Address Pre-Processing for Rooftop Geocoding

Geocoders turn addresses into coordinates, but they’re sensitive to input quality — ambiguous or malformed strings produce low-confidence matches, or none at all.

The problem

A geocoder given a raw, free-text string has to do its own parsing before it can even attempt a match. Inconsistent abbreviations, missing commas, or components in an unexpected order all reduce match confidence and accuracy.

How address standardisation helps

Standardising the address first hands the geocoder clean, labelled components (houseNumber, road, city, state, postcode) alongside standardized expansions. By expanding abbreviations ("Ave" → "Avenue", "NW" → "Northwest"), geocoding engines match street networks with maximum precision and confidence.

Example

POST /v1/address/normalize

curl -X POST "https://api.goodvat.com/v1/address/normalize" \
  -H "Authorization: Bearer $GOODVAT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{ "query": "1600 pennsylvania ave nw washington dc 20500" }'

200 OK

{
  "houseNumber": "1600",
  "road": "pennsylvania ave nw",
  "postcode": "20500",
  "city": "washington",
  "state": "dc",
  "country": "USA",
  "expansions": [
    "1600 pennsylvania avenue northwest washington dc 20500 usa",
    "1600 pennsylvania avenue northwest washington district of columbia 20500 usa"
  ]
}

Key response fields

Field	Why it matters here
expansions	Fully expanded thoroughfare and directional variations (avenue, northwest) passed to geocoders for high-confidence match rates.
houseNumber / road	The primary signal a geocoder matches against road centerlines and parcel polygons.
city / state	Narrows the search area, eliminating false-positive matches across other jurisdictions.
postcode	Strong disambiguating signal for common street names.
country	Selects the correct national geocoding index.