PyData Amsterdam 2024

How to measure a city
09-20, 14:55–15:30 (Europe/Amsterdam), Rembrandt

This story is about urbanism, geospatial analysis, and when people look for numbers to get facts, when in fact, numbers are opinions. You will learn how some cities in Canada are designing indicators to measure how livable they are, the tradeoffs for good metric design and how your methodology is encoding your opinions into the numbers. This talk will benefit anyone using data to support decisions, and no prior knowledge is required.


What if your city decided to set a goal to have 70% of its population living in “complete living environments” by 2035? And you’re the one supposed to figure out how to measure that? I’ll tell you the story of how I pulled this off, with some lessons learned along the way. More specifically:
- How did I measure a “complete living environment” ? (including a mention of geospatial tools, but no code)
- The 5 properties of a good metric, whether you’re measuring a city or a business, and how it affected our methodology choices
- How weirdly political weighting different modes of transit turned out to be, and tips to navigate this kind of situation
- What is missing from this work, and why it’s essential to consider (spoiler alert: it’s social justice)

This talk will benefit anyone using data to support decisions, for instance data scientists, and no prior knowledge is required.


Time breakdown:
(Min 0-5) Introduction: Montreal wants to measure complete living environments
(min 5-10) The 5 metrics principles
(min 10-15) General methodology : we count stuff within polygons. Link with 2 principles
(min 15-25)Transit methodology : weights are opinions. How to deal with politics? Link with 3 principles
(min 25-28) The missing ingredient : social justice
(min 28-30) Conclusion

Françoise is leading the Data team at Local Logic, a Montreal-based location-intelligence startup, where she builds data products and tells data stories. Before joining Local Logic, she did a PhD in experimental physics, a postdoctoral fellowship in machine learning, organized PyLadies Montreal and had a prolific 7 years working at Shopify. Although she loves the switch to data science, she secretly misses shooting lasers at stuff.