Which arrangement best describes the benefit of edge computing for real-time geospatial hazard monitoring?

• A. Near real-time data processing at the source, enabling rapid decision-making.
• B. Centralized processing only, with no edge handling.
• C. Delayed processing to batch data overnight.
• D. Reduced data fidelity due to on-site processing.

Answer: (A)

Edge computing means doing data analysis right where the data is created—on local sensors, gateways, or edge devices—so you don’t have to wait for a distant data center. For real-time geospatial hazard monitoring, this dramatically reduces latency, allowing alerts and decisions to be made as events unfold. In hazards such as floods, wildfires, or earthquakes, those seconds matter, so processing at the source enables rapid situational awareness, immediate filtering of noise, and timely actions, even if network connectivity is imperfect.

That’s why near real-time data processing at the source, enabling rapid decision-making, is the best fit. Centralized processing alone would create unnecessary delays, and delayed processing to batch data overnight defeats the purpose of timely hazard response. On-site processing does not inherently degrade data fidelity; it can be designed to preserve critical information while still lowering bandwidth needs.