.gitignore

@@ -1,3 +1,4 @@
 .claude
 *.out
 __pycache__/
+data-import/cache/

CHANGELOG.md

@@ -26,3 +26,9 @@ replacement, and each removed path, column or flag.
   `currency`, `flag`, `languages`, `numeric` and `tld` added; `misc.currency` the
   current ISO 4217 currencies with a minor unit, with `decimals` and `numeric`
   added, and its symbols from CLDR. `DATA-LICENSES.md` lists each source.
+- `geo.SE` and `geo.US`: five linked tables per country, `region`, `municipality`,
+  `locality`, `postal-code` and `street`, weighted by population and address counts
+  and built from SCB, GeoNames, Trafikverket NVDB and the US Census Bureau, and an
+  `address` record over one consistent draw of them. `sv_SE.address` and
+  `en_US.address` read those records, so `en_US.address.street` no longer carries
+  `name` and `suffix`, and a locale folder loads only beside `geo`.

DATA-LICENSES.md

@@ -1,10 +1,16 @@
 # Data licenses
 
 Every shipped dataset, its source, its licence and the attribution it asks for. A
-`data-import/` script rebuilds each sourced table; a curated one is hand-written.
+`data-import/` script rebuilds each sourced table, run as the README's
+[Development](README.md#development) section says; a curated one is hand-written.
 
 | Table | Source | Licence | Attribution | Rebuild |
 |-------|--------|---------|-------------|---------|
+| `geo/SE/locality.tsv`, `postal-code.tsv` | [GeoNames](https://www.geonames.org/) postal codes for SE; populations from SCB tätorter 2023 | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/); CC0 1.0 | "Postal codes from GeoNames, www.geonames.org" | `data-import/geo-se.py` |
+| `geo/SE/region.tsv`, `municipality.tsv` | [SCB](https://www.scb.se/) län and kommun codes 2026 and population 2024 | [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) | none required | `data-import/geo-se.py` |
+| `geo/SE/street.tsv` | [Trafikverket NVDB](https://www.trafikverket.se/) Gatunamn, through the open API | CC0 1.0 | none required | `data-import/geo-se.py` |
+| `geo/US/region.tsv`, `municipality.tsv`, `locality.tsv` | [Census Bureau](https://www.census.gov/) Gazetteer 2026 and population estimates 2025 | [public domain](https://www.usa.gov/government-works) | none required | `data-import/geo-us.py` |
+| `geo/US/postal-code.tsv`, `street.tsv` | Census Bureau ZCTA to place relationships 2020 and TIGER/Line 2025 address ranges and feature names | public domain | none required | `data-import/geo-us.py` |
 | `misc/country.tsv` | [datasets/country-codes](https://github.com/datasets/country-codes) | [PDDL 1.0](https://opendatacommons.org/licenses/pddl/1-0/) | none required | `data-import/country.py` |
 | `misc/currency.tsv` | [datasets/currency-codes](https://github.com/datasets/currency-codes); symbols from [Unicode CLDR](https://github.com/unicode-org/cldr) `en.xml` and `root.xml` | PDDL 1.0; [Unicode License v3](https://www.unicode.org/license.txt) | CLDR: "Copyright © 1991-2025 Unicode, Inc. Unicode and the Unicode Logo are registered trademarks of Unicode, Inc. in the United States and other countries." | `data-import/currency.py` |
 | `misc/httpstatus.tsv` | curated (IANA HTTP status codes are facts) | — | — | — |

README.md

@@ -18,6 +18,7 @@ fejkdata --seed 42 sv_SE.address               # the same address every run
 fejkdata -n 3 --separator ', ' sv_SE.word      # nät, barn, sol
 fejkdata --list                                # every path the data offers
 fejkdata 'misc.country[SE].capital'            # Stockholm — a table's row, selected by key or name
+fejkdata 'geo.SE.locality[Lund].street'         # Fjelievägen — a linked table, drawn inside the row
 fejkdata --data-path ./mydata sv_SE.word       # layer a directory over the shipped data
 fejkdata --no-shipped-data -d ./mydata --list  # only your data
 fejkdata 'name: {/sv_SE.person.last}'          # name: <a surname> — an inline template
@@ -227,6 +228,27 @@ Each locale carries `address`, `color`, `company`, `date`, `email`, `ip`,
 `misc.country[SE].capital` and `misc.currency[Euro].symbol` select a row;
 [`DATA-LICENSES.md`](DATA-LICENSES.md) names each table's source and licence.
 
+A `geo` folder holds one tree per country under its alpha-2 code: five
+[linked tables](#linked-tables) named alike, and an `address` record over one
+consistent draw of them, which the locale's `address` reads.
+
+| Table | `geo.SE` | `geo.US` | Weight |
+|-------|----------|----------|--------|
+| `region` | län, by code or name | state, by USPS abbreviation or name; `code` is the FIPS code | population |
+| `municipality` | kommun, by code or name | county, by FIPS code or name | population |
+| `locality` | postort, by name | incorporated place of 25,000 people or more with a postal code of its own, by GEOID or name; Hawaii has none | tätort population, the kommun's where the postort names it, else 200; place population |
+| `postal-code` | postnummer with street delivery, by code | ZCTA, by code | one; address ranges |
+| `street` | gatunamn, the ten with most road segments per postort | street name, the ten with most address ranges per place | segments; address ranges |
+
+`geo.SE.region[Skåne län].municipality` draws a kommun in Skåne,
+`geo.SE.locality[Lund].street` a street in Lund, and
+`geo.US.region[IL].locality[Springfield]` settles which Springfield. A region row
+carries its `timezone`, the state's predominant zone, and a locality its `lat` and
+`lon`. What ports across countries is the five table names, the `name` column,
+selection by name, and the `address` record's columns `street`, `street-number`,
+`postal-code` and `locality`; every other column is the country's own, `code` on a
+Swedish region but `abbr` on a US one.
+
 ## Data format
 
 Every value is a **node**, nestable without limit:
@@ -983,6 +1005,41 @@ renamed or retyped line is a major.
 - **A path is walked once without drawing before it is walked for real.** A path
   that fails below its first level then moves no seeded stream, at the cost of one
   draw-free walk per call, which allocates nothing.
+- **A country's postal codes and streets are siblings under its locality.** No open
+  source pairs a Swedish street with its postnummer, and pairing the US through its
+  ZIPs would shape the two trees differently, so both draw inside the pinned
+  locality and an address agrees at that level. A street's own code is the exact
+  pairing to add when a source carries it.
+- **A locale's `address` reads its country's `geo` tree, so the shipped set loads
+  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 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
+  add about 20 ms to `New`, which loads the shipped set in about 45 ms.
+- **A locale's `address` restates its country record's format.** A record cannot
+  read another whole and keep its columns, so `sv_SE.address` names the same four
+  columns as `geo.SE.address`, each a reference into it, and the format appears
+  twice; a column is spelled the same in both, `street-number`, so the two never
+  disagree on a name.
+- **A postort's kommun comes from its name, its tätort or its codes, never from
+  distance.** GeoNames leaves a fifth of Sweden's codes without a kommun and
+  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, census-designated places left out since they never ship, and ships only
+  when that place does, so a few dozen places whose every code lies mostly in a
+  bigger neighbour ship no address; 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
@@ -1032,11 +1089,17 @@ REPIN=1 docker compose run --rm --user "$(id -u):$(id -g)" test
 
 A shipped table built from a source is rebuilt by its script under
 [`data-import/`](data-import), one command per dataset, fetching the source named in
-[`DATA-LICENSES.md`](DATA-LICENSES.md):
+[`DATA-LICENSES.md`](DATA-LICENSES.md). Downloads are cached under
+`data-import/cache/`, so delete it to fetch afresh; `geo-us.py` fetches two
+TIGER/Line files per county it ships, a few hundred megabytes, and `geo-se.py` needs
+a Trafikverket API key, free at [data.trafikverket.se](https://data.trafikverket.se/),
+in `TRAFIKVERKET_API_KEY` or a `--key-file`:
 
 ```sh
 docker compose run --rm --user "$(id -u):$(id -g)" data-import data-import/country.py
 docker compose run --rm --user "$(id -u):$(id -g)" data-import data-import/currency.py
+docker compose run --rm --user "$(id -u):$(id -g)" data-import data-import/geo-us.py
+docker compose run --rm --user "$(id -u):$(id -g)" -e TRAFIKVERKET_API_KEY data-import data-import/geo-se.py
 ```
 
 To release, head `CHANGELOG.md` with the version's section in place of `Unreleased`
@@ -1067,7 +1130,7 @@ datatype.go     column datatypes: DataType, where datatype and null may sit, a c
 value.go        the value proof: what a typed column or calc operand holds, checked at load
 data.go         data loading: fs.FS folders/files -> namespace tree, multi-source merge
 cmd/fejkdata/   the fejkdata CLI
-data/           shipped data (JSON, and a TSV per table), embedded at build: locale folders + a misc folder
+data/           shipped data (JSON, and a TSV per table), embedded at build: locale folders, geo, misc
 data-import/    the scripts that rebuild each sourced table (see DATA-LICENSES.md)
 release-tooling/ the release CI publishes from the changelog heading
 testdata/       the pinned shipped shape (see Versioning)

bench_test.go

@@ -26,12 +26,12 @@ func benchPath(b *testing.B, dir, path string) {
 	}
 }
 
-func BenchmarkPerson(b *testing.B)     { benchPath(b, "data/sv_SE", "person") }
-func BenchmarkAddress(b *testing.B)    { benchPath(b, "data/sv_SE", "address") }
-func BenchmarkWord(b *testing.B)       { benchPath(b, "data/sv_SE", "word") }
-func BenchmarkCreditcard(b *testing.B) { benchPath(b, "data/misc", "creditcard") }
-func BenchmarkSSN(b *testing.B)        { benchPath(b, "data/sv_SE", "ssn") }
-func BenchmarkUUIDv7(b *testing.B)     { benchPath(b, "data/misc", "uuid") }
+func BenchmarkPerson(b *testing.B)     { benchPath(b, "data", "sv_SE.person") }
+func BenchmarkAddress(b *testing.B)    { benchPath(b, "data", "sv_SE.address") }
+func BenchmarkWord(b *testing.B)       { benchPath(b, "data", "sv_SE.word") }
+func BenchmarkCreditcard(b *testing.B) { benchPath(b, "data", "misc.creditcard") }
+func BenchmarkSSN(b *testing.B)        { benchPath(b, "data", "sv_SE.ssn") }
+func BenchmarkUUIDv7(b *testing.B)     { benchPath(b, "data", "misc.uuid") }
 
 func tmpData(b *testing.B, name, body string) string {
 	b.Helper()

cmd/fejkdata/main_test.go

@@ -14,6 +14,7 @@ import (
 const (
 	svSE = "../../data/sv_SE"
 	enUS = "../../data/en_US"
+	misc = "../../data/misc"
 )
 
 func runOut(args ...string) (int, string, string) {
@@ -405,14 +406,14 @@ func TestRunTemplateMisuse(t *testing.T) {
 }
 
 func TestRunNoShippedData(t *testing.T) {
-	code, list, errb := runOut("--no-shipped-data", "-d", svSE, "--list")
+	code, list, errb := runOut("--no-shipped-data", "-d", misc, "--list")
 	if code != 0 {
 		t.Fatalf("run = %d, stderr=%q", code, errb)
 	}
-	if strings.Contains(list, "en_US") || !strings.Contains(list, "person\n") {
+	if strings.Contains(list, "sv_SE") || !strings.Contains(list, "uuid\n") {
 		t.Errorf("--no-shipped-data --list = %q, want only the given dir", list)
 	}
-	code, out, _ := runOut("--no-shipped-data", "-d", svSE, "-s", "3", "person")
+	code, out, _ := runOut("--no-shipped-data", "-d", misc, "-s", "3", "uuid")
 	if code != 0 || strings.TrimSpace(out) == "" {
 		t.Errorf("run = %d, out=%q", code, out)
 	}

data-import/country.py

@@ -1,30 +1,24 @@
 #!/usr/bin/env python3
 """Rebuild data/misc/country.tsv from datasets/country-codes (PDDL).
 
-    data-import/country.py [--source URL_OR_FILE] [--out FILE]
+    data-import/country.py [--source URL_OR_FILE] [--cache DIR] [--out FILE]
 """
 import argparse
 import csv
 import io
 import re
-import sys
-import urllib.request
 from pathlib import Path
 
+import tsv
+
 SOURCE = "https://raw.githubusercontent.com/datasets/country-codes/main/data/country-codes.csv"
 OUT = Path(__file__).resolve().parent.parent / "data" / "misc" / "country.tsv"
+CACHE = Path(__file__).resolve().parent / "cache"
 COLUMNS = ["alpha2", "alpha3", "calling-code", "capital", "currency", "flag", "languages", "name", "numeric", "tld"]
 # Gaps in the source, keyed by alpha2.
 FIXUPS = {"TR": {"currency": "TRY"}}
 
 
-def read(source):
-    if re.match(r"^https?://", source):
-        with urllib.request.urlopen(source, timeout=60) as r:
-            return r.read().decode("utf-8")
-    return Path(source).read_text(encoding="utf-8")
-
-
 def flag(alpha2):
     return "".join(chr(0x1F1E6 + ord(c) - ord("A")) for c in alpha2)
 
@@ -58,23 +52,14 @@ def rows(text):
             yield row
 
 
-def write(out, table):
-    lines = ["\t".join(COLUMNS)]
-    for row in sorted(table, key=lambda r: r["alpha2"]):
-        cells = [row[c] for c in COLUMNS]
-        assert not any("\t" in c or "\n" in c for c in cells), row
-        lines.append("\t".join(cells))
-    Path(out).write_text("\n".join(lines) + "\n", encoding="utf-8")
-    return len(lines) - 1
-
-
 def main():
     p = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+    p.add_argument("--cache", default=str(CACHE))
     p.add_argument("--source", default=SOURCE)
     p.add_argument("--out", default=str(OUT))
     a = p.parse_args()
-    n = write(a.out, rows(read(a.source)))
-    print(f"{a.out}: {n} rows", file=sys.stderr)
+    table = rows(tsv.fetch(a.source, a.cache, "country-codes.csv").decode("utf-8"))
+    tsv.write(a.out, COLUMNS, sorted(table, key=lambda r: r["alpha2"]))
 
 
 if __name__ == "__main__":

data-import/currency.py

@@ -1,35 +1,28 @@
 #!/usr/bin/env python3
 """Rebuild data/misc/currency.tsv from datasets/currency-codes (PDDL) and CLDR's symbols (Unicode).
 
