1. When Domains Collide: Linking Databases to Determine Pupil Generation Rates Jessica Gormont, Jefferson County GIS/Addressing Office Jessica Gormont, Jefferson County GIS/Addressing Office Tori Myers, Jefferson County Assessor’s Office Tori Myers, Jefferson County Assessor’s Office Mark Schiavone, Jefferson County Department of Capital Planning and Management Mark Schiavone, Jefferson County Department of Capital Planning and Management

2. School Impact Fee Fee levied against new residential construction Calculated to ensure capacity expansion

3. School Impact Fee Impact Fee = ( Cost - Credit ) x Demand Generator Asset value per student Non-impact fee revenue per student Students per residential unit

4. Pupil Generation Rate: The number of school-aged children, usually expressed as level of schooling, per household. Traditionally Determined: Using Census/PUMS data Local census/sampling

5. Current Status: Jefferson County Pupil Generation data linked to Housing Unit Type: Single Family Detached (includes manufactured homes) Townhome/Duplex Multifamily Apartment

6. Example: Miami-Dade County Pupil Generation Rates vs. Housing Unit Size (idealized)

7. Board of Education Transportation Database Highly granular: pupil generation per address Lacks information about housing unit type or size

8. Assessor’s Database Highly granular: Housing unit type and size Addresses not accurate – Parcel_ID highly accurate

9. How to Link? BOE data (good addresses) Assessor data (good map/parcel) County GIS Link addresses to addresses Link parcel_id to parcel_id

10. The Plan Analyze BOE data and clean Analyze BOE data and clean Pass BOE data to GIS for join Pass BOE data to GIS for join GIS pass data to Assessor to add building data GIS pass data to Assessor to add building data Deliver combined dataset to consultant for analysis Deliver combined dataset to consultant for analysis

11. Preliminary Data Original parcel layer created in 2009 Original parcel layer created in 2009 IAS queries for tax code data IAS queries for tax code data

12. Finding Residential Addresses Spatial Join - address points & parcel polygons Spatial Join - address points & parcel polygons added Parcel ID to points added Parcel ID to points Tabular Join - address points & IAS data Tabular Join - address points & IAS data added tax codes to address points added tax codes to address points

13. Adding BOE Data Tabular Join - BOE data & address points Tabular Join - BOE data & address points Loss of 12.5% Loss of 12.5% Loss caused by variety of errors Loss caused by variety of errors Secondary Visual Clean Up of BOE data Secondary Visual Clean Up of BOE data Second Tabular Join of BOE data Second Tabular Join of BOE data loss of 10% - deemed acceptable loss of 10% - deemed acceptable

14. Adding Additional Information Decided to add secondary information in case needed by contractor Decided to add secondary information in case needed by contractor Spatial Join to Jurisdiction layer Spatial Join to Jurisdiction layer Allowed for removal of address points within towns if necessary Allowed for removal of address points within towns if necessary Spatial Join to Subdivision/MHP layer Spatial Join to Subdivision/MHP layer Allowed for separation of Mobile Homes in MHPs Allowed for separation of Mobile Homes in MHPs

15. Final Data Data received from GIS Data received from GIS Several queries to retrieve data for Living unit size and number of bedrooms. Several queries to retrieve data for Living unit size and number of bedrooms.

16. Final Data Final data sent to contractor contained: Final data sent to contractor contained: Physical Location Address Physical Location Address Parcel ID Parcel ID Tax Code Tax Code Number of School Kids by Grade Level Number of School Kids by Grade Level Building Assessment Data Building Assessment Data Jurisdiction Jurisdiction Subdivision/MHP name Subdivision/MHP name

17. Results Original census data from 2000 Only 3 housing unit types recognized No further granularity

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21. Results

22. Conclusion Multiple databases linked with no loss of fidelity Multiple databases linked with no loss of fidelity GIS datasets are rich and merge well with other domains GIS datasets are rich and merge well with other domains Agencies able to create sophisticated studies at low cost Agencies able to create sophisticated studies at low cost