Identifying Areas At Risk for COVID-19 and Natural Disasters: Fire and Earthquake
The COVID-19 global pandemic has impacted all of our communities. It also will exacerbate the ongoing exposure to other natural disasters in some areas of the world. To understand the coupling of this pandemic risk and other natural disasters risk, we (Team-project) decided to work on a geospatial map with different layers of COVID-19, the other disaster exposures and their combined effect on each area.
We decided to start our research on the state of California and then extend our project in future for more areas. In the state of California, wildfires have been devastating specially in recent years. Being part of the ring of wildfire and laying on top of a large number of faults, earthquakes are also another constant threat to this state. The shortage of available tools to measure the combined effect of the pandemic and these natural disasters, makes this topic a challenging project for decision makers to assess risk and make appropriate preparedness plans. Here, we provide a tool to visualize the concurrence of COVID-19 hot spots, wildfires and earthquakes in the state of California in the year 2020.
Below is the sources of the dataset for this project:
For Covid-19 dataset, we aggregated data from John Hopkins Resource Center . This data set is getting updated on a daily basis and for this project, we collected the COVID-19 data from the most recent dataset as of October 17, 2020.
Wildfire data for this project was collected from California Fire Website on October 17, 2020. During the exploratory data analysis step, other resources such as : InciWeb and San Francisco Chronicle Fire Tracker were also used to update this dataset.
Earthquake dataset was also collected from Center for Engineering Strong Motion Data on October 19, 2020.
All of these three datasets were combined into a single dataset to represent the events’ information per county-level (we tried to match the combined dataset with COVID-19 dataset which has been collected per county in the state of California). In order to summarize these datasets to the county-level, wildfire data and earthquake data needed to be manipulated slightly differently.
Disaster Analysis
Wildfires
First, we classified each fire based on the acres burned by the fire. The classifications and labels are as follows:
We then determined the number of fires per county. For fires that affected multiple counties, the fire was counted in each affected county. Fire risk scores were then calculated taking into account the extent of each fire based on the acres burned and whether they are currently active. Each event was multiplied by the corresponding event label. This manipulation is intended to add a weight to the fire based on the extent of the fire. Fires were also classified based on whether they were active (label = 1) or not (label = 0). Again, each event was weighted based on this category. The final score was calculated by adding the weighted values for all the events in each county.
The wildfires in California have been quite devastating in recent years, and 2020 has been another such catastrophic year. As of October 17, 2020, there have been 202 fires, and 19 of those are still active. The fires have also claimed the lives of 31[1]. Three percent of all land in California has burned so far. This amounts to a total of 3,549,923 acres. Not each county has been affected equally, and Riverside county in particular has been hit the hardest. There have been a total 18 wildfires, and one is still active. As seen in the below figure. As explained above, some recorded fires in the dataset have been 100% contained and some were still active at the time of this study. We used the number of fires, the extent of the areas that have burned and the status of fire whether it is active or contained to understand the fire dataset better.
Earthquakes
California is part of the ring of fire, so earthquakes are frequent and have the potential for catastrophic results. 2020 has recorded 200 earthquakes with most of them being of relatively low magnitude which can be seen in the distribution plot below. It is at a magnitude of approximately 3.5 that we begin to feel the earth tremble. As we increase from that level the chances for damage increases. Counties were scored based on the created indices and scores show that Imperial county has been affected the most this year. Similar to the wildfire risk scores, we first classified earthquakes based on the magnitude. We based our classifications on Michigan Tech’s UPSeis. These categories and their corresponding labels are as follows:
The final earthquake risk scores were calculated as the sum of all the weighted events (earthquake occurs).
These summarized values and scores were then appended to the COVID-19 dataset (generated on a county basis) to generate the dataset that we used for further analysis. The keys for this dataset are represented in this data dictionary.
COVID-19
California is not alone in that the impact of COVID-19 is not uniform across the state. Different counties have different numbers of cases or deaths. At the county-level, Imperial county has the highest per capita cases and deaths.
These risk indices are visualized in the maps below.
Combined Risk
Through the use of Principal Component Analysis and KMeans clustering we were able to construct an algorithm and build a model that allowed us to assign a risk index to the counties in California. The higher a county scored on the index the riskier it was/is to be there. That risk index was then used to construct a map that reflected the danger associated with all these disasters as it related to each county.
Risk Map
This California Risk Index for Covid-19, Fire and Earthquake can be used by the user to identify the risk of their interest region. This Web Mapping Application has been created through ArcGIS online platform and contains tools that give you the opportunity to explore different individual and combined Risk Index layers and data including Covid-19 death numbers, Fire Risk Scores, and Earthquake Risk Scores per capita in 2020 for each region of interest. Based on the legend for the combined Risk Index for these three natural disasters, the bigger the red circle on the map, the higher the risk index will be for that specific area.
While our work does not show if the prevalence of natural disasters exacerbates the degree to which a community is infected with COVID-19, we can almost certainly assume that those communities that have high combined risks have had or will have their resources stretched. Hopefully there’s a chance that a model and maps such as our can help people steer clear of these risky areas for their own safety as well as those in the community. It may also serve as an indicator of where resources and funds need to flow to provide relief.
