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Start with Data, Finish with a story.

November 16, 2021

The resurgence dashboard is a data visualisation tool that enables you to visualise the surges in new COVID-19 cases. While the dashboard does not provide you with ready-made stories, you can gather insights from the data that when woven together tell your readers a meaningful story.

These 5 steps are based on the Data Journalism Handbook, from the section Using Data Visualisation to Find Insights in Data. Together with the Resurgence dashboard, we hope to support you in being a sensemaker for your community and readers - so that you can find and transform the insights from data into meaningful stories.

Step 1: write down your question and declare your assumptions

The first thing we are going to do is write down the questions we have and write down our expectations or assumptions about this dataset. What do you hope to find? What do you believe is happening with new cases in your country / region? Answering these questions will help you be aware of any biases you may have and serves to reduce the risk of misinterpretation of the data caused by those biases.

You’ve made yourself aware of your beliefs about this sort of data and when you’re in the analysis stage of this process this awareness will help you analyse the data more accurately.

For example, this is a question I have, “Is the current lockdown regulations (level 1 - low) having an impact on the number of new confirmed cases of COVID-19?” Current assumptions I have are that stricter lockdown regulations lower the number of new cases.

Step 2: visualise

After you’ve documented your thoughts and expectations about the data, you begin the investigation by selecting how you want this data to be filtered - country, time with additional comparison metrics overlaid. You can start your inquiry by filtering the data according to what interests you. Alternatively you can explore the data in its default settings.

Tip: I’ve chosen to view the data for a country that I’m familiar with so I can sense-check the information presented while I am still learning about how to use the tool.

Once you have made your selection, the dashboard will transform to show you the data according to your filters. We offer 3 types of visualisation tools of this data - choropleth map, leaderboard, and the area graphs.

Continuing with our example, I have chosen to view the new confirmed cases per million overlaid with the stringency index (a measure of the lockdown regulations) for South Africa. This selection is shown in the image above.

Step 3: Analyse and Interpret

The next step is to learn something from the picture you have created.  To do this you can ask yourself the following questions:

  • What do I understand from this view of the data? Is it what I expected?
  • Can I see any interesting patterns?
  • What might this mean in context of the data?

What do I understand from this view?

  • Earlier on in the pandemic (March - June 2020) there were stricter policies and lockdown measures in place. The line showing the stringency index sits above 80% during this time. This is what I expected to see. The South African government’s motivation for very strict lockdown measures was to “flatten the curve” and allow enough time for hospital and care facilities to prepare enough space and equipment.
  • The lockdown regulations have relaxed over time dropping below 30% in May 2021. The restrictions have not been as strict as they initially were even though the number of cases per million have exceeded the peaks of the first wave.

Can I see any interesting patterns? I can see that South Africa had three waves  - that each subsequent wave was bigger than the last. The stringency index appears to shadow the surge in new confirmed COVID-19 cases during the second wave and by the third wave, the stringency index is more closely aligned to the resurgence in new cases.

What might this mean in context of the data? Earlier stricter policy measures were not very responsive to the number of new confirmed cases of COVID-19 but they were precautionary, particularly compared to later lockdown measures that responded to surges in cases and local hotspots. As time has passed and healthcare facilities are meant to be better equipped, the South African government’s response to new cases, as shown by the stringency index, has begun to track the resurgence in new cases more closely. The severity of lockdown restrictions are more closely linked to the severity of the rise in new cases. While the stringency index shadows the second wave, it almost pre-empted the third wave. This shows that the South African government has become better at relaxing regulations when cases are low and ramping them up when cases are expected to rise.

Step 4: Document your data insights and the steps you took

Documentation is the process of creating a map of your journey through the data and the dashboard. It is also critical that you document how you filtered the data and what insights you got from each view of the data, as you will run different queries on the data, clearly writing down the steps helps you know exactly which filters brought you to the the view where you discovered new insights or confirmed what you were looking for.

Here are some questions you can ask yourself:

  • Why have I created this chart? What did I want to see / learn?
  • What steps have I taken to create this view of the data?
  • What does this chart / view actually tell me?
The stringency index overlaid to new confirmed cases of COVID-10 per million in South Africa.

For example, in documenting how I filtered and produced the chart above I might write:

Why have I created this chart? I created this chart based on South Africa because I know this context and that knowledge would help me contextualise what I was seeing in the data visuals. In addition, a familiar country would help me focus on learning more about how the dashboard works.

What steps have I taken to create this view of the data?

I clicked on South Africa on the map, which changed out the leaderboard to a graph of South African cases. From the dropdown menu I chose the comparison metric - stringency index. I waited for this to be overlaid to new confirmed cases of COVID-10 per million. I also read the footer that explained what the stringency index is to make certain that this is the view I wanted of the data. I then pressed the download image to save the chart for analysis.

What does this chart actually mean?

It shows the number of new confirmed cases of COVID-10 per million (left y-axis) overlaid with the stringency index (a measure out of 100 on the right y-axis) over time (horizontal x-axis).

Step 5: Transform the data

While documenting your insights from the chart or view you created you might naturally be inspired by what else to look for in the dataset. Possible transformations on the Resurgence Map include: Zooming and Filtering.

Zooming: you might want to get a closer look at a particular detail in the chart. You can do this in two ways on the resurgence dashboard. You can hover your cursor over a point in the graph, a line will appear along with a card. When I do this I get a date and a value for the stringency index and new cases per million at a certain point in time.

Stringency index and new cases per million for SA.

Alternatively, you can drag the slider below the graph to zoom into different periods. For example, I have dragged the slider to the right, focusing on the third wave.

Zoomed in view

Filtering: you can add or remove datasets that you don’t want to focus on in this view. For example, if I wanted to focus on just the new cases of COVID-19 over time, I could temporarily remove the stringency index from this view. Providing us with this view below.

stringency index filtered out.

You can then continue the cycle (visualise, analyse and interpret, document and transform) until you gather the insights you need to tell the stories you want to tell.  

Stories I might be able to tell from these insights:

  • I can write a story where I compare and contrast similar African countries that varied their stringency policies to countries that did not.
  • I could write a story where I contextualise this data with data showing how stringency policies impact the spread of COVID-19.
  • I could write a story about why there were changes in the stringency index by looking at what was happening domestically in South Africa around those dates.

Some final thoughts from us:

While there are a number of international and governmental agencies talking about the pandemic, it is still super valuable right now for local newsrooms to talk about COVID-19. Finding deeper country specific, regional or continental insights about what is happening around us and how it might affect us, helps us make sense of the world.

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