README.md

@@ -1014,8 +1014,9 @@ renamed or retyped line is a major.
   whole.** `data/sv_SE` alone no longer loads: a test loads `data` and prefixes
   the locale, and `--no-shipped-data -d` takes the whole `data` folder or a set of
   one's own.
-- **The default embed holds every Swedish postort and the US places of 25,000 or
-  more.** Sweden fits whole in 700 KB; every US place of 10,000 would pass a
+- **The default embed holds every Swedish postort the import can place and give a
+  street-delivery code and a street, and the US places of 25,000 or more.** Sweden
+  fits whole in 700 KB; every US place of 10,000 would pass a
   megabyte and fetch 1,200 counties of TIGER files, so the threshold sits where the
   two countries match in size, and `--min-population` and
   `--streets-per-locality` on the import scripts build a fuller set. The two trees
@@ -1030,6 +1031,13 @@ renamed or retyped line is a major.
   carries stale spellings; the nearest code across a border named the wrong kommun
   half the time it was tried, so a postort none of the three rules place is
   dropped, as is one not cased like a place name.
+- **A highway designation is not a street, and a US postal code belongs to the place
+  holding most of its land inside places.** `I- 55 Bus` and `US Hwy 1` carry the
+  most address ranges in many places and would head every address, so the import
+  drops names spelled as a route. A ZCTA goes to the place its largest in-place part
+  lies in, and ships only when that place does; counting the land outside every
+  place too would drop a quarter of the places, whose codes straddle unincorporated
+  land, for a postal city the USPS mostly names the same way.
 - **`List` advertises direct descents only.** `region.municipality.locality` is
   listed, and `region.locality` resolves too but is not: the set of every descent
   through a chain of five tables is every subsequence of it, and the direct chain is

data-import/geo-se.py

@@ -2,12 +2,6 @@
 """Rebuild data/geo/SE/*.tsv from SCB (CC0), GeoNames (CC BY 4.0) and Trafikverket NVDB (CC0).
 
     TRAFIKVERKET_API_KEY=… data-import/geo-se.py [--key-file FILE] [--cache DIR] [--streets-per-locality N] [--out DIR]
-
-A locality is a GeoNames postort, placed in the municipality it names, else of its
-tätort, else of most of its codes, and weighted by its tätort's population, else its
-municipality's, else 200. Box codes are dropped by the digit after the postort's own
-prefix. Each NVDB street segment goes to the nearest postal code centroid; a locality
-keeps the N names with most segments.
 """
 import argparse
 import collections
@@ -95,7 +89,7 @@ def geonames(cache):
 def nvdb_segments(cache, key):
     path = cache / "nvdb-gatunamn.tsv"
     if not path.exists():
-        with open(path, "w", encoding="utf-8") as out:
+        with open(path.with_suffix(".part"), "w", encoding="utf-8") as out:
             change = "0"
             while True:
                 query = (
@@ -116,6 +110,7 @@ def nvdb_segments(cache, key):
                 change = result["INFO"]["LASTCHANGEID"]
                 if len(rows) < NVDB_PAGE:
                     break
+        path.with_suffix(".part").rename(path)
     for line in path.read_text(encoding="utf-8").splitlines():
         name, lon, lat = line.split("\t")
         yield name, float(lat), float(lon)
@@ -208,7 +203,8 @@ def streets(segments, codes, localities, per_locality):
     nearest = Nearest((r["lat"], r["lon"], r["locality"]) for r in codes if r["lat"] is not None and r["locality"] in localities)
     count = collections.Counter()
     for name, lat, lon in segments:
-        count[(nearest.find(lat, lon), name)] += 1
+        if name[0].isalpha():
+            count[(nearest.find(lat, lon), name)] += 1
     of = collections.defaultdict(list)
     for (locality, name), n in count.items():
         of[locality].append((n, name))

data-import/geo-us.py

@@ -2,11 +2,6 @@
 """Rebuild data/geo/US/*.tsv from the Census Bureau's Gazetteer, population estimates, ZCTA relationships and TIGER/Line files (public domain).
 
     data-import/geo-us.py [--cache DIR] [--min-population N] [--streets-per-locality N] [--out DIR]
-
-A locality is an incorporated place, or a consolidated city's balance, of at least N
-people, in the county holding most of it. Its postal codes are the ZCTAs mostly inside
-it, weighted by their TIGER address ranges, and its streets the N names with most
-address ranges in those codes.
 """
 import argparse
 import collections
@@ -32,10 +27,10 @@ OUT = Path(__file__).resolve().parent.parent / "data" / "geo" / "US"
 CACHE = Path(__file__).resolve().parent / "cache"
 ESTIMATE = "POPESTIMATE2025"
 CDP = "57"
+HIGHWAY = re.compile(r"\b(I- |Hwy |Rte |Route |Rd )\d")
 SUFFIX = re.compile(r" (city and borough|city|town|village|borough|municipality|comunidad|zona urbana|metropolitan government|metro government|consolidated government|unified government|urban county|corporation|plantation)( \(balance\))?$")
 # Places whose Census name is a merged government's; the postal city is what an address carries.
 NAMES = {"1303440": "Athens", "1304204": "Augusta", "1349008": "Macon", "2148006": "Louisville", "3011397": "Butte", "4732742": "Hartsville", "4752006": "Nashville"}
-# The predominant zone of each state.
 TIMEZONES = {
     "AK": "America/Anchorage", "AL": "America/Chicago", "AR": "America/Chicago", "AZ": "America/Phoenix",
     "CA": "America/Los_Angeles", "CO": "America/Denver", "CT": "America/New_York", "DC": "America/New_York",
@@ -122,7 +117,7 @@ def localities(cache, min_population, counties):
     out = {}
     for r in gazetteer(cache, "place"):
         geoid, population = r["GEOID"], place_population.get(r["GEOID"], 0)
-        if r["FUNCSTAT"] not in "AFN" or r["LSAD"] == CDP or population < min_population or not county_part.get(geoid):
+        if r["FUNCSTAT"] not in ("A", "F", "N") or r["LSAD"] == CDP or population < min_population or not county_part.get(geoid):
             continue
         county = max(county_part[geoid])[1]
         if county not in counties:
@@ -133,12 +128,13 @@ def localities(cache, min_population, counties):
 
