Stop using zip codes for geospatial analysis (2019)

That's not the important problem and there's a simple solution with ZCTAs.

The big problem is zip codes are defined in terms of convenient postal routes and aren't suitable for most geospatial analysis. Census units, as the article explains, are a much better choice.

> The consequence is that performing any analysis with an assumption that ZIP codes are polygons is bound to be error-prone.

Yeah, but any analysis you're likely to perform is approximate enough that the fact that ZIP codes aren't polygons is basically a rounding error.

Plus, it's a lot easier to get ZIP codes, and they're more reliably correct, so you might still get better results, than you would going with another indicator that is either (a) less reliable or (b) less available.

Yeah. ZIP codes are sets in the abstract-dimensional space of carrier delivery points. I suppose you could think of them as lines, but definitely not polygons.

This is a tangent, but addresses are also way more complicated than most people realize - especially if you’re relying on a user to input a correct address or if you need to support multiple countries, somewhere with unique addresses like Queens[0], or you need to differentiate between units of a specific street address that uses something other than unit numbers for a unit designation.

At that point you need something like Smarty[1] to validate and parse addresses.

[0]: https://stackoverflow.com/questions/2783155/how-to-distingui...

[1]: https://www.smarty.com/

There are point process models, but, yes, its much more common to want to aggregate to a spatial area.

Another consideration is what kind of reference information is available at different spatial units. There are plenty of Census Bureau data available by ZCTA but some data may only be available at other aggregate units. Zip Codes are often used as political boundaries.

I'd also mention the "best" areal unit depends on the data. There is a well known phenomenon called the modifiable areal unit problem in which spatial effects appear and vanish at different spatial resolutions. It can sort of be thought of as a spatial variation of the ecological fallacy.

One more advantage: ZIP codes are a good trade-off if you want to gather anonymous data in a survey or provide anonymized data to an outside entity. For example, we recently conducted a survey on mobility patterns within our university. To offer respondents a reasonable amount of anonymity, we just asked for their (German) ZIP code and the location of their primary workplace. This allows us to determine the distance and approximate route people would take between home and university campus - to a degree that is sufficient for our goals.

Well you hit on all the points that discuss the compromises that zip codes offer. Just because you have them in your data doesn't mean that they can produce anything useful. You are correct that no one knows their census unit is (if you are thinking from someone entering this on a website) but collecting location or address will be a lot better.

Fact is a lot of web data contains a zip but if you can collect something better it will usually render better results. Unless you are analyzing shipments then that is fine.

Would add that there are network effects with zip code data. If you collect H2 data, you have fewer sources with which to join.

In terms of "good enough", a Canadian postal code, broadly equivalent to a zip code, is much more granular and can often identify an individual apartment building, or single city block. Plenty of large office buildings in major Canadian cities also have their own postal code.

The functionality of it is closer to the "Zip+4" with extension used to have a more granular routing of physical mail for USPS.

https://www.canadapost-postescanada.ca/cpc/en/support/articl...

https://en.wikipedia.org/wiki/Postal_codes_in_Canada

If you are worrying about address at all instead of tax or legal jurisdiction its probable that you as a business have a physical presence. You can probably correlate better by predicting which location a given address would likely interact with if you don't know already by prior purchases/interaction which they normally do so. I would suggest actual purchase data followed by travel time.

Zip and distance as the crow flies often gives shit data. My zip suggests I'm off in bum fuck and since I'm on the puget sound things that are relatively near as the crow flies can actually be hours away.

> Easily extracted (they're part of every address, no geocoding required)

That's only true if you can also access the spatial boundaries of the zipcodes themselves.

In Australia, this turns out not to be true: the postal system considers their boundaries to be commercial confidential information and doesn't share them. The best we can do is the Australian Bureau of Statistics' approximations of them, which they dub "postal areas".

I've noticed more and more super/hypermarkets started asking for your zip/postal code sometime during self-checkout. I'm guessing they use these as approximations about where people travel from, so they can evaluate if to open more stores closer to popular areas, or something like that. Pretty sure there is more use cases for postal codes too.

Wait until you find out that this is the same way phones used to work. The number was the row/colum for the operator needed to plug your line into.

That's what I was thinking of earlier, the succinct version is "your address is where mail needs to go, the zip code is how to get it there". Or in other words, the zip code is the address(es) of the sorting centers and post offices to the destination.

What is the benefit of H3 vs a rectangular grid?

I agree.

Reading their alternatives, it strikes me with "ZCTA's are the worst form of small area aggregation except for all others."

Its not a great geography to use but it is quite useful if you know it's limitations and inaccuracies when you get into it. Stuff like multipolygon entities, island-polys, etc aren't fun to resolve but can be accounted for.

Add on that ZCTA's will historically follow some sort of actual boundary(rivers/highways/etc) they can tell a story in a way Census tracts can't.

There actually are algorithms that compute the centroid of multi part polygons. So you can in fact find the center of a non-contiguous shape. Now, whether that centroid actually has value in a real world application, I’m not sure.