The Importance of Context

Recently I was looking at some data and I noticed a trend in a neighbourhood surrounding a community centre that was evaluating the effectiveness of their poverty reduction work. The number of families classified as having a low income had decreased over recently (Neighbourhood A). Several nearby neighbourhoods (Neighbourhoods B and C) had definitely not seen this decrease.


(Shout out to Stephanie Evergreen for forever changing my life with small multiples)

At first glance this looked promising – had the poverty reduction campaign contributed to this? People were excited but I had my reservations about claiming success so quickly.

If you’ve recently visited Toronto you know that there are building cranes everywhere. Neighbourhoods are changing (read: gentrifying) very, very quickly as luxury condos go up and lower income families are driven further and further out of the core. It was possible that the income level of residents hadn’t changed – perhaps the low income residents had moved out and more affluent residents had moved in. First piece of evidence: Neighbourhood A had four condominium projects completed in that time frame whereas Neighbourhood B had one and Neighbourhood C had zero.

Next we looked at demographics. Canada completes a census every five years. We had could compare 2006 and 2011 data as the 2016 is not yet available. Second piece of evidence: Neighbourhood A had decreases in children, youth, and seniors (and families overall) but an increase in working age adults). The change wasn’t near as drastic in Neighbourhoods B and C.

Fortunately we had a lot of other data to look at in order to evaluate the program but I thought that this was a nice illustration of why it’s really important to look at the context behind the data and examine other possible explanations before claiming success.



data viz tools

Awhile ago I posted about the data viz catalogue. It’s a neat resource that helps you choose a visualization that best tells the story of your data. The creator has recently posted a roundup of the 20 best tools for data visualization. It includes tools that have no coding required as well as tools for developers. There were definitely a couple that were new to me and I look forward to checking them out.

On my 2016 to-do list: learn enough coding that I can play around with the dev tools.

Data Viz Catalogue

I just came across this great data visualization resource through BetterEvaluation – the Data Visualization Catalogue. Choosing a chart or other visualization type that best tells the story of your findings is the most fundamental part of data visualization. This site helps you by allowing you to search data visualizations by function.



Once you choose a function, it will give you some suggested visualization types:



Very neat!

The site was created by Severino Ribecca and will be added to over time.