RiskRadar: an Online Mapping Platform for Disaster Preparedness and Response via Comprehensive Data Integration and Visualization
Updatable and accurate mapping tools are essential for effective disaster management to safeguard public safety before, during, or after disasters. Maps help prioritize where assets are deployed in disasters in efficient ways and are important for compiling data layers about normal environmental risk inequalities of various indicators in general, to locate geographically the most vulnerable populations in order to improve their lives. Central to both kinds of mapping efforts are accessibility and clarity of information, whether in plotting a rapidly evolving natural or human-made hazard in a disaster zone, or for sharing with first responders, or for ongoing daily awareness and thus better administration about regular environmental risk in national populations. The RiskRadar platform prototype improves five shortcomings found regularly in other platforms for disaster risk reduction (DRR) and has more comprehensive data integration and visualization tools with its unique data indexes to enhance disaster preparedness and response. By integrating diverse environmental and urban data sets, RiskRadar shows different kinds of risk easily. From this, it facilitates informed decision-making on priorities and timely actions, while aiding more coordinated awareness and thus coordinated response to risk. This paper examines functionalities of a prototype platform for how it bolsters disaster resilience particularly in contexts desired by the United Nations Office for Disaster Risk Reduction (UNDRR). With a user-friendly interface, RiskRadar lets stakeholders, first responders, and the public at large access the same critical information. Good digitally-accessible maps aid better trust upon which better coordination for disaster management and higher group preparedness for future events can take place.
Authors
- Minji You
- Seongyun Ku
- Mark D. Whitaker
- Juho Kim (ORCID: https://orcid.org/0000-0001-6348-4127)
- Sunghwan Kim (ORCID: https://orcid.org/0000-0001-9828-2074)
- Junhyeong Park (ORCID: https://orcid.org/0000-0001-6797-4000)
Institutions
- Kyung Hee University (KR)
- Stony Brook University (US)
- Stanford University (US)
Publication Details
- Published
- 2025-03-26
- DOI
- https://doi.org/10.1109/greentech62170.2025.10977717
- Citations
- 1
- Primary Topic
- Scientific Computing and Data Management
- Type
- article
- Field-Weighted Citation Impact
- 4.17