To map oak distribution across our study area, we rely on data from iNaturalist. This crowdsourced dataset gives us the most accurate available picture oak presence in LA County. 

Because we are interested in the fire resilience of California’s native oak species (and in particular the coast live oak), we also wanted to map the damage from the Eaton Fire. Here we relied on a geospatial dataset from Cal Fire and the LA County Department of Public Works (DPW). 

Decisions about where to plant coast live oaks should be based not only on physical features like fire damage and existing oak presence, but also on the demographic characteristics of our study area. 

We use datasets for race and median household income from the ACS 5-year survey for LA County from 2022, and we use the Tiger/Line shapefile for census tract divisions. To make the race data more useful for analysis, we also created a column for the percentage of non-white residents per census tract. 

Though we used block groups in our analysis of oak tree distribution, we analyze race and income at the census-tract level because that was the only level available for 2022. 

At this point, we’re juggling multiple datasets – ones for oak presence, fire damage, race, and household income. To make our analysis more straightforward, and our maps more digestible, we created two indices: one for socioeconomic privilege, and one for fire resilience. 

The socioeconomic privilege index combines median household income with race, while the fire resilience index combines the socioeconomic privilege index with fire damage. See our “Considering Spatial Equity” page for more information on the data that went into these indices. 

As mentioned above, we wanted to use soil data in our final analysis to identify the ideal locations for coast live oak plantings. However, DPW’s soil-types dataset was a bit too unwieldy for our current purposes, containing hundreds of soil types that require specialized knowledge to decode.