Hi,
This is a known issue that stems from Connecticut's 2022 county reorganization and how the AI/BI Dashboard choropleth map resolves FIPS codes.
WHAT'S HAPPENING
In June 2022, the US Census Bureau officially replaced Connecticut's 8 traditional counties (Fairfield, Hartford, Litchfield, etc.) with 9 planning regions as the new county-equivalent entities. As part of this change, the old FIPS codes (09001-09015) were retired and replaced with entirely new codes (09110-09190).
The AI/BI Dashboard choropleth map uses an internal lookup table to match your data to geographic areas. I checked this file, and it still contains only the old Connecticut FIPS codes:
Fairfield County,Fairfield,09001,US
Hartford County,Hartford,09003,US
Litchfield County,Litchfield,09005,US
...
The new planning region codes (09110, 09120, 09130, etc.) are NOT in the lookup table. Per the map visualization docs, when a geographic value doesn't match the lookup table, "no data is shown for that locality" -- which is exactly what you're seeing for Connecticut.
You can download the actual lookup CSV used by Databricks here:
https://assets.docs.databricks.com/_static/documents/county-district-names.csv
WORKAROUNDS
Option 1: Remap FIPS codes in your query (recommended)
If your data uses the new planning region FIPS codes, you can map them back to the old county codes that Databricks recognizes. Add a CASE expression or join a mapping table in your dashboard query:
SELECT
CASE county_fips
WHEN '09110' THEN '09003' -- Capitol -> Hartford
WHEN '09120' THEN '09003' -- Greater Bridgeport -> (approximate)
WHEN '09130' THEN '09005' -- Lower CT River Valley -> (approximate)
WHEN '09140' THEN '09015' -- Naugatuck Valley -> (approximate)
WHEN '09150' THEN '09007' -- Lower CT River Valley -> Middlesex
WHEN '09160' THEN '09005' -- Northwest Hills -> Litchfield
WHEN '09170' THEN '09001' -- Western CT -> Fairfield
WHEN '09180' THEN '09009' -- South Central CT -> New Haven
WHEN '09190' THEN '09011' -- Southeastern CT -> New London
ELSE county_fips
END AS county_fips_mapped,
your_value_column
FROM your_table
Important caveat: The old counties and new planning regions don't have identical boundaries, so this mapping is approximate. Some planning regions span parts of multiple old counties.
Option 2: Use county names
The lookup table also supports matching by name (e.g., "Fairfield", "Hartford"). If you can map your data to the old county names instead of FIPS codes, set the Geographic role to "County/City" with format "Name" in the visualization editor.
Option 3: Use a Point Map
If you have latitude/longitude data (or can derive it from your records), consider using a point map visualization instead. This bypasses the FIPS lookup entirely and plots data at precise coordinates.
LONGER TERM
This is arguably a gap in the Databricks lookup data that should be updated to include the new Connecticut planning region FIPS codes. You may want to file a support ticket or product feedback request to get the lookup table updated.
DOCUMENTATION REFERENCES
- AI/BI Dashboard Map Visualizations: https://docs.databricks.com/en/dashboards/manage/visualizations/maps.html
- County lookup CSV used by Databricks: https://assets.docs.databricks.com/_static/documents/county-district-names.csv
- Census Bureau County Changes: https://www.census.gov/programs-surveys/geography/technical-documentation/county-changes.html
Hope this helps! Let me know if you have questions about any of the workaround approaches.
* This reply used an agent system I built to research and draft this response based on the wide set of documentation I have available and previous memory. I personally review the draft for any obvious issues and for monitoring system reliability and update it when I detect any drift, but there is still a small chance that something is inaccurate, especially if you are experimenting with brand new features.
