Community resilience to wildfires: A network analysis approach by utilizing human mobility data

Quantifying community resilience especially after a disaster is an open research challenge. However, with the growth in mobility datasets such as SafeGraph we are being given new opportunities to study how communities rebound from disaster.  

To this end, in a new paper with Qingqing Chen and Boyu Wang entitled "Community resilience to wildfires: A network analysis approach by utilizing human mobility data" which was published in Computers, Environment and Urban Systems we develop a framework to quantify resilience after a disaster using network analysis. To showcase this framework we us a human mobility data associated with two wildfires (Mendocino Complex and Camp wildfires) in California and measure the robustness and vulnerability of different communities over time. 

Our results show community resilience is closely tied to socio-economic and built environmental traits of the affected areas and as such our approach paves a way to study disasters and their long-term impacts on society. If this sounds of interest, below you can read the abstract to the paper, see some of the figures we use to explain and demonstrate our approach, while at the end of the post you can find the full reference along with a link to the paper. 

Abstract:

Disasters have been a long-standing concern to societies at large. With growing attention being paid to resilient communities, such concern has been brought to the forefront of resilience studies. However, there is a wide variety of definitions with respect to resilience, and a precise definition has yet to emerge. Moreover, much work to date has often focused only on the immediate response to an event, thus investigating the resilience of an area over a prolonged period of time has remained largely unexplored. To overcome these issues, we propose a novel framework utilizing network analysis and concepts from disaster science (e.g., the resilience triangle) to quantify the long-term impacts of wildfires. Taking the Mendocino Complex and Camp wildfires - the largest and most deadly wildfires in California to date, respectively - as case studies, we capture the robustness and vulnerability of communities based on human mobility data from 2018 to 2019. The results show that demographic and socioeconomic characteristics alone only partially capture community resilience, however, by leveraging human mobility data and network analysis techniques, we can enhance our understanding of resilience over space and time, providing a new lens to study disasters and their long-term impacts on society.

Keywords: Wildfire, Community resilience, Network analysis, Resilience triangle, Human mobility data.   

Resilience triangle. (a) The original resilience triangle (adapted from Bruneau et al., 2003); (b) The modified resilience triangle used in this study.

An overview of the research outline.

The zoomed in study areas of the two wildfires, where the blue areas highlight the Census Block Groups; (b) The spatial distribution of wildfire density from 2005 to 2022; (c) The distribution of annual wildfires and acres in the U.S.

The distribution of degree centrality for each census block group colored by different clusters. (a) The Camp wildfire; (b) The Mendocino Complex wildfire.

The results of resilience triangles of clustered CBGs and resilience features. (a) The determined resilience triangles of clustered CBGs for Camp wildfire; (b) The determined resilience triangles of clustered CBGs for Mendocino Complex wildfire; (c) Vulnerability of CBGs within the two wildfire areas; (d) Robustness of CBGs within the two wildfires.

Full Reference: 

Chen, Q., Wang, B. and Crooks, A.T. (2024), Community Resilience to Wildfires: A Network Analysis Approach by Utilizing Human Mobility Data, Computers, Environment and Urban System, 110: 102110. (pdf)