Flood Factor: Mapping the flood risk of 142 million properties in America with Mapbox

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October 14, 2020

Flood Factor: Mapping the flood risk of 142 million properties in America with Mapbox

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October 14, 2020

By making flood risk data freely available for all with FloodFactor.com, the non-profit First Street Foundation is equipping individuals and communities across the United States to better understand and prepare for flooding and a changing environment.

Revealing under-reported flood risks in San Francisco

Flooding is the most expensive type of natural disaster in the United States, costing over $1 trillion in inflation-adjusted dollars since 1980. While institutional real estate investors and insurers have had access to detailed property flood risk data for years, the majority of Americans have relied on Federal Emergency Management Agency (FEMA) maps. However, FEMA maps were not created to model flooding at the level of individual properties, leaving millions of households and property owners unaware of their true risk.

Flood Factor was created to address that knowledge gap. For example, I can quickly drill into Hoboken, New Jersey to see the areas most at risk.

Or I can search the zip code 20002 in the District of Columbia — and see that there is an “increasing risk” and an expected 2,385 properties will be at risk of flooding within just 30 years.

Our goal is to make the most cutting-edge flood data freely accessible to all, using a simple 1–10 score that gives property owners a straightforward way to comprehend the flood risk to what is probably their largest and most valuable financial asset: their home.Matthew Eby, Executive Director, First Street Foundation

Making complex data personal

The most important element of Flood Factor is its simplicity. Behind the scenes, the team at First Street Foundation has created a first-of-its-kind methodology that adjusts for the reality of a changing climate and how it will impact flood risk into the future, accounts for local adaptation, and incorporates the four major contributors to flooding: tidal, rain, riverine and storm surge, all at the level of granularity needed to calculate flood risk for individual properties. Despite the complexity of the modeling, the user experience is accessible and compelling.

Flood Factor models the location and depth of flooding for different probabilities within a given year.

Flood Factor’s ‘Score Map’ features allows users to visualize the changing nature of risk and make the data more relatable. By batching scores together as users zoom in and out, the interactive maps enable them to understand their Flood Risk relative to other properties in a simple and visually compelling way.

Partnering with Mapbox has been crucial for providing us with innovative ways to integrate location into Flood Factor and enhance the user experience. Our Score Maps are one of the most popular components of the site. — Colleen Ensor, Product Manager, First Street Foundation

The address search on the Flood Factor homepage, powered by the Mapbox Geocoding API, takes users directly to the content that is relevant to them and their community. For an optimized user experience the maps across the site use Mapbox GL JS for dynamic maps and static ‘snapshot’ maps to help orient the user.

Static maps help locate and confirm the property’s address
Dynamic maps let users explore flood data in more detail and in relation to neighboring areas

To calculate flood risk scores at the level of individual properties, First Street Foundation used building footprints data (from Mapbox, Microsoft, and OpenStreetMap) combined with parcel data from LightBox. The resulting flood risk datasets were processed into vector tiles and styled in Mapbox Studio. The finished maps showcase multiple data-driven styling and zoom-based styling techniques — explore how to replicate this within Mapbox Studio, directly in GL JS code using Expressions, or by editing paint properties.

Data to the people

Since its launch in June, Flood Factor has sparked a new conversation around flooding and flood preparedness, both in the scientific community, the media, and the general public. Through partnerships with Realtor.com, Flood Factor is now also reaching millions of current and future homeowners to help them understand a property’s flood risk due to a changing environment over the life of a 30-year mortgage.

Our aim has always been to democratize information that has long been inaccessible because of its complexity and cost. We have heard from people across the country who have used Flood Factor to help make important decisions about whether to purchase flood insurance, and other investments to protect their homes. — Matthew Eby, Founder and Executive Director, First Street Foundation

The First Street Foundation is also sharing their data with others working on flood risk. Nonprofits, academics, and the public sector can access aggregated flood risk summary statistics for public, non-commercial use through an AWS public dataset — and others can purchase access via an API. Leading academic and industry researchers are collaborating as part of the First Street Foundation Flood Lab to derive new insights and further understanding of flood risk, its consequences, and possible solutions. Already other projects like Neighborhoods at Risk are putting Flood Factor’s data to use to help communities prioritize improvements to stormwater systems and other climate adaptation efforts.

Understanding and effectively communicating risk is the first step towards adaptation and mitigation. Innovators like the First Street Foundation are leading the way. If you’re using location tools to change our world for the better, connect with our Community team.

If you’re working with large datasets, try the Mapbox Tiling Service to process custom vector tilesets. Combine your data with layers available in all Studio accounts, including boundaries, terrain, elevation, and high-resolution land cover and land use data.

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response = requests.get(

  "https://api.mapbox.com/v4/mapbox.temperature-raster-tileset/tilequery/40,-105.json",

    params={

        "layers": "temperature,wind_speed",

        "bands": "1708304400,1708311600",

    },

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