I am using the Traffic API to collect speed of streets. As a response, I got a list of roadways (RW), and each one has a list of flow items (FI). But reading the description (DE) of the roadways, I noticed that for the same road there are a bunch of roadway items. And each FI of a RW has a description with a name of another road (not necessarily connected to the current RW).
Example:
<RW LI="B14+05453" DE="Av Cristiano Machado" PBT="2016-04-20T22:08:55Z" mid="8a961c28-7d3c-481f-a6c4-bd1ca1b699f0|">
<FIS>
<FI>
<TMC PC="5454" DE="R Nossa Senhora De Fátima" QD="-" LE="0.04553"/>
<CF CN="0.78" FF="39.0" JF="0.0" SP="44.45" SU="44.45" TY="TR"/>
</FI>
<FI>
<TMC PC="5455" DE="Av Presidente Antônio Carlos" QD="-" LE="0.1193"/>
<CF CN="0.76" FF="39.0" JF="0.0" SP="42.03" SU="42.03" TY="TR"/>
</FI>
...
</FIS>
<\RW>
This is a small portion of a response. There is a RW called "Av Cristiano Machado" and two FI listed. The second FI, "Av Presidente Antônio Carlos" is anoter road, but it doens't intersect with "Av Cristiano Machado" in the reality.
My question is:
Based on the identifier of the RW (I think it's LI), how to indentify on a map which part of the road it represents? Is it possible to convert a RW item into a polyline or maybe get its coordinates?
Oh well, after a thorough reading I learned about responseattributes. Adding "&responseattributes=sh" to the request, it returns the shape of each flow item, which is a group of georeferenced points. You can plot and connect them through a line and I assume this is the section of the road it represents.
This figure is a result I obtained using Google MyMaps, LI="B14+05656" and LI="B14-05656". On the left hand (green) the coordinates seem really precise, not so much on the right hand of the image (red). The blue line is a valid driving route.
Related
I have a map full of markers corresponding to GPS coordinates, represented as a PostgreSQL + PostGIS database table using "geography" type for the GPS column.
Imagine, if you will, one semi-transparent square on top of each of these points corresponding to 1x1 mile, based from the centre, often intersecting with each other.
I'm trying to determine the minimum number of such "squares" and their GPS coordinates, so that they "cover" all of the markers with a minimum of 25 meters to the nearest border.
If it makes it any easier, the positions of these "squares" don't have to match the positions of any of the markers.
The purpose of this is to attempt to cut down the number of API requests to a "houses for sale" service significantly, since most of the positions are close to each other and the API takes a 1x1 mile square "bounding box" as the input for each call. It would be insanely wasteful to call the API many times for basically the same area when maybe 1 or 2 times would do it if I can first figure out where these imaginary "squares" go.
I get the feeling that this is considered a "known, common and solved" problem, but so far, I've not been able to figure out how to do it.
Sorry, but it seems like you have no idea what you are doing and are just being rude, both here and at PostGIS irc-channel.
You give no information about your api.
What is creating your maps?
Is it a wms-service or what?
What most people would do is setting up a mapservice with a tile cache. Then the mapservice will pich the tiles needed for each house you want to show ( or multiple houses).
The tiles will be prepared o will be created on the fly. But they will be cached for next time.
So, I think you should read up on things like
MapServer
Mapnik
MapCache
Mapproxy
GeoServer
That is not a complete list, but might give you some ideas about what it is that you want
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 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 question regarding graphs.
I have a street network (imagine Manhattan, NY as an example, but it could be any street network) represented as a graph (where junctions are represented as nodes und the streets are links between the nodes).
The problem now is that I somehow have to get the "city blocks" (think of the blocks in Manhattan for instance), i.e. the set of arcs that define a city block. I thought of cycle detection algorithms but that obviously won't give me the real blocks only, but also all the other cycles that I don't really need. Of course I could filter those out probably quite easily but this can't be the real solution.
Do you have any (simple) idea how I can get the "real" city block from a graph?
Thanks in advance!
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.