A geovisualization tool empowered by knowledge graph technology which enables intelligent decision making and problem solving, and further supports disaster response and humanitarian aid. There are three innovative features of this solution:
- A location-aware knowledge graph is created to link and integrate cross-domain data to make the graph analytics-ready.
- Expert-driven disaster response workflows are analyzed and modeled as machine-understandable decision paths to guide knowledge exploration via the graph.
- A scene-based visualization strategy is developed to enable interactive and heuristic visual analytics to better comprehend disaster impact situations and develop action plans for humanitarian aid.
In the project directory, you can run scripts:
Install all the required node modules for the app.
Runs the app in the development mode.
Open http://localhost:3000 to view it in the browser.
The page will reload if you make edits.
You will also see any lint errors in the console.
Launches the test runner in the interactive watch mode.
See the section about running tests for more information.
Builds the app for production to the build folder.
It correctly bundles React in production mode and optimizes the build for the best performance.
The build is minified and the filenames include the hashes.
Your app is ready to be deployed!
See the section about deployment for more information.
GeoGraphVis is released under the Apache 2.0 license.
If you wish to refer to the methods or results in your research, please cite the following paper:
@Article{ijgi12030112,
AUTHOR = {Li, Wenwen and Wang, Sizhe and Chen, Xiao and Tian, Yuanyuan and Gu, Zhining and Lopez-Carr, Anna and Schroeder, Andrew and Currier, Kitty and Schildhauer, Mark and Zhu, Rui},
TITLE = {GeoGraphVis: A Knowledge Graph and Geovisualization Empowered Cyberinfrastructure to Support Disaster Response and Humanitarian Aid},
JOURNAL = {ISPRS International Journal of Geo-Information},
VOLUME = {12},
YEAR = {2023},
NUMBER = {3},
ARTICLE-NUMBER = {112},
URL = {https://www.mdpi.com/2220-9964/12/3/112},
ISSN = {2220-9964},
DOI = {10.3390/ijgi12030112}
}