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Cluster Analysis for Microsoft MapPoint®

Guided Tour of MPCluster: Overview


MPCluster for MapPoint licenses reduced to US$60. Volume discounts continue to be available for purchases of five or more.

This guided tour gives an overview of how MPCluster is used, and the options that are available.

Sample input data for MPCluster (UK restaurant locations)

Input Pushpin Data

MPCluster finds natural groups (ie. clusters) in MapPoint datasets. These datasets are typically pushpin sets, but MPCluster also supports most of MapPoint's dataset types. For example, the map on the right shows the locations of restaurants for most of England. In this data, a cluster would represent a large number of restaurants in a small space. Therefore a restaurant supply company would be interested in finding clusters in this data if they were looking to find new sales locations or new territories for sales reps.

(This is a sample map available for download: London and the south east were removed in order to keep the map compact and downloadable.)

MPCluster's main panel

MPCluster's Main Panel

This is MPCluster's main panel. It lets you choose the dataset to use for the input data, set the clustering options, set the display options, and to start the processing.

The clustering options let you define the minimum and maximum cluster size, as well as the maximum number of clusters to find. These options are explained further on the next page.

Calculated clusters can be drawn as outline shapes of various colors, and with pushpins marking each cluster's center. The display options are used to set these options.

MPCluster's Progress Indicator


After setting all the options, press Start to start the clustering process. Processing status is displayed using the progress indicator.

UK cluster results

The Results

And here are the results. Each cluster is marked with a central pushpin, and a red outline has been drawn around all of the cluster's component pushpins. MPCluster has successfully identified the larger cities, as well as a number of smaller town/rural clusters which also meet the specified parameters.

Next, we look at shaded area datasets.

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