Assessing Quality of Geocoded Data The Florida Registry Experience

Overview What is geocoding quality? Florida’s geocoding experience – Identifying geocoding errors – Results before and after improved geocoding – Monitoring for geocoding problems 2

What is Geocoding? Spatially enable Assign geocode – Latitude/Longitude – FIPS—Census Units Match address to street file – Batch (automated) – Interactive (manual 5-10%) 3

Geocoding Quality Components Match rate – Coverage, % with spatial location Precision – Scale County center versus census block – NAACCR Items #366,#364,#365 GIS Coordinate Quality, Census Tract Certainty Accuracy – Correct location 4

Geocoding Match Software – Deterministic, Probabilistic – Parsing algorithm, Assumptions (ties) – “Black box” Underlying street files Quality of address data Batch versus manual NE 2nd, Miami, FL Did you mean: 133 NE 2nd St, Miami, 133 SE 2nd Ave, Miami, 133 NW 2nd Ave, Miami, 133 SW 2nd St, Miami, 133 SW 2nd Ave, Miami, 133 SE 2nd St, Miami,

Geocoding Precision Parcel match – “gold standard” – Match to building footprint Street level match – Most common – Interpolate along street segment Centroid – Center of polygon Block, tract, zipcode, county – Population center, physical 6

Geocoding Accuracy 7

FCDS Geocoding Proprietary, local vendor Problems found via use – Reported county does not match geocoded county – Representativeness of cases – Cases assigned to invalid or zero population block groups Problems found via scrutiny – Cases in nautical areas (not islands) – Vendor assumptions 8

Geocoding Project Test file – Created “gold standard” files – FIPS (cancer cases) – Long/Lat (well locations) Selected a vendor – Based on logistics rather than quality New vendor re-geocoded entire registry Compared Results – Before and After 9

Old versus Improved Vendor: County Match Problem 10

Old versus Improved Vendor: County Match Problem 11

Old versus Improved Vendor: % Matched 12

Old versus Improved Vendor: Representativeness of Cases Environmental Health – Re-geocoded our data Census Data – 96% Black Old Geocoding Vendor – 15% Black Cases New Geocoding Vendor – 85% Black Cases 13

Old versus Improved Vendor: Nautical, Invalid, Zero pop Cases assigned to the sea – 0 cases from new vendor Cases assigned to invalid bg – 0 cases from new vendor Cases assigned to 0, 1, 10 population bgs – 5,765 cases – 743 cases (3+ more years of data) – SF1 vs. SF3; Overlay 14

Specificity ? Old Data:Improved Data: 15

Sensitivity ? Old Data:Improved Data: 16

Validity ? Old Data : Oral Cancer by SES Wealthy – 34.0 ref Mid High – 36.6RR 1.08 Mid Low – 39.1RR 1.15 Poorest – 46.3RR 1.36 New Data : Oral Cancer by SES Wealthy – 37.3 ref Mid High – 40.1RR 1.08 Mid Low – 45.4RR 1.22 Poorest – 49.2 RR

Monitoring Geocoding Quality % County match – Florida zipcodes; military addresses geocoded to NJ % Contiguous counties Incorrect FIPS Nautical FIPS # Zero Pops Representativeness 18

Impact Fewer, smaller, lower risk clusters Greater % ungeocodable – More accurate – Less specific Ungeocodable – Rural, Poor, Old – Potential bias – Manual geocoding 19

Addressing Ungeocodables Address quality? – Implemented edits Software development – Improve matching algorithm Specific to our data Link with administrative databases DMV, Medicaid, Medicare Geo-imputation – Kevin Henry Requires institutional priority ! 20

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Acknowledgements Dr. Greg Kearny – Environmental Health, FL DOH N. Dean Powell – FCDS Jackie Button – FCDS Dr. Monique Hernandez – FCDS We acknowledge the CDC for financial support under cooperative agreement U58/DP Contents are responsibility of authors and do not represent views of CDC, FL DOH, or FCDS 22