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MPCluster for Maptitude

Navigation: Running MPCluster

Overview of MPCluster Operations

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MPCluster is started by selecting MPCluster on the Maptitude Tools -> Add-Ins sub-menu. This will display the main MPCluster panel:




The main panel is divided into six areas: Algorithm, Cluster Options, Input Data, Distances, Display Options, and the four buttons at the bottom.


MPCluster finds clusters of data (e.g. point or area centroid) locations. Use the Input Data section to set the source of these data locations. Use the Input Dataset combo-box to select the dataset (i.e. layer) to use for the source data locations. Maptitude country packs include a large number of data layers. These are usually included in the list of available input datasets. Set the Restrict datasets to user datasets only check box if you wish to restrict the list of layers to user-imported layers. As well as working with complete data view layers, MPCluster can work with selection sets within these layers. Set the Include selection sets check-box if you wish to include selection sets in the list of available input datasets.


Dataset data fields can be used during the clustering process, and these are selected by setting the Use Data Fields check box. This enables the remaining controls in the Input Data section, and these are described in more detail on the Applying Data Fields page.


The Algorithm specifies the cluster algorithm that MPCluster will use to find the clusters. MPCluster currently supports two algorithms. These have different strengths and weaknesses and are described in more detail on the Cluster Finding Algorithms page.


The Cluster Options are used to control how the clustering algorithm will work. You can set minimum and maximum limits on the size of the clusters. The exact set of options visible will depend on the chosen algorithm, and are explained further on the Changing Clustering Behavior and Options page.


The Distances box controls the use of pre-computed distances in an external distance file. Normally MPCluster works with straight line ("Great Circle") distances when creating the clusters. If you select the Hierarchical Algorithm with Median Centers, you can also provide your own pre-computed distances. These are provided as a table that is typically produced using our MileCharter add-in, and provide a 'distance' between every data point. The 'distances' could be driving distances, travel times, or actual costs. These tables and their use are described in more detail on the Using an External Table page.


MPCluster can display the clusters as new layers, in a new data view that can be joined to the input data points, and/or by writing the cluster allocations to a Microsoft Excel workbook. These are controlled with the check boxes in the Display Options. At least one of these output options must be selected. Enabling the new layers, will also enable the option to calculate an 'overlay' that calculates sums and counts of a point layer, and writes them to the cluster boundary layer. These are described on the Calculating Overlay Fields page.


Press the About button to display a standard application About Box. This will display the MPCluster version number and your license information.


Press the Help button to display the MPCluster help. This button can be found on most of MPCluster's dialog boxes.


Close closes the main MPCluster panel without any processing. When all options are set, press the Start button to start processing. Processing typically takes a few seconds or minutes depending on the number of data points and the options used. The MPCluster: Progress box is displayed to show the current status of processing:




The top bar indicates the current progress of processing through the various stages. For some stages (Improving Clusters, and Plotting Clusters) a second lower bar is displayed to show the progress within this stage:




The Stop button can also be used to abort processing. If MPCluster has cluster data that can be plotted (i.e. if processing was stopped in the Improving Clusters stage), then the option to display these clusters will be given.