-    data-import/currency.py [--source URL_OR_FILE] [--symbols URL_OR_FILE ...] [--out FILE]
+    data-import/currency.py [--source URL_OR_FILE] [--symbols URL_OR_FILE ...] [--cache DIR] [--out FILE]
 
 The symbols come from the first locale file that has one, narrow symbols before wide.
 """
 import argparse
 import csv
 import io
-import re
-import sys
-import urllib.request
 import xml.etree.ElementTree as ET
 from pathlib import Path
 
+import tsv
+
 SOURCE = "https://raw.githubusercontent.com/datasets/currency-codes/main/data/codes-all.csv"
 SYMBOLS = [
     "https://raw.githubusercontent.com/unicode-org/cldr/main/common/main/en.xml",
     "https://raw.githubusercontent.com/unicode-org/cldr/main/common/main/root.xml",
 ]
 OUT = Path(__file__).resolve().parent.parent / "data" / "misc" / "currency.tsv"
+CACHE = Path(__file__).resolve().parent / "cache"
 COLUMNS = ["code", "decimals", "name", "numeric", "symbol"]
 
 
-def read(source):
-    if re.match(r"^https?://", source):
-        with urllib.request.urlopen(source, timeout=60) as r:
-            return r.read().decode("utf-8")
-    return Path(source).read_text(encoding="utf-8")
-
-
 def symbols(xml_texts):
     """CLDR's symbol per code: the first locale's narrow symbol, else the first locale's wide one."""
     narrow, wide = {}, {}
@@ -58,24 +51,16 @@ def rows(text, symbol):
         }
 
 
-def write(out, table):
-    lines = ["\t".join(COLUMNS)]
-    for row in sorted(table, key=lambda r: r["code"]):
-        cells = [row[c] for c in COLUMNS]
-        assert all(cells) and not any("\t" in c or "\n" in c for c in cells), row
-        lines.append("\t".join(cells))
-    Path(out).write_text("\n".join(lines) + "\n", encoding="utf-8")
-    return len(lines) - 1
-
-
 def main():
     p = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+    p.add_argument("--cache", default=str(CACHE))
     p.add_argument("--source", default=SOURCE)
     p.add_argument("--symbols", nargs="+", default=SYMBOLS)
     p.add_argument("--out", default=str(OUT))
     a = p.parse_args()
-    n = write(a.out, rows(read(a.source), symbols(read(s) for s in a.symbols)))
-    print(f"{a.out}: {n} rows", file=sys.stderr)
+    symbol = symbols(tsv.fetch(s, a.cache, Path(s).name).decode("utf-8") for s in a.symbols)
+    table = rows(tsv.fetch(a.source, a.cache, "codes-all.csv").decode("utf-8"), symbol)
+    tsv.write(a.out, COLUMNS, sorted(table, key=lambda r: r["code"]))
 
 
 if __name__ == "__main__":

data-import/geo-se.py

@@ -0,0 +1,246 @@
+#!/usr/bin/env python3
+"""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]
+"""
+import argparse
+import collections
+import csv
+import io
+import json
+import math
+import os
+import re
+import sys
+import urllib.request
+import xml.etree.ElementTree as ET
+import zipfile
+from pathlib import Path
+
+import tsv
+
+CODES = "https://www.scb.se/contentassets/7a89e48960f741e08918e489ea36354a/kommunlankod-2026.xlsx"
+POPULATION = "https://api.scb.se/OV0104/v1/doris/sv/ssd/START/BE/BE0101/BE0101A/BefolkningNy"
+POPULATION_QUERY = {
+    "query": [
+        {"code": "Region", "selection": {"filter": "all", "values": ["*"]}},
+        {"code": "ContentsCode", "selection": {"filter": "item", "values": ["BE0101N1"]}},
+        {"code": "Tid", "selection": {"filter": "top", "values": ["1"]}},
+    ],
+    "response": {"format": "json"},
+}
+TATORTER = "https://geodata.scb.se/geoserver/stat/wfs?service=WFS&version=2.0.0&request=GetFeature&typeNames=stat:Tatorter_2023&outputFormat=csv&propertyName=tatort,kommun,bef"
+POSTAL_CODES = "https://download.geonames.org/export/zip/SE.zip"
+NVDB = "https://api.trafikinfo.trafikverket.se/v2/data.json"
+NVDB_PAGE = 50000
+OUT = Path(__file__).resolve().parent.parent / "data" / "geo" / "SE"
+CACHE = Path(__file__).resolve().parent / "cache"
+TIMEZONE = "Europe/Stockholm"
+ONE_POSITION = {"Stockholm", "Göteborg", "Malmö"}
+UNMATCHED_POPULATION = 200
+XLSX_NS = {"m": "http://schemas.openxmlformats.org/spreadsheetml/2006/main"}
+
+
+def xlsx_rows(data):
+    z = zipfile.ZipFile(io.BytesIO(data))
+    strings = ["".join(t.text or "" for t in si.iter("{%s}t" % XLSX_NS["m"])) for si in ET.fromstring(z.read("xl/sharedStrings.xml")).findall("m:si", XLSX_NS)]
+    sheet = ET.fromstring(z.read("xl/worksheets/sheet1.xml"))
+    for row in sheet.findall(".//m:row", XLSX_NS):
+        cells = []
+        for c in row.findall("m:c", XLSX_NS):
+            v = c.find("m:v", XLSX_NS)
+            cells.append("" if v is None else strings[int(v.text)] if c.get("t") == "s" else v.text)
+        yield cells
+
+
+def scb_codes(cache):
+    regions, municipalities = {}, {}
+    for cells in xlsx_rows(tsv.fetch(CODES, cache, "kommunlankod.xlsx", magic=b"PK")):
+        if len(cells) < 2 or not re.fullmatch(r"\d{2}|\d{4}", cells[0]):
+            continue
+        (regions if len(cells[0]) == 2 else municipalities)[cells[0]] = cells[1].strip()
+    return regions, municipalities
+
+
+def scb_population(cache):
+    body = json.dumps(POPULATION_QUERY).encode()
+    data = tsv.fetch(POPULATION, cache, "befolkning.json", data=body, headers={"Content-Type": "application/json"})
+    return {row["key"][0]: row["values"][0] for row in json.loads(data.decode("utf-8-sig"))["data"]}
+
+
+def scb_tatorter(cache):
+    text = tsv.fetch(TATORTER, cache, "tatorter.csv").decode("utf-8")
+    by_name = collections.defaultdict(list)
+    for r in csv.DictReader(io.StringIO(text)):
+        by_name[r["tatort"]].append((r["kommun"], int(r["bef"])))
+    return by_name
+
+
+def geonames(cache):
+    z = zipfile.ZipFile(io.BytesIO(tsv.fetch(POSTAL_CODES, cache, "SE.zip", magic=b"PK")))
+    rows = []
+    for line in z.read("SE.txt").decode("utf-8").splitlines():
+        f = line.split("\t")
+        lat, lon = (float(f[9]), float(f[10])) if f[9] and f[10] else (None, None)
+        rows.append({"code": f[1], "locality": f[2], "municipality": f[6], "lat": lat, "lon": lon})
+    return rows
+
+
+def nvdb_segments(cache, key):
+    path = cache / "nvdb-gatunamn.tsv"
+    if not path.exists():
+        with open(path.with_suffix(".part"), "w", encoding="utf-8") as out:
+            change = "0"
+            while True:
+                query = (
+                    f'<REQUEST><LOGIN authenticationkey="{key}"/>'
+                    f'<QUERY objecttype="Gatunamn" namespace="vägdata.nvdb_dk_o" schemaversion="1.0" limit="{NVDB_PAGE}" changeid="{change}">'
+                    "<FILTER><EQ name=\"Deleted\" value=\"false\"/></FILTER>"
+                    "<INCLUDE>Namn</INCLUDE><INCLUDE>Geometry.WKT-WGS84-3D</INCLUDE></QUERY></REQUEST>"
+                )
+                req = urllib.request.Request(NVDB, data=query.encode(), headers={"Content-Type": "text/xml"})
+                with urllib.request.urlopen(req, timeout=600) as r:
+                    result = json.load(r)["RESPONSE"]["RESULT"][0]
+                rows = result.get("Gatunamn", [])
+                for row in rows:
+                    m = re.match(r"LINESTRING Z \(([-\d.]+) ([-\d.]+) ", row.get("Geometry", {}).get("WKT-WGS84-3D", ""))
+                    name = " ".join(row.get("Namn", "").split())
+                    if m and name:
+                        out.write(f"{name}\t{m.group(1)}\t{m.group(2)}\n")
+                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)
+
+
+class Nearest:
+    """Nearest point by an equirectangular distance, over a degree grid."""
+
+    def __init__(self, points, cell=0.05):
+        self.cell = cell
+        self.grid = collections.defaultdict(list)
+        for lat, lon, value in points:
+            self.grid[(int(lat // cell), int(lon // cell))].append((lat, lon, value))
+
+    def find(self, lat, lon):
+        ci, cj = int(lat // self.cell), int(lon // self.cell)
+        best, best_d = None, math.inf
+        ring = 0
+        while ring < 400:
+            for i in range(ci - ring, ci + ring + 1):
+                for j in range(cj - ring, cj + ring + 1):
+                    if max(abs(i - ci), abs(j - cj)) != ring:
+                        continue
+                    for plat, plon, value in self.grid.get((i, j), ()):
+                        d = (plat - lat) ** 2 + ((plon - lon) * math.cos(math.radians(lat))) ** 2
+                        if d < best_d:
+                            best, best_d = value, d
+            if best is not None and math.sqrt(best_d) < ring * self.cell * math.cos(math.radians(lat)):
+                return best
+            ring += 1
+        return best
+
+
+def street_delivery(name, codes):
+    """The codes delivered to a street: the digit after the postort's own prefix says box, company or reply."""
+    if name in ONE_POSITION:
+        return [c for c in codes if c[1] != "0"]
+    largest = collections.Counter(c[:3] for c in codes).most_common(1)[0][1]
+    length = 3 if largest * 2 >= len(codes) else 2
+    return [c for c in codes if c[length] not in "018"]
+
+
+def municipality_of(name, rows, tatorter, municipalities):
+    named = [code for code, n in municipalities.items() if n == name]
+    if named:
+        return named[0], "kommun"
+    voted = collections.Counter(r["municipality"] for r in rows if r["municipality"] in municipalities)
+    matches = tatorter.get(name, [])
+    if matches:
+        in_vote = [m for m in matches if voted and m[0] == voted.most_common(1)[0][0]]
+        return max(in_vote or matches, key=lambda m: m[1])[0], "tatort"
+    if voted:
+        return voted.most_common(1)[0][0], "codes"
+    return None, "unplaced"
+
+
+def population_of(name, municipality, tatorter, municipalities, population):
+    matches = [m for m in tatorter.get(name, []) if m[0] == municipality]
+    if matches:
+        return str(max(m[1] for m in matches))
+    return population[municipality] if municipalities[municipality] == name else str(UNMATCHED_POPULATION)
+
+
+def well_cased(name):
+    return all(part[:1].isupper() and (len(part) == 1 or not part.isupper()) for part in re.split(r"[ -]", name))
+
+
+def localities(codes, tatorter, municipalities, population):
+    """Each postort with its municipality, weight, centroid and street-delivery codes."""
+    by_locality = collections.defaultdict(list)
+    for r in codes:
+        by_locality[r["locality"]].append(r)
+    out, how = {}, collections.Counter()
+    for name, rows in by_locality.items():
+        municipality, method = municipality_of(name, rows, tatorter, municipalities)
+        how[method] += 1
+        kept = street_delivery(name, [r["code"].replace(" ", "") for r in rows])
+        with_point = [r for r in rows if r["lat"] is not None]
+        if municipality is None or not kept or not with_point or not well_cased(name):
+            continue
+        lat = sum(r["lat"] for r in with_point) / len(with_point)
+        lon = sum(r["lon"] for r in with_point) / len(with_point)
+        out[name] = {"name": name, "municipality": municipality, "population": population_of(name, municipality, tatorter, municipalities, population), "lat": f"{lat:.4f}", "lon": f"{lon:.4f}", "codes": kept}
+    print(f"municipality by {dict(how)}; {len(by_locality) - len(out)} postorter dropped", file=sys.stderr)
+    return out
+
+
+def streets(segments, codes, localities, per_locality):
+    """The names with most segments per locality, each segment at its nearest code centroid."""
+    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:
+        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))
+    return {locality: [{"name": name, "locality": locality, "segments": n} for n, name in sorted(named, key=lambda s: (-s[0], s[1]))[:per_locality]] for locality, named in of.items()}
+
+
+def main():
+    p = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+    p.add_argument("--cache", default=str(CACHE))
+    p.add_argument("--key-file", help="file holding the Trafikverket API key; TRAFIKVERKET_API_KEY otherwise")
+    p.add_argument("--out", default=str(OUT))
+    p.add_argument("--streets-per-locality", type=int, default=10)
+    a = p.parse_args()
+    key = Path(a.key_file).read_text().strip() if a.key_file else os.environ.get("TRAFIKVERKET_API_KEY")
+    if not key:
+        sys.exit("set TRAFIKVERKET_API_KEY or pass --key-file")
+    cache, out = Path(a.cache), Path(a.out)
+    cache.mkdir(parents=True, exist_ok=True)
+    out.mkdir(parents=True, exist_ok=True)
+
+    regions, municipalities = scb_codes(cache)
+    population = scb_population(cache)
+    codes = geonames(cache)
+    places = localities(codes, scb_tatorter(cache), municipalities, population)
+    named = streets(nvdb_segments(cache, key), codes, places, a.streets_per_locality)
+    places = {name: l for name, l in places.items() if name in named}
+    empty = sorted(m for m in municipalities if not any(l["municipality"] == m for l in places.values()))
+    if empty:
+        sys.exit(f"municipalities without a locality: {empty}")
+
+    tsv.write(out / "region.tsv", ["code", "name", "population", "timezone"], [{"code": c, "name": n, "population": population[c], "timezone": TIMEZONE} for c, n in sorted(regions.items())])
+    tsv.write(out / "municipality.tsv", ["code", "name", "region", "population"], [{"code": c, "name": n, "region": c[:2], "population": population[c]} for c, n in sorted(municipalities.items())])
+    tsv.write(out / "locality.tsv", ["name", "municipality", "population", "lat", "lon"], [l for _, l in sorted(places.items())])
+    tsv.write(out / "postal-code.tsv", ["code", "locality"], sorted(({"code": f"{c[:3]} {c[3:]}", "locality": l["name"]} for l in places.values() for c in l["codes"]), key=lambda r: r["code"]))
+    tsv.write(out / "street.tsv", ["name", "locality", "segments"], [s for locality in sorted(named) for s in named[locality]])
+
+
+if __name__ == "__main__":
+    main()

