529 lines
19 KiB
Python
529 lines
19 KiB
Python
# -*- coding: utf-8 -*-
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import argparse
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import logging
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import operator
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import os
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import re
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import six
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import subprocess
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import sys
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this_dir = os.path.realpath(os.path.dirname(__file__))
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sys.path.append(os.path.realpath(os.path.join(os.pardir, os.pardir)))
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from geodata.address_formatting.formatter import AddressFormatter
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from geodata.coordinates.conversion import latlon_to_decimal
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from geodata.encoding import safe_decode
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from geodata.file_utils import ensure_dir, download_file
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from geodata.i18n.unicode_properties import get_chars_by_script
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from geodata.i18n.word_breaks import ideographic_scripts
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from geodata.names.deduping import NameDeduper
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from geodata.osm.components import osm_address_components
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from geodata.osm.definitions import osm_definitions
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from geodata.osm.extract import parse_osm, osm_type_and_id, NODE, WAY, RELATION, OSM_NAME_TAGS
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from geodata.polygons.index import *
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from geodata.polygons.reverse_geocode import QuattroshapesReverseGeocoder, OSMCountryReverseGeocoder, OSMReverseGeocoder
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from geodata.statistics.tf_idf import IDFIndex
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class NeighborhoodDeduper(NameDeduper):
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# Lossless conversions only
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replacements = {
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u'saint': u'st',
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u'and': u'&',
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u'〇': u'0',
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u'一': u'1',
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u'二': u'2',
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u'三': u'3',
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u'四': u'4',
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u'五': u'5',
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u'六': u'6',
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u'七': u'7',
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u'八': u'8',
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u'九': u'9',
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u'十': u'10',
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}
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discriminative_words = set([
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# Han numbers
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u'〇', u'一',
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u'二', u'三',
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u'四', u'五',
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u'六', u'七',
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u'八', u'九',
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u'十', u'百',
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u'千', u'万',
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u'億', u'兆',
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u'京', u'第',
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# Roman numerals
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u'i', u'ii',
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u'iii', u'iv',
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u'v', u'vi',
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u'vii', u'viii',
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u'ix', u'x',
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u'xi', u'xii',
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u'xiii', u'xiv',
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u'xv', u'xvi',
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u'xvii', u'xviii',
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u'xix', u'xx',
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# English directionals
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u'north', u'south',
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u'east', u'west',
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u'northeast', u'northwest',
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u'southeast', u'southwest',
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# Spanish, Portguese and Italian directionals
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u'norte', u'nord', u'sur', u'sul', u'sud',
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u'est', u'este', u'leste', u'oeste', u'ovest',
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# New in various languages
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u'new',
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u'nova',
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u'novo',
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u'nuevo',
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u'nueva',
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u'nuovo',
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u'nuova',
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# Qualifiers
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u'heights',
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u'hills',
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u'upper', u'lower',
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u'little', u'great',
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u'park',
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u'parque',
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u'village',
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])
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stopwords = set([
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u'cp',
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u'de',
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u'la',
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u'urbanizacion',
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u'do',
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u'da',
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u'dos',
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u'del',
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u'community',
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u'bairro',
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u'barrio',
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u'le',
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u'el',
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u'mah',
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u'раион',
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u'vila',
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u'villa',
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u'kampung',
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u'ahupua`a',
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])
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class ClickThatHoodReverseGeocoder(GeohashPolygonIndex):
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simplify_tolerance = 0.00001
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preserve_topology = True
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persistent_polygons = False
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cache_size = 0
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SCRATCH_DIR = '/tmp'
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# Contains accurate boundaries for neighborhoods sans weird GeoPlanet names like "Adelphi" or "Crown Heights South"
