Plies help me. I have many points. I want to get a point that does not overlap or the center of that set of points. Any ideas?set of points i am using QGIS and R.
You can't perform functions like that using only points. You need to set up some polygons, lines or something similar first. Join up the points then perform your geospatial queries. QGIS has a good python API, you could code it in there:-
https://www.qgistutorials.com/en/docs/getting_started_with_pyqgis.html
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I have a list of 10 thousand points (latitude,longitude). I want to identify suitable points to become the center point of circles with fixed radius that are able to cover all the points as shown in below.
Is there any available package to provide the below output? I am looking for the fastest way to do this.
Thanks for dropping in here.
I'm currently working on a project, and I'm not that strong with python yet. So I was hoping for some constructive feedback on this question.
I have a dataset containing core samples, all stored with sample id, latitude, longitude, content and other data irrelevant for this question.
Now I've imported this dataset and sliced it as I want it to be. For the images I'm using the rasterio module to open 2 satellite images that covers the region. I'm using the utm module to convert back and forth between latlong->UTM->Pixel values (Which also seems to be throwing me strange coordinates at some points).
Annoyingly enough, the two Sentinel-2 images are cut right across the center of the map.
As I'm doing bounding boxes on top of where the samples are taken, this is a problem as I need to extract 10x10 pixel cut outs of that region. This leads to a lot of the samples not getting a proper cut out.
So I thought why not merge the two images together into one large rectangular bit. But I still need to retain the meta data with the UTM coordinates.
How would you suggest I proceed. Can it be done in an easy way? Is there another angle on this I've overlooked?
Thank you for your time.
I'm not sure I completely understand the question, but if you are simply trying to merge 2 images, have you looked at the command line tool gdal_merge.py?
A very simple example:
gdal_merge.py -o merged_image.tif image1.tif image2.tif
I am currently working on a project that involves measuring distances all around a robot with a laser module, the robot then has to move based on the points that he gets.
I currently have access to 360 points that represent the distance from the center for each of the corresponding angles. (a distance for 0°, a distance for 1°, etc)
Here's an example of what the points look like when displayed on a 2D surface:
Circular representation of the points
What I'd like to be able to do is, rather than feeding the robot all 360 points, to feed it segments containing multiple points. For instance, the bottom part of the image would be a single segment even though the points are not completely aligned.
My question to you is, is there an existing algorithm that would help me achieve what I am trying to do?
(I'm working in python but that shouldn't really be a factor)
Thanks a lot.
Assuming your points are ordered:
For each point, look ahead by two points, if the middle point is less than some distance away from the segment between the two points, then push your endpoint 1 pt further, and check that now both of the middle points are still within some distance of your line segment. Proceed to do this until false, at which point roll back one pt and generate a segment, then set the end of that segment as the start of your next segment. Also, you could consider angles instead of just distances as there are some cases where that would be favorable. Also, if no segment can be made from a certain start point for several attempts, push the start point forward one (as not everything is going to simplify into segments)
Alternately, you could convert to Cartesian points and use the hough voting algorithm to detect lines from the resulting point-cloud.
I have a spatial dataset that consists of a large number of point measurements (n=10^4) that were taken along regular grid lines (500m x 500m) and some arbitrary lines and blocks in between. Single measurements taken with a spacing of about 0.3-1.0m (varying) along these lines (see example showing every 10th point).
The data can be assumed to be normally distributed but shows a strong small-scale variability in some regions. And there is some trend with elevation (r=0.5) that can easily be removed.
Regardless of the coding platform, I'm looking for a good or "the optimal" way to interpolate these points to a regular 25 x 25m grid over the entire area of interest (5000 x 7000m). I know about the wide range of kriging techniques but I wondered if somebody has a specific idea on how to handle the "oversampling along lines" with rather large gaps between the lines.
Thank you for any advice!
Leo
Kriging technique does not perform well when the points to interpolate are taken on a regular grid, because it is necessary to have a wide range of different inter-points distances in order to well estimate the covariance model.
Your case is a bit particular... The oversampling over the lines is not a problem at all. The main problem is the big holes you have in your grid. If think that these holes will create problems whatever the interpolation technique you use.
However it is difficult to predict a priori if kriging will behave well. I advise you to try it anyway.
Kriging is only suited for interpolating. You cannot extrapolate with kriging metamodel, so that you won't be able to predict values in the bottom left part of your figure for example (because you have no point here).
To perform kriging, I advise you to use the following tools (depending the languages you're more familiar with):
DiceKriging package in R (the one I use preferably)
fields package in R (which is more specialized on spatial fields)
DACE toolbox in matlab
Bonus: a link to a reference book about kriging which is available online: http://www.gaussianprocess.org/
PS: This type of question is more statistics oriented than programming and may be better suited to the stats.stackexchange.com website.
I got my country lat/long boundaries from koordinates.com. Now I want to fill in the interior with dots.
Since the file I have is KML, I was thinking of converting the coordinates to cartesian using the NetTopologySuite.
I do not want a polygon overlay. I want to generate dots/coordinates for the polygons interior - ideally at a density of my choosing.
I have seen algorithms like this one, http://alienryderflex.com/polygon_fill/. Is there a library that will do this for me? Alternatively, can someone share code?
Ultimately, I will convert the dot coordinates back to lat/long and populate a globe like this one
http://code.google.com/p/webgl-globe/
I'm affraid GIS isn't my area of expertise, but I've got two ideas:
Generate a set of random points. You can use a Point-In-Polygon function to determine if you're points are in the right place.
You can use a rectangle grid of points and use a 'resolution' to determine how many points there will be and how close. You can offset the grid positions to make them look more random if you need to. You'll check if the point inside the bounding rectangle of your polygon is inside the polygon or not.
Notice that the webgl-globe example uses a grid of points(similar to point(2)) converted to spherical coordinates.
Both ideas is kind of similar, only the points distribution differs.
You can find a roughly related implementation I did using actionscript here,
but I would also suggest asking on the GIS site.