Feedback on ZIP code level mapping/analysis/rating tool

[u/otter-in-a-suit]

Hello /r/gis!

I'm hoping it's OK to solicit some feedback from professionals. I recently built SkaldMaps: https://skaldmaps.com

SkaldMaps lets you explore, compare, and rank U.S. ZIP with about 500 custom attributes, from real estate market data to weather.

You can build your own rating model based on whatever criteria you care about. This is not a feature I've really seen elsewhere.

Some of the data is relatively generic (like ACS), others are fairly customized attributes. Full dictionary here: https://skaldmaps.com/docs/reference/columns

I have some neat things built in that I personally used to do with QGIS and that can get complex for a casual user (such as distance to the nearest amenities or how many hospitals are in a given area). I still use QGIS but for many things, but the aggregate data on a bigger area has been more helpful for me.

This is explicitly a different niche than Regrid and other parcel-level tools - it's more designed for people who are looking for areas - in this case, ZIP codes - to invest in / move to, rather than individual parcels. Because of that, it's also reasonably priced (I think - maybe that's up for debate).

I assume there's a decent overlap with SWEs here, so you might also enjoy my (admittedly short) technical writeup here: https://chollinger.com/blog/2026/05/skaldmaps-zip-code-research-tool/

The same blog also has some other GIS-shaped posts, in case you're curious - I'm first and foremost an SWE, not a GIS person. I have, however, dabbled in it over the years.

I am curious what people here think - is this useful? I built this based on a prototype that helped my SO and I to buy our current house (by shortlisting areas), as well as a small piece of rec land I own, so it's been useful at least for me.

I do have a free tier in case you'd like to try it. The plans and what is available on what plan might need some fine tuning.

P.S.: This is a solo/side project, so my resources are a bit limited.

[u/otter-in-a-suit]

I know! They are, however, also what's commonly used in the RE field (as in, I can search realtor or zillow for individual ZIPs) and are something that regular people would be familiar with. For me, that was an acceptable tradeoff.

[u/Kinjir0 → u/otter-in-a-suit]

You can search zip codes because its literally a database primary key. 

It is not a spatial unit. Unless you are tying to individual parcels or census tracts, you will have a significant number of incorrect edge cases. Actually probably enough that they will just be cases unstead of edge cases. 

Just because you can put data on a map does not make it useful or meaningful,  and monetizing free data with zero value add and bad spatial resolution/accuracy by using ai will get you deservedly flamed on this particular message board. 

[u/otter-in-a-suit → u/Kinjir0]

I understand the criticism. I just want to avoid confusion here: ZIP (or rather, ZCTAs) are the presentation layer for the application (and what I advertise). I outlined why earlier - it's just a very common format for other RE tools and research.

Internally, datasets that are ZCTA keyed are used just verbatim for the raw attributes (this follows a relatively standard data modelling pattern with raw, massaged, joinable, and denormalized data).

If I have tract data (for instance), I use the HUD crosswalk data to map. For some datasets (e.g. just point data or simply completely different shapes, like the "urban areas"), that's not possible and things may be a fuzzier match.

For instance, amenity distance gets less precise the larger the areas get and ex-urban areas get partially marked as such based on overlaps with, among others, urban areas. I know this well, since where I live, we have super dense subdivisions and dairy farms in the same ZIP and any metric (even a a verbatim "people/sqmi" from the ACS) is inherently a bit misleading. The model classifies it as "suburban", which is certainly partially true.

So yes, I understand that this isn't perfect (and I'm not claiming that it is), but my understanding has always been that this is exactly the use case for the HUD crosswalk files. Always happy to learn!

[u/NotObviouslyARobot → u/otter-in-a-suit]

For instance, amenity distance gets less precise the larger the areas get and ex-urban areas get partially marked as such based on overlaps with, among others, urban areas. 

You have discovered the Modifiable Areal Unit Problem. Congratulations.