In this blog post, I’ll be introducing Power BI Geospatial Analysis for building compelling and dynamic maps in reports.
Geospatial analysis in Power BI usually has a lot of restrictions. In fact, displaying actual multiple layers is not possible and requires some data manipulation. Moreover, both the default and downloadable visuals perform differently in geocoding or rendering points on the map.
Nevertheless, you can still build compelling dynamic maps that tell a story much better than you can do using charts or tables alone.
Geocoding In Power BI
When doing geospatial analysis in Power BI, we use separate geocoding software. This is because geocoding inside Power BI visuals will not always return what you expect. Hence, it is preferred to create your own geo-coded data unless you’re working with boundary maps either for postcodes or states.
There are several sources available on the web for geocoding. However, remember that it’s not always required to display latitude and longitude accuracy to the last meter. In many cases, a city or a postcode may be enough.
Advantages Of Learning Geospatial Analysis In Power BI
When it comes to geospatial analysis, you’ll learn how to properly use Power BI mapping visuals, as well as the most powerful and flexible custom geospatial visuals. In this case, I’m using the Postcode boundaries.
You’ll also understand how the mapping visuals work. This is an example from one of my location intelligence projects.
Additionally, you’ll find out more about Huff Gravity Analysis and Center of Gravity calculation. These are essential areas of geospatial analysis because they are used to estimate the potential sales for a certain location.
You’ll also find another blog discussing the calculation of distance and bearing in Excel, Power Query, and DAX. Keep in mind that distance calculations can be used for various real-world business applications.
Furthermore, by studying geospatial analysis in Power BI, you’ll learn the differences between the current allocation of customers and the newly allocated customers based on distance.
To sum up, geospatial analysis is of considerable and increasing importance today. Integrating geospatial analysis into your reports can produce valuable insights into the spatial relationship of critical variables and location-based patterns and relationships.
It can be quite confusing and overwhelming at first, but learning this will definitely contribute to better and more frequent usage of maps for your reports.
Check out the links below for more examples and related content.