I have a SpatialLinesDataFrame that is meant to represent survey tracks. These tracks are not strait lines where the route goes from a>b by the shortest distance, but instead the route twists and turns over its path (although no overlaps). My ultimate goal is to segment these curvy lines into specified distances, taking into account the twists and turns, to calculate presence/absence for each segment. Both in R and Arcmap I have segmented these lines, but it doesn't take into account the twists and turns along the path.
I believe the problem is that I need to convert the polylines into some kind of route class object, then segment that. Suggestions of how this can be done? Thanks in advance, Robin
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Visual example of the data
I used a drone to create a DOF of a small area. During the flight, it takes a photo every 20sh seconds (40sh meters of a flight). I have created a CSV file, which I transferred to a point shapefile. In total, I made with drone 10 so-called "missions", each with 100-200 points which are "shaped" as squares on the map. What I want now is to create a polygon shapefile from the point shapefile.
Because those points sometimes overlap, I cannot use the "Aggregate Points" task, as it's only distance-based. I want to make polygons automatically, using some kind of script. What could help is the fact that a maximum time between two points (AKA photos taken) is 10-20 seconds, so if the time distance is over 3 minutes, it's another "mission". Can you help with such a script, that would quickly and automatically create as many polygons as there are missions?
Okay, I think I understand what you are trying to accomplish. Since no one replied I am going to give it a quick shot, so you have something to try.
I think the best strategy would be to:
Clustering algorithm: Try running a Clustering algorithm such as DBSCAN around the timestamp dimension to classify them based on time groups, instead of the distance (since, as you said, distance based separation is not enough to properly identify and separate the points). After which, you should have all the points classified between different groups with a column group id. Maximum distance parameter in the algorithm should be around 20 seconds steps, or even a minute (since you said each mission was separated at least about 3 minutes apart).
Feature based Polygon to point: At that point, then you run your generic Polygon_from_points(...) function that transforms these clustered points to polygons shapes based on a specific discriminant feature (which in your case is going to be each group id).
How does this work?: This would properly separate the groups first (time-based) and then you should be able to find a generic point to polygon based on a feature (Arcgis should have some).
I dont have an example dataset, nor any code written, but based on what you described I think it would work, hope it helps.
is there any way to triangulate the position of an object if I have the distance to the object from three points without direction that are in a line?
that is I have the points (0,0,0), (0,1,0), (0,2,0) and point x that is 1.7,1.3, 1.7 away from the respective measuring points.
Is there a way to eliminate the ghost point if you are limited to straight line measurements?
Kind regards and appreciation for any assistance.
You can measure the distances from the object to as many points on the line as you like; if you mirror-reflect the object across the line, the new object will have all the same distances as the old one. To distinguish these two points you must take some kind of measurement that is not mirror-symmetric across the line (which distance to any point on the line is).
I am creating a line from point shapefile which is auto generated. First time when I create that line in ArcGIS, I got a line like this because the points are not in a order:
after that I ordered the points according to it's location and got a line like this:
But unable to create a line like this:
Please give me any solution to fix this in ArcGIS or R programming. If you need the shapefile I can provide you.
I think there is no bullet proof way to restore the line, as same dataset can obviously represent different lines, so you would need to use some heuristics to do this. What Rafael described is very good top-bottom heuristics if you can reliably detect start and end points.
Another heuristics could be a bottom-up process to stitch nearby segments into a line. Find nearby points for every point, sort and connect the two nearest points. Continue this process, making sure each point has at most two connections, and that there are no loops.
A simpler approach that might just work if the line follows in general some direction is your idea of sorting points. But instead of ordering by x or y coordinate, find a linear approximation of these points, project each point to this straight line, and sort using the coordinate of the projection.
One way to go about this would be to treat it as a graph problem.
Create a weighted fully connected graph where the nodes are the points and the edge weight distance between its endpoints. Heuristically identify the “starting” and “ending” points of the line (for example, pick the bottom-leftmost point as start and top-rightmost and end).
