VIDEO: Our tech team shares their favorite data visualizations
Tags: Data, Commentary, Video, Data Visualization

As a data-driven advisory organization, we often present complex data analyses to clients to inform their decisions. Data visualizations can play a key role in helping clients understand the context of a problem, their project’s results, potential avenues to scale-up, and beyond. Internally, we look to strengthen our tactics for communicating with various stakeholders, and to that end, regularly send around examples of our work from which to learn. This video features voices of our technical team members commenting on some of the data visualizations our team created this year and why they liked them.

Interested in data visualizations? Share with us some of your favorites in the comments — explain why you like them and why you think they do a good job of explaining complex information.

Transcript below:

Jeffery McManus: For this global connectivity video, the tech team wanted to showcase some of the excellent data visualizations produced by project teams in the last TRP (Triannual Review Process). Data visualizations are usually the most effective and compelling way to communicate an idea or a result to our clients. So while the vast majority of running a survey or an evaluation is non-visual in nature, all of that time and effort culminates into a single graph that has the power to change minds and influence high-stakes program decisions. The following is a small but impressive sample of our organization’s work in the past TRP.

Valentina Brailovskaya: For the Nano project in Zambia, our team was developing a monitoring system that produces data on relevant indicators from a representative sample of households that traditional leaders can use to inform policies and target resources. One of the main challenges in this project was lack of an up-to-date, reliable list of households that can be used as a sampling frame. To circumvent the problem, we employed geographic sampling as depicted here. First, a satellite picture of the chiefdom is split into 500x500 meter squares. Second, we overlay this picture with datasets from Facebook, that use machine learning to identify buildings, and a data set on structures from open street maps. This allows us to exclude areas that have no buildings at all, the green squares, and sample only from areas that have buildings — colored and beige squares. The colored squares represent sampled areas and the beige squares represent eligible, but unsampled areas This sampling approach better insures against systematic exclusion of remote households as well as allows us to track activities of enumerators (surveyors) remotely.

Crystal Huang: I really like this graph from the analysis of a large-scale survey we did in India of their flagship nutrition program delivered through social and behavioural change communication messaging. This is one of many data visualizations created by Nitya, Anirudha, and I. Through this program, messages about health and nutrition for pregnant women and recent mothers were delivered through 21 different platforms, which can be grouped into three broad types: 1. Digital Media, such as Facebook, Whatsapp, and TV, 2. Mid-media such as audio-visual, events, and posters and 3. Community-based events organized by community health workers. In this graph, we show exposure to any platform within each of the three broad types of platforms by socio-economic quintiles. I think this graph tells an interesting and visual story. Whereas platform exposure for mid and digital media increases with income across the quintile since these are platforms that require technology access, literacy, etc. Community engagement and inter-personal counseling can be seen as a great equalizer. The proportion of women who have attended a community event or have interacted with a community health worker is consistently 80 percent and up, suggesting that is the platform type capable of reaching the most poor women. This finding has implications for the targeting and the social behavior-change campaign strategy going forward.

Dan Stein: This shows a graphical representation of four impact evaluation regression results from an Orange-Fleshed Sweet Potato (OFSP) project that took place in Uganda and Tanzania. Each pair of bars shows a different regression and the way to interpret it is that the gray bar shows the control mean, while the blue bar shows the control mean plus the treatment effect. The arrow bars on the treatment are the 95 percent confidence interval of our estimate of the treatment effect. I like showing regression results this way because you can very clearly read off the treatment effect and whether or not these effects are significant. If the arrow bar passes the control mean, that means your regressions are not significant. If the arrow bars don’t cut to the control mean, they are significant at the 95 percent level. Sometimes people question why there are no arrow bars on the control mean and the answer is that we are not trying to compare control means and treatment means here. We are instead just trying to very clearly show the treatment effect.

Jeffery McManus: One of my favorite visualizations from the last TRP was created by Rebecca Bier for the endline report of a quasi-experimental impact evaluation of an education program. At the beginning of the evaluation, students in the treatment program were matched with comparison students who attended other programs, but had very similar baseline test scores and demographics. At the end of the evaluation, we found that treatment students were performing significantly better than comparison students on a battery of academic tests. In this graph I like how Rebecca presents the results of the various tests in a compact way that highlights the average treatment effects corresponding to each outcome, as well as the means and standard errors for each test for treatment and comparison students.