personal-portfolio/docs/PROJECT_REFERENCE.md

Portfolio Project Reference

Project: Analytics Portfolio Owner: Leo Miranda Status: Sprint 9 Complete (Dashboard Implementation Done) Last Updated: January 2026

---

Project Overview

Personal portfolio website with an interactive Toronto Neighbourhood Dashboard demonstrating data engineering, visualization, and analytics capabilities.

Component	Description	Status
Portfolio Website	Bio, About, Projects, Resume, Contact, Blog	Complete
Toronto Dashboard	5-tab neighbourhood analysis	Complete
Data Pipeline	dbt models, figure factories	Complete
Deployment	Production deployment	Pending

---

Completed Work

Sprint 1-6: Foundation

• Repository setup, Docker, PostgreSQL + PostGIS
• Bio landing page implementation
• Initial data model design

Sprint 7: Navigation & Theme

• Sidebar navigation
• Dark/light theme toggle
• dash-mantine-components integration

Sprint 8: Portfolio Website

• About, Contact, Projects, Resume pages
• Blog system with Markdown/frontmatter
• Health endpoint

Sprint 9: Neighbourhood Dashboard Transition

• Phase 1: Deleted legacy TRREB code
• Phase 2: Documentation cleanup
• Phase 3: New neighbourhood-centric data model
• Phase 4: dbt model restructuring
• Phase 5: 5-tab dashboard implementation
• Phase 6: 15 documentation notebooks
• Phase 7: Final documentation review

---

Application Architecture

URL Routes

URL	Page	File
/	Home	pages/home.py
/about	About	pages/about.py
/contact	Contact	pages/contact.py
/projects	Projects	pages/projects.py
/resume	Resume	pages/resume.py
/blog	Blog listing	pages/blog/index.py
/blog/{slug}	Article	pages/blog/article.py
/toronto	Dashboard	pages/toronto/dashboard.py
/toronto/methodology	Methodology	pages/toronto/methodology.py
/health	Health check	pages/health.py

Directory Structure

portfolio_app/
├── app.py                    # Dash app factory
├── config.py                 # Pydantic BaseSettings
├── assets/                   # CSS, images
├── callbacks/                # Global callbacks (sidebar, theme)
├── components/               # Shared UI components
├── content/blog/             # Markdown blog articles
├── errors/                   # Exception handling
├── figures/                  # Plotly figure factories
├── pages/
│   ├── home.py
│   ├── about.py
│   ├── contact.py
│   ├── projects.py
│   ├── resume.py
│   ├── health.py
│   ├── blog/
│   │   ├── index.py
│   │   └── article.py
│   └── toronto/
│       ├── dashboard.py
│       ├── methodology.py
│       ├── tabs/             # 5 tab layouts
│       └── callbacks/        # Dashboard interactions
├── toronto/                  # Data logic
│   ├── parsers/              # API extraction
│   ├── loaders/              # Database operations
│   ├── schemas/              # Pydantic models
│   ├── models/               # SQLAlchemy ORM
│   └── demo_data.py          # Sample data
└── utils/
    └── markdown_loader.py    # Blog article loading

---

Toronto Dashboard

Data Sources

Source	Data	Format
City of Toronto Open Data	Neighbourhoods (158), Census profiles, Parks, Schools, Childcare, TTC	GeoJSON, CSV, API
Toronto Police Service	Crime rates, MCI, Shootings	CSV, API
CMHC	Rental Market Survey	CSV

Geographic Model

City of Toronto Neighbourhoods (158) ← Primary analysis unit
CMHC Zones (~20) ← Rental data (Census Tract aligned)

Dashboard Tabs

Tab	Choropleth Metric	Supporting Charts
Overview	Livability score	Top/Bottom 10 bar, Income vs Safety scatter
Housing	Affordability index	Rent trend line, Tenure breakdown bar
Safety	Crime rate per 100K	Crime breakdown bar, Crime trend line
Demographics	Median income	Age distribution, Population density bar
Amenities	Amenity index	Amenity radar, Transit accessibility bar

Star Schema

Table	Type	Description
dim_neighbourhood	Dimension	158 neighbourhoods with geometry
dim_time	Dimension	Date dimension
dim_cmhc_zone	Dimension	~20 CMHC zones with geometry
fact_census	Fact	Census indicators by neighbourhood
fact_crime	Fact	Crime stats by neighbourhood
fact_rentals	Fact	Rental data by CMHC zone
fact_amenities	Fact	Amenity counts by neighbourhood

dbt Layers

Layer	Naming	Example
Staging	stg_{source}__{entity}	stg_toronto__neighbourhoods
Intermediate	int_{domain}__{transform}	int_neighbourhood__demographics
Marts	mart_{domain}	mart_neighbourhood_overview

---

Tech Stack

Layer	Technology	Version
Database	PostgreSQL + PostGIS	16.x
Validation	Pydantic	2.x
ORM	SQLAlchemy	2.x
Transformation	dbt-postgres	1.7+
Data Processing	Pandas, GeoPandas	Latest
Visualization	Dash + Plotly	2.14+
UI Components	dash-mantine-components	Latest
Testing	pytest	7.0+
Python	3.11+	Via pyenv

---

Branching Strategy

Branch	Purpose	Deploys To
main	Production releases	VPS (production)
staging	Pre-production testing	VPS (staging)
development	Active development	Local only

Rules:

• Feature branches from development: feature/{sprint}-{description}
• Merge into development when complete
• development → staging → main for releases
• Never delete development

---

Code Standards

Type Hints (Python 3.10+)

def process(items: list[str], config: dict[str, int] | None = None) -> bool:
    ...

Imports

Context	Style
Same directory	from .module import X
Sibling directory	from ..schemas.model import Y
External	import pandas as pd

Error Handling

class PortfolioError(Exception):
    """Base exception."""

class ParseError(PortfolioError):
    """Data parsing failed."""

class ValidationError(PortfolioError):
    """Validation failed."""

class LoadError(PortfolioError):
    """Database load failed."""

---

Environment Variables

Required in .env:

DATABASE_URL=postgresql://user:pass@localhost:5432/portfolio
POSTGRES_USER=portfolio
POSTGRES_PASSWORD=<secure>
POSTGRES_DB=portfolio
DASH_DEBUG=true
SECRET_KEY=<random>
LOG_LEVEL=INFO

---

Makefile Targets

Target	Purpose
setup	Install deps, create .env, init pre-commit
docker-up	Start PostgreSQL + PostGIS
docker-down	Stop containers
db-init	Initialize database schema
run	Start Dash dev server
test	Run pytest
dbt-run	Run dbt models
dbt-test	Run dbt tests
lint	Run ruff linter
format	Run ruff formatter
ci	Run all checks

---

Next Steps

Deployment (Sprint 10+)

• Production Docker configuration
• CI/CD pipeline
• HTTPS/SSL setup
• Domain configuration

Data Enhancement

• Connect to live APIs (currently using demo data)
• Data refresh automation
• Historical data loading

Future Projects

• Energy Pricing Analysis dashboard (planned)

---

Related Documents

Document	Purpose
README.md	Quick start guide
CLAUDE.md	AI assistant context
docs/CONTRIBUTING.md	Developer guide
notebooks/README.md	Notebook documentation

---

Reference Version: 3.0 Updated: January 2026