Thread by @MissAshes92

Happy Monday! I have been wanting to do this for a while, but I have been reviewing my social vulnerability work lately and I wanted to share some of my work with the #EMGTwitter universe. I am hoping to dive in and map some new cities soon. 1/n

As EMs, we wear MANY hats. With preparedness, outreach tasks, preplanning, and mitigation - we never have a short to-do list. This only includes blue sky days activities as well... not even talking about response/recovery!

SV can help us prioritize some tasks in our cities! 2/n

So many disaster studies have shown that there are certain demographics that cause people to have a higher risk of injury or death during disasters. These same variables also point to:
-Less storm resource access
-Less prepared
-Likely to have higher financial damage [3/n]

How is this relevant to practitioner EM?

We can use social vulnerability mapping to pinpoint areas of high vulnerability - target outreach and resource programs - improve vulnerability - and save and minimize losses [lives/money] from disasters. [4/n]

Getting into methodology: SVI is easy to make and the data is FREE! It's just demographic census data!

Tap into yearly sampled demo data through ACS data 5 year block group data. Connect that data with a GIS shapefile for mapping. [5/n]

Once you have the most recent ACS data - choose the variables to create your SV index. The main variables consistently shown in research are below. For my thesis - age, income, and education were the statistically significant vars over the others. [6/n]

After you have vars - you must choose how to weight them. Unfortunately, SV research has not determined which variable is for sure more important than the others. So many studies choose different weighting approaches. For mine - I weighted equally and took a basic ratio. [7/n]

For each census block, you take the sum of the number of people with each demographic. In the example below - Block 1 shows 17 ppl below the poverty line, 9 ppl with less than HS diploma, and 22 people ages -18 or 55+. [8/n]

After you have the census block totals and the city-wide totals, you divide each block by the city total to get a ratio for each variable. [9/n]

Finally - to create an SV index that ranges from 0 to 1, you must find another ratio. You take the largest ratio from step 2 for each variable and divide all of the ratios of that same variable. [10/n]

Last step! After you have the 0-1 variable score for each block - you sum each variable for each block and divide by # if variables chosen. This is your combines variable SV index score. [11/n]

Once the final index is created - it's a simple plug into GIS and symbolize on the map! We chose 3 classes (low vulnerability, moderate, and high) and a quantile approach. [12/n]

Here is an example of the Lubbock social vulnerability assessment on a census block level from around 2015. The red areas are areas of high vulnerability.

These areas are good targets of outreach campaigns, resource programs, and other EM actions. [13/n].

And here is an example of Williamson County TX on a census block level. Same color rule and determination but on a county level. [14/n]

SVI is such a neat and useful tool - but there are current limitations.
-ACS data is sampled (error)
-The index method I used is VERY generic
-Social science is HARD!
-Must have GIS software and skills

However look at the pros! [15/n]

Why implement SVI on a city level? Think about how we can use it to target our efforts!

-Steer outreach into areas of a city that need it the most
-Provide and prioritize storm resource projects there
-Steer mitigation projects
-Assist with response decisions
[16/n]