Then you can use a shortest path algorithm to generate the order in which you connect the points.
I have a map of a mountainous landscape, http://skimap.org/data/989/60/1218033025.jpg. It contains a number of known points, the lat-longs of which can be easily found out using Google maps. I wish to be able to pin any latitude longitude coordinate on the map, of course within the bounds of the landscape.
For this, I tried an approach that seems to be largely failing. I assumed the map to be equivalent to an aerial photograph of the Swiss landscape, without any info about the altitude or other coordinates of the camera. So, I assumed the plane perpendicular to the camera lens normal to be Ax+By+Cz-d=0.
I attempt to find the plane constants, using the known points. I fix my origin at a point, with z=0 at the sea level. I take two known points in the landscape, and using the equation for a line in 3D, I find the length of the projection of this line segment joining the two known points, on the plane. I multiply it by another constant K to account for the resizing of this length on a static 2d representation of this 3D image. The length between the two points on a 2d static representation of this image on this screen can be easily found in pixels, and the actual length of the line joining the two points, can be easily found, since I can calculate the distance between the two points with their lat-longs, and their heights above sea level.
So, I end up with an equation directly relating the distance between the two points on the screen 2d representation, lets call it Ls, and the actual length in the landscape, L. I have many other known points, so plugging them into the equation should give me values of the 4 constants. For this, I needed 8 known points (known parameters being their name, lat-long, and heights above sea level), one being my orogin, and the second being a fixed reference point. The rest 6 points generate a system of 6 linear equations in A^2, B^2, C^2, AB, BC and CA. Solving the system using a online tool, I get the result that the system has a unique solution with all 6 constants being 0.
So, it seems that the assumption that the map is equivalent to an aerial photograph taken from an aircraft, is faulty. Can someone please give me some pointers or any other ideas to get this to work? Do open street maps have a Mercator projection?
I would say that this impossible to do in an automatic way. The skimap should be considered as an image rather than a map, a map is an projection of the real world into one plane, since this doesn't fit skimaps very well they are drawn instead.
The best way is probably to manually define a lot of points in the skimap with known or estimated coordinates and use them to estimate the points betwween. To get an acceptable result you probably have to assign coordinates to each pixel in the skimap.
You could do something like the following: http://magazin.unic.com/en/2012/02/16/making-of-interactive-mobile-piste-map-by-laax/
I am solving the exact same issue. It is pretty hard and lots of maths. Taking me a few weeks to solve it. Interpolation is the key as well with lots of manual mapping. I would say that for a ski mountain it will take at least 1000/1500 points to be able to get the very basic. So, not a trivial task unless you can automate the collection of these points (what I am doing!) ;)
I have a set of dense, irregurarly distributed 2D points ("scattered all over the place"). They can be stored in a single MULTIPOINT WKT object including "holes" or - if needed - as delaunay triangles.
How would you convert this into a polygon, i.e. one outer boundary and zero, one or more inner boundaries?
P.S. It's not the largest enclosing polygon I'm looking for (that would be solved by ConvexHull or ConcaveHull). I'm looking for a true polygon with the same shape as the scattered point set (including inner boundary).
Your question reads to me like “find a polygon which has a given set of points as vertices.” Is that interpretation correct?
If so, you can do the following: Create the convex hull of your points. Remove those points from consideration, and take the convex hull of the remaining points. Proceed in this fashion until there are no more remaining points. The intermediate result will be a sequence of convex polygones nested inside one another. You can turn them into a single polygon by connecting each subsequent pair of polygons. You connect two polygons by removing an edge from each, and connecting the resulting endpoints ”the other way round”. Some care has to be taken that these connections don't overlap anything else, but that shouldn't be too hard.
Note that there are many possible results fulfilling the specification as I read it. If you need a specific one, you'll have to give details on the criteria for that choice.
Use QHull: http://www.qhull.org/
It is the de facto standard for this sort of thing.