feat: Implement Phase 3 neighbourhood data model
Add schemas, parsers, loaders, and models for Toronto neighbourhood-centric data including census profiles, crime statistics, and amenities. Schemas: - NeighbourhoodRecord, CensusRecord, CrimeRecord, CrimeType - AmenityType, AmenityRecord, AmenityCount Models: - BridgeCMHCNeighbourhood (zone-to-neighbourhood mapping with weights) - FactCensus, FactCrime, FactAmenities Parsers: - TorontoOpenDataParser (CKAN API for neighbourhoods, census, amenities) - TorontoPoliceParser (crime rates, MCI data) Loaders: - load_census_data, load_crime_data, load_amenities - build_cmhc_neighbourhood_crosswalk (PostGIS area weights) Also updates CLAUDE.md with projman plugin workflow documentation. Closes #53, #54, #55, #56, #57, #58, #59 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
@@ -6,6 +6,8 @@ from .geo import (
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NeighbourhoodParser,
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load_geojson,
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)
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from .toronto_open_data import TorontoOpenDataParser
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from .toronto_police import TorontoPoliceParser
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__all__ = [
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"CMHCParser",
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@@ -13,4 +15,7 @@ __all__ = [
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"CMHCZoneParser",
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"NeighbourhoodParser",
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"load_geojson",
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# API parsers (Phase 3)
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"TorontoOpenDataParser",
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"TorontoPoliceParser",
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]
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391
portfolio_app/toronto/parsers/toronto_open_data.py
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391
portfolio_app/toronto/parsers/toronto_open_data.py
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@@ -0,0 +1,391 @@
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"""Parser for Toronto Open Data CKAN API.
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Fetches neighbourhood boundaries, census profiles, and amenities data
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from the City of Toronto's Open Data Portal.
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API Documentation: https://open.toronto.ca/dataset/
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"""
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import json
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import logging
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from decimal import Decimal
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from pathlib import Path
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from typing import Any
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import httpx
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from portfolio_app.toronto.schemas import (
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AmenityRecord,
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AmenityType,
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CensusRecord,
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NeighbourhoodRecord,
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)
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logger = logging.getLogger(__name__)
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class TorontoOpenDataParser:
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"""Parser for Toronto Open Data CKAN API.
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Provides methods to fetch and parse neighbourhood boundaries, census profiles,
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and amenities (parks, schools, childcare) from the Toronto Open Data portal.
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"""
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BASE_URL = "https://ckan0.cf.opendata.inter.prod-toronto.ca"
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API_PATH = "/api/3/action"
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# Dataset package IDs
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DATASETS = {
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"neighbourhoods": "neighbourhoods",
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"neighbourhood_profiles": "neighbourhood-profiles",
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"parks": "parks",
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"schools": "school-locations-all-types",
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"childcare": "licensed-child-care-centres",
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}
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def __init__(
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self,
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cache_dir: Path | None = None,
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timeout: float = 30.0,
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) -> None:
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"""Initialize parser.
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Args:
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cache_dir: Optional directory for caching API responses.
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timeout: HTTP request timeout in seconds.
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"""
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self._cache_dir = cache_dir
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self._timeout = timeout
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self._client: httpx.Client | None = None
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@property
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def client(self) -> httpx.Client:
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"""Lazy-initialize HTTP client."""
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if self._client is None:
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self._client = httpx.Client(
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base_url=self.BASE_URL,
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timeout=self._timeout,
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headers={"Accept": "application/json"},
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)
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return self._client
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def close(self) -> None:
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"""Close HTTP client."""
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if self._client is not None:
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self._client.close()
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self._client = None
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def __enter__(self) -> "TorontoOpenDataParser":
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return self
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def __exit__(self, *args: Any) -> None:
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self.close()
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def _get_package(self, package_id: str) -> dict[str, Any]:
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"""Fetch package metadata from CKAN.
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Args:
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package_id: The package/dataset ID.
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Returns:
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Package metadata dictionary.
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"""
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response = self.client.get(
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f"{self.API_PATH}/package_show",
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params={"id": package_id},
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)
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response.raise_for_status()
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result = response.json()
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if not result.get("success"):
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raise ValueError(f"CKAN API error: {result.get('error', 'Unknown error')}")
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return dict(result["result"])
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def _get_resource_url(
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self,
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package_id: str,
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format_filter: str = "geojson",
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) -> str:
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"""Get the download URL for a resource in a package.
