Presenter Notes
Rendered talk track for the TorontoList story deck.
TorontoList Data Story Presenter Notes
1. A single local graph of Toronto activity.
169,269 indexed records across events, places, people, services, and City of Toronto business licence history.
Speaker note: Open by framing TorontoList as a practical local data graph: not one dataset, but a connected city index across culture, places, people, services, and municipal licensing.
Chart: Dataset Inventory
2. Business licences are the backbone dataset.
158,709 historical City licence records, 37,340 active records, and 121,369 cancelled records.
Speaker note: Use this slide to anchor credibility. The licence archive is the largest, most structured Open Data input and gives the rest of the product a durable civic backbone.
Chart: Business Category Status Stack
3. Business activity is uneven across the city.
Ward charts show where licence density concentrates and how active/cancelled balances differ by local geography.
Speaker note: Point out that this is not just a directory. It can answer geographic questions: which wards have concentration, which categories appear where, and where data needs enrichment.
Chart: Category x Ward
4. Food and drink is the largest story.
The drilldown atlas can isolate food and drink by ward, raw licence class, and active status.
Speaker note: Food and drink is the most intuitive public example. It makes the system understandable because everyone knows restaurants, takeout, retail food, and food trucks.
Chart: Licence Category Ward Matrix: Food and Drink
5. The archive spans from 1946 to current updates.
The latest local City record update in this pack is 2026-05-27.
Speaker note: This turns the dataset into a time machine. Emphasize that the system can show historical shape, current operations, and update cadence.
Chart: Issued by Year
6. Issuance patterns are visible by category.
Month-by-category heatmaps reveal waves of licensing and operational shifts.
Speaker note: Use this to show motion. Monthly heatmaps make spikes and quiet periods visible without manually reading CSVs.
Chart: Issue Month x Category
7. Streets reveal Toronto's commercial spine.
Yonge, Bloor, Dundas, Danforth, Queen, and King dominate the corridor analysis.
Speaker note: This is the corridor view. It is useful for neighbourhood, retail, and economic-development conversations because street names are legible to non-technical audiences.
Chart: Business Corridors
8. Conditions and endorsements expose operational rules.
Licence conditions show seating, zoning, subcontracting, and use constraints across categories.
Speaker note: This slide is about operational detail. The data goes beyond names and addresses into constraints, permissions, and compliance signals.
Chart: Condition x Category
9. Postal and source coverage show where the data is strongest.
FSA and source charts help explain confidence, coverage, and where additional enrichment should go next.
Speaker note: Coverage charts are important for trust. They make it clear where records are strong, where fields are missing, and what should be improved next.
Chart: Dataset x Source
10. The chart engine is reusable.
Every chart is generated from CSV/JSON into browser-ready output. Curated charts export to SVG, Vega-Lite, ECharts, Plotly, and Observable Plot.
Speaker note: Close by shifting from charts to system. The point is not one-off visualization; it is a repeatable chart engine for city data storytelling.
Chart: Open Data Coverage