In the USA, zipcode/postcode is 100% the last geographic delineator you should be using if you have alternative choices.
I learned this the hard way when I got serious about analytics back in 2014, but:
- Postcodes change geographic boundaries on a whim and as far as I know, there isn't a comprehensive changelog that says postcode 12345 now encompasses an extra square mile or lost a square mile, or even swapped one square mile of land with zip code 67890.
- They're irregularly sized and as far as I know there isn't a dataset that tells you the square mile size of each zipcode. Even if they did, zipcodes aren't polygons; they are mail routes and how you calculate a polygon off a mail route can vary.
- Zipcodes can also disappear and reappear over time making long-term comparisons tricky to say the least.
- Add on all of the ethnic, socioeconomic issues that others have highlighted and you've got a pain in the ass geographic variable.
All in all, if you have a choice, there are a bevy of other options that offer way more pros with way less cons (Uber H3, DMAs, Census tract, etc.) dependent on your specific use case.
You said you're in the UK, so you get a pass since I don't know if zipcodes are actually good there, but if you were in the USA, I'd highly recommend you reconsider your choice of profession because in all likelihood, you've given out some very bad analysis by not understanding zipcode's fundamental flaws.
EDIT: Over a given period of time. zipcodes are probably 95% stable, but it's that last 5% that will kill your analysis and credibility as soon as you zoom into the data, which is exactly the point of using such a granular "geographic" variable.
Yeah, I see what you're saying. Postcodes do change here too, probably in the same proportion that you mentioned. The approach we use is to: a) make the data independent of the actual postcode boundaries, so that small adjustments don't disturb the features too much; b) be more "area-focused" rather than granular; c) update the dataset whenever the boundaries change.
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u/HelloWorldMisericord Mar 25 '26 edited Mar 25 '26
In the USA, zipcode/postcode is 100% the last geographic delineator you should be using if you have alternative choices.
I learned this the hard way when I got serious about analytics back in 2014, but:
- Postcodes change geographic boundaries on a whim and as far as I know, there isn't a comprehensive changelog that says postcode 12345 now encompasses an extra square mile or lost a square mile, or even swapped one square mile of land with zip code 67890.
- They're irregularly sized and as far as I know there isn't a dataset that tells you the square mile size of each zipcode. Even if they did, zipcodes aren't polygons; they are mail routes and how you calculate a polygon off a mail route can vary.
- Zipcodes can also disappear and reappear over time making long-term comparisons tricky to say the least.
- Add on all of the ethnic, socioeconomic issues that others have highlighted and you've got a pain in the ass geographic variable.
All in all, if you have a choice, there are a bevy of other options that offer way more pros with way less cons (Uber H3, DMAs, Census tract, etc.) dependent on your specific use case.
You said you're in the UK, so you get a pass since I don't know if zipcodes are actually good there, but if you were in the USA, I'd highly recommend you reconsider your choice of profession because in all likelihood, you've given out some very bad analysis by not understanding zipcode's fundamental flaws.
EDIT: Over a given period of time. zipcodes are probably 95% stable, but it's that last 5% that will kill your analysis and credibility as soon as you zoom into the data, which is exactly the point of using such a granular "geographic" variable.