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NEIGHBORHOODS_REPO = 'https://github.com/blackmad/neighborhoods'
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@classmethod
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def clone_repo(cls, path):
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subprocess.check_call(['rm', '-rf', path])
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subprocess.check_call(['git', 'clone', cls.NEIGHBORHOODS_REPO, path])
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@classmethod
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def create_neighborhoods_index(cls):
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scratch_dir = cls.SCRATCH_DIR
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repo_path = os.path.join(scratch_dir, 'neighborhoods')
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cls.clone_repo(repo_path)
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neighborhoods_dir = os.path.join(scratch_dir, 'neighborhoods', 'index')
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ensure_dir(neighborhoods_dir)
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index = cls(save_dir=neighborhoods_dir)
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have_geonames = set()
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is_neighborhood = set()
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for filename in os.listdir(repo_path):
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path = os.path.join(repo_path, filename)
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base_name = filename.split('.')[0].split('gn-')[-1]
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if filename.endswith('.geojson') and filename.startswith('gn-'):
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have_geonames.add(base_name)
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elif filename.endswith('metadata.json'):
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data = json.load(open(os.path.join(repo_path, filename)))
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if data.get('neighborhoodNoun', [None])[0] in (None, 'rione'):
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is_neighborhood.add(base_name)
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for filename in os.listdir(repo_path):
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if not filename.endswith('.geojson'):
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continue
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base_name = filename.rsplit('.geojson')[0]
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if base_name in have_geonames:
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f = open(os.path.join(repo_path, 'gn-{}'.format(filename)))
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elif base_name in is_neighborhood:
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f = open(os.path.join(repo_path, filename))
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else:
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continue
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try:
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index.add_geojson_like_file(json.load(f)['features'])
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except ValueError:
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continue
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return index
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class NeighborhoodReverseGeocoder(RTreePolygonIndex):
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'''
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Neighborhoods are very important in cities like NYC, SF, Chicago, London
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and many others. We want the address parser to be trained with addresses
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that sufficiently capture variations in address patterns, including
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neighborhoods. Quattroshapes neighborhood data (in the US at least)
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is not great in terms of names, mostly becasue GeoPlanet has so many
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incorrect names. The neighborhoods project, also known as ClickThatHood
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has very accurate polygons with correct names, but only for a handful
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of cities. OSM usually lists neighborhoods and some other local admin
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areas like boroughs as points rather than polygons.
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This index merges all of the above data sets in prioritized order
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(ClickThatHood > OSM > Quattroshapes) to provide unified point-in-polygon
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tests for neighborhoods. The properties vary by source but each has
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source has least a "name" key which in practice is what we care about.
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'''
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PRIORITIES_FILENAME = 'priorities.json'
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DUPE_THRESHOLD = 0.9
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persistent_polygons = True
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cache_size = 100000
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source_priorities = {
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'clickthathood': 0, # Best names/polygons
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'osm_cth': 1, # OSM names matched with ClickThatHood polygon
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'osm_quattro': 2, # OSM names matched with Quattroshapes polygon
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'quattroshapes': 3, # Good results in some countries/areas
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}
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level_priorities = {
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'neighborhood': 0,
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'local_admin': 1,
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}
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regex_replacements = [
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# Paris arrondissements, listed like "PARIS-1ER-ARRONDISSEMENT" in Quqttroshapes
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(re.compile('^paris-(?=[\d])', re.I), ''),
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(re.compile('^prague(?= [\d]+$)', re.I), 'Praha'),
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]
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quattroshapes_city_district_patterns = [
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six.u('Praha [\d]+'),
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]
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quattroshapes_city_district_regex = re.compile('|'.join([six.u('^\s*{}\s*$').format(p) for p in quattroshapes_city_district_patterns]), re.I | re.U)
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@classmethod
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def count_words(cls, s):
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doc = defaultdict(int)
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for t, c in NeighborhoodDeduper.content_tokens(s):
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doc[t] += 1
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return doc
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@classmethod
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def create_from_osm_and_quattroshapes(cls, filename, quattroshapes_dir, country_rtree_dir, osm_rtree_dir, output_dir):
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'''
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Given an OSM file (planet or some other bounds) containing neighborhoods
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as points (some suburbs have boundaries)
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and their dependencies, create an R-tree index for coarse-grained
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reverse geocoding.
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Note: the input file is expected to have been created using
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osmfilter. Use fetch_osm_address_data.sh for planet or copy the
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admin borders commands if using other geometries.
