Noodle.ai | Senior UI Engineer | San Francisco or Palo Alto, CA | Full-time | Onsite
We’re looking for people with a love for UI and visualization to join our team. We design and build apps for industrial and enterprise customers that make complex data science output intuitive and interactive. Our stack is heavily React and D3 based; this is a chance to work on innovative bespoke creative visualization ideas in a field full of potential.
Thanks for the code compliments Zack. As I've gotten more experienced with D3, I've been trying to use it exclusively, even for jQuery type things. In this case I'm just using jQuery for old browser fallback, since D3 doesn't go to great lengths to support old browser quirks.
And btw, no code cleanup tools in use, just my OCD. ;) I haven't used Coffeescript yet. Will have to check it out.
We actually started with a linear scale, but ended up switching to a log scale. Scaling linearly says, "how much more expensive are the high end homes than the low end (subtraction)?" With the log scale we're saying, "how many more times expensive is the high end than the low end (multiplication)?"
Scaling linearly allows big values to skew the results. Imagine a neighborhood where the high end is $20m and the low end $10m, giving us an absolute difference of $10m. Another neighborhood has a $1m high end and a $500K low end, for an absolute difference of $500K. Scaled linearly, the $10m range in the first neighborhood would appear to be much much bigger than the $500K range of the second.
But if we use a log scale, instead of asking what the absolute difference is, we're asking relatively how much more expensive is the high than the low end. Using our two example neighborhoods, both would result in a 2x difference, and thus both would have the same range of prices.
It's easy to point out where all the most expensive homes are, and scaling linearly does just that. But looking at the relative differences in prices provides a much more useful way of comparing different neighborhoods (or even cities if we look nationwide), because it accounts for the natural variances in prices in different areas.
You're right. The size of the selected city is fixed, just to be visually prominent, while the size of the red/blue related cities is determined by how many searches are happening to/from there.
We’re looking for people with a love for UI and visualization to join our team. We design and build apps for industrial and enterprise customers that make complex data science output intuitive and interactive. Our stack is heavily React and D3 based; this is a chance to work on innovative bespoke creative visualization ideas in a field full of potential.
https://noodle.ai/current-openings?gh_jid=610553 I lead the Design Technology team at Noodle. If you’re interested, email me directly at [email protected].