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Right now, we are using two naïve functions for look ups and other options are available.
By Location
We are chunking the latitude and longitude into integer "zones", which approximates the functionality of a rigid QuadTree. A QuadTree would be better to cover edge cases like when the lat is 37.1 and the closest is 36.9. Even better would be a kd-tree to search the space. Both could be pre-calculated in equal space to the current zoned array.
By ZipCode
For the zip 12345, I'm storing the data in places[1][2][3][4][5]. This does not improve performance or file size, I think it was so we can load the data by chunk rather than all at once. A prefix tree might do better with we want to chuck the data. If not, a straight HashMap would probably be best (places["12345"]).
I might also remove this.
Versioning
I think semantic versioning with the last data update would work nicely:
Right now, we are using two naïve functions for look ups and other options are available.
By Location
We are chunking the latitude and longitude into integer "zones", which approximates the functionality of a rigid QuadTree. A QuadTree would be better to cover edge cases like when the lat is 37.1 and the closest is 36.9. Even better would be a kd-tree to search the space. Both could be pre-calculated in equal space to the current zoned array.
By ZipCode
For the zip 12345, I'm storing the data in
places[1][2][3][4][5]
. This does not improve performance or file size, I think it was so we can load the data by chunk rather than all at once. A prefix tree might do better with we want to chuck the data. If not, a straight HashMap would probably be best (places["12345"]
).I might also remove this.
Versioning
I think semantic versioning with the last data update would work nicely:
`{major}.{minor}.{patch}+{metadata}’
2.0.0+20230216 (data update)
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