data-import/geo-us.py

@@ -0,0 +1,201 @@
+#!/usr/bin/env python3
+"""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]
+"""
+import argparse
+import collections
+import concurrent.futures
+import csv
+import io
+import re
+import struct
+import sys
+import zipfile
+from pathlib import Path
+
+import tsv
+
+GAZETTEER = "https://www2.census.gov/geo/docs/maps-data/data/gazetteer/2026_Gazetteer/2026_Gaz_{}_national.zip"
+POPULATION = "https://www2.census.gov/programs-surveys/popest/datasets/2020-2025/{}"
+STATES = POPULATION.format("state/totals/NST-EST2025-ALLDATA.csv")
+COUNTIES = POPULATION.format("counties/totals/co-est2025-alldata.csv")
+PLACES = POPULATION.format("cities/totals/sub-est2025.csv")
+ZCTA_PLACE = "https://www2.census.gov/geo/docs/maps-data/data/rel2020/zcta520/tab20_zcta520_place20_natl.txt"
+TIGER = "https://www2.census.gov/geo/tiger/TIGER2025/{0}/tl_2025_{1}_{2}.zip"
+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 |Highway |Loop |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"}
+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",
+    "DE": "America/New_York", "FL": "America/New_York", "GA": "America/New_York", "HI": "Pacific/Honolulu",
+    "IA": "America/Chicago", "ID": "America/Boise", "IL": "America/Chicago", "IN": "America/Indiana/Indianapolis",
+    "KS": "America/Chicago", "KY": "America/New_York", "LA": "America/Chicago", "MA": "America/New_York",
+    "MD": "America/New_York", "ME": "America/New_York", "MI": "America/Detroit", "MN": "America/Chicago",
+    "MO": "America/Chicago", "MS": "America/Chicago", "MT": "America/Denver", "NC": "America/New_York",
+    "ND": "America/Chicago", "NE": "America/Chicago", "NH": "America/New_York", "NJ": "America/New_York",
+    "NM": "America/Denver", "NV": "America/Los_Angeles", "NY": "America/New_York", "OH": "America/New_York",
+    "OK": "America/Chicago", "OR": "America/Los_Angeles", "PA": "America/New_York", "PR": "America/Puerto_Rico",
+    "RI": "America/New_York", "SC": "America/New_York", "SD": "America/Chicago", "TN": "America/Chicago",
+    "TX": "America/Chicago", "UT": "America/Denver", "VA": "America/New_York", "VT": "America/New_York",
+    "WA": "America/Los_Angeles", "WI": "America/Chicago", "WV": "America/New_York", "WY": "America/Denver",
+}
+
+
+def text(data):
+    try:
+        return data.decode("utf-8-sig")
+    except UnicodeDecodeError:
+        return data.decode("latin-1")
+
+
+def gazetteer(cache, kind):
+    z = zipfile.ZipFile(io.BytesIO(tsv.fetch(GAZETTEER.format(kind), cache, f"gaz_{kind}.zip", magic=b"PK")))
+    rows = text(z.read(z.namelist()[0])).splitlines()
+    header = [h.strip() for h in rows[0].split("|")]
+    return [dict(zip(header, (c.strip() for c in row.split("|")))) for row in rows[1:]]
+
+
+def csv_rows(cache, url, name):
+    return list(csv.DictReader(io.StringIO(text(tsv.fetch(url, cache, name)))))
+
+
+def dbf_rows(data, wanted):
+    """The records of a dBASE file, the wanted fields only."""
+    count, header_len, record_len = struct.unpack("<xxxxIHH", data[:12])
+    fields, pos = [], 32
+    while data[pos] != 0x0D:
+        name = data[pos:pos + 11].split(b"\0")[0].decode()
+        fields.append((name, data[pos + 16]))
+        pos += 32
+    pos = header_len
+    for _ in range(count):
+        record = data[pos:pos + record_len]
+        pos += record_len
+        if record[:1] == b"*":
+            continue
+        row, at = {}, 1
+        for name, length in fields:
+            if name in wanted:
+                row[name] = record[at:at + length].decode("utf-8", "replace").strip()
+            at += length
+        yield row
+
+
+def tiger_zip(cache, kind, county):
+    return tsv.fetch(TIGER.format(kind.upper(), county, kind), cache, f"tl_{county}_{kind}.zip", magic=b"PK")
+
+
+def tiger(cache, kind, county, wanted):
+    z = zipfile.ZipFile(io.BytesIO(tiger_zip(cache, kind, county)))
+    return dbf_rows(z.read(f"tl_2025_{county}_{kind}.dbf"), wanted)
+
+
+def place_name(geoid, name):
+    if geoid in NAMES:
+        return NAMES[geoid]
+    stripped = SUFFIX.sub("", name)
+    if stripped == name:
+        print(f"{geoid}: no suffix stripped from {name!r}", file=sys.stderr)
+    return stripped
+
+
+def localities(cache, min_population, counties):
+    """Each shipped place with its county, population and centroid."""
+    place_population, county_part = {}, collections.defaultdict(list)
+    for r in csv_rows(cache, PLACES, "sub-est2025.csv"):
+        if r["SUMLEV"] == "162":
+            place_population[r["STATE"] + r["PLACE"]] = int(r[ESTIMATE])
+        elif r["SUMLEV"] == "157":
+            county_part[r["STATE"] + r["PLACE"]].append((int(r[ESTIMATE]), r["STATE"] + r["COUNTY"]))
+    out = {}
+    for r in gazetteer(cache, "place"):
+        geoid, population = r["GEOID"], place_population.get(r["GEOID"], 0)
+        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:
+            print(f"{geoid} {r['NAME']}: county {county} unknown, dropped", file=sys.stderr)
+            continue
+        out[geoid] = {"code": geoid, "name": place_name(geoid, r["NAME"]), "municipality": county, "population": population, "lat": r["INTPTLAT"], "lon": r["INTPTLONG"]}
+    return out
+
+
+def postal_codes(cache, localities):
+    """Each ZCTA whose largest part inside an incorporated 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"] and not r["NAMELSAD_PLACE_20"].endswith(" CDP"):
+            parts.setdefault(r["GEOID_ZCTA5_20"], []).append((int(r["AREALAND_PART"]), r["GEOID_PLACE_20"]))
+    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):
+    """The names with most TIGER address ranges per place, and the address ranges per ZCTA."""
+    with concurrent.futures.ThreadPoolExecutor(3) as pool:
+        list(pool.map(lambda c: (tiger_zip(cache, "addr", c), tiger_zip(cache, "featnames", c)), counties))
+    addresses, count = collections.Counter(), collections.Counter()
+    for county in counties:
+        zips = collections.defaultdict(set)
+        for r in tiger(cache, "addr", county, {"TLID", "ZIP"}):
+            if r["ZIP"] in locality_of_zcta:
+                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"] 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)
+    for (locality, name), n in count.items():
+        of[locality].append((n, name))
+    named = {locality: [{"name": name, "locality": locality, "addresses": n} for n, name in sorted(ranked, key=lambda s: (-s[0], s[1]))[:per_locality]] for locality, ranked in of.items()}
+    return named, addresses
+
+
+def main():
+    p = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+    p.add_argument("--cache", default=str(CACHE))
+    p.add_argument("--min-population", type=int, default=25000)
+    p.add_argument("--out", default=str(OUT))
+    p.add_argument("--streets-per-locality", type=int, default=10)
+    a = p.parse_args()
+    cache, out = Path(a.cache), Path(a.out)
+    cache.mkdir(parents=True, exist_ok=True)
+    out.mkdir(parents=True, exist_ok=True)
+
+    state_population = {r["STATE"]: r[ESTIMATE] for r in csv_rows(cache, STATES, "nst-est2025.csv") if r["SUMLEV"] == "040"}
+    regions = {r["USPS"]: {"abbr": r["USPS"], "code": r["GEOID"], "name": r["NAME"], "population": state_population[r["GEOID"]], "timezone": TIMEZONES[r["USPS"]]} for r in gazetteer(cache, "state")}
+    county_population = {r["STATE"] + r["COUNTY"]: r[ESTIMATE] for r in csv_rows(cache, COUNTIES, "co-est2025.csv") if r["SUMLEV"] == "050"}
+    counties, unestimated = {}, collections.Counter()
+    for r in gazetteer(cache, "counties"):
+        if r["GEOID"] not in county_population:
+            unestimated[r["USPS"]] += 1
+            continue
+        counties[r["GEOID"]] = {"code": r["GEOID"], "name": r["NAME"], "region": r["USPS"], "population": county_population[r["GEOID"]]}
+    print(f"counties without a population estimate, dropped: {dict(unestimated)}", file=sys.stderr)
+
+    places = localities(cache, a.min_population, counties)
+    locality_of_zcta = postal_codes(cache, places)
+    named, addresses = streets(cache, sorted({l["municipality"] for l in places.values()}), locality_of_zcta, a.streets_per_locality)
+    for geoid in [l for l in places if l not in named]:
+        print(f"{geoid} {places[geoid]['name']}: no streets, dropped", file=sys.stderr)
+        del places[geoid]
+    kept_counties = {l["municipality"] for l in places.values()}
+    kept_regions = {counties[c]["region"] for c in kept_counties}
+
+    tsv.write(out / "region.tsv", ["abbr", "code", "name", "population", "timezone"], [r for _, r in sorted(regions.items()) if r["abbr"] in kept_regions])
+    tsv.write(out / "municipality.tsv", ["code", "name", "region", "population"], [c for _, c in sorted(counties.items()) if c["code"] in kept_counties])
+    tsv.write(out / "locality.tsv", ["code", "name", "municipality", "population", "lat", "lon"], [l for _, l in sorted(places.items())])
+    tsv.write(out / "postal-code.tsv", ["code", "locality", "addresses"], [{"code": z, "locality": l, "addresses": addresses[z]} for z, l in sorted(locality_of_zcta.items()) if addresses[z] and l in places])
+    tsv.write(out / "street.tsv", ["name", "locality", "addresses"], [s for locality in sorted(named) for s in named[locality]])
+
+
+if __name__ == "__main__":
+    main()

data-import/tsv.py

@@ -0,0 +1,40 @@
+"""A source fetched once into the cache, and a table written as the loader admits it."""
+import re
+import sys
+import time
+import urllib.request
+from pathlib import Path
+
+
+def fetch(source, cache, name, magic=b"", data=None, headers=None):
+    """The bytes of a URL, downloaded into cache/name once, or of a local file."""
+    if not re.match(r"^https?://", source):
+        return Path(source).read_bytes()
+    path = Path(cache) / name
+    for attempt in range(1, 6):
+        if path.exists():
+            return path.read_bytes()
+        req = urllib.request.Request(source, data=data, headers={"User-Agent": "fejkdata data-import", **(headers or {})})
+        try:
+            with urllib.request.urlopen(req, timeout=600) as r:
+                body = r.read()
+        except OSError:
+            body = b""
+        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)
+        elif attempt < 5:
+            time.sleep(10 * attempt)
+    sys.exit(f"{source}: no valid download in 5 attempts")
+
+
+def write(path, columns, rows):
+    """Write the rows as a TSV; every cell must be non-empty and free of tabs, newlines and braces."""
+    lines = ["\t".join(columns)]
+    for row in rows:
+        cells = [str(row[c]) for c in columns]
+        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)

data/en_US/address.json

@@ -1,17 +1,8 @@
 {
   "format": "{street-number} {street}\n{locality}, {region} {postal-code}",
-  "street": {
-    "format": "{name} {suffix}",
-    "name": ["Adams", "Ashby", "Aspen", "Bay", "Birch", "Bridge", "Cedar", "Chestnut", "Church", "Clark", "Cypress", "Dogwood", "Elm", "Forest", "Franklin", "Garden", "Grove", "Hawthorn", "Hickory", "Highland", "Jackson", "Jefferson", "Juniper", "Lake", "Laurel", "Liberty", "Lincoln", "Madison", "Magnolia", "Maple", "Market", "Meadow", "Mill", "Oak", "Park", "Pine", "Poplar", "Prospect", "Ridge", "River", "Spruce", "Sunset", "Sycamore", "Union", "Walnut", "Washington", "Willow", "Wilson"],
-    "suffix": ["Avenue", "Boulevard", "Circle", "Court", "Drive", "Lane", "Place", "Road", "Street", "Terrace", "Trail", "Way"]
-  },
-  "street-number": [
-    "{int(10,99)}",
-    { "format": "{int(100,999)}", "weight": 2 },
-    { "format": "{int(1000,9999)}", "weight": 0.5 },
-    { "format": "{int(10,99)}{int(100,999)}", "weight": 0.3 }
-  ],
-  "locality": ["Albany", "Atlanta", "Austin", "Baltimore", "Boston", "Charlotte", "Chicago", "Cincinnati", "Cleveland", "Columbus", "Dallas", "Denver", "Detroit", "El Paso", "Fort Worth", "Fresno", "Houston", "Indianapolis", "Jacksonville", "Kansas City", "Las Vegas", "Long Beach", "Los Angeles", "Memphis", "Mesa", "Miami", "Milwaukee", "Minneapolis", "Nashville", "New Orleans", "Oakland", "Oklahoma City", "Omaha", "Orlando", "Philadelphia", "Phoenix", "Pittsburgh", "Portland", "Raleigh", "Sacramento", "San Antonio", "San Diego", "San Jose", "Seattle", "St. Louis", "Tampa", "Tucson", "Tulsa"],
-  "region": ["AL", "AZ", "CA", "CO", "CT", "FL", "GA", "IL", "IN", "KY", "LA", "MA", "MD", "MI", "MN", "MO", "NC", "NJ", "NV", "NY", "OH", "OK", "OR", "PA", "TN", "TX", "VA", "WA", "WI"],
-  "postal-code": ["{int(10000,99999)}", { "format": "{int(10000,99999)}-{digits(4)}", "weight": 0.3 }]
+  "street": "{/geo.US.address.street}",
+  "street-number": "{/geo.US.address.street-number}",
+  "locality": "{/geo.US.address.locality}",
+  "region": "{/geo.US.address.region}",
+  "postal-code": "{/geo.US.address.postal-code}"
 }

data/geo/SE/address.json

@@ -0,0 +1,14 @@
+{
+  "format": "{street} {street-number}\n{postal-code} {locality}",
+  "street": "{.street.name}",
+  "street-number": [
+    "{int(1,9)}",
+    "{int(10,99)}",
+    { "format": "{int(100,999)}", "weight": 0.2 },
+    { "format": "{int(1,9)}{upper(1)}", "weight": 0.2 },
+    { "format": "{int(10,99)}{upper(1)}", "weight": 0.1 },
+    { "format": "{int(100,999)}{upper(1)}", "weight": 0.05 }
+  ],
+  "postal-code": "{.postal-code.code}",
+  "locality": "{.locality.name}"
+}

