Convert lat/lon to zipcode / neighborhood name

Question

I have a large collection of pictures with GPS locations, encoded as lat/lon coordinates, mostly in Los Angeles. I would like to convert these to (1) zipcodes, and (2) neighborhood names. Are there any free web services or databases to do so?

The best I can come up with so far is scrape the neighborhood polygons from the LA times page and try to find out in which polygon every coordinate is. However this might be quite a lot of work, and not all of my coordinates are in LA. As for the zipcodes, this 2004 database is the best I can find, however zipcodes are encoded as a single coordinates instead of a polygon. So the best I can do is find the minimum distance from a given coordinate to the given zipcode-coordinates, which is not optimal.

I was under the impression that google-maps or open-street-maps should be able to do this (as they seem to 'know' exactly where every neighboorhood and zipcode is), however I cannot find any API's to do the lookups / queries.

Answer 1

You can now do this directly within R itself thanks to the rather awesome ggmap package.

Like others mention, you'll be reverse geocoding using the google maps API (and therefore limited to 2,500 queries daily), but it's as simple as:

library("ggmap")

# generate a single example address
lonlat_sample <- as.numeric(geocode("the hollyood bowl"))
lonlat_sample  # note the order is longitude, latitiude

res <- revgeocode(lonlat_sample, output="more")
# can then access zip and neighborhood where populated
res$postal_code
res$neighborhood

Answer 2

Here is a nice blog post with examples how to geocode and reverse geocode using google-maps.

Answer 3

Try this one:

http://www.usnaviguide.com/zip.htm

There is some limit as to how many queries per day you can do on the site, but they also sell the complete database, which changes every few months. Sorry that I don't know of any free resources.

Answer 4

Use Reverse Geocoding to convert your lat/lon to addresses. It has some limit on the number of queries per day though.

Answer 5

As others suggested, geocode them into street address should work fine for zip code. i am not too sure about neighborhood, because you may have to look if street number is odd/even to see if it is located which side of a road that determines neighborhood.

An alternative way is to prepare GIS polygon feature (ESRI shape file for example), test each point against this set of polygons see which one it intersects.

zip code is very straighforward, you can download shape file from the census.

http://www.census.gov/cgi-bin/geo/shapefiles2010/main

neighborhood is harder, i'd guess. In another part of US i had to create my shape file on my own by combining definitions from municipal government, real-estate website, newspaper etc so that it looks like what people thinks neighborhood in the city are without having any overlap or gap. It can take some time to compose such set of polygons. you may crab census "block group", or even census "block" from the above page and merge them

Once you prepared polygon features, there are couple of GIS tools on different environment (stand-alone executable, GUI program, c/python/sql etc API, probably R as well, to do intersection of polygons and points.