Hi Zach! The long-term Intention of this Data Journalism piece is to start a conversation about the role that data visualizers can play to support policymakers in knowing how to support a clean energy transition in the transportation sector, and where it might be most effective for them to allocate local funds/attention considering local and national factors (like climate, typology, existing infrastructure for bikes/scooters, etc.) There is so much complexity and uncertainty embedded in decisions about how to most effectively support this transition. Clear and comprehensive data journalism can help frame these problems and reveal potential solution spaces!

The vision I have for how this piece would ideally be visualized is as a webpage with text callouts that slide up as the reader scrolls, over a map that transitions in scale and data visualization shown, underneath the text (somewhat like this: https://www.nytimes.com/interactive/2021/01/14/climate/hottest-year-2020-global-map.html)

Since data at the state level is so biased by freight and long-haul transportation, it might not support my narrative about urban commuter behavior too closely, but is just another scale to pass when zooming in to the city level where the problem space in question really lies

//WIP coding goals for the graphic above // I'd love to remake this and visualize it as unfilled bubbles (or some other way of showing a "void" in barrels of oil that might have been consumed— added to the graphic above showing quantity of oil used since 1965

BNEF is one of the best sources of data on this topic, but man they really need someone on their team who knows how to visualize anything but bar charts!

// WIP coding goals for the graphic above // (I had a failed attempt to add a cool map here from another Observable page (https://observablehq.com/d/880a53d2f9888395) because the link to the original dataset wouldn't copy over... still leaving this thought as a placeholder to find something else)

// WIP coding goals for graphic above // to use the size of each circle associated with a city to correlate with the number of people in that city who drive a personal petrol/diesel car. Since all of the circles will be related to raw numbers, and not localized proportions, the biggest bubbles will highlight where the most national attention could be most effectively targeted to address national emission reduction goals. (The map below (from: https://observablehq.com/@uwdata/cartographic-visualization?collection=@uwdata/visualization-curriculum) currently shows data about airport use, not carbon emissions from the full transportations sector, because I can't yet find the *census* data i'd need... but haven't given up hope that it exists)

this sketch was done on my ipad, I can easily import it into illustrator and copy the code into an observable doc to have a lot of the cluttering text descriptions appear on hover or click?

// In order to make the problem space more measurable, zooming in on a particular case study city for this pilot seems necessary //

// Help! // I found a helpful dataset of data in Denver, but wanted to visualize it in likeness of the *Monthly Maximum air Temperatures in San Francisco 2002-2021* plot here (https://observablehq.com/@spren9er/two-tone-pseudo-color-scales) and I think the only issue i'm having with the code not working, is knowing how to read what the column names might have been for that dataset, and how I can replace them with the column names in my Excel file, so the same code otherwise can read my Denver data I have a few lines of code below, and more in the Appendix, that I think have me 90% there...

// here is a pre-loaded visualization of my data, but not yet shown in a meaningful way //

yay! this worked! looking forward to it being able to relate to other data in a meaningful way!

// RIP to the diesel burning map attempt //

I feel so close!! I think I just need the column headers in my dataset to match the ones in the lost dataset from the SF file in the original Observable doc that I can't open... i'm assuming that UTCMonth was a column name in that file?

this question, and the following ones are still placeholders for me to research and think about further

// I still need to list what isn't included/considered and could/should be here // I have the methodology behind SUMC's tool (https://learn.sharedusemobilitycenter.org/wp-content/uploads/Shared-Mobility-Benefits-Calculator-Methodology-10.28.20.pdf) all marked up on paper, but need to translate those learnings here