Insurance
Last updated
Last updated
Many government agencies such as FEMA in the United States, provide flood zone data for long term flooding risk, but what about areas that may be prone to flash floods or wildfire? This analysis takes synthetic policy data in Florida and analyzes it for Flash Flood risk using historic storms and historic fires, along with current weather warnings.
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This example demonstrates how an insurance company could use Workflows to assess the number of people and the value of the properties affected by a volcano eruption, in the spanish island of La Palma. It takes into account the actual lava flow, but also, separately, the surrounding area.
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Underwriting or reinsuring a home or property insurance combines many different factors about the property but also the location where the property sits. While nationwide datasets exist for analysis like this such as the national FEMA risk index, other datasets like crime or emergency facilities are often shared by municipalities.
This workflow shows how you can combine many different data layers to make a spatial determination about a property.
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While flooding is a major risk in many areas, coastal areas are particularly prone to flooding both in long-term and short-term time horizons. In addition, each location has different factors that can impact flooding on a local level such as proximity to a storm drain or elevation.
This workflow shows how you can combine many different data layers to make a spatial determination using hyper-local data in Savannah, GA.
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This example demonstrate how to use Workflows to combine traffic data such as road collisions and traffic count with car's telemetry data to generate a risk score that can later be used to enrich a specific journey's path.
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