This is interesting since zip codes came up in consideration for how we built out our pay choropleth map in the US: https://levels.fyi/heatmap
Though ultimately it was far too granular (for example the Bay Area would be so many different zip codes). Instead we went with Nielsen's DMA (Designated Market Area) mappings within the US to abstract aggregated data a bit better. And of course this DMA dataset also had a different original use case. It was used for TV / media market surveys so it has some weird vestiges. Some regions are grouped very far and wide (you'll notice there's a bit of Denver within Nevada and its just a remnant of how it used to be categorized), but it still provides a bit of a broader level grouping than something acute like zip code.
We've also been considering using Combined Statistical Areas using population instead. This is something that is under way, and in the interim we've considered charting styles that don't necessarily need borders (for example this bubble map: https://www.levels.fyi/bubble-plot/europe/). The benefit with DMAs is that it offers full border coverage of the entire US whereas some hubs can still be missing from CSAs if relying on a population threshold. But the plan is to create some of our own regional definitions and borders using our own submissions combined with population. Will be an interesting project.
How do you think it should be treated? I think at the individual granular data point level adding a tag or note about the equity not being immediately liquid is a good start. But I don't think it'd be a good idea to weigh the stock differently since that can depend on so many things. For example SpaceX and some other private companies do offer regular liquidity and I would consider their equity close to liquid.
Appreciate the feedback though, and definitely agree we can work on how we display the data and make it more clear.
1. Salary (straightforward, on regular schedule, and you'll get it)
2. Bonuses and RSUs (various vesting rules, and ways you can never see it)
3. Startup stock and (worse) stock options (probably worthless, vesting rules, and you might need an advisor to make sure you don't exercise and come out with a big negative)
This is a great point, and something we plan to address. We currently use Nielsen's DMA (Designated Market Area) mappings within the US to separate out regional areas which was used for TV / media market surveys. We happen to use DMA categories for our regional pages on Levels.fyi which is why it was easiest to start with since we already had this data captured. The features can sometimes be a bit off and seem like they're grouped very far and wide (you'll notice there's a bit of Denver within Nevada and its just a vestige of how it used to be categorized), but it still provides a bit of a broader level grouping than something like zip code. We've also been considering using Combined Statistical Areas using population instead, but the benefit with DMAs is that it offers full coverage of the entire US whereas some major tech hubs are still missing from CSAs if relying solely on population.
We're planning to create some of our own regional definitions and borders using our own submissions and that should offer some more tighter bounds. This was just a v1, and I think its already resonating with folks.
> We've also been considering using Combined Statistical Areas using population instead, but the benefit with DMAs is that it offers full coverage of the entire US whereas some major tech hubs are still missing from CSAs if relying solely on population.
I think just using the 387 MSAs [1] instead of the 181 CSAs would get you far enough to cover all the major tech hubs.
If that data is submitted by individuals to a particular company, is it possible to see a lot more detailed heatmap, perhaps down to each address of each company?
I think that part is still a bit ambiguous, it's almost how people and companies self-identify the role. But that said, we collect a bit of a taxonomy for our role structure, and we specifically look at Software Engineers focused on AI. The responsibilities can still differ from company to company, but that's what we used for our dataset.
Congrats on the launch! Awesome to see this release as an early user who was able to check it out. The shared workspaces and shared browser windows with context in place has been incredible for collaborating with folks. We have our Figma design, Notion doc, and Gitlab MR all in the same space so we don't have to go searching for each one independently or have them cross-linked to each other.
Though ultimately it was far too granular (for example the Bay Area would be so many different zip codes). Instead we went with Nielsen's DMA (Designated Market Area) mappings within the US to abstract aggregated data a bit better. And of course this DMA dataset also had a different original use case. It was used for TV / media market surveys so it has some weird vestiges. Some regions are grouped very far and wide (you'll notice there's a bit of Denver within Nevada and its just a remnant of how it used to be categorized), but it still provides a bit of a broader level grouping than something acute like zip code.
I do like this map from the article though and the granularity you can get with zip code when zooming: https://clausa.app.carto.com/map/29fd0873-64cb-42a6-a90d-c83...
We've also been considering using Combined Statistical Areas using population instead. This is something that is under way, and in the interim we've considered charting styles that don't necessarily need borders (for example this bubble map: https://www.levels.fyi/bubble-plot/europe/). The benefit with DMAs is that it offers full border coverage of the entire US whereas some hubs can still be missing from CSAs if relying on a population threshold. But the plan is to create some of our own regional definitions and borders using our own submissions combined with population. Will be an interesting project.
GeoJSON data for the map borders: https://github.com/PublicaMundi/MappingAPI/blob/master/data/...
Nielsen DMA regions: https://blocks.roadtolarissa.com/simzou/6459889
reply