data/geo/SE/locality.json

@@ -0,0 +1 @@
+{ "format": "{name}", "rows": "locality.tsv", "key": "name", "parent": "municipality", "weight": "population" }

data/geo/SE/municipality.json

@@ -0,0 +1 @@
+{ "format": "{name}", "rows": "municipality.tsv", "key": "code", "name": "name", "parent": "region", "weight": "population" }

data/geo/SE/municipality.tsv

@@ -0,0 +1,291 @@
+code	name	region	population
+0114	Upplands Väsby	01	50323
+0115	Vallentuna	01	35119
+0117	Österåker	01	49787
+0120	Värmdö	01	46635
+0123	Järfälla	01	88950
+0125	Ekerö	01	28910
+0126	Huddinge	01	114304
+0127	Botkyrka	01	95905
+0128	Salem	01	17507
+0136	Haninge	01	100895
+0138	Tyresö	01	49179
+0139	Upplands-Bro	01	32868
+0140	Nykvarn	01	12342
+0160	Täby	01	77744
+0162	Danderyd	01	32425
+0163	Sollentuna	01	77624
+0180	Stockholm	01	995574
+0181	Södertälje	01	102911
+0182	Nacka	01	112112
+0183	Sundbyberg	01	56274
+0184	Solna	01	85789
+0186	Lidingö	01	48377
+0187	Vaxholm	01	11822
+0188	Norrtälje	01	66585
+0191	Sigtuna	01	52767
+0192	Nynäshamn	01	30579
+0305	Håbo	03	22973
+0319	Älvkarleby	03	9552
+0330	Knivsta	03	21193
+0331	Heby	03	14345
+0360	Tierp	03	21104
+0380	Uppsala	03	248016
+0381	Enköping	03	48591
+0382	Östhammar	03	22138
+0428	Vingåker	04	8750
+0461	Gnesta	04	11458
+0480	Nyköping	04	58344
+0481	Oxelösund	04	12031
+0482	Flen	04	15362
+0483	Katrineholm	04	34154
+0484	Eskilstuna	04	107203
+0486	Strängnäs	04	39313
+0488	Trosa	04	14927
+0509	Ödeshög	05	5237
+0512	Ydre	05	3626
+0513	Kinda	05	9957
+0560	Boxholm	05	5517
+0561	Åtvidaberg	05	11467
+0562	Finspång	05	21623
+0563	Valdemarsvik	05	7525
+0580	Linköping	05	168035
+0581	Norrköping	05	144980
+0582	Söderköping	05	14789
+0583	Motala	05	43505
+0584	Vadstena	05	7490
+0586	Mjölby	05	28695
+0604	Aneby	06	6797
+0617	Gnosjö	06	9131
+0642	Mullsjö	06	7594
+0643	Habo	06	13456
+0662	Gislaved	06	28936
+0665	Vaggeryd	06	14825
+0680	Jönköping	06	147654
+0682	Nässjö	06	31587
+0683	Värnamo	06	34542
+0684	Sävsjö	06	11563
+0685	Vetlanda	06	27528
+0686	Eksjö	06	17792
+0687	Tranås	06	18604
+0760	Uppvidinge	07	9061
+0761	Lessebo	07	8289
+0763	Tingsryd	07	11966
+0764	Alvesta	07	19830
+0765	Älmhult	07	17653
+0767	Markaryd	07	9938
+0780	Växjö	07	98334
+0781	Ljungby	07	28280
+0821	Högsby	08	5321
+0834	Torsås	08	6984
+0840	Mörbylånga	08	16224
+0860	Hultsfred	08	13673
+0861	Mönsterås	08	13069
+0862	Emmaboda	08	9006
+0880	Kalmar	08	72704
+0881	Nybro	08	19951
+0882	Oskarshamn	08	26923
+0883	Västervik	08	36447
+0884	Vimmerby	08	15384
+0885	Borgholm	08	10666
+0980	Gotland	09	60971
+1060	Olofström	10	13000
+1080	Karlskrona	10	66301
+1081	Ronneby	10	28741
+1082	Karlshamn	10	31751
+1083	Sölvesborg	10	17430
+1214	Svalöv	12	14543
+1230	Staffanstorp	12	27303
+1231	Burlöv	12	20101
+1233	Vellinge	12	37816
+1256	Östra Göinge	12	13978
+1257	Örkelljunga	12	10277
+1260	Bjuv	12	15985
+1261	Kävlinge	12	32477
+1262	Lomma	12	24715
+1263	Svedala	12	23581
+1264	Skurup	12	17099
+1265	Sjöbo	12	19337
+1266	Hörby	12	15562
+1267	Höör	12	17518
+1270	Tomelilla	12	13639
+1272	Bromölla	12	12470
+1273	Osby	12	12947
+1275	Perstorp	12	7235
+1276	Klippan	12	17714
+1277	Åstorp	12	16449
+1278	Båstad	12	16026
+1280	Malmö	12	365644
+1281	Lund	12	131590
+1282	Landskrona	12	47309
+1283	Helsingborg	12	152091
+1284	Höganäs	12	28430
+1285	Eslöv	12	34922
+1286	Ystad	12	32106
+1287	Trelleborg	12	47269
+1290	Kristianstad	12	86379
+1291	Simrishamn	12	18890
+1292	Ängelholm	12	45110
+1293	Hässleholm	12	52114
+1315	Hylte	13	10196
+1380	Halmstad	13	106084
+1381	Laholm	13	26595
+1382	Falkenberg	13	47337
+1383	Varberg	13	69070
+1384	Kungsbacka	13	85792
+1401	Härryda	14	40003
+1402	Partille	14	41060
+1407	Öckerö	14	12771
+1415	Stenungsund	14	27851
+1419	Tjörn	14	16092
+1421	Orust	14	15352
+1427	Sotenäs	14	9104
+1430	Munkedal	14	10354
+1435	Tanum	14	12773
+1438	Dals-Ed	14	4606
+1439	Färgelanda	14	6376
+1440	Ale	14	32576
+1441	Lerum	14	43570
+1442	Vårgårda	14	12474
+1443	Bollebygd	14	9802
+1444	Grästorp	14	5555
+1445	Essunga	14	5560
+1446	Karlsborg	14	7023
+1447	Gullspång	14	5031
+1452	Tranemo	14	11839
+1460	Bengtsfors	14	9076
+1461	Mellerud	14	9052
+1462	Lilla Edet	14	14442
+1463	Mark	14	35155
+1465	Svenljunga	14	10747
+1466	Herrljunga	14	9497
+1470	Vara	14	16088
+1471	Götene	14	13286
+1472	Tibro	14	11338
+1473	Töreboda	14	9043
+1480	Göteborg	14	608993
+1481	Mölndal	14	71420
+1482	Kungälv	14	50313
+1484	Lysekil	14	13907
+1485	Uddevalla	14	57010
+1486	Strömstad	14	13482
+1487	Vänersborg	14	40041
+1488	Trollhättan	14	59003
+1489	Alingsås	14	42722
+1490	Borås	14	114872
+1491	Ulricehamn	14	24985
+1492	Åmål	14	11906
+1493	Mariestad	14	24583
+1494	Lidköping	14	40425
+1495	Skara	14	18707
+1496	Skövde	14	57995
+1497	Hjo	14	9350
+1498	Tidaholm	14	12805
+1499	Falköping	14	32806
+1715	Kil	17	12061
+1730	Eda	17	8412
+1737	Torsby	17	11321
+1760	Storfors	17	3788
+1761	Hammarö	17	16992
+1762	Munkfors	17	3625
+1763	Forshaga	17	11520
+1764	Grums	17	9004
+1765	Årjäng	17	9825
+1766	Sunne	17	13356
+1780	Karlstad	17	98084
+1781	Kristinehamn	17	23756
+1782	Filipstad	17	9776
+1783	Hagfors	17	11418
+1784	Arvika	17	25547
+1785	Säffle	17	14899
+1814	Lekeberg	18	8606
+1860	Laxå	18	5423
+1861	Hallsberg	18	16120
+1862	Degerfors	18	9278
+1863	Hällefors	18	6321
+1864	Ljusnarsberg	18	4369
+1880	Örebro	18	160140
+1881	Kumla	18	22681
+1882	Askersund	18	11477
+1883	Karlskoga	18	30180
+1884	Nora	18	10639
+1885	Lindesberg	18	23141
+1904	Skinnskatteberg	19	4256
+1907	Surahammar	19	9845
+1960	Kungsör	19	8694
+1961	Hallstahammar	19	16653
+1962	Norberg	19	5452
+1980	Västerås	19	160634
+1981	Sala	19	22843
+1982	Fagersta	19	13072
+1983	Köping	19	25729
+1984	Arboga	19	13980
+2021	Vansbro	20	6752
+2023	Malung-Sälen	20	10254
+2026	Gagnef	20	10384
+2029	Leksand	20	16137
+2031	Rättvik	20	10998
+2034	Orsa	20	6851
+2039	Älvdalen	20	6882
+2061	Smedjebacken	20	10823
+2062	Mora	20	20540
+2080	Falun	20	59945
+2081	Borlänge	20	51425
+2082	Säter	20	11223
+2083	Hedemora	20	15281
+2084	Avesta	20	22417
+2085	Ludvika	20	26634
+2101	Ockelbo	21	5715
+2104	Hofors	21	9281
+2121	Ovanåker	21	11341
+2132	Nordanstig	21	9262
+2161	Ljusdal	21	18445
+2180	Gävle	21	103838
+2181	Sandviken	21	38360
+2182	Söderhamn	21	24545
+2183	Bollnäs	21	26243
+2184	Hudiksvall	21	37528
+2260	Ånge	22	9044
+2262	Timrå	22	17521
+2280	Härnösand	22	24515
+2281	Sundsvall	22	99048
+2282	Kramfors	22	17491
+2283	Sollefteå	22	18396
+2284	Örnsköldsvik	22	55443
+2303	Ragunda	23	5146
+2305	Bräcke	23	6035
+2309	Krokom	23	15680
+2313	Strömsund	23	11023
+2321	Åre	23	12693
+2326	Berg	23	7108
+2361	Härjedalen	23	10175
+2380	Östersund	23	64979
+2401	Nordmaling	24	6942
+2403	Bjurholm	24	2359
+2404	Vindeln	24	5417
+2409	Robertsfors	24	6690
+2417	Norsjö	24	3968
+2418	Malå	24	2962
+2421	Storuman	24	5577
+2422	Sorsele	24	2357
+2425	Dorotea	24	2294
+2460	Vännäs	24	9132
+2462	Vilhelmina	24	6229
+2463	Åsele	24	2694
+2480	Umeå	24	134249
+2481	Lycksele	24	12118
+2482	Skellefteå	24	78150
+2505	Arvidsjaur	25	6089
+2506	Arjeplog	25	2599
+2510	Jokkmokk	25	4701
+2513	Överkalix	25	3201
+2514	Kalix	25	15391
+2518	Övertorneå	25	4057
+2521	Pajala	25	5857
+2523	Gällivare	25	17233
+2560	Älvsbyn	25	7774
+2580	Luleå	25	79645
+2581	Piteå	25	42447
+2582	Boden	25	28049
+2583	Haparanda	25	9151
+2584	Kiruna	25	22426

data/geo/SE/postal-code.json

@@ -0,0 +1 @@
+{ "format": "{code}", "rows": "postal-code.tsv", "key": "code", "parent": "locality" }

data/geo/SE/region.json

@@ -0,0 +1 @@
+{ "format": "{name}", "rows": "region.tsv", "key": "code", "name": "name", "weight": "population" }

data/geo/SE/region.tsv

@@ -0,0 +1,22 @@
+code	name	population	timezone
+01	Stockholms län	2473307	Europe/Stockholm
+03	Uppsala län	407912	Europe/Stockholm
+04	Södermanlands län	301542	Europe/Stockholm
+05	Östergötlands län	472446	Europe/Stockholm
+06	Jönköpings län	370009	Europe/Stockholm
+07	Kronobergs län	203351	Europe/Stockholm
+08	Kalmar län	246352	Europe/Stockholm
+09	Gotlands län	60971	Europe/Stockholm
+10	Blekinge län	157223	Europe/Stockholm
+12	Skåne län	1428626	Europe/Stockholm
+13	Hallands län	345074	Europe/Stockholm
+14	Västra Götalands län	1772821	Europe/Stockholm
+17	Värmlands län	283384	Europe/Stockholm
+18	Örebro län	308375	Europe/Stockholm
+19	Västmanlands län	281158	Europe/Stockholm
+20	Dalarnas län	286546	Europe/Stockholm
+21	Gävleborgs län	284558	Europe/Stockholm
+22	Västernorrlands län	241458	Europe/Stockholm
+23	Jämtlands län	132839	Europe/Stockholm
+24	Västerbottens län	281138	Europe/Stockholm
+25	Norrbottens län	248620	Europe/Stockholm

data/geo/SE/street.json

@@ -0,0 +1 @@
+{ "format": "{name}", "rows": "street.tsv", "parent": "locality", "weight": "segments" }

data/geo/US/address.json

@@ -0,0 +1,13 @@
+{
+  "format": "{street-number} {street}\n{locality}, {region} {postal-code}",
+  "street": "{.street.name}",
+  "street-number": [
+    "{int(10,99)}",
+    { "format": "{int(100,999)}", "weight": 2 },
+    { "format": "{int(1000,9999)}", "weight": 0.5 },
+    { "format": "{int(10,99)}{int(100,999)}", "weight": 0.3 }
+  ],
+  "locality": "{.locality.name}",
+  "region": "{.region.abbr}",
+  "postal-code": "{.postal-code.code}"
+}

data/geo/US/locality.json

@@ -0,0 +1 @@
+{ "format": "{name}", "rows": "locality.tsv", "key": "code", "name": "name", "parent": "municipality", "weight": "population" }

data/geo/US/municipality.json

@@ -0,0 +1 @@
+{ "format": "{name}", "rows": "municipality.tsv", "key": "code", "name": "name", "parent": "region", "weight": "population" }