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Args:
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package_id: The package/dataset ID.
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format_filter: Resource format to filter by (e.g., 'geojson', 'csv').
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Returns:
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Resource download URL.
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Raises:
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ValueError: If no matching resource is found.
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"""
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package = self._get_package(package_id)
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resources = package.get("resources", [])
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for resource in resources:
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resource_format = resource.get("format", "").lower()
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if format_filter.lower() in resource_format:
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return str(resource["url"])
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available = [r.get("format") for r in resources]
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raise ValueError(
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f"No {format_filter} resource in {package_id}. Available: {available}"
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)
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def _fetch_geojson(self, package_id: str) -> dict[str, Any]:
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"""Fetch GeoJSON data from a package.
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Args:
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package_id: The package/dataset ID.
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Returns:
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GeoJSON FeatureCollection.
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"""
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# Check cache first
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if self._cache_dir:
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cache_file = self._cache_dir / f"{package_id}.geojson"
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if cache_file.exists():
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logger.debug(f"Loading {package_id} from cache")
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with open(cache_file, encoding="utf-8") as f:
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return dict(json.load(f))
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url = self._get_resource_url(package_id, format_filter="geojson")
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logger.info(f"Fetching GeoJSON from {url}")
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response = self.client.get(url)
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response.raise_for_status()
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data = response.json()
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# Cache the response
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if self._cache_dir:
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self._cache_dir.mkdir(parents=True, exist_ok=True)
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cache_file = self._cache_dir / f"{package_id}.geojson"
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with open(cache_file, "w", encoding="utf-8") as f:
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json.dump(data, f)
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return dict(data)
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def _fetch_csv_as_json(self, package_id: str) -> list[dict[str, Any]]:
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"""Fetch CSV data as JSON records via CKAN datastore.
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Args:
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package_id: The package/dataset ID.
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Returns:
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List of records as dictionaries.
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"""
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package = self._get_package(package_id)
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resources = package.get("resources", [])
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# Find a datastore-enabled resource
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for resource in resources:
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if resource.get("datastore_active"):
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resource_id = resource["id"]
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break
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else:
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raise ValueError(f"No datastore resource in {package_id}")
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# Fetch all records via datastore_search
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records: list[dict[str, Any]] = []
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offset = 0
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limit = 1000
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while True:
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response = self.client.get(
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f"{self.API_PATH}/datastore_search",
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params={"id": resource_id, "limit": limit, "offset": offset},
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)
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response.raise_for_status()
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result = response.json()
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if not result.get("success"):
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raise ValueError(f"Datastore error: {result.get('error')}")
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batch = result["result"]["records"]
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records.extend(batch)
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if len(batch) < limit:
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break
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offset += limit
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return records
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def get_neighbourhoods(self) -> list[NeighbourhoodRecord]:
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"""Fetch 158 Toronto neighbourhood boundaries.
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Returns:
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List of validated NeighbourhoodRecord objects.
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"""
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geojson = self._fetch_geojson(self.DATASETS["neighbourhoods"])
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features = geojson.get("features", [])
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records = []
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for feature in features:
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props = feature.get("properties", {})
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geometry = feature.get("geometry")
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# Extract area_id from various possible property names
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area_id = props.get("AREA_ID") or props.get("area_id")
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if area_id is None:
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# Try AREA_SHORT_CODE as fallback
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short_code = props.get("AREA_SHORT_CODE", "")
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if short_code:
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# Extract numeric part
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area_id = int("".join(c for c in short_code if c.isdigit()) or "0")
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area_name = (
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props.get("AREA_NAME")
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or props.get("area_name")
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or f"Neighbourhood {area_id}"
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)
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area_short_code = props.get("AREA_SHORT_CODE") or props.get(
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"area_short_code"
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)
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records.append(
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NeighbourhoodRecord(
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area_id=int(area_id),
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area_name=str(area_name),
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area_short_code=area_short_code,
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geometry=geometry,
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)
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)
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logger.info(f"Parsed {len(records)} neighbourhoods")
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return records
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def get_census_profiles(self, year: int = 2021) -> list[CensusRecord]:
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"""Fetch neighbourhood census profiles.
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Note: Census profile data structure varies by year. This method
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extracts key demographic indicators where available.
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Args:
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year: Census year (2016 or 2021).
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Returns:
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List of validated CensusRecord objects.
