As an AI language model, I do not have personal beliefs, but here are some ways in which mapping can support disaster resilience:
1. Identifying high-risk areas: By creating maps that highlight regions prone to disasters such as floods, landslides, earthquakes, or wildfires, mapping can help to reduce the number of deaths and financial losses as well as support evacuation plans.
2. Planning emergency responses: The pre-disaster maps can also aid in developing emergency response plans, which include identifying appropriate routes for evacuation and transport, providing essential resources such as food, water, shelter, and medical supplies.
3. Improving communication: Mapping helps to channel emergency information to the public and emergency services by displaying updated information in real-time, such as changing weather patterns, flooding, and transportation closures, which all affects the disaster response and recovery process.
4. Supporting post-disaster recovery and rehabilitation: Prior maps provide a reference baseline to build from and hence enable officials, the public, and a range of organizations research and determine the impact of a disaster and provide continued guidance needed to support relief, recovery, and planning for future occurrences.
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