# 2024 Week 15 – Bivariant Maps

## Introduction

Here is a fun one to keep the map theme going: Bivariant maps!

These maps are a good way to encode two variables on a map and give the user a way to see the relationship between the two at a glance. But beware, correlation does not imply causation. So, while for this map we can clearly see a correlation between education and poverty, we cannot – with certainty – say whether poor education means you earn less money or whether less money doesn’t enable you to get a good education!

I recently stumbled upon this blogpost by Will Heikes and thought it would be a great one to try myself.

I tried to make it work with compare tables like Will did, however I think I had too many contradicting requirements to make it work. If you enjoy torturing yourself, feel free to make it all work within a CRMA dashboard. If you value your sanity, I recommend leveraging recipes do get the right fields!

## Requirements

• Create the dashboard below
• Include a map of the continental US as well as Hawaii and Alaska
• Colour each county based on the poverty and education level within the county
• Thresholds:
• Poverty: <10%, 10%-13.5%, >=13.5%
• Education: <27.72%, 27.72%-28%, >28%
• The buckets cover about 50%,25% and 25% of all counties
• Create a dynamic legend that explains the colours and lets you highlight certain buckets
• Use the following colours:
• rgb(254, 239, 227), rgb(155, 207, 228), rgb(43, 175, 229)
• rgb(252, 177, 137), rgb(174, 151, 140), rgb(70, 122, 141)
• rgb(245, 115, 52), rgb(167, 94, 59), rgb(91, 71, 62)
• Match the tooltips

The resulting dataset should look something like this:

Tipps…

## Dataset

You can download the dataset and the geojson for the counties here on data.world.
I removed all the unnecessary columns from the data and had to re-format the geojson. You are better off downloading the clean data but feel free to try it yourself:

https://gist.github.com/sdwfrost/d1c73f91dd9d175998ed166eb216994a

## Share

After you finish your workout, share a screenshot of your solutions or interesting insights.

Either on Twitter using the hashtags #WOW2024 and #CRMA and tag @genetis, @LaGMills @msayantani, and @simplysfdc. (Or you can use this handy link to do that)

Or on LinkedIn, tagging Will Heikes for the inspiration and Alex Waleczek, Lauren Mills, Sayantani Mitra, Phillip Schrijnemaekers and Johan Yu using the hashtags #WOW2024

Also make sure to fill out the Submission Tracker to track your progress and help us judge the difficulty of our challenges.

Coming later…
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