An audit-ready R toolkit for cleaning, geocoding, validating, reviewing, and exporting messy address and location data. It sits on top of tidygeocoder: tidygeocoder fetches coordinates; locatr decides whether those coordinates are trustworthy and produces a dashboard- and GIS-ready crosswalk.
How locatr relates to tidygeocoder
locatr is built on top of tidygeocoder, not as a replacement. tidygeocoder is the geocoding engine: hand it an address, pick a service, and it returns coordinates and service results. locatr uses that engine for its main geocoding passes and adds the workflow layer around it: deciding whether a coordinate can be trusted, recording how it was produced, and tying it to local geography for review and reuse.
If you just need coordinates from already-clean addresses via one service, use tidygeocoder::geo() / geocode() directly. locatr earns its place when the data is messy, the matches need to be defensible, and you need local geography enrichment.
| Concern | tidygeocoder | locatr |
|---|---|---|
| Address -> coordinates | core purpose | delegates main geocoding passes to tidygeocoder |
| Breadth of services / reverse geocoding | broad support | focused workflow |
| Normalise messy address text | assumes prepared input | clean_addresses() |
| Flag PO boxes / placeholders before calls | outside scope | flag_bad_addresses() |
| Reject out-of-region false matches | outside scope |
validate_geocodes(), region_bbox()
|
| Multi-service cascade |
geocode_combine() cascades not-found records |
geocode_records() adds region validation, tier stamps, and name-match score/type gates |
| Audit trail / review status | service results, but no standard review trail | method, pass, match, validation, review, and override fields |
| Local geography crosswalk | Census geographies possible for Census results | point-in-polygon/key joins to Census or user boundary layers |
| Human review + reusable crosswalk | outside scope | review export, manual overrides, final crosswalk |
| Single-address candidate lookup |
geo(limit = ..., full_results = TRUE) can return candidates |
geocode_address() adds cleaning, score filtering, context hints, and geography |
| No-code app | outside scope | Shiny app / Hugging Face Space |
In short: tidygeocoder turns an address into geocoder results; locatr decides whether those results can be trusted, records why, and ties them to local geography for review and reuse.
Why It Exists
Geocoders are imperfect. Strict services miss real addresses; fuzzy services can confidently place a bad match far outside the intended service area. locatr wraps that reality with three guards:
- Flagging - PO boxes, placeholders, and missing address/city fields never hit the geocoder. ZIP is helpful but optional; missing ZIP is audited without blocking address + city + state geocoding.
- Fallback passes - ArcGIS and name-based lookup can pick up what a stricter geocoder misses.
- Region validation - anything that lands outside the configured bounding box or boundary polygon is rejected, not mapped.
Every function records an audit trail (geocode_method, geocode_pass, match_status, validation_status, review_status, manual_override_used) so a reviewer can see how each coordinate was produced. In finished geocoding output, review_status uses outcome-oriented values such as auto_accepted, needs_manual_review, manual_override_applied, and rejected.
Those signals are also distilled into a single match_confidence (0-1) with a plain-language confidence_reason, via add_match_confidence(). It is attached automatically by geocode_records(), carried through the crosswalk, and included in geocode_address() candidates, so you can sort or threshold on one trust column instead of reading four.
Workflow
library(locatr)
cleaned <- records %>%
clean_addresses(id = `Facility ID`, address = Address,
city = City, zip = Zip, name = `Facility Name`,
state = "NJ") %>%
flag_bad_addresses()
geocoded <- geocode_records(cleaned, bbox = region_bbox("NJ"))
with_geography <- add_county_muni(geocoded, state = "NJ")
write_geocode_review(with_geography, "manual_review.csv")
final <- with_geography %>%
apply_manual_overrides("manual_review_completed.csv", bbox = region_bbox("NJ")) %>%
export_location_crosswalk("location_crosswalk.csv")At minimum, clean_addresses() needs an address and city. If no ID is supplied, locatr creates row-number IDs; if no ZIP is supplied, zip_clean stays NA and the single-line address omits the trailing ZIP:
cleaned <- records %>%
clean_addresses(address = Address, city = City, state = "NJ") %>%
flag_bad_addresses()Rows with missing ZIP remain geocodable. Rows missing address/city, PO boxes, placeholder addresses, and test records still go to manual review.
