Geospatial and Semantic Mapping Platform for Massive COVID-19 Scientific Publication Search

Abstract

Thousands of results of scientific publications on the topic of COVID-19 have emerged within a short time, raising significant challenges for scholars to organize the research, or even synthesize the knowledge in a timely or comprehensive manner. To facilitate this, we have developed a geospatial and semantic mapping platform to search and organize these large and unmapped digital collections. The semantic map visualizes research topics based on customized natural language processing algorithms, which helps users to identify their content of specific interest beyond keyword searches in web search engines. The resultant geospatial map extracts all the location names mentioned in the publications, illustrating where COVID-19 studies have been conducted and where neglected study areas might exist. The system has been deployed as a web service for public access.

Impact of COVID-19 Pandemic on Air Pollution in Poland Based on Surface Measurements and Satellite Data

“…This has led to a sharp decline in industrial and commercial activity, traffic on the roads and the movement of people. Despite negatively affecting economic growth, the decline in anthropogenic activity had a positive effect on the environment (Chakraborty and Maity, 2020;Somani et al, 2020;Yunus et al, 2020;Ye et al, 2021).…”

Towards an AI-driven framework for multi-scale urban flood resilience planning and design

“…An ontology is a template defining and representing entities, ideas, and events, with all their interdependent properties and relations, according to a system of categories (i.e., flood resilience in this project). To create such a knowledge graph, there are many technical challenges that can be addressed through AI innovations in computer vision, natural language processing, and geographic information system (Amiruzzaman et al, 2021;Ye et al, 2021). Massive resilience relevant information can reside in different forms of data types, such as documents and images.…”

Map Design for Public Health Emergencies: a Novel Conceptual Framework for Thematic Content Selection

“…This requires to define the spatiotemporal properties and attributes for the data according to their corresponding thematic content and their own characteristics. To clearly depict the specific geographic objects and their characteristics on the COVID-19 maps, it is also important to set appropriate principles and modes for cartographic representation depending on maps' thematic content (Wildbur and Burke 1998;Li 2021).…”

“…Hence, epidemic outbreak numbers, generalized reports, web applications, in-depth investigations of official responses from authoritative media, and the mainstream internet media are all used as major forms of information exchange and disclosure that enable the public to understand the epidemic and its spatiotemporal risk pattern (Kim and Jung 2018;Xu et al 2020a;Zhang L et al 2020). To work out and delineate the pattern and relationships between human activities, epidemic evolution processes, and risk trends, many scientific research institutions, governmental institutions, and mainstream media outlets have thrown into COVID-19 scientific researches as well as design of different types of maps and geovisualization for this public health emergency (Akella 2009;Kostelnick and Hoeniges 2019;Ye et al 2021). These maps and visualization forms integrated risk information to depict the dynamic patterns and properties of the epidemic as well as its adverse effect on the public from different aspects (Guillette et al 2020).…”