Using just three of many datasets for my example, these latitude longitude points from three files represent three different price-points. I am going to combine the three datasets and wish to cluster them geographically–within some physical distance tolerance (?DBScan, K Nearest Neighbor, whatever you suggest). I have attached the Latitude and Longitude points and a base ID for each of the three groups here.
I would love the GIS distance algorithm to automatically create/present the shapefiles (clusters) and assign the shapefile ID to each property, both as data and as a map image.
From there I get to do all kinds of cool analyses.
As always, any assistance, suggestions, or workflow you can offer is more appreciated than you know.
Hi Scott, I have a grotesque amount of data for each property associated with each lat-long point. I have used and will use further several data clustering algorithms including K-Means and likely Fuzzy C-Means. But for this part of the project I am focusing on actual physical location clustering and analyses.
I have already used K-Means to cluster the properties (actual houses) by criteria such as Price, Size, Etc. I created sub-groups from these clusters (similar properties based on monitored criteria) and have now clustered each sub-group geographically using DBScan.
At this point I used DBScan and tweaked the adjustments until the clusters look appropriate/optimized geographically.
Using the lat-long points associated with the DBScan derived geographic sub-group clusters, I would love to understand how to create fully enclosed shapefiles or at least the centroid of each series of lat-long clusters from DBScan. I don’t know if KNIME can do this or if I need to use outside GIS software to do this.
Yes, I could now hand draw the shapefiles inferred through DBScan using google tools, import the WKT, and derive the centroid–but hand-drawing a thousand shapefiles sounds kind of sucky. I was hoping for an automated/magic solution.
This one fell off my radar completely, sorry! Maybe this forum thread would be useful to you - it contains a nice workflow from @LukasS as well that makes use of the KNIME Spatial Processing Nodes extension.