To catch data-entry errors the geocoder would silently accept, run flag_field_conflicts(): it flags a ZIP that cannot belong to the stated state (a conservative USPS-region check that never false-flags) and, given a stated county column, a county that disagrees with the geocoded location_county. It adds zip_state_conflict, county_conflict, and a combined field_conflict column without changing any coordinate.
checked <- flag_field_conflicts(with_geography, stated_county = "County")Quick single-address lookup
To check one address interactively - no data frame required - use geocode_address(). It cleans the text, asks ArcGIS for candidate matches ranked by confidence (highest first), and attaches county/municipality when state is supplied, a candidate state can be inferred, or you provide a geography layer:
geocode_address("1600 Pennsylvania Ave NW")
# ambiguous street-only searches often need a little location context
geocode_address("24 Peachton", state = "NJ")
# keep only high-confidence matches, coordinates only
geocode_address("1 City Hall Sq", city = "Boston", state = "MA",
min_score = 90, geography = FALSE)Set min_score to a confidence threshold (0-100), max_candidates to cap how many come back, and geography = FALSE to skip the county/municipality join. min_score filters candidates ArcGIS already returned; use city, state, zip, or bbox when you need to change where ArcGIS searches. In interactive sessions, geocode_address() prints short progress messages; set show_progress = FALSE to silence them.
Audit helpers
After a run, use the small audit helpers to understand and compare results:
summarise_geocoding(geocoded)
explain_geocode_result(geocoded, row = 1)
suggest_geography_level("PA")
compare_geocode_runs(previous_geocoded, geocoded)
if (interactive()) {
plot_geocode_review_map(geocoded)
}No-code web app
For users who would rather not write R, the same pipeline is available as a Shiny app: upload a CSV/Excel/Parquet file, geocode it with a session cache, and download the geocoded records immediately as CSV, Excel, or Parquet. If you also need county/locality fields, attach geography from Census TIGER/Line or an uploaded shapefile first, optionally append tract/ZCTA/district/school-district GEOIDs, then download the geography crosswalk. The app also exposes field conflict flags, cache/provenance summaries, and a downloadable Markdown audit report with a methods paragraph. The download step lets you remove columns before exporting. In the app, Unique ID and ZIP are optional: leave them as (auto) / (none) when your file only has address and city.
install.packages(c("shiny", "bslib", "DT", "leaflet",
"readr", "readxl", "tibble", "writexl",
"arrow", "tigris"))
run_locatr_app()The app is also published as a Hugging Face Space (Docker): https://huggingface.co/spaces/Prigas89/locatr_reviewer. The deployment scaffolding lives in the huggingface/ folder on GitHub.
Map-based review app
A separate Shiny app takes records from upload through geocoding to map-based review: upload a file, map the ID and address columns, then geocode the addresses (clean, flag, cascade, and attach county/municipality) or use existing coordinates. The flagged records appear on a map to accept, reject, or relocate, and the app produces a completed override CSV that feeds straight into apply_manual_overrides().
The decision logic is a plain, testable function you can also use headless:
overrides <- build_review_overrides(geocoded, decisions) # accept / reject / relocate
final <- apply_manual_overrides(geocoded, "manual_review_completed.csv")accept confirms the automated coordinate, relocate substitutes a reviewer coordinate, and reject leaves the point unplaced - all recorded in the override table’s manual_note.
The Geocoding Cascade
geocode_records() runs progressively fuzzier internet services, retrying only rows that began with geocodable addresses and are still unplaced after each validation pass:
| Tier | Function | Service | What it catches |
|---|---|---|---|
| 0 | backfill_from_reference() |
trusted table (no network) | records already verified in a prior cycle |
| 1 | geocode_census() |
US Census (structured) | clean, in-range street addresses |
| 2 | geocode_arcgis() |
ArcGIS (address line) | typos, range gaps, fuzzy streets |
| 3 | geocode_by_name() |
ArcGIS (name + city) | named places, campuses, landmarks |
| manual | apply_manual_overrides() |
human | the rest |
Tier 0 is optional and authoritative: pass geocode_records(cleaned, reference = verified) or call backfill_from_reference() directly to place records whose coordinates were checked before. These rows are bbox-validated, stamped pass_0_reference, and skipped by every later tier.
The input readiness value ready_for_geocoding is internal to the cascade; after geocode_records() completes, valid matched rows are marked auto_accepted.
Name lookup is score-gated when the geocoder returns score/type fields. Precise point-address hits above min_score pass cleanly; fuzzier POI, locality, or low-score hits keep coordinates for reviewer context but remain in needs_manual_review. The Shiny app exposes the name score threshold and the address types that are allowed to pass without review.
Reproducible Runs
Geocoding hits external services, so a run is only reproducible if you can replay it without re-querying. Pass a locatr_cache() to geocode_records() (it also works on the single tiers and on geocode_address()): repeated addresses are served from the cache, and a later run reproduces the same coordinates offline instead of re-hitting Census or ArcGIS.
cache <- locatr_cache("nj_geocode_cache.rds") # persistent; omit the path for memory-only
geocoded <- geocode_records(cleaned, bbox = region_bbox("NJ"), cache = cache)
# a second run reuses the cache - no network for addresses already placed
geocoded_again <- geocode_records(cleaned, bbox = region_bbox("NJ"), cache = cache)
cache_info(cache) # rows, distinct keys, per-method counts, file size, oldest/newestThe cache stores one row per candidate result (plus a no-match sentinel, so misses are replayed too), keyed by the exact query and request parameters. Pass refresh = TRUE to force a re-query and overwrite. Nothing is written to disk unless you give locatr_cache() a path.
