5 Tips for More Accurate Power BI Maps

Alternative desc

Although Microsoft Power BI's drag-and-drop interface makes it possible to quickly create visuals and KPIs without a technical developer, there are still some situations where a little additional skill and knowledge may be needed to ensure certain data visualizations, such as maps, display the most accurate information. 

Challenges of Data Accuracy in Power BI Map Visualizations

The biggest obstacle to data accuracy in Power BI map visualizations is that most organizations do not capture latitudinal and longitudinal data points. Latitude and longitude are the most precise way of plotting map data, but it’s not surprising that while customers may provide you with an address, they’re unlikely to know their exact coordinates. To fill this gap, Power BI uses Bing’s unstructured URL template service to convert an address into coordinates through a process called geocoding. During geocoding, Bing makes its best guess of the coordinates using the location information you provide. It’s important to be aware though, that without the right data, Bing may not make the correct guess, especially when a location value (such as city and state/province names) is not unique worldwide. The way you input and format your data can have a huge impact on your visualizations. 

5 Tips for Building a More Accurate Power BI Map

Here are some best practices to help you display your data as accurately on a Power BI map:

 

1) Use more than one location column (field).

Since you most likely will not be capturing longitude and latitude in your dataset, it’s important to provide Power BI with as much location information as possible to get the best plotting results. For example, if you want to display a map of where products have been purchased by city, be sure to also include the state and even the country fields, whenever possible, to avoid the map plotting the wrong location. For example, Paris is not only a city in France but also a city in Texas as well.

 

 

2) Categorize your data.

When address fields are loaded into Power BI, we find Power BI often defaults to interpreting address fields as text fields and without recognizing the geography that they represent. We recommend that you take the time to categorize your fields and ensure Power BI knows which columns are cites, states, countries, etc. for improved plotting accuracy.

 

How to categorize your data:

  1. Select the field in the fields list
  2. Choose the Column Tools tab
  3. Set the Data Category of the field to the appropriate value.

 

You can also create custom columns that combine multiple attributes together and set the Data Category as a “Place” for Power BI to tell Bing that several pieces of address information are included in the single column.

 

3) Consider which visualization style will communicate your data best.

Power BI has two standard map visualizations – a filled map and a shape map. Depending on your dataset, one map may offer a clearer presentation of information than the other. For example, if you are plotting states and every state in America is in your dataset, a filled map will just highlight the entire country and may not be as useful as a shape map that would not only put a bubble on each state but also provide additional information based on the bubble size. For example, if you track sales by location, you can set it so that the bubbles will be larger in the states that have the most sales.

 

An ArcGIS map by Esri can also be created in Power BI that provides even more mapping options such as the ability to create heat maps.

Alternative desc
Alternative desc

4) Don’t forget to use tooltips.

The tooltips feature is a quick way to validate that your data was correctly interpreted by geocoding. When you hover over a plotted point a pop-up displaying the data that the location was derived from appears, making it simple to verify its accuracy against the visualization. You can even add any column in your dataset to the tooltips display.

Alternative desc

5) Use full names instead of abbreviations when possible.

If your data set captures states, countries, etc. as abbreviations, consider creating custom tables and columns to calculate the full name of an address value from an abbreviation in Power BI to reduce data confusion.

Interested in learning more about Power BI or Power BI maps? Read the Microsoft documentation or talk to an Altriva team member

Other articles you might be interested in...

FacebookTwitterLinkedIn