We, G were tasked by the Huffington Post to use the latest Covid-19 data to analyse if areas were going to move into tighter restrictions. The background to this was the announcement of a new tier system after the end of the second lockdown. The article was published the day before the government announced which areas would be placed into which tier. We produced numerous data visualisations which covered the brief we were given. We looked at the following: coronavirus cases in England’s regions, case rates in local authorities, the R rate, and weekly positive tests for age groups over
The first impact was that we were able to educate and inform readers of what tier their area could be placed in. One of the ways we achieved this was by producing a searchable table. Users could look up their local authority and compare case rates from the previous two weeks.
Not everyone has the capabilities to either access this data or understand it. We were able to simplify the information and display important trends in the data. The data journalism profession revolves around communicating complex data to the public. We achieved this by keeping the visuals simple and effective.
Furthermore, we focused on the criteria that was used to determine what tier an area was placed in. That is why we kept the visualisations focused and conveyed each measure the government stated would influence the tiering system.
In terms of the overall accuracy of the piece, we successfully predicted which areas would go into what tier. In addition to showing figures nationally, we were able to also show the areas with the highest rates. These areas were facing inevitable alterations in restrictions.
Instead of downloading the data from the government coronavirus dashboard, we decided to query the API in the programming language R. We made this decision due to the API being faster than downloading the CSVs manually. This was important as the editors at the Huffington Post wanted to publish the story before the government’s announcement. Otherwise, it would have lost all newsworthiness. We cleaned the data in R and then filtered for cases from the relevant time frame. This allowed us to compare weekly rates and highlight changes.
Huffington Post wanted case rates in counties to be displayed on a map. To get the case rates for each county, we had to look up which local authorities form a county as well as then work out the case rate per 100,000 population. We used the population estimates from the ONS for this.
A second map that we produced was to visualise the changes in the R rate. We made the editorial decision to use the Huffington Post colour palette. It was accessible for colour blind readers and showed which areas in England had the highest reproduction rates. Text annotations also supply complimentary details to aid the interpretation of the chart.
We used Datawrapper to create the maps and Flourish for the multi-chart and tables. Flourish allowed us to make the visualisations more interactive and incorporate animations.
What was the hardest part of this project?
There were a plethora of challenging aspects to this piece. Firstly, time constraints. We both were completing this article whilst carrying out our academic studies. Not only did this add to the pressure, but the tight deadline set by the Huffington Post meant we had little room for error. Ideally, we would have prepared using previous releases. However, on this occasion, this was not possible.
Within the short amount of time we had for this article, we had to familiarise ourselves with the five areas Huffington Post wanted us to explore. These were:
Case rates in all age groups
Case rates in the over-60s
The rate at which cases are rising or falling
Positivity rate (the number of positive cases detected as a percentage of tests taken)
Pressure on the NHS, including current and projected occupancy
This gave us an idea, but only from looking at the actual data, could we decide how to best visualise it. (Due to a colleague working on another story about hospital bed occupancy, we didn’t look at this data to avoid duplication.)
Due to the Covid-19 pandemic, we were working remotely. Therefore, the communication took place on Slack. This is a different way of working that we both had to adapt to. Progress is harder to track and any technical problems are harder to fix.
This was the first time that we had looked at testing and case figures which made the process even more challenging. But we were able to help each other out whenever we came across any obstacles.
What can others learn from this project?
Other journalists can learn to focus on the core message which was what area could be placed into which tier. There are so many ways Covid-19 data can be visualised. So it is important to be selective and emphasize the most relevant angles. Whilst the pandemic has shown the need for data, it is necessary not to overload the user.
Journalists can also learn the importance of giving readers a chance to interact with the visualisations. A less author-driven approach stimulates greater engagement. You can see this in the tables that we produced. They were searchable which added a sense of relatability to the work that we were producing. Displaying this information on a map would have been too much detail and would have made it harder for the readers to find the local authority they are interested in.
It is also important to contextualise the data. We achieved this by giving a more representative time frame of trends. Only showing the case rates of one week could be misleading as they would only present a snapshot. Disseminating a trend requires showing data for more than one week.