I have a list of latitude-longitude coordinates (and addresses if that helps). I want to find which voting precintcs they are located in. Is there any easy way to do this in R? Here is a sample of my data:
Related
(Sorry my English is not good, but I will try to phrase it clearly)
For example, I've got road data in a form like this:
Latitude Longitude
RoadA(consists of 2 dots)
31.263319 121.5555711
31.2619722 121.5564754
RoadB(consists of 3 dots)
31.2619722 121.5564754
31.2611567 121.557023
31.2610903 121.557088
As you can see, each road consists of several (2~x) dots. The road may be a curve and need many dots to describe it. Between two dots they are connected by a straight line.
Once I have read in all the road data, I will read in a set of dots, my task here is that once a new dot is given, I need to find out if it is on any of the roads. If not, I need to draw a perpendicular towards the nearest road and find out the coordinate of the pedal foot(the nearest point on road).
The amount of query is huge, so I need the speed to be as fast as possible.What kind of data structure should I use?
There are some Spatial Partitioning methods in Game Development and theory.
Maybe you should use one of them.
link
You should partition your locations in Binary,Quad,Oct, ... trees.
I think the best way, is to use a map of Pairs.
Hi frens I am using geopy to calculate the latitude and longitude. Now I want to get the list of areas given distance from a zipcode.How to get that?
Well, as I can see, geopy doesn't have any built-in capability to get a list of areas around some coordinates.
But you can use a workaround. Take your geocode and calculate coordinates (latitue and longitude). Then imagine a grid on the map with a cell size equal to area of the smallest one you need to find around your location.
Use geopy to get an area name belonging to the each cell corner of your grid. Is that ok for you? It will get you some kind of approximation because a grid is not a circle and you may miss some small areas. But I think in most cases the solution will work fine.
It is much easier to locate zipcodes inside a rectangle than in a circle so I would recommend that you approximate your problem by looking for zipcodes inside a given rectangle.
Here are answers to the question of how to get list of zipcodes in given polygone: Find zipcodes inside polygon shape using google maps api
Summary
You need geometry for each zipcode. Once you have that you need to be able to query it using database that supports geoquery. One such database is Google's Fusion Table and there is already a geometry data table for zipcodes available here: https://www.google.com/fusiontables/DataSource?docid=1AxB511DayCdtmyBuYOIPHIe_WM9iG87Q3jKh6EQ#rows:id=1
Here's the sample query for Fusion Table data.
Another approach is server side code using PHP and CSV data. Here's live demo: http://daim.snm.ku.dk/demo/zip/. The page also has download for code.
If you use any of above technique please make sure to upvote answers of original authors :).
I am currently reading into the topic of stereo vision, using the book of Hartley&Zimmerman alongside some papers, as I am trying to develop an algorithm capable of creating elevation maps from two images.
I am trying to come up with the basic steps for such an algorithm. This is what I think I have to do:
If I have two images I somehow have to find the fundamental matrix, F, in order to find the actual elevation values at all points from triangulation later on. If the cameras are calibrated this is straightforward if not it is slightly more complex (plenty of methods for this can be found in H&Z).
It is necessary to know F in order to obtain the epipolar lines. These are lines that are used in order to find image point x in the first image back in the second image.
Now comes the part were it gets a bit confusing for me:
Now I would start taking a image point x_i in the first picture and try to find the corresponding point x_i’ in the second picture, using some matching algorithm. Using triangulation it is now possible to compute the real world point X and from that it’s elevation. This process will be repeated for every pixel in the right image.
In the perfect world (no noise etc) triangulation will be done based on
x1=P1X
x2=P2X
In the real world it is necessary to find a best fit instead.
Doing this for all pixels will lead to the complete elevation map as desired, some pixels will however be impossible to match and therefore can't be triangulated.
What confuses me most is that I have the feeling that Hartley&Zimmerman skip the entire discussion on how to obtain your point correspondences (matching?) and that the papers I read in addition to the book talk a lot about disparity maps which aren’t mentioned in H&Z at all. However I think I understood correctly that the disparity is simply the difference x1_i- x2_i?
Is this approach correct, and if not where did I make mistakes?
Your approach is in general correct.
You can think of a stereo camera system as two points in space where their relative orientation is known. This are the optical centers. In front of each optical center, you have a coordinate system. These are the image planes. When you have found two corresponding pixels, you can then calculate a line for each pixel, wich goes throug the pixel and the respectively optical center. Where the two lines intersect, there is the object point in 3D. Because of the not perfect world, they will probably not intersect and one may use the point where the lines are closest to each other.
There exist several algorithms to detect which points correspond.
When using disparities, the two image planes need to be aligned such that the images are parallel and each row in image 1 corresponds to the same row in image 2. Then correspondences only need to be searched on a per row basis. Then it is also enough to know about the differences on x-axis of the single corresponding points. This is then the disparity.
I'm building a store locator based on in-house geocoding data. Effectively I need to query stories near City X or Zip Y within a certain radius. The data sets I'm working with are relatively comprehensive and include things such as population.
One issue is that large cities (Los Angeles for example) are many miles in radius so you could be within the city but miles from the coordinate we have loaded.
Is there a rule of thumb, or a free data feed which would list an approximate radius of a city, or perhaps even outlines of the city points?
Also, assuming I have a shape defining the city what calculation would I use to say "stores within X miles of this area"?
Why don't you use the zip codes and latitude/longitude of the stores, instead of the cities? You know the addresses of the stores, so use its zip code, look up the coordinates, and calculate the distance from the origin zip code. Then it wouldn't matter how big the city is, because big cities have many zip codes, but each store has its own zip code.
It would only be a problem for states with big zip codes like Texas, but then there is likely not more than 1 store per zip code anyways so not a big deal.
Ultimately we didn't implement this feature, but before it was cancelled I had a fair amount of success using the below approach:
Finding coordinates for the city itself, as well as all zip codes of the city
"Connecting the dots" of all the above coordinates to create a polygon of the (very rough shape of the city)
Checking if the user's input coordinate was within the given range of the polygon
The above approach worked relatively well and may have ultimately developed into a sound solution with some more enhancements and tuning.
I have an image which was shown to groups of people with different domain knowledge of its content. I than recorded gaze fixation data of them watching the image.
I now kind of want to compare the results of the two groups - so what I need to know is, if there is a correlation of the positions of the sampling data between the two groups or not.
I have the original image as well as the fixation coords. Do you have any good idea how to start analyzing the data?
It's more about the idea or the plan so you don't have to be too technical on that one.
Thanks
Simple idea: render all the coordinates on the original image in a 'heat map' like way, one image for each group. You can then visually compare the images for correlation, and you have some nice graphics for in your paper.
There is something like the two-dimensional correlation coefficient. With software like R or Matlab you can do the number crunching for the correlation.
Matlab has a function for this:
Two Dimensional Correlation Function: corr2
Computes two dimensional correlation coefficient between two matrices
and the matrices must be of the same size. r = corr2 (A,B)
In gaze tracking, the most interesting data lies in two areas.
In where all people look, for that you can use the heat map Daan suggests. Make a heat map for all people, and heat maps for separate groups of people.
In when people look there. For that I would recommend you start by making heat maps as above, but for short time intervals starting from the time the picture was first shown. Again, for all people, and for the separate groups you have.
The resulting set of heat-maps, perhaps animated for the ones from the second point, should give you some pointers for further analysis.