Precisely Wildfire

Addresscloud offers comprehensive coverage of key perils for the US and these are sourced from a number of trusted, high quality data providers with a proven track record of providing data to the insurance industry.  This section focuses on Wildfire hazard scores provided by Precisely.  The scores and values you see in Addresscloud are as they are provided by Precisely, any derivations or modelling from Addresscloud are noted as such.

1.What is it

Wildfire risk is a comprehensive resource designed to evaluate the exposure of critical infrastructure and property to wildfire impacts across the United States. Developed by wildfire scientists with over 30 years of experience in firefighting and fire modelling, this data product integrates various datasets to provide a detailed assessment for underwriting, pricing, portfolio analysis, and loss estimation.

Wildfire Risk is a cutting-edge database that helps assess wildfire hazards and risks. It considers various factors like terrain, climate, and human activities, such as the slope of the land, type of vegetation, frequency of past fires, proximity to water, wind patterns, non-burnable surfaces, and the distance to fire stations.

Using these factors, the model creates unique fireshed polygons across the United States, each with its own risk score. Plus, it provides detailed information at the address level, including proximity measurements to help understand the specific risk to individual structures.

Ultimately this data set will

1. Provide a wildfire model for all 50 states that scores the risk profile of every location from 0-49 along with detailed scores on factors like fuel, frequency, and mitigation efforts
2. Utilise three Fire Environments: It accounts for Wildland, Intermix, and Interface environments, allowing for comprehensive analysis of threats from direct flames, smoke, and embers. 
3. Include a geo-enrichment file which appends all wildfire risk information to every affected address location using a unique identifier
4. Provide state-wide Coverage: It offers probability and severity ratings for all 50 states, providing a consistent, address-level evaluation of wildfire risk.
5. Contain proximity measurements for added insight at the address level
6. Include a supporting layer of recent burn perimeters
7. Deliver a supporting layer of areas impacted by the Mountain Pine Beetle

2. How can I use it?

Insurance underwriters and risk analysts can use the Precisely Wildfire dataset to evaluate property-level exposure to wildfires. The dataset provides critical insights that allow users to quantify and classify risks in areas prone to wildfires, helping to determine appropriate coverage, set premiums, and inform underwriting policies based on landscape risk categories. This detailed segmentation helps insurers address specific risk characteristics in wildfire-prone areas, allowing for more effective management of portfolios in high-risk zones.

3. How is the data created?

The Precisely Wildfire data is derived from a combination of geospatial analysis, historical wildfire data, climate conditions, and land cover assessments. Precisely integrates data from multiple sources, including vegetation and fuel data, historical fire occurrence records, and satellite imagery, to model potential wildfire behaviour. The resulting analysis provides insights into different landscape types, offering precise risk assessments for Wildland, Intermix, and Interface areas to identify both the probability and potential severity of wildfire impacts.

In maps, a vector refers to a type of data representation where geographic features are stored as points, lines, or polygons. Unlike raster data, which is based on a grid of pixels, vector data is represented by mathematical expressions that define shapes and locations, making it scalable and precise. Here’s a breakdown:

1. Points: Represent specific locations, such as cities, landmarks, or GPS points. Each point is a single pair of coordinates (latitude and longitude).
2. Lines: Used to show linear features like roads, rivers, and trails. They connect a series of points and can vary in length and shape.
3. Polygons: Enclosed shapes that represent areas, such as lakes, parks, or country borders. Polygons are formed by connecting lines that close to form a loop.

Because vectors describe locations and shapes with precise coordinates, they allow for high-resolution detail without pixelation when zoomed in, which makes them ideal for digital maps that need to scale and adapt to different levels of detail.

4. Are there any limitations?

While the Precisely Wildfire dataset provides robust data for wildfire risk, there are some limitations to consider:

• Geographic Scope: The dataset currently covers only the United States, so it is limited in its application for international risk assessments.
• Update Frequency: This dataset is updated annually, which may not capture rapid shifts in risk factors due to sudden environmental or human-related changes.

5. What data points are available?

The Precisely wildfire data includes the following attributes, each providing insights into different aspects of wildfire risk:

• risktype: Specifies the type of landscape risk (e.g., wildland, intermix, interface) for wildfire exposure.
• riskdesc: Describes the assessed wildfire risk level, such as "Intermix High" or "Interface Moderate."
• risk50: Overall risk rating (0–50), quantifying the general wildfire risk level at the property location.
• severity: Measures potential severity (0–50) of wildfires based on environmental and geographic factors.
• frequency: Rates how often fires are likely to occur in the area on a scale from 0 to 50.
• community: Rates community-level vulnerability or susceptibility to wildfire risk (0–50).
• damage: Estimated damage rating (0–50) indicating potential losses if a wildfire were to occur.
• mitigation: Rates the degree of existing fire-mitigation measures in place (0–50).
• fstatprox: Distance-based proximity to the nearest fire station (0–50), impacting emergency response times.
• pastfires: Indicates the number of historical fire perimeters nearby (0–50), reflecting previous wildfire activity.
• intensity: Fireline intensity rating (0–50) predicting the strength and heat of potential fires.
• crown_fire: Assesses the percentage of area within half a mile of a fire-shed (0–50) for crown fires.
• wind_spd: Relative wind speed (0–50) which affects fire spread potential.
• ember_cast: Likelihood of embers traveling to the property, facilitating spread (0–50).
• burn_prob: Burn probability (0–50) based on the FSim model for predicting fire likelihood.
• prob_ignit: Probability of ignition (0–50) showing how likely the property is to catch fire.
• line_dist: Proximity to the nearest electrical transmission line (0–50), as sparks can ignite fires.
• structure: Structural density rating (0–50) indicating how close buildings are, which could accelerate fire spread.
• w_align_rd: Number of wind-aligned roads (0–50), where wind direction may facilitate fire spread along roads.
• acc_egress: Access and egress score (0–50), assessing road distance and evacuation route availability.
• veg_cover: Vegetation cover density (0–50), indicating available fuel for wildfire.
• hist_loss: Number of past fires with property losses (0–50), indicating loss history in the area.
• i_and_d: Rates vegetation affected by insects or disease (0–50), as weakened vegetation may increase fire risk.
• water_dist: Distance to the nearest water source (0–50), which could mitigate fire risk.
• topo_pos: Topographic position (0–50), indicating terrain and elevation that may affect fire behavior.
• burnable: Percent of burnable land around the property, indicating available fuel for fire spread.

6. Where can I find out more?

For more information, you can visit Precisely's website.