July 22nd, 2021
Hello, this is John, Datawrapper’s summer intern. I’m currently working on a Master’s in Cartography and Geovisualization, and so I am excited to be doing a blog post for Datawrapper’s Weekly Chart – or in this case, map!
Since moving to Germany last year, I’m often asked where in the US I’m from. While I am used to saying “the Bay Area,” I’ve found it doesn’t always get much of a reaction. I’ll follow up by saying “San Francisco”, and then it clicks for people. It has made me think a lot about how and why we identify where we are from.
Why do I like to say that I’m from the Bay Area, not San Francisco? Because it acknowledges that it is a place made up of many parts and not just centered around one urban core like Berlin. San Francisco may be the most well known, but everyone who lives in the area knows that San Jose, Oakland and the East Bay, Silicon Valley, and wine country are all vital parts. The longer you live in San Francisco or a city nearby, the more likely you are to say you are from the Bay Area (or simply the Bay).
We can see the scattered Bay Area population on the following symbol map. The size of each square shows the population of the town/city:
This physical map gives you a good sense for how big the Bay Area really is and the true location of the towns and cities (within the framework of this map projection, of course). But it also causes the squares to be small and far apart. That’s why I created an alternative version; a proper cartogram:
The cartogram makes it easier to compare population sizes, because the shapes are closer to each other. Compressing the area removes the excess space between towns, space that contains no thematically relevant data. It is therefore easy to notice that the big square in the south, San Jose, is actually bigger than San Francisco.
This cartogram also alters the shape and size of the administrative boundaries. For example, the northern counties, despite their large geographic size, have smaller populations than other regions of the Bay. The map allows the population data to be the focus of attention as opposed to the geographically largest area.
But – and it’s a big but – if you’re not familiar with the area, the map just looks like a bunch of randomly distributed colorful squares. Even if you are from the area, it may take you a second to recognize the location. But once you do, it’s a really fun map to look at and explore.
An issue with the cartogram map is the fairly subjective placement of the squares. I tried to keep the relative latitudinal and longitudinal city locations true to reality (e.g. San Francisco and San Ramon share the same latitude and are therefore aligned in the cartogram). But because the cities in the north bay are much smaller and further apart, they are the least true to their real geography.
Another big design decision: To show the strong regional identities within the Bay with colors. When you ask someone from the Bay where they are from, they will often tell you something like “the north bay” (in yellow-orange; known for wine country) or the “east bay” (in green; a vast, dense urban network of medium-sized towns and cities). And residents rarely say they live in Silicon Valley, typically opting for “the peninsula” instead (in blue).
To create these maps, I used the custom GeoJSON upload for choropleth maps and the area import for locator maps. Learn more about them in these Academy articles:
I hope you enjoyed this week’s map! As always, do let us know if you have feedback, suggestions or questions. I’d be happy to hear from you at email@example.com or in the comments below. See you next week!
Many urban areas have a sort of hub-and-spoke model, where there is a main urban center surrounded by suburbs. Berlin, where most of the Datawrapper team works, is like this. If you were to take the entire Berlin-Brandenburg greater metro area, Berlin makes up well over 50% of the population. San Francisco, on the other hand, makes up only 10% of the Bay Area’s population, and it’s not even the most populous city despite what many think.↩︎