 
 def postal_codes(cache, localities):
-    """Each ZCTA and the shipped place holding most of its land."""
+    """Each ZCTA whose largest part inside any place lies in a shipped place."""
     parts = {}
     for r in csv.DictReader(io.StringIO(text(tsv.fetch(ZCTA_PLACE, cache, "zcta-place.txt"))), delimiter="|"):
-        if r["GEOID_ZCTA5_20"] and r["GEOID_PLACE_20"] in localities:
+        if r["GEOID_ZCTA5_20"] and r["GEOID_PLACE_20"]:
             parts.setdefault(r["GEOID_ZCTA5_20"], []).append((int(r["AREALAND_PART"]), r["GEOID_PLACE_20"]))
-    return {zcta: max(p)[1] for zcta, p in parts.items()}
+    largest = {zcta: max(p)[1] for zcta, p in parts.items()}
+    return {zcta: place for zcta, place in largest.items() if place in localities}
 
 
 def streets(cache, counties, locality_of_zcta, per_locality):
@@ -153,7 +149,7 @@ def streets(cache, counties, locality_of_zcta, per_locality):
                 zips[r["TLID"]].add(r["ZIP"])
                 addresses[r["ZIP"]] += 1
         for r in tiger(cache, "featnames", county, {"TLID", "FULLNAME", "PAFLAG"}):
-            if r["PAFLAG"] == "P" and r["FULLNAME"]:
+            if r["PAFLAG"] == "P" and r["FULLNAME"] and not HIGHWAY.search(r["FULLNAME"]):
                 for z in zips.get(r["TLID"], ()):
                     count[(locality_of_zcta[z], r["FULLNAME"])] += 1
     of = collections.defaultdict(list)

data-import/tsv.py

@@ -20,10 +20,10 @@ def fetch(source, cache, name, magic=b"", data=None, headers=None):
                 body = r.read()
         except OSError:
             body = b""
-        if body.startswith(magic) and b"Request Rejected" not in body[:512]:
+        if body and body.startswith(magic) and b"Request Rejected" not in body[:512]:
             path.parent.mkdir(parents=True, exist_ok=True)
             path.write_bytes(body)
-        else:
+        elif attempt < 5:
             time.sleep(10 * attempt)
     sys.exit(f"{source}: no valid download in 5 attempts")
 
@@ -33,7 +33,8 @@ def write(path, columns, rows):
     lines = ["\t".join(columns)]
     for row in rows:
         cells = [str(row[c]) for c in columns]
-        assert all(cells) and not any(re.search(r"[\t\n{}]", c) for c in cells), row
+        if not all(cells) or any(re.search(r"[\t\n{}]", c) for c in cells):
+            raise ValueError(f"{path}: a cell is empty or holds a tab, newline or brace: {row}")
         lines.append("\t".join(cells))
     Path(path).write_text("\n".join(lines) + "\n", encoding="utf-8")
     print(f"{path}: {len(lines) - 1} rows", file=sys.stderr)

table_test.go

@@ -44,7 +44,6 @@ var (
 	regionOf       = map[string]string{"0180": "01", "0184": "01", "1280": "12", "1281": "12", "1480": "14"}
 )
 
-// siblings adds two child tables under locality: postal-code, keyed, and street, keyless.
 func siblings() map[string]string {
 	return with(geo(), map[string]string{
 		"postal-code.json": `{"format":"{code}","rows":"postal-code.tsv","key":"code","parent":"locality"}`,

todo.md

@@ -54,10 +54,10 @@ countries; the README maps each to the native term.
 | Table | SE | US | Weight |
 |---|---|---|---|
 | `region` | län (21) | state and DC (50; Hawaii has no incorporated place) | population |
-| `municipality` | kommun (290) | county with a shipped place (666) | population |
-| `locality` | postort (1,522), tätort population | place of 25,000+ (1,601) | population |
-| `postal-code` | postnummer with street delivery (13,712) | ZCTA of a shipped place (7,401) | one; address ranges |
-| `street` | gatunamn, top 10 per postort (14,764) | street name, top 10 per place (16,010) | segments; address ranges |
+| `municipality` | kommun (290) | county with a shipped place (663) | population |
+| `locality` | postort (1,522), tätort population | place of 25,000+ (1,579) | population |
+| `postal-code` | postnummer with street delivery (13,712) | ZCTA of a shipped place (5,946) | one; address ranges |
+| `street` | gatunamn, top 10 per postort (14,764) | street name, top 10 per place (15,790) | segments; address ranges |
 
 - Shipped in step 2, README Data. `geo.SE.address` is a record over one consistent
   draw. Each region row carries its timezone, each locality its centroid.