2026-09-18 01:44:02 +02:00
43 changed files with 55810 additions and 142 deletions
@@ -1,3 +1,4 @@
 .claude
 *.out
-__pycache__/
+__pycache__/
 data-import/cache/
@@ -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`.
@@ -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) | — | — | — |
@@ -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)
@@ -26,12 +26,12 @@ func benchPath(b *testing.B, dir, path string) {
 	}
 }
-func BenchmarkPerson(b *testing.B)     { benchPath(b, "data/sv_SE", "person") }
+func BenchmarkPerson(b *testing.B)     { benchPath(b, "data", "sv_SE.person") }
-func BenchmarkAddress(b *testing.B)    { benchPath(b, "data/sv_SE", "address") }
+func BenchmarkAddress(b *testing.B)    { benchPath(b, "data", "sv_SE.address") }
-func BenchmarkWord(b *testing.B)       { benchPath(b, "data/sv_SE", "word") }
+func BenchmarkWord(b *testing.B)       { benchPath(b, "data", "sv_SE.word") }
-func BenchmarkCreditcard(b *testing.B) { benchPath(b, "data/misc", "creditcard") }
+func BenchmarkCreditcard(b *testing.B) { benchPath(b, "data", "misc.creditcard") }
-func BenchmarkSSN(b *testing.B)        { benchPath(b, "data/sv_SE", "ssn") }
+func BenchmarkSSN(b *testing.B)        { benchPath(b, "data", "sv_SE.ssn") }
-func BenchmarkUUIDv7(b *testing.B)     { benchPath(b, "data/misc", "uuid") }
+func BenchmarkUUIDv7(b *testing.B)     { benchPath(b, "data", "misc.uuid") }
 func tmpData(b *testing.B, name, body string) string {
 	b.Helper()
@@ -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)
 	}
@@ -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)))
+    table = rows(tsv.fetch(a.source, a.cache, "country-codes.csv").decode("utf-8"))
-    print(f"{a.out}: {n} rows", file=sys.stderr)
+    tsv.write(a.out, COLUMNS, sorted(table, key=lambda r: r["alpha2"]))
 if __name__ == "__main__":
@@ -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)))
+    symbol = symbols(tsv.fetch(s, a.cache, Path(s).name).decode("utf-8") for s in a.symbols)
-    print(f"{a.out}: {n} rows", file=sys.stderr)
+    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__":
@@ -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()
@@ -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()
@@ -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)
@@ -1,17 +1,8 @@
 {
  "format": "{street-number} {street}\n{locality}, {region} {postal-code}",
-  "street": {
+  "street": "{/geo.US.address.street}",
-    "format": "{name} {suffix}",
+  "street-number": "{/geo.US.address.street-number}",
-    "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"],
+  "locality": "{/geo.US.address.locality}",
-    "suffix": ["Avenue", "Boulevard", "Circle", "Court", "Drive", "Lane", "Place", "Road", "Street", "Terrace", "Trail", "Way"]
+  "region": "{/geo.US.address.region}",
-  },
+  "postal-code": "{/geo.US.address.postal-code}"
  "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 }]
 }
@@ -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}"
 }
@@ -0,0 +1 @@
 { "format": "{name}", "rows": "locality.tsv", "key": "name", "parent": "municipality", "weight": "population" }
@@ -0,0 +1 @@
 { "format": "{name}", "rows": "municipality.tsv", "key": "code", "name": "name", "parent": "region", "weight": "population" }
@@ -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
@@ -0,0 +1 @@
 { "format": "{code}", "rows": "postal-code.tsv", "key": "code", "parent": "locality" }
@@ -0,0 +1 @@
 { "format": "{name}", "rows": "region.tsv", "key": "code", "name": "name", "weight": "population" }
@@ -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
@@ -0,0 +1 @@
 { "format": "{name}", "rows": "street.tsv", "parent": "locality", "weight": "segments" }
@@ -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}"
 }
@@ -0,0 +1 @@
 { "format": "{name}", "rows": "locality.tsv", "key": "code", "name": "name", "parent": "municipality", "weight": "population" }
@@ -0,0 +1 @@
 { "format": "{name}", "rows": "municipality.tsv", "key": "code", "name": "name", "parent": "region", "weight": "population" }
@@ -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
@@ -0,0 +1 @@
 { "format": "{code}", "rows": "postal-code.tsv", "key": "code", "parent": "locality", "weight": "addresses" }
@@ -0,0 +1 @@
 { "format": "{name}", "rows": "region.tsv", "key": "abbr", "name": "name", "weight": "population" }
@@ -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