data/geo/US/municipality.tsv

@@ -0,0 +1,666 @@
+code	name	region	population
+01001	Autauga County	AL	61920
+01003	Baldwin County	AL	267761
+01031	Coffee County	AL	56953
+01055	Etowah County	AL	103886
+01069	Houston County	AL	110318
+01073	Jefferson County	AL	665742
+01077	Lauderdale County	AL	97135
+01081	Lee County	AL	189881
+01083	Limestone County	AL	122928
+01089	Madison County	AL	433516
+01097	Mobile County	AL	411658
+01101	Montgomery County	AL	225891
+01103	Morgan County	AL	126483
+01113	Russell County	AL	58898
+01117	Shelby County	AL	238552
+01125	Tuscaloosa County	AL	241368
+02020	Anchorage Municipality	AK	287155
+02090	Fairbanks North Star Borough	AK	93972
+02110	Juneau City and Borough	AK	31609
+04003	Cochise County	AZ	126332
+04005	Coconino County	AZ	144368
+04013	Maricopa County	AZ	4689558
+04015	Mohave County	AZ	228102
+04019	Pima County	AZ	1074685
+04021	Pinal County	AZ	539380
+04025	Yavapai County	AZ	252552
+04027	Yuma County	AZ	224449
+05007	Benton County	AR	332554
+05031	Craighead County	AR	116957
+05045	Faulkner County	AR	133979
+05051	Garland County	AR	99695
+05055	Greene County	AR	47411
+05069	Jefferson County	AR	62987
+05085	Lonoke County	AR	76664
+05091	Miller County	AR	42357
+05115	Pope County	AR	64976
+05119	Pulaski County	AR	404611
+05125	Saline County	AR	133288
+05131	Sebastian County	AR	130641
+05143	Washington County	AR	271213
+06001	Alameda County	CA	1636630
+06007	Butte County	CA	209211
+06013	Contra Costa County	CA	1170070
+06019	Fresno County	CA	1035456
+06023	Humboldt County	CA	131647
+06025	Imperial County	CA	181411
+06029	Kern County	CA	927068
+06031	Kings County	CA	154327
+06037	Los Angeles County	CA	9694934
+06039	Madera County	CA	167927
+06041	Marin County	CA	253694
+06047	Merced County	CA	297260
+06053	Monterey County	CA	433729
+06055	Napa County	CA	132949
+06059	Orange County	CA	3149507
+06061	Placer County	CA	442081
+06065	Riverside County	CA	2544916
+06067	Sacramento County	CA	1618460
+06069	San Benito County	CA	70082
+06071	San Bernardino County	CA	2224091
+06073	San Diego County	CA	3282248
+06075	San Francisco County	CA	826079
+06077	San Joaquin County	CA	823815
+06079	San Luis Obispo County	CA	282367
+06081	San Mateo County	CA	743568
+06083	Santa Barbara County	CA	442065
+06085	Santa Clara County	CA	1914391
+06087	Santa Cruz County	CA	258852
+06089	Shasta County	CA	181648
+06095	Solano County	CA	455376
+06097	Sonoma County	CA	486444
+06099	Stanislaus County	CA	557719
+06101	Sutter County	CA	98787
+06107	Tulare County	CA	485146
+06111	Ventura County	CA	830851
+06113	Yolo County	CA	224410
+08001	Adams County	CO	554668
+08005	Arapahoe County	CO	673820
+08013	Boulder County	CO	328560
+08014	Broomfield County	CO	79174
+08031	Denver County	CO	740613
+08035	Douglas County	CO	399396
+08041	El Paso County	CO	757040
+08059	Jefferson County	CO	580451
+08069	Larimer County	CO	377292
+08077	Mesa County	CO	162845
+08101	Pueblo County	CO	169277
+08123	Weld County	CO	378426
+09110	Capitol Planning Region	CT	994115
+09120	Greater Bridgeport Planning Region	CT	337697
+09130	Lower Connecticut River Valley Planning Region	CT	177311
+09140	Naugatuck Valley Planning Region	CT	463349
+09160	Northwest Hills Planning Region	CT	114690
+09170	South Central Connecticut Planning Region	CT	578741
+09180	Southeastern Connecticut Planning Region	CT	284015
+09190	Western Connecticut Planning Region	CT	640482
+10001	Kent County	DE	194786
+10003	New Castle County	DE	588026
+11001	District of Columbia	DC	693645
+12001	Alachua County	FL	290028
+12005	Bay County	FL	204479
+12009	Brevard County	FL	663982
+12011	Broward County	FL	2013317
+12031	Duval County	FL	1062963
+12033	Escambia County	FL	333834
+12035	Flagler County	FL	140360
+12057	Hillsborough County	FL	1574115
+12061	Indian River County	FL	172799
+12069	Lake County	FL	456068
+12071	Lee County	FL	875607
+12073	Leon County	FL	299048
+12081	Manatee County	FL	468200
+12083	Marion County	FL	442660
+12086	Miami-Dade County	FL	2802029
+12091	Okaloosa County	FL	221810
+12095	Orange County	FL	1528002
+12097	Osceola County	FL	481718
+12099	Palm Beach County	FL	1575726
+12103	Pinellas County	FL	948563
+12105	Polk County	FL	874790
+12111	St. Lucie County	FL	402449
+12115	Sarasota County	FL	479958
+12117	Seminole County	FL	491884
+12127	Volusia County	FL	606573
+13015	Bartow County	GA	120800
+13021	Bibb County	GA	157556
+13031	Bulloch County	GA	86949
+13045	Carroll County	GA	131036
+13051	Chatham County	GA	311855
+13057	Cherokee County	GA	299273
+13059	Clarke County	GA	129921
+13067	Cobb County	GA	793345
+13077	Coweta County	GA	160240
+13089	DeKalb County	GA	774394
+13095	Dougherty County	GA	82616
+13097	Douglas County	GA	154293
+13113	Fayette County	GA	125156
+13115	Floyd County	GA	101378
+13121	Fulton County	GA	1098791
+13135	Gwinnett County	GA	1018099
+13139	Hall County	GA	226568
+13151	Henry County	GA	264922
+13153	Houston County	GA	178214
+13179	Liberty County	GA	70313
+13185	Lowndes County	GA	122867
+13215	Muscogee County	GA	202171
+13245	Richmond County	GA	206559
+13285	Troup County	GA	72844
+13313	Whitfield County	GA	106212
+16001	Ada County	ID	546141
+16005	Bannock County	ID	91591
+16019	Bonneville County	ID	135771
+16027	Canyon County	ID	275123
+16055	Kootenai County	ID	191864
+16057	Latah County	ID	41842
+16065	Madison County	ID	55172
+16069	Nez Perce County	ID	42905
+16083	Twin Falls County	ID	97539
+17001	Adams County	IL	64267
+17007	Boone County	IL	53568
+17019	Champaign County	IL	209972
+17031	Cook County	IL	5194625
+17037	DeKalb County	IL	101835
+17043	DuPage County	IL	934298
+17089	Kane County	IL	525757
+17093	Kendall County	IL	145470
+17095	Knox County	IL	47767
+17097	Lake County	IL	719339
+17111	McHenry County	IL	317751
+17113	McLean County	IL	171419
+17115	Macon County	IL	99300
+17119	Madison County	IL	263110
+17143	Peoria County	IL	178553
+17161	Rock Island County	IL	141869
+17163	St. Clair County	IL	250708
+17167	Sangamon County	IL	194170
+17179	Tazewell County	IL	130049
+17183	Vermilion County	IL	71259
+17197	Will County	IL	712253
+17201	Winnebago County	IL	283674
+18003	Allen County	IN	402329
+18005	Bartholomew County	IN	85729
+18011	Boone County	IN	80689
+18019	Clark County	IN	130451
+18035	Delaware County	IN	113106
+18039	Elkhart County	IN	208774
+18043	Floyd County	IN	82153
+18053	Grant County	IN	66524
+18057	Hamilton County	IN	387036
+18059	Hancock County	IN	90969
+18063	Hendricks County	IN	193510
+18067	Howard County	IN	83904
+18081	Johnson County	IN	174262
+18089	Lake County	IN	504612
+18091	LaPorte County	IN	111294
+18095	Madison County	IN	135088
+18097	Marion County	IN	992196
+18105	Monroe County	IN	143345
+18127	Porter County	IN	176049
+18141	St. Joseph County	IN	272861
+18157	Tippecanoe County	IN	190456
+18163	Vanderburgh County	IN	181995
+18167	Vigo County	IN	106512
+18177	Wayne County	IN	66169
+19013	Black Hawk County	IA	131532
+19033	Cerro Gordo County	IA	42372
+19049	Dallas County	IA	118457
+19061	Dubuque County	IA	99381
+19103	Johnson County	IA	160044
+19113	Linn County	IA	232028
+19127	Marshall County	IA	39890
+19153	Polk County	IA	516546
+19155	Pottawattamie County	IA	92996
+19163	Scott County	IA	175259
+19169	Story County	IA	101291
+19179	Wapello County	IA	35210
+19193	Woodbury County	IA	106649
+20045	Douglas County	KS	120920
+20055	Finney County	KS	37505
+20057	Ford County	KS	33993
+20091	Johnson County	KS	636906
+20103	Leavenworth County	KS	84590
+20155	Reno County	KS	61539
+20161	Riley County	KS	72598
+20169	Saline County	KS	53377
+20173	Sedgwick County	KS	538433
+20177	Shawnee County	KS	178607
+20209	Wyandotte County	KS	170597
+21015	Boone County	KY	145316
+21047	Christian County	KY	70115
+21059	Daviess County	KY	104898
+21067	Fayette County	KY	329751
+21073	Franklin County	KY	52649
+21093	Hardin County	KY	113482
+21101	Henderson County	KY	44255
+21111	Jefferson County	KY	795222
+21113	Jessamine County	KY	57147
+21117	Kenton County	KY	175779
+21145	McCracken County	KY	67553
+21151	Madison County	KY	101696
+21209	Scott County	KY	62262
+21227	Warren County	KY	149375
+22015	Bossier Parish	LA	131867
+22017	Caddo Parish	LA	224226
+22019	Calcasieu Parish	LA	208466
+22033	East Baton Rouge Parish	LA	456180
+22045	Iberia Parish	LA	66846
+22051	Jefferson Parish	LA	431398
+22071	Orleans Parish	LA	362154
+22073	Ouachita Parish	LA	158542
+22079	Rapides Parish	LA	125877
+22103	St. Tammany Parish	LA	279108
+22109	Terrebonne Parish	LA	104163
+23001	Androscoggin County	ME	116487
+23005	Cumberland County	ME	317222
+23019	Penobscot County	ME	157967
+24003	Anne Arundel County	MD	603380
+24021	Frederick County	MD	302883
+24031	Montgomery County	MD	1074582
+24033	Prince George's County	MD	970374
+24043	Washington County	MD	157731
+24045	Wicomico County	MD	106899
+24510	Baltimore city	MD	569997
+25001	Barnstable County	MA	233539
+25003	Berkshire County	MA	128224
+25005	Bristol County	MA	593640
+25009	Essex County	MA	826653
+25013	Hampden County	MA	464338
+25015	Hampshire County	MA	164065
+25017	Middlesex County	MA	1669979
+25021	Norfolk County	MA	739749
+25023	Plymouth County	MA	546829
+25025	Suffolk County	MA	791891
+25027	Worcester County	MA	888502
+26017	Bay County	MI	102123
+26025	Calhoun County	MI	133408
+26049	Genesee County	MI	401093
+26065	Ingham County	MI	289709
+26075	Jackson County	MI	159552
+26077	Kalamazoo County	MI	263795
+26081	Kent County	MI	675232
+26099	Macomb County	MI	886221
+26111	Midland County	MI	83754
+26121	Muskegon County	MI	177901
+26125	Oakland County	MI	1288337
+26139	Ottawa County	MI	308459
+26145	Saginaw County	MI	187688
+26147	St. Clair County	MI	160486
+26161	Washtenaw County	MI	370214
+26163	Wayne County	MI	1769038
+27003	Anoka County	MN	381605
+27013	Blue Earth County	MN	70634
+27019	Carver County	MN	114379
+27027	Clay County	MN	67734
+27037	Dakota County	MN	457710
+27053	Hennepin County	MN	1284784
+27099	Mower County	MN	40971
+27109	Olmsted County	MN	166731
+27123	Ramsey County	MN	541623
+27131	Rice County	MN	69939
+27137	St. Louis County	MN	200518
+27139	Scott County	MN	159017
+27141	Sherburne County	MN	104194
+27145	Stearns County	MN	164110
+27147	Steele County	MN	37464
+27163	Washington County	MN	286895
+27169	Winona County	MN	50523
+28033	DeSoto County	MS	197918
+28035	Forrest County	MS	79034
+28047	Harrison County	MS	217136
+28049	Hinds County	MS	211888
+28071	Lafayette County	MS	59597
+28075	Lauderdale County	MS	70317
+28081	Lee County	MS	83731
+28089	Madison County	MS	116298
+28105	Oktibbeha County	MS	51896
+28121	Rankin County	MS	162181
+28151	Washington County	MS	40446
+29019	Boone County	MO	191746
+29021	Buchanan County	MO	83540
+29031	Cape Girardeau County	MO	83999
+29037	Cass County	MO	115859
+29043	Christian County	MO	96725
+29047	Clay County	MO	265032
+29051	Cole County	MO	77908
+29077	Greene County	MO	309286
+29095	Jackson County	MO	732994
+29097	Jasper County	MO	127428
+29183	St. Charles County	MO	426499
+29189	St. Louis County	MO	990911
+29510	St. Louis city	MO	278144
+30013	Cascade County	MT	85029
+30029	Flathead County	MT	115429
+30031	Gallatin County	MT	128740
+30049	Lewis and Clark County	MT	75331
+30063	Missoula County	MT	123513
+30093	Silver Bow County	MT	36118
+30111	Yellowstone County	MT	172692
+31001	Adams County	NE	31071
+31019	Buffalo County	NE	51172
+31053	Dodge County	NE	38057
+31055	Douglas County	NE	606460
+31079	Hall County	NE	63633
+31109	Lancaster County	NE	334049
+31119	Madison County	NE	36106
+31141	Platte County	NE	35649
+31153	Sarpy County	NE	208303
+32003	Clark County	NV	2407226
+32019	Lyon County	NV	65088
+32031	Washoe County	NV	509386
+32510	Carson City	NV	58571
+33011	Hillsborough County	NH	433415
+33013	Merrimack County	NH	158078
+33017	Strafford County	NH	135043
+34001	Atlantic County	NJ	278657
+34003	Bergen County	NJ	977026
+34007	Camden County	NJ	535799
+34011	Cumberland County	NJ	157148
+34013	Essex County	NJ	896379
+34017	Hudson County	NJ	735033
+34021	Mercer County	NJ	399289
+34023	Middlesex County	NJ	883335
+34025	Monmouth County	NJ	651035
+34031	Passaic County	NJ	531624
+34039	Union County	NJ	601863
+35001	Bernalillo County	NM	667601
+35005	Chaves County	NM	63364
+35009	Curry County	NM	46655
+35013	Doña Ana County	NM	229091
+35015	Eddy County	NM	62509
+35025	Lea County	NM	74749
+35035	Otero County	NM	70368
+35043	Sandoval County	NM	159565
+35045	San Juan County	NM	120340
+35049	Santa Fe County	NM	156907
+36001	Albany County	NY	321225
+36007	Broome County	NY	195736
+36011	Cayuga County	NY	74365
+36013	Chautauqua County	NY	124126
+36015	Chemung County	NY	80415
+36027	Dutchess County	NY	300708
+36029	Erie County	NY	946741
+36047	Kings County	NY	2653963
+36055	Monroe County	NY	750506
+36059	Nassau County	NY	1398939
+36063	Niagara County	NY	208912
+36065	Oneida County	NY	226392
+36067	Onondaga County	NY	466584
+36071	Orange County	NY	417669
+36083	Rensselaer County	NY	160510
+36091	Saratoga County	NY	241343
+36093	Schenectady County	NY	162581
+36103	Suffolk County	NY	1546090
+36109	Tompkins County	NY	104047
+36119	Westchester County	NY	1015743
+37001	Alamance County	NC	186177
+37019	Brunswick County	NC	174702
+37021	Buncombe County	NC	277417
+37025	Cabarrus County	NC	249725
+37035	Catawba County	NC	170172
+37049	Craven County	NC	105025
+37051	Cumberland County	NC	338473
+37057	Davidson County	NC	180182
+37063	Durham County	NC	347240
+37067	Forsyth County	NC	401718
+37071	Gaston County	NC	246558
+37081	Guilford County	NC	562234
+37097	Iredell County	NC	211798
+37101	Johnston County	NC	256448
+37105	Lee County	NC	70258
+37119	Mecklenburg County	NC	1233383
+37127	Nash County	NC	99365
+37129	New Hanover County	NC	245959
+37133	Onslow County	NC	217175
+37135	Orange County	NC	152498
+37147	Pitt County	NC	182936
+37151	Randolph County	NC	149516
+37159	Rowan County	NC	155096
+37179	Union County	NC	267674
+37183	Wake County	NC	1257235
+37191	Wayne County	NC	122278
+37195	Wilson County	NC	81150
+38015	Burleigh County	ND	103251
+38017	Cass County	ND	201794
+38035	Grand Forks County	ND	74501
+38059	Morton County	ND	34601
+38089	Stark County	ND	34013
+38101	Ward County	ND	68233
+38105	Williams County	ND	41767
+39003	Allen County	OH	100881
+39009	Athens County	OH	63197
+39017	Butler County	OH	400128
+39023	Clark County	OH	135340
+39035	Cuyahoga County	OH	1232925
+39041	Delaware County	OH	242032
+39045	Fairfield County	OH	169752
+39049	Franklin County	OH	1361536
+39057	Greene County	OH	174322
+39061	Hamilton County	OH	838418
+39063	Hancock County	OH	75034
+39085	Lake County	OH	232217
+39089	Licking County	OH	185564
+39093	Lorain County	OH	323219
+39095	Lucas County	OH	423347
+39099	Mahoning County	OH	224706
+39101	Marion County	OH	65115
+39103	Medina County	OH	185025
+39109	Miami County	OH	112634
+39113	Montgomery County	OH	539598
+39119	Muskingum County	OH	87014
+39133	Portage County	OH	163404
+39139	Richland County	OH	124893
+39151	Stark County	OH	373771
+39153	Summit County	OH	538376
+39155	Trumbull County	OH	198972
+39159	Union County	OH	73446
+39165	Warren County	OH	257181
+39169	Wayne County	OH	116758
+39173	Wood County	OH	134176
+40027	Cleveland County	OK	303973
+40031	Comanche County	OK	122158
+40047	Garfield County	OK	61779
+40101	Muskogee County	OK	66708
+40109	Oklahoma County	OK	822125
+40119	Payne County	OK	83889
+40125	Pottawatomie County	OK	75102
+40143	Tulsa County	OK	698782
+40147	Washington County	OK	54037
+41003	Benton County	OR	97728
+41005	Clackamas County	OR	426280
+41017	Deschutes County	OR	213072
+41029	Jackson County	OR	221795
+41033	Josephine County	OR	87867
+41039	Lane County	OR	381584
+41043	Linn County	OR	132843
+41047	Marion County	OR	355777
+41051	Multnomah County	OR	795391
+41067	Washington County	OR	611708
+41071	Yamhill County	OR	110024
+42003	Allegheny County	PA	1225035
+42011	Berks County	PA	440072
+42013	Blair County	PA	119541
+42027	Centre County	PA	157393
+42043	Dauphin County	PA	293351
+42045	Delaware County	PA	580937
+42049	Erie County	PA	265832
+42069	Lackawanna County	PA	216502
+42071	Lancaster County	PA	563159
+42075	Lebanon County	PA	146380
+42077	Lehigh County	PA	384383
+42079	Luzerne County	PA	332126
+42081	Lycoming County	PA	112587
+42091	Montgomery County	PA	877643
+42095	Northampton County	PA	324411
+42101	Philadelphia County	PA	1574281
+42133	York County	PA	473197
+44003	Kent County	RI	173495
+44007	Providence County	RI	678179
+45003	Aiken County	SC	181515
+45007	Anderson County	SC	219930
+45013	Beaufort County	SC	204433
+45015	Berkeley County	SC	274666
+45019	Charleston County	SC	436200
+45035	Dorchester County	SC	178397
+45041	Florence County	SC	138504
+45045	Greenville County	SC	583125
+45051	Horry County	SC	427551
+45063	Lexington County	SC	317588
+45077	Pickens County	SC	139198
+45079	Richland County	SC	434956
+45083	Spartanburg County	SC	380857
+45085	Sumter County	SC	105067
+45091	York County	SC	306887
+46011	Brookings County	SD	37635
+46013	Brown County	SD	37561
+46099	Minnehaha County	SD	212691
+46103	Pennington County	SD	116792
+47001	Anderson County	TN	82066
+47003	Bedford County	TN	55273
+47009	Blount County	TN	143820
+47011	Bradley County	TN	115465
+47037	Davidson County	TN	745904
+47063	Hamblen County	TN	68843
+47065	Hamilton County	TN	390833
+47093	Knox County	TN	511453
+47113	Madison County	TN	100790
+47119	Maury County	TN	118131
+47125	Montgomery County	TN	249935
+47141	Putnam County	TN	86612
+47149	Rutherford County	TN	386352
+47157	Shelby County	TN	910226
+47163	Sullivan County	TN	163759
+47165	Sumner County	TN	215538
+47179	Washington County	TN	141199
+47187	Williamson County	TN	272061
+47189	Wilson County	TN	175033
+48005	Angelina County	TX	88154
+48027	Bell County	TX	402248
+48029	Bexar County	TX	2160088
+48037	Bowie County	TX	92696
+48039	Brazoria County	TX	419080
+48041	Brazos County	TX	249088
+48061	Cameron County	TX	433946
+48085	Collin County	TX	1297179
+48091	Comal County	TX	209166
+48099	Coryell County	TX	85592
+48113	Dallas County	TX	2661397
+48121	Denton County	TX	1069346
+48135	Ector County	TX	173801
+48139	Ellis County	TX	240867
+48141	El Paso County	TX	877858
+48157	Fort Bend County	TX	975191
+48167	Galveston County	TX	372207
+48181	Grayson County	TX	153613
+48183	Gregg County	TX	126095
+48187	Guadalupe County	TX	201111
+48201	Harris County	TX	5045026
+48209	Hays County	TX	304390
+48215	Hidalgo County	TX	921549
+48231	Hunt County	TX	123336
+48245	Jefferson County	TX	254321
+48251	Johnson County	TX	218048
+48257	Kaufman County	TX	209235
+48265	Kerr County	TX	54037
+48277	Lamar County	TX	51503
+48303	Lubbock County	TX	328906
+48309	McLennan County	TX	272020
+48323	Maverick County	TX	58823
+48329	Midland County	TX	187855
+48339	Montgomery County	TX	781194
+48347	Nacogdoches County	TX	66035
+48349	Navarro County	TX	57181
+48355	Nueces County	TX	352992
+48367	Parker County	TX	184767
+48381	Randall County	TX	152351
+48397	Rockwall County	TX	140738
+48423	Smith County	TX	252549
+48439	Tarrant County	TX	2248466
+48441	Taylor County	TX	150077
+48451	Tom Green County	TX	120602
+48453	Travis County	TX	1389670
+48465	Val Verde County	TX	47835
+48469	Victoria County	TX	92656
+48471	Walker County	TX	83842
+48479	Webb County	TX	281224
+48485	Wichita County	TX	129555
+48491	Williamson County	TX	752827
+49005	Cache County	UT	145000
+49011	Davis County	UT	381227
+49021	Iron County	UT	67141
+49035	Salt Lake County	UT	1220916
+49045	Tooele County	UT	87461
+49049	Utah County	UT	759859
+49053	Washington County	UT	213670
+49057	Weber County	UT	278174
+50007	Chittenden County	VT	169115
+51059	Fairfax County	VA	1167873
+51107	Loudoun County	VA	449749
+51121	Montgomery County	VA	98434
+51510	Alexandria city	VA	160662
+51540	Charlottesville city	VA	44388
+51550	Chesapeake city	VA	255332
+51590	Danville city	VA	41647
+51600	Fairfax city	VA	26772
+51630	Fredericksburg city	VA	30393
+51650	Hampton city	VA	137315
+51660	Harrisonburg city	VA	50839
+51680	Lynchburg city	VA	81347
+51683	Manassas city	VA	44332
+51700	Newport News city	VA	183230
+51710	Norfolk city	VA	231013
+51730	Petersburg city	VA	33734
+51740	Portsmouth city	VA	96777
+51760	Richmond city	VA	237257
+51770	Roanoke city	VA	99111
+51775	Salem city	VA	25816
+51790	Staunton city	VA	26801
+51800	Suffolk city	VA	104699
+51810	Virginia Beach city	VA	453737
+51840	Winchester city	VA	28272
+53005	Benton County	WA	221722
+53007	Chelan County	WA	81941
+53011	Clark County	WA	532119
+53015	Cowlitz County	WA	114885
+53021	Franklin County	WA	102612
+53025	Grant County	WA	105727
+53033	King County	WA	2344939
+53035	Kitsap County	WA	283374
+53053	Pierce County	WA	946288
+53057	Skagit County	WA	132975
+53061	Snohomish County	WA	870656
+53063	Spokane County	WA	558344
+53067	Thurston County	WA	304261
+53071	Walla Walla County	WA	62361
+53073	Whatcom County	WA	236392
+53075	Whitman County	WA	48512
+53077	Yakima County	WA	259185
+54011	Cabell County	WV	91183
+54039	Kanawha County	WV	172381
+54061	Monongalia County	WV	107991
+54069	Ohio County	WV	40496
+54107	Wood County	WV	82385
+55009	Brown County	WI	275803
+55025	Dane County	WI	590375
+55031	Douglas County	WI	43990
+55035	Eau Claire County	WI	109033
+55039	Fond du Lac County	WI	104669
+55059	Kenosha County	WI	168448
+55063	La Crosse County	WI	121339
+55071	Manitowoc County	WI	81710
+55073	Marathon County	WI	139432
+55079	Milwaukee County	WI	924216
+55087	Outagamie County	WI	195894
+55089	Ozaukee County	WI	94346
+55097	Portage County	WI	71943
+55101	Racine County	WI	198919
+55105	Rock County	WI	166472
+55117	Sheboygan County	WI	118047
+55131	Washington County	WI	139238
+55133	Waukesha County	WI	417210
+55139	Winnebago County	WI	174218
+56001	Albany County	WY	38558
+56005	Campbell County	WY	48145
+56021	Laramie County	WY	102938
+56025	Natrona County	WY	80526