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'''
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index = cls(save_dir=output_dir)
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logger = logging.getLogger('neighborhoods')
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qs_scratch_dir = os.path.join(quattroshapes_dir, 'qs_neighborhoods')
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ensure_dir(qs_scratch_dir)
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logger.info('Creating Quattroshapes neighborhoods')
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qs = QuattroshapesNeighborhoodsReverseGeocoder.create_neighborhoods_index(quattroshapes_dir, qs_scratch_dir)
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logger.info('Creating ClickThatHood neighborhoods')
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cth = ClickThatHoodReverseGeocoder.create_neighborhoods_index()
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country_rtree = OSMCountryReverseGeocoder.load(country_rtree_dir)
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osm_admin_rtree = OSMReverseGeocoder.load(osm_rtree_dir)
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osm_admin_rtree.cache_size = 1000
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logger.info('Creating IDF index')
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idf = IDFIndex()
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char_scripts = get_chars_by_script()
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for idx in (cth, qs):
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for i in xrange(idx.i):
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props = idx.get_properties(i)
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name = props.get('name')
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if name is not None:
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doc = cls.count_words(name)
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idf.update(doc)
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for key, attrs, deps in parse_osm(filename):
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for k, v in six.iteritems(attrs):
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if any((k.startswith(name_key) for name_key in OSM_NAME_TAGS)):
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doc = cls.count_words(v)
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idf.update(doc)
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qs.matched = [False] * qs.i
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cth.matched = [False] * cth.i
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logger.info('Matching OSM points to neighborhood polygons')
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# Parse OSM and match neighborhood/suburb points to Quattroshapes/ClickThatHood polygons
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num_polys = 0
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for element_id, attrs, deps in parse_osm(filename):
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try:
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lat, lon = latlon_to_decimal(attrs['lat'], attrs['lon'])
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except ValueError:
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continue
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osm_name = attrs.get('name')
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if not osm_name:
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continue
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id_type, element_id = element_id.split(':')
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element_id = long(element_id)
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props['type'] = id_type
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props['id'] = element_id
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possible_neighborhood = osm_definitions.meets_definition(attrs, osm_definitions.NEIGHBORHOOD)
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is_neighborhood = attrs.get('place') in ('neighbourhood', 'neighborhood')
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country, candidate_languages = country_rtree.country_and_languages(lat, lon)
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component_name = None
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component_name = osm_address_components.component_from_properties(country, attrs)
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ranks = []
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osm_names = []
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for key in OSM_NAME_TAGS:
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name = attrs.get(key)
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if name:
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osm_names.append(name)
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for name_key in OSM_NAME_TAGS:
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osm_names.extend([v for k, v in six.iteritems(attrs) if k.startswith('{}:'.format(name_key))])
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for idx in (cth, qs):
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candidates = idx.get_candidate_polygons(lat, lon, return_all=True)
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if candidates:
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max_sim = 0.0
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arg_max = None
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normalized_qs_names = {}
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for osm_name in osm_names:
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contains_ideographs = any(((char_scripts[ord(c)] or '').lower() in ideographic_scripts
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for c in safe_decode(osm_name)))
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for i in candidates:
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props = idx.get_properties(i)
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name = normalized_qs_names.get(i)
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if not name:
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name = props.get('name')
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if not name:
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continue
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for pattern, repl in cls.regex_replacements:
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name = pattern.sub(repl, name)
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normalized_qs_names[i] = name
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if is_neighborhood and idx is qs and props.get(QuattroshapesReverseGeocoder.LEVEL) != 'neighborhood':
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continue
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if not contains_ideographs:
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sim = NeighborhoodDeduper.compare(osm_name, name, idf)
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else:
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# Many Han/Hangul characters are common, shouldn't use IDF
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sim = NeighborhoodDeduper.compare_ideographs(osm_name, name)
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if sim > max_sim:
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max_sim = sim
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poly = idx.get_polygon(i)
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arg_max = (max_sim, props, poly.context, idx, i)
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if arg_max:
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ranks.append(arg_max)
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ranks.sort(key=operator.itemgetter(0), reverse=True)
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if ranks and ranks[0][0] >= cls.DUPE_THRESHOLD:
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score, props, poly, idx, i = ranks[0]
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existing_osm_boundaries = osm_admin_rtree.point_in_poly(lat, lon, return_all=True)
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skip_node = False
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for poly_index, osm_props in enumerate(existing_osm_boundaries):
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containing_component = None
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name = osm_props.get('name')
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# Only exact name matches here since we're comparins OSM to OSM
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if name and name.lower() != attrs.get('name', '').lower():
