The world is changing and previous models around all aspects of weather patterns, climate change, floods, wildfires, and more are all changing rapidly. The good news is that we have access to information and models around these topics, but this data is traditionally hard to access at scale. CARTO helps solve this by analyzing data at scale to surface important insights.

Accessing detailed spatial data at scale isn't easy

Geospatial data about many different phenomena in our world already exists and is maintained by expert teams - everything from snow cover depth to wildfire ignition fuel types. The problem? How do I keep these massive datasets up to date and share useful insights for my teams to use at scale?

With the volume and frequency of spatial data increasing, using a cloud-native spatial experience can help you centralize your data and surface usable insights to your team.

On the following slides we’ll show how a BigQuery + CARTO spatial analytics solution can help insurers navigate this new world of limitless geospatial data efficiently and quickly across several common use cases.  

MAP 1 OF 4

Improving underwriting insights

In this map we can analyze four different datasets to provide more granular insights about different properties. This includes FEMA Hazard indexes, FEMA Flood Zones, reported violent crimes, and proximity to a fire station.

While this analysis represents 4 different datasets, you can extend this to any number of datasets and scale this to a production workload for any existing location or new locations being evaluated.

Interact with the widgets to adjust the map dynamically.

MAP 2 OF 4

Identifying costal flooding risk

Flood plain zones represent one of the easiest indicators of risk related to any flood related damage. However determining areas of risk that are close too a flood zone or at risk of flash flooding can be a lot harder.

By looking at a coastal city, in this case Savannah, GA, we can see that flood zones can give us a basline indicator of long term flood risk, but by analyzing other factors such as elevation, proximity to flood levees and drainage can also give a local level indication of flood risk not captured by nationwide flood plains.

Adjust the widgets to identify high/low risk properties.

MAP 3 OF 4

Wildfire risk index

Every single day the Copernicus Emergency Management Service for the European Forest Fire Information System (EFFIS) publishes a series of datasets that provides wildfire risk indexes with data going back to 1940.

This data is incredibly valuable but at the same time difficult to extract and use. Using the CARTO Analytics Toolbox this data can be automated as an import into your data warehouse and easily analyzed and visualized to provide a regular  

Customers can use ML to create fire forecasting models.

MAP 4 OF 4

Understanding wildfire spread

Understanding, or even better predicting, where active wildfires will spread is another critical analysis to understand the potential future risk of a fire. To do so in addition to understanding the overall area that has already burned, wind patterns, and slope, understanding the underlying fuel ignition types is key.

This map presents a data layer showing the current extent of the Texas Wildfires in March 2024 and the LANDFIRE Fire Behavior Fuel Model 13 Attributes which allow us to see areas at risk and the fuel types in those areas to better determine risk.

Orchestration at scale

Everything you have seen today is orchestrated with CARTO Workflows, a low code interface to Google BigQuery.

This allows you to construct complex spatial and non-spatial data pipelines to analyze your data at scale and keep analytics up to date, even with no background in data engineering.

Thank you!

A story map by