This document discusses how to create a wildfire risk index using spatial data and analysis. It describes using data on past wildfires, climate/weather, land cover, demographics and more to build annual and seasonal wildfire risk indexes for California. Different variables are analyzed for their relationship to burned areas to select predictors for the index. The indexes can help various groups like insurers better understand risk and mitigate losses from wildfires.

1. How to Use Spatial Data to
Create a Wildfire Risk Index
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2. CARTO — Unlock the power of spatial analysis
Introductions
Lucía García-Duarte
Data Scientist at CARTO
Mark Gibbas
CEO at Weather Source

3. CARTO — Unlock the power of spatial analysis
Last year, more
than 8,000
wildfires burned
almost 2.6 million
acres in the state of
California alone.

4. CARTO — Unlock the power of spatial analysis
Wildfire Risk is Only Increasing

5. CARTO — Unlock the power of spatial analysis
Organizations need spatial data and
analysis to answer pressing questions
Government
Agencies
Insurance
Businesses
Power &
Utilities
How should I develop
wildfire suppression
plans, direct resources,
and aid in emitting
wildfire alerts when
needed?
Landscape
Managers
How can I design
contingency plans that
guarantee energy supply
during disaster recovery to
critical facilities and
vulnerable neighbors?
How can I prioritize and
allocate investments to
treat wildland fuels and
identify areas with a high
estimated loss?
How can I provide affordable
coverage, mitigate risks, and
manage losses, while staying
competitive by offering
wildfire insurance while
others choose not to?

6. CARTO — Unlock the power of spatial analysis
POLL 1
Are you currently using weather data and/or spatial analysis
when analyzing wildfire risk?
Yes, I use it almost every day
Somewhat, I use it occasionally
No, I have never used location data or spatial analysis
before

7. Key Ingredients For Wildfires
Vegetation Fuel Persistent Heat & Drought Wind
Spark Wildfires

8. Climate Crisis Increased Wildfire Risk
Warming Planet:
More Atmospheric Energy
Stronger Atmospheric Dynamics
More Weather Extremes
More Drought
Stronger Winds
More Wildfires
NOAA

9. Weather Source Data & Solutions
OnPoint Weather
OnPoint Climatology
ECMWF Forecast
Historical GFS Forecast
Historical ECMWF Forecast
GFS and ECMWF Ensembles
HRRR Forecast
WRF Forecast & Modeling
Dynamic Weather Alerting System (DWAS)
OnPoint Geospatial
The Weather Insights Platform (WIP)
Weather Impact Indices
Condition-Based Ad Triggering (C-BAT)
Much of this is available
via CARTO !

10. CARTO — Unlock the power of spatial analysis
Building the
Wildfire Risk
Index with
Weather Data &
Spatial Analysis
+

11. CARTO — Unlock the power of spatial analysis
Area under study: California

12. CARTO — Unlock the power of spatial analysis
Area under study: California

13. CARTO — Unlock the power of spatial analysis
Data Sources
● Wildfires data (2001-2020): perimeters of
different fires (Weather Source)
● Geographical Data:
○ Land cover type (Microsoft Planetary
Computer): physical coverage of the
Earth’s surface (i.e.: crops, trees, water…)
○ Köpper-Geiger climate classification:
assigns a different climate group based
on seasonal patterns.
○ Population and elevation (CARTO Spatial
features)
○ Kilometers of roads and distance to
the closest road (Roads North America -
Global - Natural Earth, from CARTO DO)

14. CARTO — Unlock the power of spatial analysis
Data Sources
● Meteorological data (Weather Source):
○ SPEI - standardized precipitation
evapotranspiration index (2001 - 2020):
drought index, measured at different
time scales
○ SM - soil moisture (2014 - 2020): the
water content of the soil, measured at
different depths
● Sociodemographics data:
○ Median income (Sociodemographics -
American Community Survey dataset
from CARTO DO)

15. CARTO — Unlock the power of spatial analysis
Building the WRI
1. Cluster the data → built a different index for each cluster
2. Fire precursors selection → find those variables related with burned area
3. Index definition → built a WRI combining selected variables

16. CARTO — Unlock the power of spatial analysis
Building the WRI
1. Cluster the data → built a different index for each cluster
Building the WRI

17. CARTO — Unlock the power of spatial analysis
Building the WRI
1. T
2. Fire precursors selection → find those variables
related with burned area

18. CARTO — Unlock the power of spatial analysis
1. Cluster the data → built a different index for each cluster
2. Fire precursors selection → find those variables related with burned area
3. Index definition → built a WRI combining selected variables
Building the WRI
● Annual index

19. CARTO — Unlock the power of spatial analysis
1. Cluster the data → built a different index for each cluster
2. Fire precursors selection → find those variables related with burned area
Building the WRI
● Seasonal index

20. CARTO — Unlock the power of spatial analysis
Building the WRI
Annual Seasonal

21. CARTO — Unlock the power of spatial analysis
Building the WRI
Annual Seasonal

22. CARTO — Unlock the power of spatial analysis
Building the WRI
Annual Seasonal
https://gcp-us-east1.app.carto.com/map/2f397711-4bb3-41fa-9040-55990c237327 https://gcp-us-east1.app.carto.com/map/32fae6c5-dd8b-4805-ae59-f93ff9c29c29

23. CARTO — Unlock the power of spatial analysis
Provides competitive advantage in assessing
wildfire risk:
● Defining coverage costs
● Adjust coverages
● Design specific plans to ensure resource
and personnel assistance
Can be used to design targeted marketing
campaigns
Use Case:
Using the WRI to
improve home insurance

24. CARTO — Unlock the power of spatial analysis
Weather Source in the Spatial Data Catalog

25. Thanks for listening!
Any questions?
Request a demo at CARTO.COM
Mark Gibbas
CEO at Weather Source // mark.gibbas@weathersource.com
Lucía García-Duarte
Data Scientist at CARTO // lgarciaduarte@cartodb.com