Forum Discussion
Highlighting in an Azure Map Visual
- Anonymous1 year ago
Hi mikemagill,
Fully understand that.😂
As another alternative solution, you could consider using a Heatmap Instead of Points.
If the goal is to show the general area rather than individual postcodes, switching to a heatmap layer in Azure Maps can help preserve visibility while emphasizing high-value areas.
Or, custom Map Visual (via Power BI Custom Visuals or Python/R)
If you need full control, consider creating a custom map visual using Power BI's Python/R scripting or a custom visual like Mapbox, which may support dynamic radius filtering.Either of above is a workaround, you could consider it based on your customer's need.
In short, precumputing by SQL database is a way fully matched with your need, however, it is huge workload.
Anyway, good luck.😀
Best Regards,
Qi
Community Support TeamIf there is any post helps, then please consider Accept it as the solution to help the other members find it more quickly.
If I misunderstand your needs or you still have problems on it, please feel free to let us know. Thanks a lot!
Hi mikemagill - Yes! You can achieve this effect in Power BI’s Azure Map without filtering out nearby postcodes.
Click on the Azure Map visual.
Go to Format > Edit Interactions.
Select your postcode selection visual and change its interaction type from "Filter" to "Highlight".
This will keep other postcodes visible while emphasizing the selected one.
Hope this helps.
HI. That's exactly what I hoped that I could do but the only options available are Filter or None.
- Anonymous1 year agoNot applicable
Hi mikemagill,
You are right that Azure Maps in Power BI only provides "Filter" or "None" under Edit Interactions, so you can't use a "Highlight" effect like you would on a bar chart.
Please check the doc for details:
If you want this feature to be supported, you could raise an idea here:
https://ideas.fabric.microsoft.com/
Since you have mentioned that 90,000 postcodes and working out the distance between each will kill the report performance, I think the only way is to claclualte the distance, however, you could consider precomputing distances between postcodes and storing them in a table (SQL or Power BI), which is not that easy😂
- Precompute distances between postcodes in SQL Server.
- Store only nearby postcodes (e.g., within 5-10 km) for each postcode.
- Create a disconnected table of postcodes in Power BI and use a measure to check if a postcode falls within the selected one's precomputed nearby list.
Here are some related blogs for your reference:
Best Regards,
Qi
Community Support TeamIf there is any post helps, then please consider Accept it as the solution to help the other members find it more quickly.
If I misunderstand your needs or you still have problems on it, please feel free to let us know. Thanks a lot!- mikemagill1 year agoHelper I
Many thanks. I've submitted an idea.
I might explore the SQL database solution but I suspect my client might baulk at an 8 billion row table even if it is only three columns. 😉- Anonymous1 year agoNot applicable
Hi mikemagill,
Fully understand that.😂
As another alternative solution, you could consider using a Heatmap Instead of Points.
If the goal is to show the general area rather than individual postcodes, switching to a heatmap layer in Azure Maps can help preserve visibility while emphasizing high-value areas.
Or, custom Map Visual (via Power BI Custom Visuals or Python/R)
If you need full control, consider creating a custom map visual using Power BI's Python/R scripting or a custom visual like Mapbox, which may support dynamic radius filtering.Either of above is a workaround, you could consider it based on your customer's need.
In short, precumputing by SQL database is a way fully matched with your need, however, it is huge workload.
Anyway, good luck.😀
Best Regards,
Qi
Community Support TeamIf there is any post helps, then please consider Accept it as the solution to help the other members find it more quickly.
If I misunderstand your needs or you still have problems on it, please feel free to let us know. Thanks a lot!