Geoff Boeing

great! there is also the python package osmnx[1] to calculate isochrones by yourself. Also check geoff boeing's blog[2]. if you are into geo spatial things. [1]: https://github.com/gboeing/osmnx [2]: https://geoffboeing.com

I imagine it would look similar to the examples from his follow-up post on other cities from around the world, such as Rome or Paris, where distribution becomes fairly even: http://geoffboeing.com/2018/07/city-street-orientations-world/

Yes I have. It’s mostly a north/south grid everywhere, broken up a bit by neighborhood (eg belltown is different than Queen Anne). Some of the smaller neighborhoods (Mt Baker, near university, etc) aren’t great grids but you shouldn’t need to traverse them unless you live in it. It varies a bit by neighborhood, but for the most part Avenues run north/south and streets run east west. Makes it very easy to navigate. Much of the city also uses numbered names, for avenues. If you’re trying to get to 22nd ave and you’re on 17th, you know you need to travel west - along a street (any street) and you should reach it. On a larger level, the city planners have done a great job wrt gradual density increases in the city, and consistent up zoning along transit corridors. They’ve done a good job at promoting biking and alternative transit by building out a useful set of bike paths (if imperfect). I think the link expansions missed the mark, cost too much, and will take too long to build, but that doesn’t impact driving experience. I think the city should have dumped a ton of street cars (like in SLU and cap hill) across the city instead. Boston, by comparison, is based on animal trails, and the grid is not consistent within a neighborhood, and is non existent across neighborhoods. The street names give you almost no insight into where you’re going. Compare the cities here: https://geoffboeing.com/2018/07/comparing-city-street-orientations/ Oh and as far as drivers go, Boston drivers…

Made me think of this much less elaborate city road layout analysis: https://geoffboeing.com/2018/07/comparing-city-street-orientations/

These things are all about intersections and the intersections they connect with. The angled portions of Seattle's downtown just doesn't have anywhere near the number of intersections as the NSEW part of the whole city. [1] > it calculates the compass bearing from each directed edge’s origin node u to its destination node v. Now we can visualize these bearings, binned together as a histogram to get a sense of the relative frequency of the streets’ spatial orientations. [1] https://geoffboeing.com/2018/02/street-network-orientation/

It's a short exerpt from this link, which probably should be the OP: https://geoffboeing.com/2019/09/urban-street-network-orientation/

Here is a post with an Jupyter notebook producing such visualizations with the OSMnx library: https://geoffboeing.com/2018/02/street-network-orientation/

I'm not sure what they used for the web visualizations but I have seen this before with the python osmnx tool (https://geoffboeing.com/2019/09/urban-street-network-orientation/)

These graphs looked very familiar. I'm still digging to see if these types of polar histograms were used before this 2019 research from Geoff Boeing [0], but I haven't found it yet. [0] https://geoffboeing.com/2019/09/urban-street-network-orientation/ [edit] yes, they're also called rose diagrams. Here at least one reference from 2013 https://www.researchgate.net/publication/258954759_Entropy_and_order_i…

Not an uninteresting question. Have a look at the map of LA in the Metropolitan Patterns section of this report, about halfway in. Maybe you can track down more detailed version and/or the underlying data. https://geoffboeing.com/2016/08/craigslist-rental-housing-insights/ I'd look at Ventura, maybe. Inland is cheaper, but too hot.

Most of the US is definitely very regular, the exception being Boston. There was an interesting article about city street orientations. https://geoffboeing.com/2018/07/comparing-city-street-orientations/

I think you are right, one-way streets. >The street networks are directed and preserve one-way directionality. http://geoffboeing.com/2016/11/osmnx-python-street-networks/ >OSMnx automatically calculates all of the streets’ bearings. Specifically it calculates the compass bearing from each directed edge’s origin node u to its destination node v. http://geoffboeing.com/2018/02/street-network-orientation/