The Azure Maps Power BI Visual preview offers a rich set of data visualizations to help you bring location context into your data. With the March release of Power BI, the Azure Maps visual is getting two new tools, the addition of Geocoding capabilities and a Pie Chart layer.
Geocoding in Power BI
If your data has a location context, it probably contains addresses or other geographic information. This can include items like a house number with a street name and postcode, but you do not have the point location (latitude-longitude) that you need to plot the address on a map. With the new geocode capabilities in the Azure Maps Power BI visual, you can now geocode address data into location data right in Power BI. The Azure Maps geocoder is flexible and will still do it’s best to work with incomplete address information or spelling mistakes. The Azure Maps Power BI visual supports regional geocoding for country, state or province, city, county, postal code, and partial address data.
The Location Field
You can add multiple data items in the Location field and the more you add, the better the context the geocoder has, and will get you a more precise point. For example “London” could be “London, England” or “London, Ontario, Canada”. By adding more data you can add the necessary context required to disambiguate the request and get a better location result from the geocoder.
Adding the Pie Chart layer
Following the geocoder, the pie chart layer lets you place pie charts at the specific location relevant to the data it represents. The pie chart itself shows the numerical proportion for a specific location, like understanding the market share of each of your products in a region, the distribution of voters in districts, what product category is selling the best in which store location, etc. In this month's release, you can now turn a bubble layer into a pie chart layer to visualize the data using this new visualization.
To render a pie chart, drag categorical data into the legend field and numeric data of your map. The categorical data will determine how many pie slices will be in the chart, where numeric data will decide the proportion of the slices.