Collaborations are a great way for our community to work on different types of data challenges and to see the potential impact and reach their work can have.

We were delighted to collaborate with PATH.org this week and in particular use simulated data from their Visualize No Malaria project to generate visualizations and dashboards that will help the PATH team in their efforts with their local counterparts in Zambia who are driving the efforts in the communities to eradicate Malaria from Zambia by 2021.

 

Makeover Monday live at London TUG

In addition to the collaboration, Andy and I were invited to run a Makeover Monday live event at the London Tableau User Group this Monday, bringing together over 100 people who geeked out with data, beer and pizza at the Tableau office to show the insights they could find and visualize in 60 min.

We were joined by Jeff Bernson, Senior Director, Results Management, Measurement & Learning at PATH, who talked briefly about the project and the impact that data driven initiatives are having in local communities, as well as the support Visualize No Malaria has given community health workers and health facilities by visualizing results and the effectiveness of different campaigns and by identifying where resources are most needed.

It was great having Jeff as a stakeholder in the room and getting his introduction to the topic which made it all more real and exciting for the people in the room as well as those watching the live stream from other places around the world.

 

Thank you!

At this point we also want to say a big THANK YOU to the TUG leaders Sarah Bartlett, Pablo Gomez and Nick Bignell, as well as Paul Chapman and David Pires for having us there and giving us a chance to connect with our community. Another big THANK YOU goes to the team from i for ideas who created a great short video capturing the atmosphere at the event. Check it out below:

Andy and I continue to be impressed and actually blown away by the enthusiasm and passion all of YOU put into this project week after week. Sometimes it may just feel like a data visualization exercise, sometimes you’re really excited about a particular topic or chart and sometimes you get the opportunity to have your work reach much further than you originally intended.

I would love to organize these collaborations much more frequently and the only reason we don’t is that they are significantly more time consuming than typical Makeover Monday weeks. And that is only natural as we engage with organizations and their people. We have done it a number of times in the past and we enjoy giving them a chance to crowd-source data visualizations and see the talent in this community. We enjoy giving you unique challenges and opportunities to make an impact.

 

The next collaborations

So at this point I want to announce two upcoming collaborations.

The first will be with the Global Footprint Network next week to mark Earth Day, followed by a joint project with Viz for Social Good which will kick off on April 30. More details will come closer to the time and I’m excited to have Chloe work with us to extend the Makeover Monday challenge for those who want to dig deeper, find more insights, create data stories and share them with the world. We have a new and interesting topic that is very timely and important to be talked about. Stay tuned for details…

For this week’s lessons we looked at the many many submissions to see what stood out as a challenge across the board. Here is what we saw most frequently:

 

LESSON 1: STATE YOUR SOURCES. ACCURATELY.

This is a really important lesson and one that every analyst should adhere to. You MUST state your sources. We’ve discussed image sources in the past because we had a number of topics that resulted in heavy use of icons and images and incorrect attribution.

Aside from images though, please always state your data sources as well. We ALWAYS list the sources on data.world and on this website where the datasets are listed. It is just a matter of copy and paste.

As you post your visualizations online where they can be found, shared, copied, etc., it is important to have the data sources stated clearly on your viz, the footer being the most common location for it.

This week it was just as important to include the statement around the data being simulated. Again, this was clearly stated on our site here and on data.world.

Please (Please!) don’t just go straight to the data and build a viz. Make sure you read the accompanying text, check for any instructions, notes and disclaimers. Paying attention to these details is an important part of your skills as an analyst.

 

LESSON 2: VISUALIZING PATTERNS

For this week’s design lesson I want to share some great examples where patterns become clearly visible in the (simulated) data. Yes, aggregated data is okay to use and telling a story of the overall impact is one way to tackle the dataset.

What is fascinating in addition, though, is to look at patterns based on the seasonality of malaria outbreaks, which are linked to the wet season in Zambia (as stated in the text accompanying the original viz).

While the seasonal pattern recurred every year, the overall impact of malaria had decreased and there were a number of visualizations which showed this trend really well.

 

Philip Riggs used a line chart with annotations to show the decline and added small maps for each full year to also indicate that numbers are decreasing over time.

Anthony Hipp used bar charts instead of lines and added a heatmap with white borders to clearly show the pattern of malaria cases over the years and seasons. Adding borders to a heatmap in this way can be beneficial for your audience as it makes adjoining cells with similar colors easier to see and interpret than in a heatmap without borders where everything becomes simply a gradient of colors.

Daniel Caroli created very neat small multiple area charts which show the cases over time for each district and added a summary level line chart at the top with the overall pattern to give context. These small multiples are another way to show patterns in the data.

FAVORITES

 

This week’s Favorites feature PATH’s / Jonathan Drummey’s Top 3 visualizations from this week, followed by my own round-up of stand-outs. Thanks again to everyone who participated!

 

PATH Favorites

 

Author: Nils Macher
Link: Tableau Public

Author: Staticum
Tool: Canva

Eva’s Favorites