I have a list of locations with zipcodes. I have another list of Distribution Centers that serve these locations. Is there anyway to map the nearest DC to each of these locations? I am an extremely green coder, but i have some experience with R
I'd need more information to give you some possible code to solve your problem however, here is one approach to solving your problem.
Convert your zipcodes to longitudes and latitudes.
Not sure what location data you have on your distribution centers, but you should be able to find a way to retrieve the long/lat of each of these.
For each zipcode, compute the the distance to each DC (using their respective longs/lats). To compute the distance, use the haversine formula. Find the minimum of these distances. This is your solution.
I am participating in a starter Kaggle competition(Crimes in San Francisco) in which I want to predict the category of a crime using a bunch of predictor variables including X and Y coordinates of a crime. As I doubt of the predictive power of the coordinates, I want to transform these variables to something more relevant to the crime category.
So I am thinking that if I had the neighbourhood of San Francisco in which the crime took place, it would be more informative than the actual coordinates of the crime. I can find the neighbourhoods online but of course I cant use the borders of each neighbour to classify the corresponding crime because their shapes are not rectangular or anything like that.
Does anyone have any idea about how I could solve this one?
Thanks guys
Well that's interesting AntoniosK and it's getting close to what I want to accomplish. The problem is that the information " south-east and 2km from city center" can lead to more than one neighborhoods.
I am still thinking that the partition of the city in neighborhoods is valuable because the socio-economic and structural differences between them ( there is a reason why the neighborhoods of each city are separated as such, right?) can lead to a higher probability for a certain category crime and a lower one for another.
That said, your idea made me thinking of using the south-east etc mapping and then use the angle of the segment(point to city center) with x axis to map the point to appropriate neighborhood. I am on it right now. Thanks
After some time on the problem I found that the procedure I want to perform is titled " reverse geocoding". It also turns out that there are some api's to solve this. The best according to my opinion is revgeocode() function contained in ggmap package(google's edition). This one though has a query limit per day(2500 queries) unless you pay for extra.
The one that I turned to though is geonames package and GNneighbourhood function that turns coordinates to neighbours. It is free, though I have experienced some errors(keep in mind that this one is only for US and Canada cities)
revgeocode function-ggmap package
Gnneighbourhood-geonames package
I am wondering whether the reference coordinates of the base map acquired using get_googlemap() of ggmap can be converted to UTM coordinate system ?
I would like to display a heatmap on a base map of a city acquired by get_googlemap(). The data of my heatmap is referenced in UTMs (i.e. metres) as I find these coordinates more meaningful at the city scale than decimal degrees. I have researched SO but it seems the solutions are to go the other way i.e. to convert the heat map UTM data into lat long system, but this is not my preference.
Has anyone a solution for this ? Any guidance and help is greatly appreciated.
Many thanks !
I try to find a way to get the projection type of the following coords.
I need to convert these coords in WGS84 latitude longitude format. I have only 1 hint: these coords are located in Florida-USA (Broward County)
<XCoordinate>9082520</XCoordinate>
<YCoordinate>6563620</YCoordinate>
Thanks
I suspect your data are in "SPC" (State Plane Coordinate) system. This is often used for "local" coordinates - it makes calculations for such things as distances and routes much easier. If that is the case, and the county in question is Broward (which is zone 0901), then there is still some guessing to do... because one can use "US feet", "International feet", or meters in this system.
If you go to http://www.earthpoint.us/Convert.aspx you can enter your coordinates in the "Position" box. Depending on what you choose you will get different answers. I suggest you try to see which makes sense (if you suspect the coordinates are for a post office and you land in a lake you probably are using the wrong number):
0901 908252.0ftE 656362.0ftN gives 26°08'14.5036", -080°13'53.8483"
That puts you pretty much on top of a bus stop - which I guess is what the Hastus system might be giving you...
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.