data/geo/US/postal-code.json

@@ -0,0 +1 @@
+{ "format": "{code}", "rows": "postal-code.tsv", "key": "code", "parent": "locality", "weight": "addresses" }

data/geo/US/region.json

@@ -0,0 +1 @@
+{ "format": "{name}", "rows": "region.tsv", "key": "abbr", "name": "name", "weight": "population" }

data/geo/US/region.tsv

@@ -0,0 +1,51 @@
+abbr	code	name	population	timezone
+AK	02	Alaska	737270	America/Anchorage
+AL	01	Alabama	5193088	America/Chicago
+AR	05	Arkansas	3114791	America/Chicago
+AZ	04	Arizona	7623818	America/Phoenix
+CA	06	California	39355309	America/Los_Angeles
+CO	08	Colorado	6012561	America/Denver
+CT	09	Connecticut	3688496	America/New_York
+DC	11	District of Columbia	693645	America/New_York
+DE	10	Delaware	1059952	America/New_York
+FL	12	Florida	23462518	America/New_York
+GA	13	Georgia	11302748	America/New_York
+IA	19	Iowa	3238387	America/Chicago
+ID	16	Idaho	2029733	America/Boise
+IL	17	Illinois	12719141	America/Chicago
+IN	18	Indiana	6973333	America/Indiana/Indianapolis
+KS	20	Kansas	2977220	America/Chicago
+KY	21	Kentucky	4606864	America/New_York
+LA	22	Louisiana	4618189	America/Chicago
+MA	25	Massachusetts	7154084	America/New_York
+MD	24	Maryland	6265347	America/New_York
+ME	23	Maine	1414874	America/New_York
+MI	26	Michigan	10127884	America/Detroit
+MN	27	Minnesota	5830405	America/Chicago
+MO	29	Missouri	6270541	America/Chicago
+MS	28	Mississippi	2954160	America/Chicago
+MT	30	Montana	1144694	America/Denver
+NC	37	North Carolina	11197968	America/New_York
+ND	38	North Dakota	799358	America/Chicago
+NE	31	Nebraska	2018006	America/Chicago
+NH	33	New Hampshire	1415342	America/New_York
+NJ	34	New Jersey	9548215	America/New_York
+NM	35	New Mexico	2125498	America/Denver
+NV	32	Nevada	3282188	America/Los_Angeles
+NY	36	New York	20002427	America/New_York
+OH	39	Ohio	11900510	America/New_York
+OK	40	Oklahoma	4123288	America/Chicago
+OR	41	Oregon	4273586	America/Los_Angeles
+PA	42	Pennsylvania	13059432	America/New_York
+RI	44	Rhode Island	1114521	America/New_York
+SC	45	South Carolina	5570274	America/New_York
+SD	46	South Dakota	935094	America/Chicago
+TN	47	Tennessee	7315076	America/Chicago
+TX	48	Texas	31709821	America/Chicago
+UT	49	Utah	3538904	America/Denver
+VA	51	Virginia	8880107	America/New_York
+VT	50	Vermont	644663	America/New_York
+WA	53	Washington	8001020	America/Los_Angeles
+WI	55	Wisconsin	5972787	America/Chicago
+WV	54	West Virginia	1766147	America/New_York
+WY	56	Wyoming	588753	America/Denver

