Im using Annotations in IOS to display London Tube stations, but im looking at numbers and there are 280 or so.
Whats the easiest way to do this?
Individually or is there another option?
Cheers for all the advice
David
The performance is good with 280 annotations, the appearance is not. You have to group them into clusters when the user zooms out.
One way to do it is:
Decide how many cluster annotations you want to show.
Split the screen in x*y tiles so roughly x*y =~ numClusters and x/y=480/320=1.5
Add a cluster annotation per tile (it's a normal cluster with an array containing 0 or more annotations).
Run the k-means algorithm:
Iterate all annotations and add each one to the closest cluster.
Calculate a new center for each cluster, which will be an average of the centers of all its members.
Empty each cluster.
Repeat until no cluster moves any longer.
Remove empty clusters, if any.
You end up with numClusters clusters positioned according to the annotation density.
You can also leave a number of normal annotations on their own if they are away from the clusters. Depends on how you want it to look.
Related
I create a network add nodes and edges. I view it (it creates a dot and pdf file automatically). Later, I want to create a second network with the same nodes but different edges. I want to place the nodes in the same coordinates, so that I can make a comparison of both graphs easily. I tried to get the coordinates of the first graph, and tried to set the coordinates of the nodes) but I couldn't find proper functions to do that. I also checked networkx package. I also tried to get a copy of the first network, and delete the edges with no success. Can someone please show me how to create a second network with the same node coordinates?
This is the simple network creation code
import graphviz as G
network1 = G.Digraph(
graph_attr={...},
node_attr={...},
edge_attr={...} )
network.node("xxx")
network.node("yyy")
network.node("zzz")
network.edge("xxx", "yyy")
network.edge("yyy", "zzz")
network1.view(file_name)
First, calculate the node positions for the first graph using the layout of your choice (say, the spring layout):
node_positions = nx.layout.spring_layout(G1)
Now, you can draw this graph and any other graph with the same nodes in the same positions:
nx.draw(G1, with_labels=True, pos=node_positions)
nx.draw(G2, with_labels=True, pos=node_positions)
Graphviz's layers feature might also be interesting:
https://graphviz.org/faq/#FaqOverlays
Here is a working example of using layers - ignore the last two lines that create a video.
https://forum.graphviz.org/t/stupid-dot-tricks-2-making-a-video/109
And here is some more background:
https://forum.graphviz.org/t/getting-layers-to-work-with-svg/107
I'm using multiple Leaflet.markercluster groups to cluster data from different layers, meaning a given cluster contains only markers from a single layer. I'm able to customize the icon for each type (i.e. provide iconCreateFunction) so the user can distinguish them visually. So far, so good.
What I'd like to do is combine clusters of different types, or even markers, when their icons are very close to one another. That is, maxClusterRadius for homogeneous might be 80 pixels, but heterogeneous clusters would use a radius of something like 5 pixels.
Has anyone tackled this, or at least given serious thought to what a solution might look like?
I would like to know your opinion about dbscan clustering, I am trying to implement algorithm as published here. In my opinion there is possibility for one point from border of some cluster to be an core point of another one as shown in picture:
.
I think there are some of the possible solutions:
we could consider point as written to cluster and that cannot be changed - but we could lost second cluster because of that
we could be able to change border points cluster but without recomputing epsilon neighbourhood.
we could be able to add point into multiple clusters (worst one).
What do you think is the best? Or am I getting something completely wrong?
The core-point property is not cluster specific.
Either the point is a core point, or it is not; independent of which cluster it is in.
If it is a core point, then it cannot be a noise or border point anymore.
Whenever two core points are neighbors, they by definition are in the same cluster.
The known special case that can happen is that one point is border to more than one cluster. See end of page 229.
I have some data points which I have devided into them into some clusters with some clustering algorithms as the picture below:(it might takes some time for the image to appear)
alt text http://www.freeimagehosting.net/uploads/05a807bc42.png
Each color represents different cluster. I have to draw polygons around each cluster. I use convhull for this reason. But as you can see the polygon for the red cluster is very big and covers a lot of areas, which is not the one I am looking for. I need to draw lines(ploygons) exactly around my data sets. For example in the picture above I want a polygon that is drawn exactly the same(and around) as the red cluster with the 3 branches. In other words, in this case I need a polygon with 3 branches to cover my red clusters not that big polygon that covers the whole area. Can anyone help me with this?
