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What Is Natural Disaster Data?

This is the collection of data about natural disasters of all kinds: earthquakes, wildfires, floods, hurricanes, etc. Human-caused disasters that affect the environment, such as chemical spills or gas leaks also count toward natural disaster data.

Where Does Natural Disaster Data Come From?

Sensors, satellites, and observation posts collect most of the natural disaster data. However, social networking sites are also important sources of natural disaster data as people in these disaster-stricken areas post about their situations in real time.

What Types of Columns/Attributes Should I Expect When Working with This Data?

Columns in natural disaster datasets include type of disaster, location, loss of life, property damage, and year.

What Is This Data Used For?

Governments and international organizations use this data to plan and implement disaster relief programs. They also work with individuals and communities like churches and schools to educate people about how to prepare for and survive disasters.

Other users of this data include insurance companies, city planners, architects, and more. Even social media companies use this data; people stuck in natural disasters use their platforms to reach out for help or to let their loved ones know they are alive.

How Should I Test the Quality of Natural Disaster Data?

The most important aspect of natural disaster data collection is the quality of the sensors that collect the data. To that end, perform regular checks on the sensors that record the data to make sure they are in peak condition. You can also check that the sensors are working by checking there are no missing data points as this may indicate a technical problem.

Most datasets, however, are of good quality as they are maintained by government bodies who are responsible for the outcomes of any flawed data. There are also numerous open-source datasets as well, with active communities that analyze the data which you can join.

What Are the Most Important Factors I Should Vet when Selecting This Data?

For natural disaster data specifically, be sure that that data sensors are reliable and in good condition. Beyond this, there remains a wide range of data collection methods and tools. However, the main principles of data quality remain the same: accuracy, relevancy, completeness, timeliness, and consistency.

Interesting Case Studies and Blogs to Look Into

FEMA Case Study: Mashpee Wampanoag Tribe case study
Selerity: Managing natural disasters with predictive data analytics.

Tangible Examples of Impact

Andrew Forrest’s philanthropic Minderoo Foundation believes that within five years technology will make it possible to detect and snuff out any potentially dangerous bushfire within one hour of it starting.

Minderoo’s high-tech plan to extinguish any bushfire within one hour

Relevant datasets

BreezoMeter Fires API

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Fires API alerts your customers to active fires in their area and give them the power to protect themselves from rapidly changing air quality levels at their location.

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BreezoMeter Global Pollen API

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Global Pollen API provides the necessary data and map that allows customers track their allegies.

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HazardHub API

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HazardHub’s API pass all the data updates to the API automatically as soon as the database gets updated.

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