@@ -0,0 +1 @@
 { "format": "{name}", "rows": "street.tsv", "parent": "locality", "weight": "addresses" }
@@ -1,21 +1,7 @@
 {
  "format": "{street} {street-number}\n{postal-code} {locality}",
-  "street": [
+  "street": "{/geo.SE.address.street}",
-    {
+  "street-number": "{/geo.SE.address.street-number}",
-      "format": "{first}{last}",
+  "postal-code": "{/geo.SE.address.postal-code}",
-      "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"],
+  "locality": "{/geo.SE.address.locality}"
      "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"]
 }
@@ -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))
+	f := newGenerator(t, "data", WithSeed(1))
 	sv := newGenerator(t, "data/sv_SE", 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))
+	f := newGenerator(t, "data", WithSeed(11))
-	for _, path := range []string{"person", "person.last"} {
+	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, "data", WithSeed(7))
-		f := newGenerator(t, dir, 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) {
+			if name := fake(t, f, path); len(name) < 3 || !regexp.MustCompile(`\S \S`).MatchString(name) {
-				t.Fatalf("%s person %q lacks first and last name", dir, 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) {
+func TestShippedStreetIsARegisteredName(t *testing.T) {
-	// street is a choice of composed {first}{last} templates and literal names.
+	f := newGenerator(t, "data", WithSeed(5))
-	f := newGenerator(t, "data/sv_SE", 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)
 		}
 	}
@@ -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
 		}
 	}
@@ -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 {
@@ -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))
+	f := newGenerator(t, "data", WithSeed(1))
-	_, err := f.Fake("person.typo")
+	_, 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)
 	}
@@ -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)
@@ -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}"}`,
@@ -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
@@ -53,19 +53,25 @@ countries; the README maps each to the native term.
 | Table | SE | US | Weight |
 |---|---|---|---|
-| `region` | län (21) | state (56) | population |
+| `region` | län (21) | state and DC (50; Hawaii has no incorporated place) | population |
-| `municipality` | kommun (290) | county (3,234) | population |
+| `municipality` | kommun (290) | county with a shipped place | population |
-| `locality` | postort (~1,780), tätort population | place (~19,500) | population |
+| `locality` | postort, tätort population | place of 25,000+ | population |
-| `postal-code` | postnummer (~10,500 deliverable) | ZCTA (33,791) | address count or 1 |
+| `postal-code` | postnummer with street delivery | ZCTA of a shipped place | one; address ranges |
-| `street` | gatunamn, top N per locality | street name, top N per place | address count |
+| `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
+- Shipped in step 2, README Data. `geo.SE.address` is a record over one consistent
-  code, locality; `geo.SE.locality[Lund].address` stays inside Lund. Each region
+  draw. Each region row carries its timezone, each locality its centroid.
-  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,
+- Revisit an application to Lantmäteriet for the exact street to postnummer
-  box codes dropped by the third-digit rule; the pairing is approximate. Revisit
+  pairing after v0.1.0; today a street goes to the nearest postal code centroid.
  an application to Lantmäteriet for the exact pairing after v0.1.0.
 #### 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/`.
+   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.
+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.
		`@@ -0,0 +1 @@`
							`{ "format": "{name}", "rows": "locality.tsv", "key": "name", "parent": "municipality", "weight": "population" }`
		`@@ -0,0 +1 @@`
							`{ "format": "{name}", "rows": "municipality.tsv", "key": "code", "name": "name", "parent": "region", "weight": "population" }`
		`@@ -0,0 +1 @@`
							`{ "format": "{code}", "rows": "postal-code.tsv", "key": "code", "parent": "locality" }`
		`@@ -0,0 +1 @@`
							`{ "format": "{name}", "rows": "region.tsv", "key": "code", "name": "name", "weight": "population" }`
		`@@ -0,0 +1 @@`
							`{ "format": "{name}", "rows": "street.tsv", "parent": "locality", "weight": "segments" }`