Performing spatial clustering of polygons based on attribute

Question

I want to perform spatial clustering on the polygon which contains a number of total points inside it. I would like to ask if there is any ways that the polygons can be clustered based on the total number of points inside it. Since I am new to ArcGIS and python, I seek a solution without any code.

My map is as shown below.

The expected results look like this (each color represents a cluster).

But in my case, the cluster should be formed from the regular grids shown.

Answer 1

With spatial autocorrelation you can measure locations with attribute values. See Spatial Autocorrelation Definition.

Hot Spot Analysis (Getis-Ord Gi*) identifies statistically significant hot spots and cold spots using the Getis-Ord Gi* statistic.

For polygon features, feature centroids are used in distance computations.

Basic knowledge of statistics is required for.

1. Open Hot Spot Analysis (Getis-Ord Gi*) :

   Arctoolbox =>Spatial Statistics Tools => Mapping Clusters => Hot spot Analysis(Getis-Ord Gi*)

2. Select Polygon layer in Input Feature Class.

3. Select "Count_" field in Input Field.

4. Define and Output Feature Class.

For more information about clustering tools please consider this link: An overview of the Mapping Clusters toolset

Answer 2

This type of clustering is a bit dubious but, there is a work flow that you could follow in statistical software such as R. Sorry, there is no clear "no code" solution other than the advice that you muck with symbology to find breaks in the counts. This however, does not account for spatial clustering.

Here is a worked example illustrating why you do not want to cluster the data using just the counts.

library(spatialEco)
library(sp)

data(meuse)
coordinates(meuse) <- ~x+y 

First we create some polygons and random points to emulate your data.

hex <- hexagons(meuse, res=100) 
pts <- spsample(hex, n=10000, type="random")
  pts <- SpatialPointsDataFrame(pts, data.frame(ID=1:length(pts)))
pts.hex <- point.in.poly(pts, hex)
pt.count <- tapply(pts.hex$ID, pts.hex$HEXID, length)

We need to account for polygons with no points.

na.idx <- which(!rownames(hex@data) %in% names(pt.count))
  if(length(na.idx) > 0) {
    pt.count <- insert.values(pt.count, 0, na.idx)
    hex@data$count <- pt.count  
    hex@data$count <- ifelse(hex@data$count < 2, 0, hex@data$count)
  } else {
    hex@data$count <- pt.count
  }

#plot counts
spplot(hex, "count")

Here we create k-means clusters with k=3 using both counts and coordinates to define the clusters then we plot our results.

clust <- kmeans( scale(cbind(coordinates(hex), hex@data$count)), 3 )$cluster
  hex@data$k3 <- clust
spplot(hex, "k3")

Now we the clusters using k=3 and count only to define our clusters.

clust <- kmeans( scale(hex@data$count), 3 )$cluster
  hex@data$k3count <- clust
spplot(hex, "k3count")

As you can see, including the spatial coordinates constrains the clustering in a way that a univariate (count only) does not thus, resulting in more spatially uniform and congruent results.