I have a list of GPS coordinates and I want to see if they fall in a certain range of GPS coordinates. My list is about 6000 points, but I have 44000 ranges. These GPS coordinates are based on street addresses so I was thinking to narrow my ranges by street name first and then seeing if the coordinates fall within the subset of ranges, which are street blocks. Otherwise I would have 6000x44000 searches and that would take forever. Anyone have any idea on what would be the most efficient way to do this? I'm completely new to R and coding in general so I'm have no idea where to start.
Inbound https://maps.googleapis.com/maps/api/distancematrix/json?origins=Mount+Monodnak&destinations=Boston+MA
Outbound: https://maps.googleapis.com/maps/api/distancematrix/json?origins=Boston+MA&destinations=Mount+Monadnock
You can see that data is returned on the outbound leg to the mountain but not on the return back. Travel mode needs to be driving.
I Geocoded Mount Monadnock to get the latitude and longitude and tried the nearestRoad api call but that also returned {}
Here is the URL with my API key removed (but is required) roads.googleapis.com/v1/nearestRoads?points=42.8610804,-72.1083267&key=
A solution I am considering is to find the lat long bounds of the zip code area and find which point is closest to the destination to calculate distance and duration but that is obviously not ideal.
Any ideas or insight would be much appreciated, thanks.
I have heading that i get with this computeHeading(from:LatLng, to:LatLng), latlng of one point and distance to second point(in meters) is there a way using these three things to get latlng of second point ?
From google docs Navigation Functions
Given a particular heading, an origin location, and the distance to travel (in meters), you can calculate the destination coordinates using computeOffset().
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.
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
Use Reverse Geocoding to convert your lat/lon to addresses. It has some limit on the number of queries per day though.
Here is a nice blog post with examples how to geocode and reverse geocode using google-maps.
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.
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.
I am trying to divide a certain city into several blocks, each representing North, North-West, North-East, South...and so on. I just need the coordinates of the region boundaries (e.g.: North is between X and Y latitude and between Z and T longitude), so that I can check in my app whether a point belongs to a region or another. The regions should not depend on a certain zoom level's boundaries and they don't need to be the same size (maybe the North part of a city is a little bit larger then the South one).
Any idea how can I "draw" these region boundaries? Thank you!
For boundary data, you would have to do a search. Depends on the city and country. In the US, many municipalities provide this data directly through a city or country web site. Generally it will be in a GIS data format such as a shapefile. You have a number of different options for working programmatically with GIS data formats. I recommend using the GDAL libraries,
particularly ogr2ogr. Once you've got the boundary data, you can draw it on the map using polyline overlays or create a raster images of the data, say using gdal_rasterize. Or you can convert the data to KML using ogr2ogr, and upload it to Google Fusion Tables using Google Docs and overlay it using a FusionTablesLayer.