|Problems? Is your data what you think it is?|
as there is really no easy way to sort the points and process them in image-based batches.
Assuming your zipcode polygons are obtained from somewhere like here, the each of the .e00 files contains the polygons for the zipcodes in each state. By building one image per state you will have 48/50 images and 48/50 bounding boxes. Pre-selecting or ordering the points by those bounding boxes (much as you currently do using the bounding box of the individual polys0, is simple, and as there are many fewer states than zips, much quicker.
If the points originate in a DB, then something like
would allow you to load the state images one at a time and select only the points (roughly) within state for lookup.
The pixels in the image can be mapped to the smallest unit of the coordinate, but fractional units for a point like 1.24435, 455.22452 get all fudged up.
Taking Texas as an example (guessing that it is the largest state from looking at a map), width covers 13 degrees of Longitude and 773 miles. That means that each arc minute represents ~1 mile and each arc second ~88 feet. In decimal degrees, each 0.0001 of a degree represents ~32 feet. (Check my math, it's getting late!)
So, if you represent the biggest state by an image 13000x11000 pixels, and multiply all the coordinates in your polys by 10,000 and truncate, each pixels will represent ~32ft X 32 feet. The image takes under 600MB when loaded in memory. If you have fully populated hardware, you could perhaps increase your resolution by a factor of 10 if need be. Remember that the majority of states are considerably smaller.
There will obviously be some overlap between bounding boxes, which means some points will be lookup in 2 or 3 maps, but it is convenient that in large part, most state boundaries follow lines of longitude and latitude.
There is even a module Geo::E00 for processing these files :)
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