Articles filed under Data Stories


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Guest Post: Using Folium to Visualize Distribution of Public Services in 140 Toronto Neighbourhoods

Lisa Chen reached out to the open data team with the visualizations she created from two open datasets: Toronto Neighbourhood Boundaries and Youth Wellness Resources.

Towards a Data Quality Score in open data (Part 2)

In my first story on Open Data Toronto’s Data Quality Score (DQS), I shared why data quality matters to us and what the DQS is at a high-level; in this story, I walk through the steps of exactly how we created it so it is more detailed and a little more technical. Read on if that’s your jam.

Open Data: A product approach

Open data at the City of Toronto has been up and running for a decade this year, with an amazing and active community. However, this is not the case everywhere, and outside of our exceptional community, open data is still unknown by many. Why?

Towards a Data Quality Score in Open Data

Traditionally, Open Data Toronto program performance has been tied to the number of datasets in the catalogue. Today, however, catalogue size is less relevant primarily because it fails to measure progress towards the program’s vision of enabling anyone, anywhere, to improve life in Toronto with open data. To solve this issue, we created a score to assess data quality and what it measures.

Analyzing Lobbyist Data

The Office of the Lobbyist Registrar ensures the public disclosure of lobbying activities and oversees the regulation of lobbyists’ conduct. The City of Toronto publishes this data through the Lobbyist Registry Disclosure Site and through the Open Data Portal.

Strategies for working with new data

We’re excited to present our first community story by Sharla Gelfand, which looks at how to start working with open data in R.

Exploring Toronto voter statistics using Golang

Yizhao Tan takes us through how to use the GO programming language to better understand voter turnouts and patterns in Toronto.

Measuring Walking Times Across Toronto to Nearest TTC Stop

In this tutorial, we analyze walk times from every address point within the City of Toronto limits to the closest Toronto Transit Commission (TTC) Stops. TTC stops can include subway, LRT, street car and bus. The analysis uses Pandana to perform the network distance calculations on a new open data set called the “Pedestrian Network” that our team created in conjunction with Transportation Services to better understand walkable access to various amenities across Toronto.

Towards Data Analytics: Data literacy in R

Data is everywhere, and is a critical component in every faction of work. Understanding data is essential for evidence based decision making. Within most organizations Excel is King, the application that is used to collect, share, manipulate, analyze, and communicate information from data.

Exploring Bike Share Ridership

The weather is finally warming up and Torontonians are venturing outside to catch some sun. One thing is for certain: we love our bikes, and we’ll ride them everywhere. The Open Data team’s own Yizhao Tan decided to work with the open data provided by the City to better understand how Torontonians use bike share. Learn more about how Yizhao uses data visualization and analysis libraries, as well as data cleaning tools, to demonstrate how to turn cycling open data into insights.

Exploring Cleared Building Permits

The City of Toronto publishes data on building permits going as far back as 2000. Excited about this, we at the Open Data Team asked ourselves: how might we learn from the Cleared Building Permits? Could we use it for improving our understanding of Toronto and how we deliver services to the community?