I would like know if there are any tools/commands in MATLAB or any other software that helps to cut the dendrogram (where points represent states) at a certain height and represent it in a geographical map like the one in the below images.
Could you please let me know if there is any better way to do it
Thank you.
You can draw polar dendrogram (as on your example) with File Exchange submission - Draw a Polar Dendrogram.
To apply threshold to distance between nodes and get the cluster data you can use CLUSTER or CLUSTERDATA function.
Then you can use USAMAP function from Mapping Toolbox to draw the states and apply colors based on your clusters. See the example 3 in the documentation.
Related
Having centroids of superpixels for an image, is there any MATLAB function for drawing region adjacency graph ?
L = superpixels(A, 200);
K=regionprops(L, 'Centroid'); % Detemining centroid coordinates of each superpixels
P.S. Similar but not exact solutions :
https://www.mathworks.com/matlabcentral/fileexchange/16938-region-adjacency-graph-rag
https://www.mathworks.com/matlabcentral/fileexchange/53614-image-graphs
There are a huge amount of ways of generating graphs from nodes, and you have not specified which one you want.
One that resembles the image you provided (but its not the same) would be triangulating the domain with delaunay(). You can generate a triangulation() object from that, which contains more usable information than the output of delaunay
Alternatively, if you have your own criteria for connecting the nodes that you decided not to share, you can use graph() to generate any topology of graphs.
If you have it in a triangulation format, plotting it can be done with triplot(), trimesh() or some others. With a hold on and triplot() you will find the closest to the figure you posted.
If you want working code I am happy to provide if you add a runnable snippet in the question.
Context: I want to create an interactive heatmap with areas separated by a ZIP code. I've found no way of displaying it directly (i.e. using Google Maps or OSM), so I want to create curves or lines that are separating those areas, and visualize it in maps.
I have a set of points, represented by their coordinates and their according class (ZIP code). I want to get a curve separating them. The problem is that these points are not linearly separable.
I tried to use softmax regression, but that doesn't work well with non-linearly separable classes. The only methods I know which are able to separate non-linearly are nearest neighbors and neural networks. But such classifiers only classify, they don't tell me the borders between classes.
Is there a way to get the borders somehow?
If you have a dense cloud of known points within each Zip code with coordinates [latitude. longitude, zip code], using machine learning to find the boundary enclosing those points sounds like overkill.
You could probably get a good approximation of the boundary by using computational geometry, e.g finding the 2D convex hull of each Zip code's set of points using the Matlab convhull function
K = convhull(X,Y)
The result K would be a vector of points enclosing the input X, Y vector of points, that could be used to draw a polygon.
The only complication would be what coordinate system to work in, you might need to do a bit of work going between (lat, lon) and map (x,y) coordinates. If you do not have the Matlab Mapping Toolbox, you could look at the third party library M_Map M_Map home page, which offers some of the same functionality.
Edit: If the cloud of points for Zip codes has a bounding region that is non convex, you may need a more general computational geometry technique to find a better approximation to the bounding region. Performing a Voronoi tesselation of the region, as suggested in the comments, is one such possibility.
I am working with structured 2.5D and unstructured 3D data, which generally is available in (X,Y,Z) coordinates, i.e. point clouds. Now I want to impose a regular voxel grid onto the data. This is not meant for visualization purposes, but rather for "cleaning" or fusing the data. I imagine cases, where e.g. 3 points fall within the volume of one voxel. Then I would aim at either just setting this voxel to "activated" and discarding the 3 original points or alternatively I would like to calculate the euclidian mean of the points and return the thus "cleaned" point cloud as an irregularly structured one again.
I hope I could make my intentions clear enough: It's not about visualization and not necessarily about using volumetric cubes instead of points. It's only about manipulating possibily irregular point clouds in a structured way.
I was thinking about kd-tree or octree based solutions in this context, but can anybody point me in the proper direction? Hinting at existing MATLAB implementations would be most appreciated.
If the data is irregularly spaced, what you want to use is something which both smooths and interpolates your data points. A very good method for doing so is Gaussian process regression. Here's an example for the same problem but in 2D.
I have a 3 dimensional data set which I intend to analyse. After analysing the data set, basically running an algorithm to find a range of points, I this range of points to have a specific colour so that when someone sees the surface plot, they know which are the points of interest. How can this be achieved?
I have tried to find some help in the mathworks forums, but so far I am not able to find a satisfactory solution.
If you are using the surface function, you can use the 4 parameter version surf(x,y,z,c) where c lets you specify colour based on the currently used colour map. See this link at the mathworks site for more detail http://www.mathworks.co.uk/help/matlab/ref/surf.html
the output of some processing consists of a binary map with several connected areas.
The objective is, for each area, to compute and draw on the image a line crossing the area on its longest axis, but not extending further. It is very important that the line lies just inside the area, therefore ellipse fitting is not very good.
Any hint on how to do achieve this result in an efficient way?
If you have the image processing toolbox you can use regionprops which will give you several standard measures of any binary connected region. This includes
You can also get the tightest rectangular bounding box, centroid, perimeter, orientation. These will all help you in ellipse fitting.
Depending on how you would like to draw your lines, the regionprops function also returns the length for major and minor axes in 2-D connected regions and does it on a per-connected-region basis, giving you a vector of axis lengths. If you specify 4 neighbor connected you are fairly sure that the length will be exclusively within the connected region. But this is not guaranteed since `regionprops' calculates major axis length of an ellipse that has the same normalized second central moment as the connected region.
My first inclination would be to treat the pixels as 2D points and use principal components analysis. PCA will give you the major axis of each region (princomp if you have the stat toolbox).
Regarding making line segments and not lines, not knowing anything about the shape of these regions, an efficient method doesn't occur to me. Assuming the region could have any arbitrary shape, you could just trace along each line until you reach the edge of the region. Then repeat in the other direction.
I assumed you already have the binary image divided into regions. If this isn't true you could use bwlabel (if the regions aren't touching) or k-means (if they are) first.