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"""
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# Census profiles are typically in CSV/datastore format
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try:
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raw_records = self._fetch_csv_as_json(
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self.DATASETS["neighbourhood_profiles"]
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)
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except ValueError as e:
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logger.warning(f"Could not fetch census profiles: {e}")
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return []
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# Census profiles are pivoted - rows are indicators, columns are neighbourhoods
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# This requires special handling based on the actual data structure
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logger.info(f"Fetched {len(raw_records)} census profile rows")
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# For now, return empty list - actual implementation depends on data structure
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# TODO: Implement census profile parsing based on actual data format
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return []
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def get_parks(self) -> list[AmenityRecord]:
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"""Fetch park locations.
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Returns:
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List of validated AmenityRecord objects.
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"""
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return self._fetch_amenities(
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self.DATASETS["parks"],
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AmenityType.PARK,
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name_field="ASSET_NAME",
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address_field="ADDRESS_FULL",
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)
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def get_schools(self) -> list[AmenityRecord]:
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"""Fetch school locations.
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Returns:
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List of validated AmenityRecord objects.
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"""
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return self._fetch_amenities(
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self.DATASETS["schools"],
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AmenityType.SCHOOL,
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name_field="NAME",
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address_field="ADDRESS_FULL",
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)
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def get_childcare_centres(self) -> list[AmenityRecord]:
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"""Fetch licensed childcare centre locations.
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Returns:
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List of validated AmenityRecord objects.
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"""
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return self._fetch_amenities(
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self.DATASETS["childcare"],
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AmenityType.CHILDCARE,
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name_field="LOC_NAME",
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address_field="ADDRESS",
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)
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def _fetch_amenities(
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self,
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package_id: str,
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amenity_type: AmenityType,
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name_field: str,
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address_field: str,
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) -> list[AmenityRecord]:
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"""Fetch and parse amenity data from GeoJSON.
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Args:
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package_id: CKAN package ID.
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amenity_type: Type of amenity.
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name_field: Property name containing amenity name.
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address_field: Property name containing address.
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Returns:
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List of AmenityRecord objects.
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"""
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try:
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geojson = self._fetch_geojson(package_id)
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except (httpx.HTTPError, ValueError) as e:
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logger.warning(f"Could not fetch {package_id}: {e}")
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return []
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features = geojson.get("features", [])
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records = []
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for feature in features:
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props = feature.get("properties", {})
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geometry = feature.get("geometry")
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# Get coordinates from geometry
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lat, lon = None, None
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if geometry and geometry.get("type") == "Point":
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coords = geometry.get("coordinates", [])
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if len(coords) >= 2:
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lon, lat = coords[0], coords[1]
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# Try to determine neighbourhood_id
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# Many datasets include AREA_ID or similar
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neighbourhood_id = (
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props.get("AREA_ID")
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or props.get("area_id")
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or props.get("NEIGHBOURHOOD_ID")
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or 0 # Will need spatial join if not available
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)
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name = props.get(name_field) or props.get(name_field.lower()) or "Unknown"
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address = props.get(address_field) or props.get(address_field.lower())
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# Skip if we don't have a neighbourhood assignment
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if neighbourhood_id == 0:
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continue
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records.append(
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AmenityRecord(
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neighbourhood_id=int(neighbourhood_id),
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amenity_type=amenity_type,
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amenity_name=str(name)[:200],
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address=str(address)[:300] if address else None,
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latitude=Decimal(str(lat)) if lat else None,
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longitude=Decimal(str(lon)) if lon else None,
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)
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)
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logger.info(f"Parsed {len(records)} {amenity_type.value} records")
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return records
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371
portfolio_app/toronto/parsers/toronto_police.py
Normal file
371
portfolio_app/toronto/parsers/toronto_police.py
Normal file
@@ -0,0 +1,371 @@
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"""Parser for Toronto Police crime data via CKAN API.
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Fetches neighbourhood crime rates and major crime indicators from the
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Toronto Police Service data hosted on Toronto Open Data Portal.