Every geocode_records() result also carries a run manifest and two per-row provenance columns:
geocode_provenance(geocoded) # run id, timestamp, versions, tiers, cache activity, status counts
geocoded[, c("record_id", "placed_at", "cache_status")]cache_status is one of fresh (geocoded this run), cached (the cache already held the coordinate before this run), reference, manual, or unplaced. placed_at records when a coordinate actually entered the output - the cached timestamp for cached rows, or an explicit reference/override timestamp when your data carries one, rather than the current run time. Both columns flow through export_location_crosswalk(). The manifest is attached as an attribute (attr(geocoded, "locatr_run")), so read it with geocode_provenance() directly off the geocode_records() result, before further data-frame operations that might drop attributes.
For a shareable summary, geocode_report() rolls the manifest and audit columns into counts by review status, placing tier, and cache status, a match_confidence summary, and an auto-generated plain-language methods paragraph for a report or paper. Print it, or write Markdown with file =:
geocode_report(geocoded) # printed summary + methods paragraph
geocode_report(geocoded, file = "run.md") # Markdown reportRegions And Geography
region_bbox() ships coarse guard boxes for every US state and DC (case-insensitive), so validation works out of the box beyond NJ:
geocoded <- geocode_records(cleaned, bbox = region_bbox("CA"))These presets are deliberately generous sanity boxes, not precise boundaries. For a tighter or non-state region, pass your own bbox, or derive one from an sf layer with bbox_from_sf():
custom_bbox <- c(lat_min = 38.0, lat_max = 39.0, lon_min = -77.5, lon_max = -76.0)
geocoded <- geocode_records(cleaned, bbox = custom_bbox)For stricter validation, pass an sf boundary polygon with boundary =.
Local Geography (NJ Municipalities)
In production, location_locality and location_county come from a spatial join against an authoritative NJ municipal boundary layer, not from the geocoder response or Census reverse-geocoding. The workflow is: load boundaries, derive a bbox, geocode, validate, convert to sf points, spatially join to the polygons, and write location_county, location_locality, geography_match_status.
munis <- sf::st_read("path/to/Municipal_Boundaries_of_NJ.shp") %>%
sf::st_make_valid() %>%
dplyr::transmute(location_county = COUNTY, location_locality = MUN)
bbox <- bbox_from_sf(munis)
geocoded <- geocode_records(cleaned, bbox = bbox)
with_geography <- add_local_geography(
geocoded,
geography_shapes = munis,
county_col = "location_county",
locality_col = "location_locality"
)Or build the packaged default once with data-raw/local_geography.R, sourced from NJGIN/NJOGIS Municipal Boundaries of NJ.
Any State, With Census TIGER/Line
add_county_muni() is the shortest path when Census TIGER/Line boundaries are good enough for your workflow:
final <- add_county_muni(geocoded, state = "PA", geography = "county_subdivision")Under the hood, build_local_geography() pulls Census boundaries through tigris and standardises them to the location_county / location_locality schema, plus stable join fields when the source provides them: county_code, county_fips, municipality_code, municipality_geoid, municipality_name_standard, municipality_type, and muni_join_key.
areas <- build_local_geography(state = "PA", geography = "county_subdivision")
bbox <- bbox_from_sf(areas)
geocoded <- geocode_records(cleaned, bbox = bbox)
final <- add_local_geography(
geocoded,
geography_shapes = areas,
county_col = "location_county",
locality_col = "location_locality"
)Pick geography for the state: county is easy everywhere; county_subdivision maps well to townships/municipalities in NJ, PA, NY, and New England; place covers incorporated places/CDPs but misses townships and many unincorporated areas; tract uses tract GEOIDs. For high-stakes, state-specific reporting where “municipality” has legal meaning, use an official state GIS layer and pass it directly. Muni Key is kept as a readable fallback, but production joins should prefer muni_join_key, municipality_geoid, or another official code from your boundary source.
Use add_muni_from_shapes() for point-in-polygon joins against your own boundary layer, or add_muni_from_key() when your records and geography share a code column such as ZIP, FIPS, or GEOID.
For analysis and policy geographies beyond county/municipality - census tract, block group, ZCTA, congressional and state legislative districts, or unified school districts - add_census_geographies() attaches any combination in one call, each as a <level>_geoid / <level>_name pair:
enriched <- add_census_geographies(
geocoded, state = "NJ",
levels = c("tract", "congressional_district", "school_district")
)