Too Good To Be True (TGTBT) - Data for Good | Climate Change + Vulnerable Populations

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Team Name	Team Too Good To Be True (TGTBT)
Track	Student Track
Use Case	Student Track \| Option 1 (Data for Good \| Climate Change + Vulnerable Populations) - Identifying the link between economically vulnerable population and geographic areas which are most at risk from climate change impacts, and helping to inform targeted interventions.
Technology	Python, SAS Viya, Jupyter Notebook
Region	AP
Team lead	Patrick Daniel B. Belarmino
Team members	@PatrickPDB @seanpica @Allysson_T @raincarinoxx
Social media handles	https://fb.com/danielbelarmino7.7.7/ https://fb.com/FNC.xPICA https://fb.com/Allysson.Montefalco https://fb.com/rainellaross.carino
Is your team interested in participating in an interview?	N
Optional: Expand on your technology expertise	Fourth-year IT undergraduates specializing in Business Analytics, with experience in Python, Jupyter, web development, machine learning, and data analytics.

Jury Video

Too Good To Be True (TGTBT) - Student Track | Option 1 - Jury.mp4
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Pitch Video

Too Good To Be True (TGTBT) - Student Track | Option 1 - Pitch.mp4
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LGroves · ‎09-05-2024

Great work, @PatrickPDB +Too Good To Be True (TGTBT) - Data for Good | Climate Change + Vulnerable Populations!

Your Team Profile is complete and looks great. Thank you for putting the correct tag – “ Student Track | Option 1 (Data for Good | Climate Change + Vulnerable Populations)” so it’ll be easier to find and judge, when it’s time.

If you’re excited to learn more about the Hack before September 16^th– including a sneak-peak of the use case – please see my post here: https://communities.sas.com/t5/SAS-Hacker-s-Hub/SAS-Hackathon-2024-Student-Track-Details/ba-p/941054

Good luck!

LGroves · ‎10-28-2024

Very nice work, @TGTBT! I love how critically you thought about the data and how you pulled everything together in your presentation. Moreover, it's great to see that you create a new index - the Worrisome Score - as a way to combine several measures into a single metric.

I noted that Wake County was number 50 on the list. Whoa. I live in Wake County... and it's also the location of SAS HQ. So, I was a bit surprised to see us on the list... given that we're not near the coast in a traditional "hotspot" for weather events. In investigating your Worrisome Score a bit more, I'm wondering if the population measure isn't driving things a bit too much. In fact, the top 50 counties are all large - and I didn't see any traditional rural/isolated counties in the list. So, for robustness, I'd love to see your results if you (1) normalized all the subcomponents to range between 0 and 1 (you can do that with a standardized Z-Score) and then (2) aggregate the variables, as specified in your current approach.

Wake County concerns aside, great job!!!

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