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Data Sources:
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- Neighbourhood Crime Rates: Annual crime rates by neighbourhood
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- Major Crime Indicators (MCI): Detailed incident-level data
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"""
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import contextlib
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import logging
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from decimal import Decimal
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from typing import Any
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import httpx
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from portfolio_app.toronto.schemas import CrimeRecord, CrimeType
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logger = logging.getLogger(__name__)
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# Mapping from Toronto Police crime categories to CrimeType enum
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CRIME_TYPE_MAPPING: dict[str, CrimeType] = {
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"assault": CrimeType.ASSAULT,
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"assaults": CrimeType.ASSAULT,
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"auto theft": CrimeType.AUTO_THEFT,
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"autotheft": CrimeType.AUTO_THEFT,
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"auto_theft": CrimeType.AUTO_THEFT,
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"break and enter": CrimeType.BREAK_AND_ENTER,
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"breakenter": CrimeType.BREAK_AND_ENTER,
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"break_and_enter": CrimeType.BREAK_AND_ENTER,
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"homicide": CrimeType.HOMICIDE,
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"homicides": CrimeType.HOMICIDE,
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"robbery": CrimeType.ROBBERY,
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"robberies": CrimeType.ROBBERY,
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"shooting": CrimeType.SHOOTING,
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"shootings": CrimeType.SHOOTING,
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"theft over": CrimeType.THEFT_OVER,
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"theftover": CrimeType.THEFT_OVER,
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"theft_over": CrimeType.THEFT_OVER,
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"theft from motor vehicle": CrimeType.THEFT_FROM_MOTOR_VEHICLE,
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"theftfrommv": CrimeType.THEFT_FROM_MOTOR_VEHICLE,
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"theft_from_mv": CrimeType.THEFT_FROM_MOTOR_VEHICLE,
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}
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def _normalize_crime_type(crime_str: str) -> CrimeType:
|
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"""Normalize crime type string to CrimeType enum.
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|
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Args:
|
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crime_str: Raw crime type string from data source.
|
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|
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Returns:
|
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Matched CrimeType enum value, or CrimeType.OTHER if no match.
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"""
|
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normalized = crime_str.lower().strip().replace("-", " ").replace("_", " ")
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return CRIME_TYPE_MAPPING.get(normalized, CrimeType.OTHER)
|
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|
||||
|
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class TorontoPoliceParser:
|
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"""Parser for Toronto Police crime data via CKAN API.
|
||||
|
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Crime data is hosted on Toronto Open Data Portal but sourced from
|
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Toronto Police Service.
|
||||
"""
|
||||
|
||||
BASE_URL = "https://ckan0.cf.opendata.inter.prod-toronto.ca"
|
||||
API_PATH = "/api/3/action"
|
||||
|
||||
# Dataset package IDs
|
||||
DATASETS = {
|
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"crime_rates": "neighbourhood-crime-rates",
|
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"mci": "major-crime-indicators",
|
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"shootings": "shootings-firearm-discharges",
|
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}
|
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|
||||
def __init__(self, timeout: float = 30.0) -> None:
|
||||
"""Initialize parser.
|
||||
|
||||
Args:
|
||||
timeout: HTTP request timeout in seconds.
|
||||
"""
|
||||
self._timeout = timeout
|
||||
self._client: httpx.Client | None = None
|
||||
|
||||
@property
|
||||
def client(self) -> httpx.Client:
|
||||
"""Lazy-initialize HTTP client."""
|
||||
if self._client is None:
|
||||
self._client = httpx.Client(
|
||||
base_url=self.BASE_URL,
|
||||
timeout=self._timeout,
|
||||
headers={"Accept": "application/json"},
|
||||
)
|
||||
return self._client
|
||||
|
||||
def close(self) -> None:
|
||||
"""Close HTTP client."""
|
||||
if self._client is not None:
|
||||
self._client.close()
|
||||
self._client = None
|
||||
|
||||
def __enter__(self) -> "TorontoPoliceParser":
|
||||
return self
|
||||
|
||||
def __exit__(self, *args: Any) -> None:
|
||||
self.close()
|
||||
|
||||
def _get_package(self, package_id: str) -> dict[str, Any]:
|
||||
"""Fetch package metadata from CKAN."""
|
||||
response = self.client.get(
|
||||
f"{self.API_PATH}/package_show",
|
||||
params={"id": package_id},
|
||||
)
|
||||
response.raise_for_status()
|
||||
result = response.json()
|
||||
|
||||
if not result.get("success"):
|
||||
raise ValueError(f"CKAN API error: {result.get('error', 'Unknown error')}")
|
||||
|
||||
return dict(result["result"])
|
||||
|
||||
def _fetch_datastore_records(
|
||||
self,
|
||||
package_id: str,
|
||||
filters: dict[str, Any] | None = None,
|
||||
) -> list[dict[str, Any]]:
|
||||
"""Fetch records from CKAN datastore.
