Mapinfo for Telecom - Part 4 (Using Voronoi for LAC, Performance KPI and other Analysis)

Thursday, June 28, 2012 3:26:00 PM Categories: Mapinfo Reports and Presentations
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Whether in IT or Telecommunications area, any new knowledge acquired becomes an extra point in our professional experience, a bonus. Even if we use it or not, is worth at least meet new possibilities. So, in case you may need in the future, you know where you can find this resource.

An example of this is the use of Voronoi Function in programs such as Mapinfo, and that only a few people know.

 

So let's find out today what is Voronoi, and also see a practical (and very interesting) application of it.

 

 

Note: All telecomHall articles are originally written in Portuguese. Following we translate to English and Spanish. As our time is short, maybe you find some typos (sometimes we just use the automatic translator, with only a final and 'quick' review). We apologize and we have an understanding of our effort. If you want to contribute translating / correcting of these languages, or even creating and publishing your tutorials, please contact us: contact.

 

What is Voronoi?

Voronoi comes from the name of the Russian mathematician Georgy Voronoy, who created a model or a function that creates a polygon region, based on a set of points. For each point, it created a region around it, containing the area closest to that point.

Voronoi region is also known as 'Dirichlet Tessellation' or 'Thiessen Polygon'.

It is not hard to understand the basic concept applied in a region (for example a square) with 1, 2 or 4 points evenly distributed.

 

But when these points are not evenly distributed, or our region is much more complex?

There is no longer that simple. For this we need the help of a mathematical function. But as always, it becomes easier to understand viewing a demo.

So, let's find out how to use Voronoi Function in our daily work?

 

Voronoi polygons for network cells

As an example, let's create the Voronoi polygons using the cells of our network.

To do this, we will use the data from our fictitious network, already used in other tutorials.

 

To help you to follow these steps, please download the sample files.

 

Download

To download the files for this tutorial, click here.

To learn how to get all Hunter Tool Files and Codes, click here.

 

Creating polygons in Mapinfo

The way to get the network Map in Mapinfo, based on the data from an Excel table (as provided in the sample) has already been demonstrated in other tutorials, so we can continue from this point: where we have this Map opened.

Important: the creation of the polygons will be made based on the points used when you created the points on the Mapinfo Base Map. If you want to view the cell level, you should not use the Latitude and Longitude of the Site (1), instead use of cell (2)! (A value of 50 metres away from the site, in the direction of azimuth is a good approach). Of course this will depend on your analysis. If you want to have polygons per Site, you can use the Sites coordinates.

 

Accessing the Menu: 'Table' (1)-& gt; 'Voronoi' (2) we can start creating our new map. In the 'Voronoi Table' window you choose which table will use the points (3) and where the results should be stored (4). In our case, we leave the option '& lt;New & gt;' (5) to create the data into a new table. Click the 'Next' button (5).

 

In the 'New Table' window, with the definitions of the new table, we also have some options. We will leave default options as it is, opening a new map (1) and creating the new structure based on our original table (2). Click on the 'Create' button (3).

 

We now have the 'New Table Structure' window, where we can define the structure of our new table, and we can even add new fields. But let's leave it as is and simply click on the 'Create' button (1).

 

With everything ready to create our new table, Mapinfo opens the 'Create New Table' window. Navigate to where we want to save the new table, and give a name to it, for example 'tbl_Network_VORONOI.TAB' (1). Click the 'Save' button (2).

 

Finally, the 'Data Aggregation' window, where we indicate the field used to aggregate the data. Choose for example 'CELLNAME' (1) and click on the 'OK' button (2).

 

Finally our new table is created, containing the Voronoi polygons!

 

From this point on, the possibilities are many. We only need to practice, for example by creating thematic maps using our Voronoi Table. (We have also seen in other tutorials how to create thematic maps, so there is no need to explain it here).

 

Creating for example a thematic map for 'LAC' (for the new table that we created, and that contains the polygons), we have the result below.

 

On this map, we can check that something is strange: there's only 2 cells with LAC equal to 5 (yellow polygons on the map). So we have identified a possible problem, and therefore, the opportunity to fix it!

Several other analyses are also possible, as for example the map below that shows the 'Mechanic Tilt' distribution.

 

The same can be done for any other type of analysis that you want, such as Traffic, Dropped Calls, etc. In this case, the field needs to be present in the original file.

Note: of course that this approach we have here does not represent the replacement of more accurate analyses, such as prediction tools that uses heights and clutters. But for sure is a very reasonable approach, and certainly can and should be applied.

Even because it represents another way of data visualization, and may even be faced with predictions (for example): since we have the regions where each point is the closest, 'theoretically' it should be the best server.

 

JAVA Voronoi Applet

If you have an interest in the subject, you can do an Internet search, and you'll find enough material about this subject.

We only highlight a site with a Java Applet where you can practice the creation of Voronoi, simply by clicking (and adding) new points. The Voronoi is automatically created and modified.

http://www.cs.cornell.edu/Info/People/chew/Delaunay.html

 

 

Conclusion

This was another tip to make your daily work each time more productive, showing a new way of viewing your network data, allowing for a more efficient analysis.

Using Mapinfo, we can quickly create a map with the closest polygons around each cell. From this map, we can run several types of analyses, such as Performance and Parameters!

 

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