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Timmons Group is a leading provider of geospatial, information architecture and engineering services focusing on developing intuitive, enterprise geospatial web applications, highly-usable, cross-device mobile applications, and integrated, geospatially-enabled enterprise solutions. When you are signed in, this message, the ads in this row, and the red-underlined link ads all go away.
Previously we looked at cluster analysis using Microsoft MapPoint, finishing with the MPCluster add-in. First we import this data into Microsoft MapPoint as a pushpin set using MapPoint’s Data Import Wizard.
Due to this, MPCluster can sometimes require a little bit of trial and error to find good parameters that produce a usable result. For this example, we only apply two constraints: the minimum and maximum number of data points (pushpins) per cluster. Some delivery locations are more important than others because they receive more deliveries. Although we do not use the ability here, MPCluster can also export all of the data points and cluster allocations to Excel. For this run, MPCluster reported 99% stability, and has done a good job of clustering the data into groups. The Manage Clusters menu option also provides the ability to draw circles around the cluster centers, and to delete clusters. After using MPCluster to find and allocate cluster centers, sometimes it is more practical to define their territories using simple circles. This completes this example of MPCluster being used to find delivery territories and warehouse locations for a European retailer.
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The geo-location feature of this application allows the user to locate the exact location of underwater fish structures, guide their boat to their favorite fish attractor, and start fishing!
Timmons Group worked with us to develop an application that dynamically pulls this map data as we update it. The objective is to find warehouse locations for a retail company operating in Western Europe. These have already been consolidated over a month, so each postcode location has multiple stops, plus total delivered weight and total number of packages. We have data for the total weight of deliveries, the number of packages, and the number of stops (ie. This is controlled using the Export to Excel checkbox and the Excel Options button on the main MPCluster panel.
Note that this can be time consuming for large numbers of clusters, so it is strongly recommended that you restrict the range as much as possible.
Lets use this by pressing the Use this estimate button (this is enabled after an estimate has been calculated). First, the geometric center is in the sea – obviously we would choose a real world location on the neighbouring coast.
All of the shapes are standard closed freeform shapes, so it is possible to manually modify the shapes.
The Draw Circles management function will draw a circle of specified radius around each of a cluster run’s cluster centers. The Delete management function can be used to delete old cluster runs which are no longer needed.
The mobile application was developed by Timmons Group, a national leader in innovative geospatial solutions. These locations will be based on delivery locations, with each warehouse located at the center of a delivery location cluster.


This is European data so we shall use MapPoint Europe 2013, but it should also work in MapPoint North America due to the presence of longitude, latitude coordinates. This is only an estimate and should only be used after setting all of the other parameters. As it is, these are only estimated locations – we would always choose actual warehouse locations near suitable infrastructure such as major highways.
Then, using the MPCluster Manage Clusters menu option, you may export the modified new clusters to Excel.
A wiA™c prawda jest taka: Sylwia Grzeszczak nie prowadzi na dzieA„ dzisiejszy A?adnego osobistego konta na Facebook'u.
Timmons Group has been a recipient of both the Esri Business Partner of the Year award and the Esri Foundation Partner of the Year award. It is also possible to apply too many constraints making stable solutions impossible (or very difficult) to find. Hence after computing a set of clusters, MPCluster will report the number of clusters found and a stability percentage. This might not be the most economical allocation, and these locations would be candidates for re-location. This is the percentage of data points that are stable and are not changing with each iteration. 20%) typically indicates that a stable solution satisfying the constraints cannot be found.



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