|
||||
|
||||
Args:
|
||||
package_id: CKAN package ID.
|
||||
filters: Optional filters to apply.
|
||||
|
||||
Returns:
|
||||
List of records as dictionaries.
|
||||
"""
|
||||
package = self._get_package(package_id)
|
||||
resources = package.get("resources", [])
|
||||
|
||||
# Find datastore-enabled resource
|
||||
resource_id = None
|
||||
for resource in resources:
|
||||
if resource.get("datastore_active"):
|
||||
resource_id = resource["id"]
|
||||
break
|
||||
|
||||
if not resource_id:
|
||||
raise ValueError(f"No datastore resource in {package_id}")
|
||||
|
||||
# Fetch all records
|
||||
records: list[dict[str, Any]] = []
|
||||
offset = 0
|
||||
limit = 1000
|
||||
|
||||
while True:
|
||||
params: dict[str, Any] = {
|
||||
"id": resource_id,
|
||||
"limit": limit,
|
||||
"offset": offset,
|
||||
}
|
||||
if filters:
|
||||
params["filters"] = str(filters)
|
||||
|
||||
response = self.client.get(
|
||||
f"{self.API_PATH}/datastore_search",
|
||||
params=params,
|
||||
)
|
||||
response.raise_for_status()
|
||||
result = response.json()
|
||||
|
||||
if not result.get("success"):
|
||||
raise ValueError(f"Datastore error: {result.get('error')}")
|
||||
|
||||
batch = result["result"]["records"]
|
||||
records.extend(batch)
|
||||
|
||||
if len(batch) < limit:
|
||||
break
|
||||
offset += limit
|
||||
|
||||
return records
|
||||
|
||||
def get_crime_rates(
|
||||
self,
|
||||
years: list[int] | None = None,
|
||||
) -> list[CrimeRecord]:
|
||||
"""Fetch neighbourhood crime rates.
|
||||
|
||||
The crime rates dataset contains annual counts and rates per 100k
|
||||
population for each neighbourhood.
|
||||
|
||||
Args:
|
||||
years: Optional list of years to filter. If None, fetches all.
|
||||
|
||||
Returns:
|
||||
List of validated CrimeRecord objects.
|
||||
"""
|
||||
try:
|
||||
raw_records = self._fetch_datastore_records(self.DATASETS["crime_rates"])
|
||||
except (httpx.HTTPError, ValueError) as e:
|
||||
logger.warning(f"Could not fetch crime rates: {e}")
|
||||
return []
|
||||
|
||||
records = []
|
||||
|
||||
for row in raw_records:
|
||||
# Extract neighbourhood ID (Hood_ID maps to AREA_ID)
|
||||
hood_id = row.get("HOOD_ID") or row.get("Hood_ID") or row.get("hood_id")
|
||||
if not hood_id:
|
||||
continue
|
||||
|
||||
try:
|
||||
neighbourhood_id = int(hood_id)
|
||||
except (ValueError, TypeError):
