Sexy webcam chat with only zipcode

If USPS wanted to a grow up a bit, they'd just convert to the latest ZCTA and provide authoritative GIS polygons. Here we query for intersections between TIGER State and TIGER ZCTA datasets.Note, we qualify states by 1% of the total ZCTA area.Zip Codes are defined by a bounding box of Streets delivered to by carriers from a particular distribution center.So you would need to take the USPS AIS data and extract by Zip Codes the streets that are delivered by a given Post Office, then Join these a street grid.The relationship files use Census geography IDs, so you'll want to grab a gazetteer file to help you convert the numeric IDs into the place or county names you're expecting.As other answers have stated, any mapping of ZIP codes to places is likely to be approximate, but I've had good luck with the Census data files.This is what all the commercial vendors do (Nokia/Tom Tom) to create the Psuedo shape that they use to show postal boundaries.This inexact process is the reason why the USPS does not provide spatial data.

Most of the other cases are in the lower sector range (reserved for post office boxes) and in Sector 99 (reserved for the postmaster and business mail return).

As others have mentioned, there are a few different ways to figure out the area covered by a ZIP Code, but ZCTAs are the easiest, and the only official version that I know of.

So your example of 42223 does span a state border, 02861 Massachusetts 02861 Rhode Island 42223 Kentucky 42223 Tennessee 59221 Montana 59221 North Dakota 63673 Illinois 63673 Missouri 71749 Arkansas 71749 Louisiana 73949 Oklahoma 73949 Texas 81137 Colorado 81137 New Mexico 84536 Arizona 84536 Utah 86044 Arizona 86044 Utah 86515 Arizona 86515 New Mexico 88063 New Mexico 88063 Texas 89439 California 89439 Nevada 97635 California 97635 Oregon import pandas as pd zcta_to_place_url = ' # load relevant data df = pd.read_csv( zcta_to_place_url, dtype=, usecols=['ZCTA5', 'STATE']) # the data often repeats the same (ZCTA, state) pair.

Remove these df = df.drop_duplicates() # get number of times each ZCTA appears (most are only 1) counts = df['ZCTA5'].value_counts() # get those listed more than once multi_state_zips = df[df.

ZCTA5.isin(counts[counts 1].index)] # the census uses numeric state codes # replace these with state names census_codes_to_names_url = ' states = pd.read_csv(census_codes_to_names_url, sep='|') merged = pd.merge( multi_state_zips, states, on='STATE' )'ZCTA5', 'STATE_NAME' print merged.sort(['ZCTA5', 'STATE_NAME']).to_string(index=False) : It seems the Census has two different two-digit codings for states.

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