand English and Brazilian Portuguese natural language

“…Such methods rely on the ability to determine the geographic area referred to by spatial relation terms in everyday language. For example, computational extraction of geographic content from text (e.g., extraction of disease, disaster impact, or environmental data from social media) can incorporate models for spatial relation terms to more accurately locate phenomena (Bassi, Manna, & Sun, 2016; Zhang, Wu, & Su, 2017). In locations where many languages are spoken, a multilingual approach provides access to larger amounts of data that are more representative of the population, better supporting current machine learning models.…”

Earthquake Damage Assessment Based on User Generated Data in Social Networks

“…It proved that users tend to share their status during a crisis on the social network, indicating the potential of social networks for a quick damage assessment. Research has shown that the severity of damage in a region was correlated with the amount of crisis-related activities in the social network in that region [19].…”