|
||||
continue
|
||||
|
||||
# Crime rate data typically has columns like:
|
||||
# ASSAULT_2019, ASSAULT_RATE_2019, AUTOTHEFT_2020, etc.
|
||||
# We need to parse column names to extract crime type and year
|
||||
|
||||
for col_name, value in row.items():
|
||||
if value is None or col_name in (
|
||||
"_id",
|
||||
"HOOD_ID",
|
||||
"Hood_ID",
|
||||
"hood_id",
|
||||
"AREA_NAME",
|
||||
"NEIGHBOURHOOD",
|
||||
):
|
||||
continue
|
||||
|
||||
# Try to parse column name for crime type and year
|
||||
# Pattern: CRIMETYPE_YEAR or CRIMETYPE_RATE_YEAR
|
||||
parts = col_name.upper().split("_")
|
||||
if len(parts) < 2:
|
||||
continue
|
||||
|
||||
# Check if last part is a year
|
||||
try:
|
||||
year = int(parts[-1])
|
||||
if year < 2014 or year > 2030:
|
||||
continue
|
||||
except ValueError:
|
||||
continue
|
||||
|
||||
# Filter by years if specified
|
||||
if years and year not in years:
|
||||
continue
|
||||
|
||||
# Check if this is a rate column
|
||||
is_rate = "RATE" in parts
|
||||
|
||||
# Extract crime type (everything before RATE/year)
|
||||
if is_rate:
|
||||
rate_idx = parts.index("RATE")
|
||||
crime_type_str = "_".join(parts[:rate_idx])
|
||||
else:
|
||||
crime_type_str = "_".join(parts[:-1])
|
||||
|
||||
crime_type = _normalize_crime_type(crime_type_str)
|
||||
|
||||
try:
|
||||
numeric_value = Decimal(str(value))
|
||||
except (ValueError, TypeError):
|
||||
continue
|
||||
|
||||
if is_rate:
|
||||
# This is a rate column - look for corresponding count
|
||||
# We'll skip rate-only entries and create records from counts
|
||||
continue
|
||||
|
||||
# Find corresponding rate if available
|
||||
rate_col = f"{crime_type_str}_RATE_{year}"
|
||||
rate_value = row.get(rate_col)
|
||||
rate_per_100k = None
|
||||
if rate_value is not None:
|
||||
with contextlib.suppress(ValueError, TypeError):
|
||||
rate_per_100k = Decimal(str(rate_value))
|
||||
|
||||
records.append(
|
||||
CrimeRecord(
|
||||
neighbourhood_id=neighbourhood_id,
|
||||
year=year,
|
||||
crime_type=crime_type,
|
||||
count=int(numeric_value),
|
||||
rate_per_100k=rate_per_100k,
|
||||
)
|
||||
)
|
||||
|
||||
logger.info(f"Parsed {len(records)} crime rate records")
|
||||
return records
|
||||
|
||||
def get_major_crime_indicators(
|
||||
self,
|
||||
years: list[int] | None = None,
|
||||
) -> list[CrimeRecord]:
|
||||
"""Fetch major crime indicators (detailed MCI data).
|
||||
|
||||
MCI data contains incident-level records that need to be aggregated
|
||||
by neighbourhood and year.
|
||||
|
||||
Args:
|
||||
years: Optional list of years to filter.
|
||||
|
||||
Returns:
|
||||
List of aggregated CrimeRecord objects.
|
||||
"""
|
||||
try:
|
||||
raw_records = self._fetch_datastore_records(self.DATASETS["mci"])
|
||||
except (httpx.HTTPError, ValueError) as e:
|
||||
logger.warning(f"Could not fetch MCI data: {e}")
|
||||
return []
|
||||
|
||||
# Aggregate counts by neighbourhood, year, and crime type
|
||||
aggregates: dict[tuple[int, int, CrimeType], int] = {}
|
||||
|
||||
for row in raw_records:
|
||||
# Extract neighbourhood ID
|
||||
hood_id = (
|
||||
row.get("HOOD_158")
|
||||
or row.get("HOOD_140")
|
||||
or row.get("HOOD_ID")
|
||||
or row.get("Hood_ID")
|
||||
)
|
||||
if not hood_id:
|
||||
continue
|
||||
|
||||
try:
|
||||
neighbourhood_id = int(hood_id)
|
||||
except (ValueError, TypeError):
|
||||
continue
|
||||
|
||||
# Extract year from occurrence date
|
||||
occ_year = row.get("OCC_YEAR") or row.get("REPORT_YEAR")
|
||||
if not occ_year:
|
||||
continue
|
||||
|
||||
try:
|
||||
year = int(occ_year)
|
||||
if year < 2014 or year > 2030:
|
||||
continue
|
||||
except (ValueError, TypeError):
|
||||
continue
|
||||
|
||||
# Filter by years if specified
|
||||
if years and year not in years:
|
||||
continue
|
||||
|
||||
# Extract crime type
|
||||
mci_category = row.get("MCI_CATEGORY") or row.get("OFFENCE") or ""
|
||||
crime_type = _normalize_crime_type(str(mci_category))
|
||||
|
||||
# Aggregate count
|
||||
key = (neighbourhood_id, year, crime_type)
|
||||
aggregates[key] = aggregates.get(key, 0) + 1
|
||||
|
||||
# Convert aggregates to CrimeRecord objects
|
||||
records = [
|
||||
CrimeRecord(
|
||||
neighbourhood_id=neighbourhood_id,
|
||||
year=year,
|
||||
crime_type=crime_type,
|
||||
count=count,
|
||||
rate_per_100k=None, # Would need population data to calculate
|
||||
)
|
||||
for (neighbourhood_id, year, crime_type), count in aggregates.items()
|
||||
]
|
||||
|
||||
logger.info(f"Parsed {len(records)} MCI records (aggregated)")
|
||||
return records
|
||||
Reference in New Issue
Block a user