265 lines
12 KiB
Python
265 lines
12 KiB
Python
#!/usr/bin/env python3
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"""Rebuild data/geo/SE/*.tsv from SCB (CC0), GeoNames (CC BY 4.0) and Trafikverket NVDB (CC0).
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TRAFIKVERKET_API_KEY=… data-import/geo-se.py [--key-file FILE] [--cache DIR] [--streets-per-locality N] [--out DIR]
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A locality is a GeoNames postort, placed in the municipality it names, else of its
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tätort, else of most of its codes, and weighted by its tätort's population, else its
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municipality's, else 200. Box codes are dropped by the digit after the postort's own
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prefix. Each NVDB street segment goes to the nearest postal code centroid; a locality
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keeps the N names with most segments.
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"""
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import argparse
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import collections
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import csv
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import io
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import json
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import math
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import os
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import re
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import sys
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import urllib.request
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import xml.etree.ElementTree as ET
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import zipfile
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from pathlib import Path
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CODES = "https://www.scb.se/contentassets/7a89e48960f741e08918e489ea36354a/kommunlankod-2026.xlsx"
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POPULATION = "https://api.scb.se/OV0104/v1/doris/sv/ssd/START/BE/BE0101/BE0101A/BefolkningNy"
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POPULATION_QUERY = {
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"query": [
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{"code": "Region", "selection": {"filter": "all", "values": ["*"]}},
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{"code": "ContentsCode", "selection": {"filter": "item", "values": ["BE0101N1"]}},
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{"code": "Tid", "selection": {"filter": "top", "values": ["1"]}},
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],
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"response": {"format": "json"},
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}
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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"
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POSTAL_CODES = "https://download.geonames.org/export/zip/SE.zip"
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NVDB = "https://api.trafikinfo.trafikverket.se/v2/data.json"
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NVDB_PAGE = 50000
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OUT = Path(__file__).resolve().parent.parent / "data" / "geo" / "SE"
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CACHE = Path(__file__).resolve().parent / "cache"
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TIMEZONE = "Europe/Stockholm"
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ONE_POSITION = {"Stockholm", "Göteborg", "Malmö"}
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UNMATCHED_POPULATION = 200
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XLSX_NS = {"m": "http://schemas.openxmlformats.org/spreadsheetml/2006/main"}
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def fetch(url, cache, name, data=None, headers=None):
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path = cache / name
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if not path.exists():
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req = urllib.request.Request(url, data=data, headers=headers or {})
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with urllib.request.urlopen(req, timeout=300) as r:
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path.write_bytes(r.read())
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return path.read_bytes()
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def xlsx_rows(data):
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z = zipfile.ZipFile(io.BytesIO(data))
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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)]
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sheet = ET.fromstring(z.read("xl/worksheets/sheet1.xml"))
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for row in sheet.findall(".//m:row", XLSX_NS):
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cells = []
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for c in row.findall("m:c", XLSX_NS):
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v = c.find("m:v", XLSX_NS)
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cells.append("" if v is None else strings[int(v.text)] if c.get("t") == "s" else v.text)
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yield cells
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def scb_codes(cache):
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regions, municipalities = {}, {}
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for cells in xlsx_rows(fetch(CODES, cache, "kommunlankod.xlsx")):
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if len(cells) < 2 or not re.fullmatch(r"\d{2}|\d{4}", cells[0]):
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continue
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(regions if len(cells[0]) == 2 else municipalities)[cells[0]] = cells[1].strip()
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return regions, municipalities
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def scb_population(cache):
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body = json.dumps(POPULATION_QUERY).encode()