data/geo/US/street.json

@@ -0,0 +1 @@
+{ "format": "{name}", "rows": "street.tsv", "parent": "locality", "weight": "addresses" }

data/sv_SE/address.json

@@ -1,21 +1,7 @@
 {
   "format": "{street} {street-number}\n{postal-code} {locality}",
-  "street": [
-    {
-      "format": "{first}{last}",
-      "first": ["Ängs", "Bergs", "Björk", "Drottning", "Eke", "Furu", "Hamn", "Köpmans", "Kungs", "Linné", "Norra", "Nybro", "Oden", "Öster", "Park", "Skogs", "Skol", "Slotts", "Söder", "Stations", "Stor", "Strand", "Trädgårds", "Vasa", "Väster"],
-      "last": ["gatan", "vägen", "stigen", "gränd", "backen", "torget", "allén"]
-    },
-    ["Avenyn", "Birger Jarlsgatan", "Promenaden", "Staby", "Sveavägen", "Vintjärn"]
-  ],
-  "street-number": [
-    "{int(1,9)}",
-    "{int(10,99)}",
-    { "format": "{int(100,999)}", "weight": 0.2 },
-    { "format": "{int(1,9)}{upper(1)}", "weight": 0.2 },
-    { "format": "{int(10,99)}{upper(1)}", "weight": 0.1 },
-    { "format": "{int(100,999)}{upper(1)}", "weight": 0.05 }
-  ],
-  "postal-code": "{int(100,999)} {int(10,99)}",
-  "locality": ["Alingsås", "Alvesta", "Ängelholm", "Arboga", "Arvika", "Avesta", "Boden", "Bollnäs", "Borås", "Borlänge", "Enköping", "Eskilstuna", "Eslöv", "Fagersta", "Falkenberg", "Falköping", "Falun", "Finspång", "Gällivare", "Gävle", "Göteborg", "Halmstad", "Haparanda", "Härnösand", "Hässleholm", "Helsingborg", "Huddinge", "Hudiksvall", "Jönköping", "Kalmar", "Karlshamn", "Karlskoga", "Karlskrona", "Karlstad", "Katrineholm", "Kiruna", "Köping", "Kramfors", "Kristianstad", "Kristinehamn", "Landskrona", "Lidingö", "Lidköping", "Lindesberg", "Linköping", "Ljungby", "Ludvika", "Luleå", "Lund", "Lycksele", "Malmö", "Mariestad", "Mjölby", "Mölndal", "Mora", "Motala", "Nacka", "Nässjö", "Norrköping", "Norrtälje", "Nyköping", "Nynäshamn", "Örebro", "Örnsköldsvik", "Oskarshamn", "Östersund", "Piteå", "Rabbalshede", "Ronneby", "Säffle", "Sandviken", "Sävsjö", "Sigtuna", "Skara", "Skellefteå", "Skövde", "Söderhamn", "Södertälje", "Sollentuna", "Solna", "Sölvesborg", "Stockholm", "Strängnäs", "Sundbyberg", "Sundsvall", "Täby", "Tierp", "Tranås", "Trelleborg", "Trollhättan", "Uddevalla", "Ulricehamn", "Umeå", "Upplands Väsby", "Uppsala", "Vänersborg", "Varberg", "Värnamo", "Västerås", "Västervik", "Växjö", "Vetlanda", "Vimmerby", "Visby", "Ystad"]
+  "street": "{/geo.SE.address.street}",
+  "street-number": "{/geo.SE.address.street-number}",
+  "postal-code": "{/geo.SE.address.postal-code}",
+  "locality": "{/geo.SE.address.locality}"
 }

data_test.go

@@ -51,14 +51,13 @@ func TestShippedDataCategories(t *testing.T) {
 			regexp.MustCompile(`^\d{1,3}( \d{3})?(,\d{2})? kr$`)},
 	}
 
-	en := newGenerator(t, "data/en_US", WithSeed(1))
-	sv := newGenerator(t, "data/sv_SE", WithSeed(1))
+	f := newGenerator(t, "data", WithSeed(1))
 	for _, c := range cases {
 		for i := 0; i < 200; i++ {
-			if v := fake(t, en, c.path); !c.en.MatchString(v) {
+			if v := fake(t, f, "en_US."+c.path); !c.en.MatchString(v) {
 				t.Fatalf("en_US %s = %q, want %s", c.path, v, c.en)
 			}
-			if v := fake(t, sv, c.path); !c.sv.MatchString(v) {
+			if v := fake(t, f, "sv_SE."+c.path); !c.sv.MatchString(v) {
 				t.Fatalf("sv_SE %s = %q, want %s", c.path, v, c.sv)
 			}
 		}
@@ -134,8 +133,8 @@ func TestShippedMiscReferenceData(t *testing.T) {
 // TestSwedishPersonNamesHaveNoTripleLetter pins an orthographic rule the shape
 // regexes miss: no generated name repeats a character three times over.
 func TestSwedishPersonNamesHaveNoTripleLetter(t *testing.T) {
-	f := newGenerator(t, "data/sv_SE", WithSeed(11))
-	for _, path := range []string{"person", "person.last"} {
+	f := newGenerator(t, "data", WithSeed(11))
+	for _, path := range []string{"sv_SE.person", "sv_SE.person.last"} {
 		for i := 0; i < 20000; i++ {
 			name := fake(t, f, path)
 			r := []rune(name)
@@ -152,10 +151,10 @@ func TestSwedishPersonNamesHaveNoTripleLetter(t *testing.T) {
 // is a real calendar date (so month-length variants never emit e.g. Apr 31 or
 // Feb 30) and the trailing digit is a valid Luhn checksum over the other nine.
 func TestSwedishPersonnummer(t *testing.T) {
-	sv := newGenerator(t, "data/sv_SE", WithSeed(1))
+	sv := newGenerator(t, "data", WithSeed(1))
 	sawLongMonthEnd := false
 	for i := 0; i < 2000; i++ {
-		v := fake(t, sv, "ssn")
+		v := fake(t, sv, "sv_SE.ssn")
 		d := digitsOnly(v)
 		if len(d) != 10 {
 			t.Fatalf("ssn %q has %d digits, want 10", v, len(d))
@@ -176,49 +175,49 @@ func TestSwedishPersonnummer(t *testing.T) {
 }
 
 func TestShippedSwedishPhone(t *testing.T) {
-	f := newGenerator(t, "data/sv_SE", WithSeed(11))
+	f := newGenerator(t, "data", WithSeed(11))
 	re := regexp.MustCompile(`^0\d{1,2}-\d{3} \d{2} \d{2}$`)
 	for i := 0; i < 50; i++ {
-		if n := fake(t, f, "phone"); !re.MatchString(n) {
+		if n := fake(t, f, "sv_SE.phone"); !re.MatchString(n) {
 			t.Fatalf("phone %q does not match %s", n, re)
 		}
 	}
 }
 
 func TestShippedSwedishAddress(t *testing.T) {
-	f := newGenerator(t, "data/sv_SE", WithSeed(3))
+	f := newGenerator(t, "data", WithSeed(3))
 	digit := regexp.MustCompile(`\d`)
 	for i := 0; i < 30; i++ {
-		a := fake(t, f, "address")
+		a := fake(t, f, "sv_SE.address")
 		if !regexp.MustCompile(`\n`).MatchString(a) || !digit.MatchString(a) {
 			t.Fatalf("address %q is not a multi-line address with a number", a)
 		}
 	}
 
-	locality := regexp.MustCompile(`^\p{L}+( \p{L}+)*$`)
+	locality := regexp.MustCompile(`^\p{L}+([ -]\p{L}+)*$`)
 	for i := 0; i < 30; i++ {
-		if c := fake(t, f, "address.locality"); !locality.MatchString(c) {
+		if c := fake(t, f, "sv_SE.address.locality"); !locality.MatchString(c) {
 			t.Fatalf("locality %q is not a Swedish place name", c)
 		}
 	}
 }
 
 func TestShippedPersonHasParts(t *testing.T) {
-	for _, dir := range []string{"data/sv_SE", "data/en_US"} {
-		f := newGenerator(t, dir, WithSeed(7))
+	f := newGenerator(t, "data", WithSeed(7))
+	for _, path := range []string{"sv_SE.person", "en_US.person"} {
 		for i := 0; i < 30; i++ {
-			if name := fake(t, f, "person"); len(name) < 3 || !regexp.MustCompile(`\S \S`).MatchString(name) {
-				t.Fatalf("%s person %q lacks first and last name", dir, name)
+			if name := fake(t, f, path); len(name) < 3 || !regexp.MustCompile(`\S \S`).MatchString(name) {
+				t.Fatalf("%s %q lacks first and last name", path, name)
 			}
 		}
 	}
 }
 
 func TestShippedUSPhone(t *testing.T) {
-	f := newGenerator(t, "data/en_US", WithSeed(11))
+	f := newGenerator(t, "data", WithSeed(11))
 	re := regexp.MustCompile(`^(\(\d{3}\) \d{3}-\d{4}|\d{3}-\d{3}-\d{4})$`)
 	for i := 0; i < 50; i++ {
-		if n := fake(t, f, "phone"); !re.MatchString(n) {
+		if n := fake(t, f, "en_US.phone"); !re.MatchString(n) {
 			t.Fatalf("phone %q does not match %s", n, re)
 		}
 	}
@@ -270,11 +269,11 @@ func luhnValid(s string) bool {
 // tests so shipped name lists can grow without re-enumerating them here.
 var swedishName = regexp.MustCompile(`^\p{L}+([ -]\p{L}+)*$`)
 
-func TestShippedStreetComposition(t *testing.T) {
-	// street is a choice of composed {first}{last} templates and literal names.
-	f := newGenerator(t, "data/sv_SE", WithSeed(5))
+func TestShippedStreetIsARegisteredName(t *testing.T) {
+	f := newGenerator(t, "data", WithSeed(5))
+	street := regexp.MustCompile(`^\p{L}[\p{L}\d:.-]*([ -][\p{L}\d:.-]+)*$`)
 	for i := 0; i < 300; i++ {
-		if s := fake(t, f, "address.street"); !swedishName.MatchString(s) {
+		if s := fake(t, f, "sv_SE.address.street"); !street.MatchString(s) {
 			t.Fatalf("street %q is not a Swedish street name", s)
 		}
 	}
@@ -283,9 +282,9 @@ func TestShippedStreetComposition(t *testing.T) {
 func TestShippedLastNameComposition(t *testing.T) {
 	// last is a choice of patronymic {first}sson templates, compound
 	// {first}{last} templates and literal surnames.
-	f := newGenerator(t, "data/sv_SE", WithSeed(6))
+	f := newGenerator(t, "data", WithSeed(6))
 	for i := 0; i < 300; i++ {
-		if s := fake(t, f, "person.last"); !swedishName.MatchString(s) {
+		if s := fake(t, f, "sv_SE.person.last"); !swedishName.MatchString(s) {
 			t.Fatalf("last name %q is not a Swedish surname", s)
 		}
 	}
@@ -293,10 +292,10 @@ func TestShippedLastNameComposition(t *testing.T) {
 
 func TestShippedStreetNumberFormats(t *testing.T) {
 	// Reachable via a hyphenated path; covers all five weighted number variants.
-	f := newGenerator(t, "data/sv_SE", WithSeed(8))
+	f := newGenerator(t, "data", WithSeed(8))
 	re := regexp.MustCompile(`^[1-9]\d{0,2}[A-Z]?$`)
 	for i := 0; i < 300; i++ {
-		if n := fake(t, f, "address.street-number"); !re.MatchString(n) {
+		if n := fake(t, f, "sv_SE.address.street-number"); !re.MatchString(n) {
 			t.Fatalf("street-number %q does not match %s", n, re)
 		}
 	}

fejkdata_test.go

@@ -64,18 +64,18 @@ func TestNewReportsAnEntropyFailure(t *testing.T) {
 }
 
 func TestWithSeedIsDeterministic(t *testing.T) {
-	a, b := newGenerator(t, "data/sv_SE", WithSeed(42)), newGenerator(t, "data/sv_SE", WithSeed(42))
+	a, b := newGenerator(t, "data", WithSeed(42)), newGenerator(t, "data", WithSeed(42))
 	for i := 0; i < 50; i++ {
-		if x, y := fake(t, a, "person"), fake(t, b, "person"); x != y {
+		if x, y := fake(t, a, "sv_SE.person"), fake(t, b, "sv_SE.person"); x != y {
 			t.Fatalf("same seed diverged at %d: %q != %q", i, x, y)
 		}
 	}
 }
 
 func TestDifferentSeedsDiffer(t *testing.T) {
-	a, b := newGenerator(t, "data/en_US", WithSeed(1)), newGenerator(t, "data/en_US", WithSeed(2))
+	a, b := newGenerator(t, "data", WithSeed(1)), newGenerator(t, "data", WithSeed(2))
 	for i := 0; i < 50; i++ {
-		if fake(t, a, "person") != fake(t, b, "person") {
+		if fake(t, a, "en_US.person") != fake(t, b, "en_US.person") {
 			return
 		}
 	}

graph.go

@@ -63,6 +63,9 @@ func contained(n node) []namedNode {
 	case *table:
 		return append([]namedNode{{node: n.format}}, named(n.fields)...)
 	case *column:
+		if len(n.t.tokens) == 0 {
+			return nil
+		}
 		var out []namedNode
 		for r := 0; r < n.t.rows(); r++ {
 			if cell := n.t.cellNode(r, n.i); cell != nil {

path_test.go

@@ -130,8 +130,8 @@ func TestPathKeyIsUnambiguous(t *testing.T) {
 // single-variant choice always picks the same item, so it needs no every-variant
 // guard and the error can name the field that is missing.
 func TestMissingFieldNamesItself(t *testing.T) {
-	f := newGenerator(t, "data/sv_SE", WithSeed(1))
-	_, err := f.Fake("person.typo")
+	f := newGenerator(t, "data", WithSeed(1))
+	_, err := f.Fake("sv_SE.person.typo")
 	if err == nil || !strings.Contains(err.Error(), `no field "typo"`) {
 		t.Errorf("Fake(person.typo) = %v, want it to name the missing field", err)
 	}

table.go

@@ -285,7 +285,7 @@ func (t *table) checkCells() error {
 		if (col == t.key || col == t.name) && strings.ContainsAny(cell, inSelector) {
 			return fmt.Errorf("line %d: %s %q contains %q, which a selector cannot spell", row+2, t.columns[col], cell, cell[strings.IndexAny(cell, inSelector):][:1])
 		}
-		if !strings.ContainsAny(cell, "{}") {
+		if strings.IndexByte(cell, '{') < 0 && strings.IndexByte(cell, '}') < 0 {
 			continue
 		}
 		n, err := compileString(cell)

table_test.go

@@ -44,6 +44,20 @@ var (
 	regionOf       = map[string]string{"0180": "01", "0184": "01", "1280": "12", "1281": "12", "1480": "14"}
 )
 
