Image Flare Symbol For Clustered Graphics

This is a long overdue post following the clustering post where I wanted each flare element in a cluster symbol to flare to a different image symbol when the flare expands out. In addition, the default flare symbol expands upon a rollover mouse event, by me giving you the source code, you can customize it to expand on click, double click, shift click, whatever you want - you have the source code. The same applies to the flare elements. The ImageFlareSymbol has the same properties as the basic FlareSymbol but has two additional properties (backgroundWeights, backgroundColors) to color code the clusters based on their weight value. Each cluster is composed of one or more Graphic instance. In this implementation, each graphic must be symbolized with an instance of ImageFlareElementSymbol. The latter has a property ‘href’ that reference a bitmap url that will be displayed as a flare element. Here is a sample application to show the image flare in action. And like usual, here is the source code.
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Weighted Map Feature Clustering With Attributes

Was asked if the WeightedClusterer can cluster features not just by proximity but by proximity and attribute property value. The idea is that there should be at least one cluster per unique attribute property value. Let me explain further:



The above snapshot is a clustering of cities based on proximity and country - note that despite the proximity of Belgium and The Netherlands, two distinct clusters have been created one for BE and the other for NL - now for France - there is a cluster containing one feature over Paris, however due to the distance from Paris (at that scale) a cluster was created based on 3 cities in the south of France. Make sense ?

In the implementation of the AttrClusterer class that extends the ESRIClusterer class, we override the clusterGraphics function. This function is invoked indirectly by the Flex framework during the update of the display list. In this function, we overlay a virtual grid on the map. This grid acts as a spatial index. Features that are in the same cell will be initially clustered together. Next a process of searching and merging will be executed till no cluster overlaps another cluster. Let me explain this process; for each cluster, search the immediately adjacent cell for any overlapping cluster (that is why we need that spatial index grid :-). If a neighboring cluster is found, then the two cluster merge into one cluster and the location of the new cluster is based on the weight of each original cluster. the new cluster is spatially indexed and the original two clusters are removed. BTW, this process is guaranteed to converge to a steady state solution, so there is no need for a terminal condition like a counter. Now something of importance here is the index used to locate neighbors. The spatial index is implemented as a hash map, the cluster key is a combination of the cluster position relative to the center of the map and the cluster attribute - remember - that what started this whole implementation. You can see the application in action here. I’ve loaded the top 1000 cities by population from geonames. And like usual, you can find the source code here.