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data = fetch(POPULATION, cache, "befolkning.json", data=body, headers={"Content-Type": "application/json"})
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return {row["key"][0]: row["values"][0] for row in json.loads(data.decode("utf-8-sig"))["data"]}
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def scb_tatorter(cache):
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text = fetch(TATORTER, cache, "tatorter.csv").decode("utf-8")
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by_name = collections.defaultdict(list)
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for r in csv.DictReader(io.StringIO(text)):
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by_name[r["tatort"]].append((r["kommun"], int(r["bef"])))
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return by_name
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def geonames(cache):
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z = zipfile.ZipFile(io.BytesIO(fetch(POSTAL_CODES, cache, "SE.zip")))
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rows = []
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for line in z.read("SE.txt").decode("utf-8").splitlines():
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f = line.split("\t")
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lat, lon = (float(f[9]), float(f[10])) if f[9] and f[10] else (None, None)
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rows.append({"code": f[1], "locality": f[2], "municipality": f[6], "lat": lat, "lon": lon})
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return rows
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def nvdb_segments(cache, key):
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path = cache / "nvdb-gatunamn.tsv"
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if not path.exists():
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with open(path, "w", encoding="utf-8") as out:
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change = "0"
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while True:
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query = (
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f'<REQUEST><LOGIN authenticationkey="{key}"/>'
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f'<QUERY objecttype="Gatunamn" namespace="vägdata.nvdb_dk_o" schemaversion="1.0" limit="{NVDB_PAGE}" changeid="{change}">'
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"<FILTER><EQ name=\"Deleted\" value=\"false\"/></FILTER>"
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"<INCLUDE>Namn</INCLUDE><INCLUDE>Geometry.WKT-WGS84-3D</INCLUDE></QUERY></REQUEST>"
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)
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req = urllib.request.Request(NVDB, data=query.encode(), headers={"Content-Type": "text/xml"})
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with urllib.request.urlopen(req, timeout=600) as r:
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result = json.load(r)["RESPONSE"]["RESULT"][0]
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rows = result.get("Gatunamn", [])
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for row in rows:
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m = re.match(r"LINESTRING Z \(([-\d.]+) ([-\d.]+) ", row.get("Geometry", {}).get("WKT-WGS84-3D", ""))
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name = " ".join(row.get("Namn", "").split())
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if m and name:
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out.write(f"{name}\t{m.group(1)}\t{m.group(2)}\n")
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change = result["INFO"]["LASTCHANGEID"]
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if len(rows) < NVDB_PAGE:
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break
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for line in path.read_text(encoding="utf-8").splitlines():
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name, lon, lat = line.split("\t")
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yield name, float(lat), float(lon)
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class Nearest:
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"""Nearest point by an equirectangular distance, over a degree grid."""
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def __init__(self, points, cell=0.05):
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self.cell = cell
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self.grid = collections.defaultdict(list)
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for lat, lon, value in points:
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self.grid[(int(lat // cell), int(lon // cell))].append((lat, lon, value))
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def find(self, lat, lon):
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ci, cj = int(lat // self.cell), int(lon // self.cell)
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best, best_d = None, math.inf
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ring = 0
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while ring < 400:
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for i in range(ci - ring, ci + ring + 1):
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for j in range(cj - ring, cj + ring + 1):
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if max(abs(i - ci), abs(j - cj)) != ring:
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continue
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for plat, plon, value in self.grid.get((i, j), ()):
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d = (plat - lat) ** 2 + ((plon - lon) * math.cos(math.radians(lat))) ** 2
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if d < best_d:
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best, best_d = value, d
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if best is not None and math.sqrt(best_d) < ring * self.cell * math.cos(math.radians(lat)):
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return best
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ring += 1
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return best
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def street_delivery(name, codes):
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"""The codes delivered to a street: the digit after the postort's own prefix says box, company or reply."""
