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Is H3 specifically made for this? No. Is it a good fit? Yes, I think it could work really well. See this notebook for an example implementation: https://observablehq.com/@nrabinowitz/privacy-preserving-spatial-indexing-with-h3 The technique here is a fairly good fit for your use case. Grid cells work well for privacy-preserving location, because you can use progressively larger cells to mask exact coordinates, but it’s generally a good idea to use an additional variable like population to help choose a sufficiently large cell. Then you can convert large cells to their children at some fine resolution to get the benefits of a uniform resolution for joins, etc. |
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We applied a similar idea but with varying resolution based on a trust
metric.. some details in figure 4 here
https://www.tandfonline.com/doi/full/10.1080/13658816.2021.1931236#d1e734
…On Wed, Mar 11, 2026 at 2:22 PM James A Rosen ***@***.***> wrote:
I use this exact technique on Pick My Fruit! We store
<https://github.com/jamesarosen/PickMyFruit/blob/main/apps/www/src/lib/h3-resolutions.ts#L9>
produce locations at resolution 13, but limit the public
<https://github.com/jamesarosen/PickMyFruit/blob/main/apps/www/src/lib/h3-resolutions.ts#L24>
to resolution 8.
Population-relative maximums are a great idea. Resolution 8 is
privacy-preserving in a small city, but not all that useful in a rural
community.
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I want to store (anonimised but close enough) user locations, so that we can do queries like
Is this what H3 is made for? Or should I just stick with lat/lng calculations for that? I came to H3 because it offers a secure way to store a user location (that's privacy safe)
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