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/schemas/neighbourhood.py

@@ -0,0 +1,106 @@
+"""Pydantic schemas for Toronto neighbourhood data.
+
+Includes schemas for neighbourhood boundaries, census profiles, and crime statistics.
+"""
+
+from decimal import Decimal
+from enum import Enum
+from typing import Any
+
+from pydantic import BaseModel, Field
+
+
+class CrimeType(str, Enum):
+    """Major crime indicator types from Toronto Police data."""
+
+    ASSAULT = "assault"
+    AUTO_THEFT = "auto_theft"
+    BREAK_AND_ENTER = "break_and_enter"
+    HOMICIDE = "homicide"
+    ROBBERY = "robbery"
+    SHOOTING = "shooting"
+    THEFT_OVER = "theft_over"
+    THEFT_FROM_MOTOR_VEHICLE = "theft_from_motor_vehicle"
+    OTHER = "other"
+
+
+class NeighbourhoodRecord(BaseModel):
+    """Schema for Toronto neighbourhood boundary data.
+
+    Based on City of Toronto's 158 neighbourhoods dataset.
+    AREA_ID maps to neighbourhood_id for consistency with police data (Hood_ID).
+    """
+
+    area_id: int = Field(description="AREA_ID from Toronto Open Data (1-158)")
+    area_name: str = Field(max_length=100, description="Official neighbourhood name")
+    area_short_code: str | None = Field(
+        default=None, max_length=10, description="Short code (e.g., 'E01')"
+    )
+    geometry: dict[str, Any] | None = Field(
+        default=None, description="GeoJSON geometry object"
+    )
+
+    model_config = {"str_strip_whitespace": True}
+
+
+class CensusRecord(BaseModel):
+    """Census profile data for a neighbourhood.
+
+    Contains demographic and socioeconomic indicators from Statistics Canada
+    census data, aggregated to the neighbourhood level.
+    """
+
+    neighbourhood_id: int = Field(
+        ge=1, le=200, description="Neighbourhood ID (AREA_ID)"
+    )
+    census_year: int = Field(ge=2016, le=2030, description="Census year")
+    population: int | None = Field(default=None, ge=0, description="Total population")
+    population_density: Decimal | None = Field(
+        default=None, ge=0, description="Population per square kilometre"
+    )
+    median_household_income: Decimal | None = Field(
+        default=None, ge=0, description="Median household income (CAD)"
+    )
+    average_household_income: Decimal | None = Field(
+        default=None, ge=0, description="Average household income (CAD)"
+    )
+    unemployment_rate: Decimal | None = Field(
+        default=None, ge=0, le=100, description="Unemployment rate percentage"
+    )
+    pct_bachelors_or_higher: Decimal | None = Field(
+        default=None, ge=0, le=100, description="Percentage with bachelor's degree+"
+    )
+    pct_owner_occupied: Decimal | None = Field(
+        default=None, ge=0, le=100, description="Percentage owner-occupied dwellings"
+    )
+    pct_renter_occupied: Decimal | None = Field(
+        default=None, ge=0, le=100, description="Percentage renter-occupied dwellings"
+    )
+    median_age: Decimal | None = Field(
+        default=None, ge=0, le=120, description="Median age of residents"
+    )
+    average_dwelling_value: Decimal | None = Field(
+        default=None, ge=0, description="Average dwelling value (CAD)"
+    )
+
+    model_config = {"str_strip_whitespace": True}
+
+
+class CrimeRecord(BaseModel):
+    """Crime statistics for a neighbourhood.
+
+    Based on Toronto Police neighbourhood crime rates data.
+    Hood_ID in source data maps to neighbourhood_id (AREA_ID).
+    """
+
+    neighbourhood_id: int = Field(
+        ge=1, le=200, description="Neighbourhood ID (Hood_ID -> AREA_ID)"
+    )
+    year: int = Field(ge=2014, le=2030, description="Year of crime statistics")
+    crime_type: CrimeType = Field(description="Type of crime (MCI category)")
+    count: int = Field(ge=0, description="Number of incidents")
+    rate_per_100k: Decimal | None = Field(
+        default=None, ge=0, description="Rate per 100,000 population"
+    )
+
+    model_config = {"str_strip_whitespace": True}