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continue
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containing_ids = [(boundary['type'], boundary['id']) for boundary in existing_osm_boundaries[poly_index + 1:]]
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containing_component = osm_address_components.component_from_properties(country, osm_props, containing=containing_ids)
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if containing_component and containing_component != component_name and AddressFormatter.component_order[containing_component] <= AddressFormatter.component_order[AddressFormatter.CITY]:
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skip_node = True
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break
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# Skip this element
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if skip_node:
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continue
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containing_ids = [(boundary['type'], boundary['id']) for boundary in existing_osm_boundaries]
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component = osm_address_components.component_from_properties(country, attrs, containing=containing_ids)
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attrs['component'] = component
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if idx is cth:
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attrs['polygon_type'] = 'neighborhood'
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source = 'osm_cth'
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else:
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level = props.get(QuattroshapesReverseGeocoder.LEVEL, None)
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source = 'osm_quattro'
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if level == 'neighborhood':
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attrs['polygon_type'] = 'neighborhood'
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else:
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attrs['polygon_type'] = 'local_admin'
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attrs['source'] = source
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index.index_polygon(poly)
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index.add_polygon(poly, attrs)
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idx.matched[i] = True
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num_polys += 1
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if num_polys % 1000 == 0 and num_polys > 0:
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logger.info('did {} neighborhoods'.format(num_polys))
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for idx, source in ((cth, 'clickthathood'), (qs, 'quattroshapes')):
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for i in xrange(idx.i):
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props = idx.get_properties(i)
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poly = idx.get_polygon(i)
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if idx.matched[i]:
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continue
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props['source'] = source
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if idx is cth or props.get(QuattroshapesReverseGeocoder.LEVEL, None) == 'neighborhood':
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component = AddressFormatter.SUBURB
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if source == 'quattroshapes':
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name = props.get('name')
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if not name:
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continue
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for pattern, repl in cls.regex_replacements:
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name = pattern.sub(repl, name)
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props['name'] = name
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if cls.quattroshapes_city_district_regex.match(name):
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component = AddressFormatter.CITY_DISTRICT
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props['component'] = component
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props['polygon_type'] = 'neighborhood'
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else:
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# We don't actually care about local admin polygons unless they match OSM
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continue
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index.index_polygon(poly.context)
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index.add_polygon(poly.context, props)
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return index
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def setup(self):
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self.priorities = []
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def index_polygon_properties(self, properties):
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self.priorities.append((self.level_priorities[properties['polygon_type']], self.source_priorities[properties['source']]))
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def load_polygon_properties(self, d):
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self.priorities = json.load(open(os.path.join(d, self.PRIORITIES_FILENAME)))
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def save_polygon_properties(self, d):
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json.dump(self.priorities, open(os.path.join(d, self.PRIORITIES_FILENAME), 'w'))
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def priority(self, i):
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return self.priorities[i]
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def get_candidate_polygons(self, lat, lon):
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candidates = super(NeighborhoodReverseGeocoder, self).get_candidate_polygons(lat, lon)
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return sorted(candidates, key=self.priority)
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class QuattroshapesNeighborhoodsReverseGeocoder(GeohashPolygonIndex, QuattroshapesReverseGeocoder):
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persistent_polygons = False
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cache_size = None
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@classmethod
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def create_neighborhoods_index(cls, quattroshapes_dir,
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output_dir,
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index_filename=None,
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polys_filename=DEFAULT_POLYS_FILENAME):
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local_admin_filename = os.path.join(quattroshapes_dir, cls.LOCAL_ADMIN_FILENAME)
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neighborhoods_filename = os.path.join(quattroshapes_dir, cls.NEIGHBORHOODS_FILENAME)
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return cls.create_from_shapefiles([local_admin_filename, neighborhoods_filename],
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output_dir, index_filename=index_filename,
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polys_filename=polys_filename)
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if __name__ == '__main__':
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||
# Handle argument parsing here
|
||
parser = argparse.ArgumentParser()
|
||
|
||
parser.add_argument('-q', '--quattroshapes-dir',
|
||
help='Path to quattroshapes dir')
|
||
|
||
parser.add_argument('-a', '--osm-admin-rtree-dir',
|
||
help='Path to OSM admin rtree dir')
|
||
|
||
parser.add_argument('-c', '--country-rtree-dir',
|
||
help='Path to country rtree dir')
|
||
|
||
parser.add_argument('-n', '--osm-neighborhoods-file',
|
||
help='Path to OSM neighborhoods file (no dependencies, .osm format)')
|
||
|
||
parser.add_argument('-o', '--out-dir',
|
||
default=os.getcwd(),
|
||
help='Output directory')
|
||
|
||
logging.basicConfig(level=logging.INFO)
|
||
|
||
args = parser.parse_args()
|
||
if args.osm_neighborhoods_file and args.quattroshapes_dir and args.osm_admin_rtree_dir and args.country_rtree_dir:
|
||
index = NeighborhoodReverseGeocoder.create_from_osm_and_quattroshapes(
|
||
args.osm_neighborhoods_file,
|
||
args.quattroshapes_dir,
|
||
args.country_rtree_dir,
|
||
args.osm_admin_rtree_dir,
|
||
args.out_dir
|
||
)
|
||
else:
|
||
parser.error('Must specify quattroshapes dir or osm admin borders file')
|
||
|
||
index.save()
|