Please Note that the solution should be general, because the clusters will change in each run of the algorithm, so it needs to be in a way that is general.
I am not sure this is a fully specified question. I see this variants on this question come up quite often.
Why this can not really be answered here: Imagine six points, three in an equilateral triangle with another three in an equilateral triangle inside it in the same orientation.
What is the correct hull around this? Is it just the convex hull? Is it the inner triangle with three line spurs coming out from it? Does it matter what the relative sizes of the triangles are? Should you have to specify that parameter then?
If your clusters are very compact, you could try the following:
Create a grid, say with a spacing of 0.1.
Set every pixel in the grid to 1 if there's at least one data point covering it, set the pixel to 0 if there is no data point covering the pixel.
You may need to run imclose on your mask in order to fill little holes inside that have not been colored due to sheer bad luck.
Extract the border pixels using, e.g. bwperim. This is the outline of the polygon you're looking for.
I have an application in which users interact with each-other. I want to visualize these interactions so that I can determine whether clusters of users exist (within which interactions are more frequent).
I've assigned a 2D point to each user (where each coordinate is between 0 and 1). My idea is that two users' points move closer together when they interact, an "attractive force", and I just repeatedly go through my interaction logs over and over again.
Of course, I need a "repulsive force" that will push users apart too, otherwise they will all just collapse into a single point.
First I tried monitoring the lowest and highest of each of the XY coordinates, and normalizing their positions, but this didn't work, a few users with a small number of interactions stayed at the edges, and the rest all collapsed into the middle.
Does anyone know what equations I should use to move the points, both for the "attractive" force between users when they interact, and a "repulsive" force to stop them all collapsing into a single point?
Edit: In response to a question, I should point out that I'm dealing with about 1 million users, and about 10 million interactions between users. If anyone can recommend a tool that could do this for me, I'm all ears :-)
In the past, when I've tried this kind of thing, I've used a spring model to pull linked nodes together, something like: dx = -k*(x-l). dx is the change in the position, x is the current position, l is the desired separation, and k is the spring coefficient that you tweak until you get a nice balance between spring strength and stability, it'll be less than 0.1. Having l > 0 ensures that everything doesn't end up in the middle.
In addition to that, a general "repulsive" force between all nodes will spread them out, something like: dx = k / x^2. This will be larger the closer two nodes are, tweak k to get a reasonable effect.
I can recommend some possibilities: first, try log-scaling the interactions or running them through a sigmoidal function to squash the range. This will give you a smoother visual distribution of spacing.
Independent of this scaling issue: look at some of the rendering strategies in graphviz, particularly the programs "neato" and "fdp". From the man page:
neato draws undirected graphs using ``spring'' models (see Kamada and
Kawai, Information Processing Letters 31:1, April 1989). Input files
must be formatted in the dot attributed graph language. By default,
the output of neato is the input graph with layout coordinates
appended.
fdp draws undirected graphs using a ``spring'' model. It relies on a
force-directed approach in the spirit of Fruchterman and Reingold (cf.
Software-Practice & Experience 21(11), 1991, pp. 1129-1164).
Finally, consider one of the scaling strategies, an attractive force, and some sort of drag coefficient instead of a repulsive force. Actually moving things closer and then possibly farther later on may just get you cyclic behavior.
Consider a model in which everything will collapse eventually, but slowly. Then just run until some condition is met (a node crosses the center of the layout region or some such).
Drag or momentum can just be encoded as a basic resistance to motion and amount to throttling the movements; it can be applied differentially (things can move slower based on how far they've gone, where they are in space, how many other nodes are close, etc.).
Hope this helps.
The spring model is the traditional way to do this: make an attractive force between each node based on the interaction, and a repulsive force between all nodes based on the inverse square of their distance. Then solve, minimizing the energy. You may need some fairly high powered programming to get an efficient solution to this if you have more than a few nodes. Make sure the start positions are random, and run the program several times: a case like this almost always has several local energy minima in it, and you want to make sure you've got a good one.
Also, unless you have only a few nodes, I would do this in 3D. An extra dimension of freedom allows for better solutions, and you should be able to visualize clusters in 3D as well if not better than 2D.