Document Type

Conference Proceeding

Publication Date

5-2022

Abstract

We tracked census tract level population change along California's wild land-urban interface (WUI) during the past decade (2010-2019), an ecological sensitive region transitioning from developed land to wilderness. Our results from Mann-Kendall analysis, a method employed for monotonic trend detection showed that about one-third (29.1%) of census tracts in California’s WUI have seen a significant population increase from 2010 to 2019, affecting 12.7% population in California.

The population increase along WUI is largely driven by the sixteen counties in the San Francisco Bay Area (10) and Southern California (6). We also found that higher proportion of WUI residents in Bay Area and larger number of WUI residents in Southern California. Bay Area counties in general have a higher proportion of population living in WUI tracts with significant population increase than Southern California counties. However, the lower proportion of residents living in WUI in Southern California counties account for a much larger population. Riverside is the county with the highest number of residents living in WUI tracts that have experienced significant population increase during the past decade. These residents also account for a high proportion (29.2%) of total population in Riverside. Preliminary results showed that the increase of population along WUI is driven by the house affordability and house ownership in 16 counties of Bay Area and Southern California. These factors can still explain a significant amount of the spatial pattern if extended to all counties in California.

Comments

This work was presented during International Wildland Fire Association’s Fire and Climate Conference of 2022 in Pasadena, California from May 23 to May 27, 2022. This presentation targets audience from academia and government agencies who study or work on wildfire management and related policymaking.

Copyright

Shenyue Jia

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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