![]() Researchers also collected data from the Kincade Fire in Sonoma County, which burned 77,000 acres and destroyed 374 structures over 13 days in October 2019. ![]() The data for the map below came from several rounds of surveys that encompassed 1,887 households and represented some 5,345 people and their experiences across the 20 fire seasons. Irva Hertz-Picciotto and the UC Davis Environmental Health Sciences Center (EHSC) began conducting the California Wildfire and Health Impacts Survey as part of a larger research project, “Wildfires and Health: Assessing the Toll in NOrthWest California” (WHAT NOW California). As a result, and because of how we used the Zip+4 centroid data to match to granular wildfire risk data, the rural data may be less accurate in flagging wildfire risk, either over-counting or under-counting households within high-risk areas.Use our interactive map to see how long people went without essentials after California's historic wildfires in 2017-2019 Where our data comes from Though this data is more granular than Census data with regard to geographic location, the Zip+4 centroids associate with capture areas of varying geographic size, such that rural Zip+4 centroids tend to include addresses that are more geographically dispersed. ², which are assigned a color based on the number of at-risk households.Ī note of caution regarding USPS vacancy data. The household counts are then aggregated within small area hexagons measuring approximately four-mi. The maps only display counts of households that are at high or very high risk. For this analysis, Zip+4 centroids that matched with WHP risk of High or Very High are used to convey high-risk households. The result is a national map of wildfire potential at 270- m. This national wildfire risk data is an index generated from multiple data sources that measure attributes such as wildfire likelihood and intensity, fuel and vegetation, and past fire occurrences. To assess wildfire risk, researchers matched Zip+4 centroids to the 2018 Wildfire Hazard Potential (WHP) Map produced by the US Forest Service 2. The data of occupied residential units can be geographically plotted at the Zip+4 centroid, which typically encompasses 10-30 actual housing units. The USPS vacancy data is a quarterly updated dataset of the universe of all addresses in the United States and are, thus, the most granular geographic housing unit data available to HUD. To produce these maps, researchers used 2017 United States Postal Service (USPS) address vacancy data 1. Therefore, estimates of concentrations can serve as a guide to conduct further data analysis and vulnerability assessments, emergency planning, and resource allocation. These orange and yellow areas, then, are the most likely to involve conflagrations that impact large populations. ![]() ![]() A hexagon that is orange or yellow does not necessarily indicate that wildfire risk is highest, overall, but rather that the hexagon contains the most households in high risk areas, relative to other hexagons. Lighter yellow hues signify the highest concentration of individual households in highest risk areas. ![]() Estimates of the number of households that fall within areas with the highest wildfire risk are aggregated within small area hexagons and then displayed on the map using a color-coding scheme described in the legend. These maps display wildfire risk exposure of households in the state of California. ![]()
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