+func siblings() map[string]string {
+	return with(geo(), map[string]string{
+		"postal-code.json": `{"format":"{code}","rows":"postal-code.tsv","key":"code","parent":"locality"}`,
+		"postal-code.tsv":  "code\tlocality\n111 20\tL1\n111 21\tL1\n171 41\tL2\n211 20\tL3\n221 00\tL4\n223 50\tL4\n411 01\tL5\n417 05\tL6\n247 45\tL7\n170 71\tL8\n",
+		"street.json":      `{"format":"{name}","rows":"street.tsv","parent":"locality","weight":"segments"}`,
+		"street.tsv":       "name\tlocality\tsegments\nDrottninggatan\tL1\t30\nSveavägen\tL1\t12\nRåsundavägen\tL2\t8\nStorgatan\tL3\t20\nStora Södergatan\tL4\t9\nKlostergatan\tL4\t4\nAvenyn\tL5\t15\nHisingsgatan\tL6\t3\nSandbyvägen\tL7\t2\nSandbyvägen\tL8\t2\n",
+	})
+}
+
+var (
+	localityOfCode   = map[string]string{"111 20": "L1", "111 21": "L1", "171 41": "L2", "211 20": "L3", "221 00": "L4", "223 50": "L4", "411 01": "L5", "417 05": "L6", "247 45": "L7", "170 71": "L8"}
+	localityOfStreet = map[string][]string{"Drottninggatan": {"L1"}, "Sveavägen": {"L1"}, "Råsundavägen": {"L2"}, "Storgatan": {"L3"}, "Stora Södergatan": {"L4"}, "Klostergatan": {"L4"}, "Avenyn": {"L5"}, "Hisingsgatan": {"L6"}, "Sandbyvägen": {"L7", "L8"}}
+)
+
 func with(files map[string]string, more map[string]string) map[string]string {
 	out := map[string]string{}
 	for k, v := range files {
@@ -271,6 +285,41 @@ func TestLinkedTablesDrawApartAcrossGroupsAndRepeats(t *testing.T) {
 	}
 }
 
+func TestSiblingTablesDrawInsideOneAncestor(t *testing.T) {
+	files := with(siblings(), map[string]string{
+		"addr.json": `{"format":"{s}|{p}|{l}","s":"{/street.name}","p":"{/postal-code.code}","l":"{/locality.code}"}`,
+	})
+	f := newGenerator(t, writeFiles(t, files), WithSeed(3))
+	seen := map[string]bool{}
+	for i := 0; i < 300; i++ {
+		parts := strings.Split(fake(t, f, "addr"), "|")
+		if s, p, l := parts[0], parts[1], parts[2]; !slices.Contains(localityOfStreet[s], l) || localityOfCode[p] != l {
+			t.Fatalf("addr = %v, want the street and the postal code inside the locality", parts)
+		}
+		seen[parts[2]] = true
+	}
+	if len(seen) < 4 {
+		t.Fatalf("only %v drawn in 300 renders", seen)
+	}
+	for i := 0; i < 100; i++ {
+		if s := fake(t, f, "locality[L4].street.name"); s != "Stora Södergatan" && s != "Klostergatan" {
+			t.Fatalf("locality[L4].street.name = %q, outside L4", s)
+		}
+		if p := fake(t, f, "region[01].postal-code"); localityOfCode[p] != "L1" && localityOfCode[p] != "L2" && localityOfCode[p] != "L8" {
+			t.Fatalf("region[01].postal-code = %q, outside region 01", p)
+		}
+	}
+	if _, err := f.Fake("street[Avenyn]"); err == nil || !strings.Contains(err.Error(), "no key") {
+		t.Fatalf("Fake(street[Avenyn]) = %v, want no column to select by", err)
+	}
+	paths := f.List()
+	for _, p := range []string{"locality.postal-code", "locality.street", "region.municipality.locality.street.name"} {
+		if !slices.Contains(paths, p) {
+			t.Fatalf("List() lacks %s: %v", p, paths)
+		}
+	}
+}
+
 func TestTableSelectorInAReference(t *testing.T) {
 	files := with(geo(), map[string]string{
 		"x.json": `{"format":"{a} {b} {c}","a":"{/region[12].name}","b":"{/region[12].municipality.code}","c":"{/region[12].locality.code}"}`,

testdata/shipped_shape.txt

@@ -1,11 +1,9 @@
-en_US.address	format "{street-number} {street}\n{locality}, {region} {postal-code}"
+en_US.address	format "{street-number} {street}\n{locality}, {region} {postal-code}"	reads geo.US.address
 en_US.address.locality	string
 en_US.address.postal-code	string
 en_US.address.region	string
 en_US.address.street	string
 en_US.address.street-number	string
-en_US.address.street.name
-en_US.address.street.suffix
 en_US.color
 en_US.company	format "{base} {suffix}"
 en_US.company.base	string
@@ -52,6 +50,147 @@ en_US.version.n	string
 en_US.version.pre	string
 en_US.version.suffix	string
 en_US.word
+geo.SE.address	format "{street} {street-number}\n{postal-code} {locality}"	reads geo.SE.locality geo.SE.postal-code geo.SE.street
+geo.SE.address.locality	string
+geo.SE.address.postal-code	string
+geo.SE.address.street	string
+geo.SE.address.street-number	string
+geo.SE.locality	format "{name}"	key name	weight population	parent municipality
+geo.SE.locality.lat	string
+geo.SE.locality.lon	string
+geo.SE.locality.municipality	string
+geo.SE.locality.name	string
+geo.SE.locality.population	string
+geo.SE.locality.postal-code
+geo.SE.locality.postal-code.code
+geo.SE.locality.postal-code.locality
+geo.SE.locality.street
+geo.SE.locality.street.locality
+geo.SE.locality.street.name
+geo.SE.locality.street.segments
+geo.SE.municipality	format "{name}"	key code	name name	weight population	parent region
+geo.SE.municipality.code	string
+geo.SE.municipality.locality
+geo.SE.municipality.locality.lat
+geo.SE.municipality.locality.lon
+geo.SE.municipality.locality.municipality
+geo.SE.municipality.locality.name
+geo.SE.municipality.locality.population
+geo.SE.municipality.locality.postal-code
+geo.SE.municipality.locality.postal-code.code
+geo.SE.municipality.locality.postal-code.locality
+geo.SE.municipality.locality.street
+geo.SE.municipality.locality.street.locality
+geo.SE.municipality.locality.street.name
+geo.SE.municipality.locality.street.segments
+geo.SE.municipality.name	string
+geo.SE.municipality.population	string
+geo.SE.municipality.region	string
+geo.SE.postal-code	format "{code}"	key code	parent locality
+geo.SE.postal-code.code	string
+geo.SE.postal-code.locality	string
+geo.SE.region	format "{name}"	key code	name name	weight population
+geo.SE.region.code	string
+geo.SE.region.municipality
+geo.SE.region.municipality.code
+geo.SE.region.municipality.locality
+geo.SE.region.municipality.locality.lat
+geo.SE.region.municipality.locality.lon
+geo.SE.region.municipality.locality.municipality
+geo.SE.region.municipality.locality.name
+geo.SE.region.municipality.locality.population
+geo.SE.region.municipality.locality.postal-code
+geo.SE.region.municipality.locality.postal-code.code
+geo.SE.region.municipality.locality.postal-code.locality
+geo.SE.region.municipality.locality.street
+geo.SE.region.municipality.locality.street.locality
+geo.SE.region.municipality.locality.street.name
+geo.SE.region.municipality.locality.street.segments
+geo.SE.region.municipality.name
+geo.SE.region.municipality.population
+geo.SE.region.municipality.region
+geo.SE.region.name	string
+geo.SE.region.population	string
+geo.SE.region.timezone	string
+geo.SE.street	format "{name}"	weight segments	parent locality
+geo.SE.street.locality	string
+geo.SE.street.name	string
+geo.SE.street.segments	string
+geo.US.address	format "{street-number} {street}\n{locality}, {region} {postal-code}"	reads geo.US.locality geo.US.postal-code geo.US.region geo.US.street
+geo.US.address.locality	string
+geo.US.address.postal-code	string
+geo.US.address.region	string
+geo.US.address.street	string
+geo.US.address.street-number	string
+geo.US.locality	format "{name}"	key code	name name	weight population	parent municipality
+geo.US.locality.code	string
+geo.US.locality.lat	string
+geo.US.locality.lon	string
+geo.US.locality.municipality	string
+geo.US.locality.name	string
+geo.US.locality.population	string
+geo.US.locality.postal-code
+geo.US.locality.postal-code.addresses
+geo.US.locality.postal-code.code
+geo.US.locality.postal-code.locality
+geo.US.locality.street
+geo.US.locality.street.addresses
+geo.US.locality.street.locality
+geo.US.locality.street.name
+geo.US.municipality	format "{name}"	key code	name name	weight population	parent region
+geo.US.municipality.code	string
+geo.US.municipality.locality
+geo.US.municipality.locality.code
+geo.US.municipality.locality.lat
+geo.US.municipality.locality.lon
+geo.US.municipality.locality.municipality
+geo.US.municipality.locality.name
+geo.US.municipality.locality.population
+geo.US.municipality.locality.postal-code
+geo.US.municipality.locality.postal-code.addresses
+geo.US.municipality.locality.postal-code.code
+geo.US.municipality.locality.postal-code.locality
+geo.US.municipality.locality.street
+geo.US.municipality.locality.street.addresses
+geo.US.municipality.locality.street.locality
+geo.US.municipality.locality.street.name
+geo.US.municipality.name	string
+geo.US.municipality.population	string
+geo.US.municipality.region	string
+geo.US.postal-code	format "{code}"	key code	weight addresses	parent locality
+geo.US.postal-code.addresses	string
+geo.US.postal-code.code	string
+geo.US.postal-code.locality	string
+geo.US.region	format "{name}"	key abbr	name name	weight population
+geo.US.region.abbr	string
+geo.US.region.code	string
+geo.US.region.municipality
+geo.US.region.municipality.code
+geo.US.region.municipality.locality
+geo.US.region.municipality.locality.code
+geo.US.region.municipality.locality.lat
+geo.US.region.municipality.locality.lon
+geo.US.region.municipality.locality.municipality
+geo.US.region.municipality.locality.name
+geo.US.region.municipality.locality.population
+geo.US.region.municipality.locality.postal-code
+geo.US.region.municipality.locality.postal-code.addresses
+geo.US.region.municipality.locality.postal-code.code
+geo.US.region.municipality.locality.postal-code.locality
+geo.US.region.municipality.locality.street
+geo.US.region.municipality.locality.street.addresses
+geo.US.region.municipality.locality.street.locality
+geo.US.region.municipality.locality.street.name
+geo.US.region.municipality.name
+geo.US.region.municipality.population
+geo.US.region.municipality.region
+geo.US.region.name	string
+geo.US.region.population	string
+geo.US.region.timezone	string
+geo.US.street	format "{name}"	weight addresses	parent locality
+geo.US.street.addresses	string
+geo.US.street.locality	string
+geo.US.street.name	string
 misc.car
 misc.car.maker
 misc.car.model
@@ -93,7 +232,7 @@ misc.timezone
 misc.useragent
 misc.uuid	format "{hex(8)}-{hex(4)}-4{hex(3)}-{variant}{hex(3)}-{hex(12)}"
 misc.uuid.variant	string
-sv_SE.address	format "{street} {street-number}\n{postal-code} {locality}"
+sv_SE.address	format "{street} {street-number}\n{postal-code} {locality}"	reads geo.SE.address
 sv_SE.address.locality	string
 sv_SE.address.postal-code	string
 sv_SE.address.street	string

todo.md

@@ -53,19 +53,25 @@ countries; the README maps each to the native term.
 
 | Table | SE | US | Weight |
 |---|---|---|---|
-| `region` | län (21) | state (56) | population |
-| `municipality` | kommun (290) | county (3,234) | population |
-| `locality` | postort (~1,780), tätort population | place (~19,500) | population |
-| `postal-code` | postnummer (~10,500 deliverable) | ZCTA (33,791) | address count or 1 |
-| `street` | gatunamn, top N per locality | street name, top N per place | address count |
+| `region` | län (21) | state and DC (50; Hawaii has no incorporated place) | population |
+| `municipality` | kommun (290) | county with a shipped place | population |
+| `locality` | postort, tätort population | place of 25,000+ | population |
+| `postal-code` | postnummer with street delivery | ZCTA of a shipped place | one; address ranges |
+| `street` | gatunamn, top 10 per postort | street name, top 10 per place | segments; address ranges |
 
-- `geo.SE.address` is a record over one consistent draw: street, number, postal
-  code, locality; `geo.SE.locality[Lund].address` stays inside Lund. Each region
-  row carries its timezone, each locality its centroid.
+- 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.
+- Let `geo.SE.locality[Lund].address` descend from a selected row into the
+  template beside the family: the path step must reach a sibling category and the
+  outer selector's pins seed every draw group of the render.
+- Ship the fuller sets, every US place of 10,000 and more streets per locality, as
+  packs; `--min-population` and `--streets-per-locality` on the scripts build them.
+- Fill the 398 Swedish localities weighted 200 from SCB småorter before v0.1.0.
+- Give the address records one column set across countries: `region` and
+  `municipality` as building-block columns on `geo.SE.address` too, in step 6.
 - v0.1.0 ships SE and US; then NO, DK, FI, NL, FR, AU, CA, ES, GB, DE.
-- SE streets come from NVDB per kommun and postnummer from GeoNames per postort,
-  box codes dropped by the third-digit rule; the pairing is approximate. Revisit
-  an application to Lantmäteriet for the exact pairing after v0.1.0.
+- Revisit an application to Lantmäteriet for the exact street to postnummer
+  pairing after v0.1.0; today a street goes to the nearest postal code centroid.
 
 #### Builtins the data cannot express
 
@@ -157,8 +163,8 @@ address, phone, national id, company and date names each.
 #### Order of work
 
 1. Table node, key and name selection, parent links, consistent draws, the
-   choice-of-rows fence, `DATA-LICENSES.md`, `data-import/`.
-2. `geo/SE` and `geo/US`, and `address` in both locales on top of them.
+   choice-of-rows fence, `DATA-LICENSES.md`, `data-import/` — done.
+2. `geo/SE` and `geo/US`, and `address` in both locales on top of them — done.
 3. Weighted person names and valid ids in both locales; `date()`.
 4. `misc` conversions and the new `misc` tables.
 5. The remaining locale categories: company, phone, finance, vehicle, words.