[GIS] Performing spatial clustering of polygons based on attribute

arcgis-desktopclustering

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.

Best Answer

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.

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

Arctoolbox =>Spatial Statistics Tools => Mapping Clusters => Hot spot Analysis(Getis-Ord Gi*)
Select Polygon layer in Input Feature Class.
Select "Count_" field in Input Field.
Define and Output Feature Class.

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

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Use Openlayers Cluster strategy which lets you display points representing clusters of features (geopoints) within some pixel distance. visit http://openlayers.org/dev/examples/strategy-cluster.html for complete source code.

or check out http://gmaps-utility-library-dev.googlecode.com/svn/tags/markerclusterer/1.0/examples/simple_example.html to create cluster maps from geo-points.

[GIS] Run spatial clustering by inputting coordinates to cluster algorithm

Just a technicality (should be a comment but I lack reputation):

In any case this needs a coordinate reference system that gives you euclidean distances in your attribute space, for example UTM:

library("rgdal")
# specify original CRS:
proj4string(house) <- CRS("+proj=longlat +datum=WGS84")
# transform to new CRS (need to specify correct UTM zone):
house <- spTransform(house, CRS("+proj=utm +zone=50 ellps=WGS84"))

Conceptually, three things from the top of my head that may be relevant (?):

how do you weight geographical distance against distances between attributes? E.g. how does 10 years difference in building age weight against 10 meter difference in x/y direction? Then again, the same problem exists for non-spatial attributes anyway.
I am not certain about this, but maybe in some contexts, X and Y location shouldn't be seen as independent attributes. Say two buildings have identical position on X, but very different position on Y. Does the closeness in dimension X really mean anything? Moreover, if the Y values are very similar, it should make the similarity in X a lot more important, but that doesn't seem to be the case either. There are cases where X and Y can of course be used independently; E.g. if it is important how close you are to the equator, the latitude alone contains important information independent from the longitude.
In many cases I think you would be interested not in the absolute location, but in some kind of relative location to other things. Depending on what the goal of the clustering is, instead of x and y you could include for example the distance to the (city-?) center or the distance to the nearest neighbour (both would solve 2.)

Best Answer

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[GIS] Run spatial clustering by inputting coordinates to cluster algorithm

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