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if name in ONE_POSITION:
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return [c for c in codes if c[1] != "0"]
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largest = collections.Counter(c[:3] for c in codes).most_common(1)[0][1]
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length = 3 if largest * 2 >= len(codes) else 2
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return [c for c in codes if c[length] not in "018"]
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def municipality_of(name, rows, tatorter, municipalities):
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named = [code for code, n in municipalities.items() if n == name]
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if named:
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return named[0], "kommun"
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voted = collections.Counter(r["municipality"] for r in rows if r["municipality"] in municipalities)
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matches = tatorter.get(name, [])
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if matches:
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in_vote = [m for m in matches if voted and m[0] == voted.most_common(1)[0][0]]
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return max(in_vote or matches, key=lambda m: m[1])[0], "tatort"
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if voted:
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return voted.most_common(1)[0][0], "codes"
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return None, "unplaced"
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def population_of(name, municipality, tatorter, municipalities, population):
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matches = [m for m in tatorter.get(name, []) if m[0] == municipality]
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if matches:
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return str(max(m[1] for m in matches))
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return population[municipality] if municipalities[municipality] == name else str(UNMATCHED_POPULATION)
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def well_cased(name):
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return all(part[:1].isupper() and (len(part) == 1 or not part.isupper()) for part in re.split(r"[ -]", name))
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def write(path, columns, rows):
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lines = ["\t".join(columns)]
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for row in rows:
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cells = [str(row[c]) for c in columns]
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assert not any(re.search(r"[\t\n{}]", c) for c in cells), row
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lines.append("\t".join(cells))
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path.write_text("\n".join(lines) + "\n", encoding="utf-8")
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print(f"{path}: {len(rows)} rows", file=sys.stderr)
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def main():
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p = argparse.ArgumentParser(description=__doc__.splitlines()[0])
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p.add_argument("--cache", default=str(CACHE))
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p.add_argument("--key-file", help="file holding the Trafikverket API key; TRAFIKVERKET_API_KEY otherwise")
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p.add_argument("--out", default=str(OUT))
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p.add_argument("--streets-per-locality", type=int, default=10)
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a = p.parse_args()
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key = Path(a.key_file).read_text().strip() if a.key_file else os.environ.get("TRAFIKVERKET_API_KEY")
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if not key:
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sys.exit("set TRAFIKVERKET_API_KEY or pass --key-file")
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cache, out = Path(a.cache), Path(a.out)
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cache.mkdir(parents=True, exist_ok=True)
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out.mkdir(parents=True, exist_ok=True)
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regions, municipalities = scb_codes(cache)
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population = scb_population(cache)
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tatorter = scb_tatorter(cache)
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codes = geonames(cache)
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by_locality = collections.defaultdict(list)
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for r in codes:
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by_locality[r["locality"]].append(r)
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localities, how = {}, collections.Counter()
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for name, rows in by_locality.items():
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municipality, method = municipality_of(name, rows, tatorter, municipalities)
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how[method] += 1
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kept = street_delivery(name, [r["code"].replace(" ", "") for r in rows])
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with_point = [r for r in rows if r["lat"] is not None]
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if municipality is None or not kept or not with_point or not well_cased(name):
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continue
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lat = sum(r["lat"] for r in with_point) / len(with_point)
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lon = sum(r["lon"] for r in with_point) / len(with_point)
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localities[name] = {"name": name, "municipality": municipality, "population": population_of(name, municipality, tatorter, municipalities, population), "lat": f"{lat:.4f}", "lon": f"{lon:.4f}", "codes": kept}
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print(f"municipality by {dict(how)}; {len(by_locality) - len(localities)} postorter dropped", file=sys.stderr)
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centroids = [(r["lat"], r["lon"], r["locality"]) for r in codes if r["lat"] is not None and r["locality"] in localities]
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nearest_locality = Nearest(centroids)
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segments = collections.Counter()
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for name, lat, lon in nvdb_segments(cache, key):
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segments[(nearest_locality.find(lat, lon), name)] += 1
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streets_of = collections.defaultdict(list)
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for (locality, name), n in segments.items():
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streets_of[locality].append((n, name))
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streets = []
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for locality in sorted(streets_of):
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for n, name in sorted(streets_of[locality], key=lambda s: (-s[0], s[1]))[: a.streets_per_locality]:
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streets.append({"name": name, "locality": locality, "segments": n})
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for name in [l for l in localities if l not in streets_of]:
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del localities[name]
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empty = sorted(m for m in municipalities if not any(l["municipality"] == m for l in localities.values()))
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if empty:
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sys.exit(f"municipalities without a locality: {empty}")
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write(out / "region.tsv", ["code", "name", "population", "timezone"], [{"code": c, "name": n, "population": population[c], "timezone": TIMEZONE} for c, n in sorted(regions.items())])
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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())])
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write(out / "locality.tsv", ["name", "municipality", "population", "lat", "lon"], [l for _, l in sorted(localities.items())])
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write(out / "postal-code.tsv", ["code", "locality"], sorted(({"code": f"{c[:3]} {c[3:]}", "locality": l["name"]} for l in localities.values() for c in l["codes"]), key=lambda r: r["code"]))
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write(out / "street.tsv", ["name", "locality", "segments"], streets)
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if __name__ == "__main__":
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main()
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