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module Y2016.M12.D23.Solution where
import Codec.Compression.GZip
import Control.Monad (void)
import System.Environment
-- below imports available from 1HaskellADay git repository
import Data.SAIPE.USCounties
import Graph.KMeans
import Graph.ScoreCard
import Graph.ScoreCard.Clusters
import Y2016.M12.D15.Solution
import Y2016.M12.D20.Solution
import Y2016.M12.D21.Solution
So, a couple of days ago we were able to cluster US Counties by SAIPE/poverty
statistics. Great!
Data is gzipped here:
Y2016/M12/D15/SA IPESNC_15DEC16_11_35_13_00.csv.gz
The problem is that there are so many counties. And, clustering them, we lost
our reference to their US States.
Well, clusters are supposed to help by grouping large data sets into (smaller)
groups. Today, as a first step, let's visualize these clusters.
Using whatever data visualization tool you prefer, show the US Counties in
their clusters.=
So, first we load the data and create score cards from them
*Y2016.M12.D23.Solution> readSAIPERaw "Y2016/M12/D15/SAIPESNC_15DEC16_11_35_13_00.csv.gz" ~> raw
showClusteredUSCounties :: [ScoreCard USCounty Axes Float] -> IO ()
showClusteredUSCounties scorecards = getEnv "CYPHERDB_ACCESS" >>= \endpoint ->
let (_gens, clusters) = kmeans 30 scorecards
colors = colorization clusters scorecards
in void (relateClusteredCells endpoint (NC ("US Counties", colors)))
Now we upload this information to our graph database:
*Y2016.M12.D23.Solution> showClusteredUSCounties (saipeRows2SC raw)
... ,\"errors\":[]}\n"
Now let's add the clustering nodes:
*Y2016.M12.D23.Solution> let cclusters = relateClusters (NC ("US Counties", colors))
*Y2016.M12.D23.Solution> uploadClusters endpoint cclusters
Sample graphs show in this directory.