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>

portfolio_app/toronto/loaders/census.py

@@ -0,0 +1,68 @@
+"""Loader for census data to fact_census table."""
+
+from sqlalchemy.orm import Session
+
+from portfolio_app.toronto.models import FactCensus
+from portfolio_app.toronto.schemas import CensusRecord
+
+from .base import get_session, upsert_by_key
+
+
+def load_census_data(
+    records: list[CensusRecord],
+    session: Session | None = None,
+) -> int:
+    """Load census records to fact_census table.
+
+    Args:
+        records: List of validated CensusRecord schemas.
+        session: Optional existing session.
+
+    Returns:
+        Number of records loaded (inserted + updated).
+    """
+
+    def _load(sess: Session) -> int:
+        models = []
+        for r in records:
+            model = FactCensus(
+                neighbourhood_id=r.neighbourhood_id,
+                census_year=r.census_year,
+                population=r.population,
+                population_density=float(r.population_density)
+                if r.population_density
+                else None,
+                median_household_income=float(r.median_household_income)
+                if r.median_household_income
+                else None,
+                average_household_income=float(r.average_household_income)
+                if r.average_household_income
+                else None,
+                unemployment_rate=float(r.unemployment_rate)
+                if r.unemployment_rate
+                else None,
+                pct_bachelors_or_higher=float(r.pct_bachelors_or_higher)
+                if r.pct_bachelors_or_higher
+                else None,
+                pct_owner_occupied=float(r.pct_owner_occupied)
+                if r.pct_owner_occupied
+                else None,
+                pct_renter_occupied=float(r.pct_renter_occupied)
+                if r.pct_renter_occupied
+                else None,
+                median_age=float(r.median_age) if r.median_age else None,
+                average_dwelling_value=float(r.average_dwelling_value)
+                if r.average_dwelling_value
+                else None,
+            )
+            models.append(model)
+
+        inserted, updated = upsert_by_key(
+            sess, FactCensus, models, ["neighbourhood_id", "census_year"]
+        )
+        return inserted + updated
+
+    if session:
+        return _load(session)
+    with get_session() as sess:
+        return _load(sess)