FHM Press Conference 9 July 2020

Source: https://www.youtube.com/watch?v=NrFOihWYXeA&list=PLLqBo3UjMccAyAkJ9uiJkQpPjDYUoWlHp&index=1


Socialstyrelsen: “Only 3 out of 290 municipalities are experiencing a serious impact [due to the pandemic], citing staffing concerns.”


“The pandemic, primarily or at least for the most part, is right now affecting non-risk groups, younger people… younger people aren’t heeding our recommendations.”

“The threat level is high. This is due to that we still have a substantial contagion effect, despite that it is spreading right now amongst groups that don’t think they may get infected.”

“Hard to say” about tracking the number of infected citizens

“Still not ready with more extensive analyses” about herd immunity

Socialstyrelsen: “Only 3 out of 290 municipalities are experiencing a serious impact [due to the pandemic], citing staffing concerns.”


“The pandemic, primarily or at least for the most part, is right now affecting non-risk groups, younger people… younger people aren’t heeding our recommendations.”

“The threat level is high. This is due to that we still have a substantial contagion effect, despite that it is spreading right now amongst groups that don’t think they may get infected.”

“Hard to say” about tracking the number of infected citizens

“Still not ready with more extensive analyses” about herd immunity

Continue reading FHM Press Conference 9 July 2020

FHM pressträff – 9 July 2020


Socialstyrelsen: “Endast 3 av 290 kommuner ser en allvarlig påverkan och det handlar om en viss oro för bemanning.”


“Vi har en spridning, som i huvudsak då eller till stor del, finns inom icke-riskgrupper, yngre människor…. Yngre människor respekterar inte riktlinjer som ges.”

“Hotnivån är hög. Det beror på att vi fortfarande har en ganska omfattande smittspridning även om det sker huvudsakligen i grupper som man inte har anledning att frukta att man blir svårt sjuk.”

“Svårt att säga” om hur många som bär på smittan

“Inte färdiga än med mer djupgående analyser” angående flockimmunitet

Continue reading FHM pressträff – 9 July 2020

Difference between government and private sector vaccines

Both government and private offer eight vaccines from the list of routine immunisation recommendations. How do you do start to answer the question? From the vaccinations offered and who (government and private, here) offers what.

The World Health Organization (WHO) compiles key information on routine immunisation recommendations. These recommendations are based on position papers, published in the health organisation’s Weekly Epidemiological Record. The recommendations are broken down into two types. The first is a list of 10 vaccines for all age groups and populations. The second are supplementary lists that are tailored for specific population characteristics, like risk for polio.

The vaccination schedule, published by Amayeza, an online and independent resource for medicine information, shows what the private sector and government offers. However, the schedule is based on the 2009 schedule for the Department of Health’s Expanded Programme on Immunisation (EPI). Using the Amayeza information for the private sector and looking at the 2010 (last known update) government immunisation programme, the following table summarises what’s on offer from both:

vaccine code vaccine for on govt schedule? on private schedule?
BCG tuberculosis yes yes
DTP diphtheria, pertussis, tetanus yes yes
HBV hep B yes yes
HiB haemophilus influenza type b (causes mennigitis, pneumonia) yes yes
HPV papillomavirus (cause cervical cancer, genital warts) no no
IPV Polio yes no
MMR Measles, rubella only offers measles yes
OPV Polio yes yes
PCV pneumococcal yes yes
RV rotavirus (causes severe diahorrea) yes yes

Further remarks

  • Government offers a vaccination for tetanus that is not on the health organisation’s schedules.
  • Private sector offers three vaccinations on the health organisation’s supplementary lists – Meningococcal (MCV), Varicella for chicken pox (VCV), and hepatitis A.
  • Since April 2009 (page 6), government has been offering the pentavalent vaccine, a “five-in-one” vaccine that protects children from diphtheria, tetanus, whooping cough, hepatitis B and Haemophilus influenzae type b (Hib). From comparing the schedules with and without this vaccine, it wasn’t possible to see any discernable difference.

‘We offer everything on WHO list’

Spokesman for Department of Health, Joe Maila, disagreed with the findings above and insisted,”the fact is that we actually provide 11 vaccines in this country.” When asked to see the source of this claim, no response was received. It also wasn’t possible to find a more recent version of the government immunisation schedule.

From Idea to [the start of a] Story

This is an introductory guide on how to produce the beginnings of a piece of data journalism. We’re going to walk through it together, as I outline the key things to consider before starting, how to structure your work, a basic process to follow, and then use a real case study to show how the process works with a real story.

Be at ease, there is hope

The glitz and glamour of data journalism (the animations, the striking maps, those great infographics) are all over the Internet. It’s easy to think then that it’s about the data and how cool you can make it look, sing, or dance. Our wise friends at Code4SARaymond and Adi, keep reminding us (and the salivating Internet-at-large) that the focus should be on data journalism, and not data journalism.

Data journalism is no different than the journalism we all know and consume every day. Where traditional journalism relies on human sources (insiders, experts, scholars, scientists), data journalism treats data sources (spreadsheets, websites, databases) with all the rigor and scrutiny journalists treat human sources.

The animations and snazzy work is a part of communicating the final product – the story – but they will never replace the actual story.

The grand start

A data journalism story can start from an important event or it can simply be a question. You could have seen a breaking headline and wondered, how much x did it take for y to happen? Or, you start thinking about, say, food in a supermarket and wonder, what percentage of dog food features on the average shopper’s bill? Both questions are equally valid and are great starts for considering a piece of data journalism.

What I’ve learned so far in my work is that there is little difference between doing the work of basic science and that of data journalism. You make an observation, you come up with a question (hypothesis for purists and fancy people), and then you go about, doing some work to answer that question. Your work will show either that your initial hypothesis was incorrect or yes, it was indeed correct.

So, as I mentioned earlier, it’s not about the fancy graphic or how much data you trawled through. It’s about, what was your question and did you answer it or not?

Don’t believe the hype.

Who’s your data and what does he do‘?

I live and work in South Africa, so I’ll be basing this guide on data on the workforce from the country’s statistics agency. (The results of the quarterly survey was released just recently and the official unemployment rate is at a grim 25%.) The agency cares (in my head) about my feelings and thus have released the data in a Excel spreadsheet format. I will write other posts about how to deal with sources of data, where the publishers don’t care as much about your feelings.

The dataset is here and there are enough sheets in there to warrant exploration. This exploration is important because an excited and hurried deep dive into the data, without knowing what it’s about, what it covers, and so on, may end up on looking at the wrong data that doesn’t answer your question, attempting to answer the wrong question or – the nightmare of every data journalist – hours wasted achieving little.

So, before we talk about the process, let’s look at the data and see what it tells us. We don’t usually work with all the data (unless our initial idea or question requires this). It’s better to first spending some time looking at all the data and then focus on a particular section that catches their attention.

Looking through the spreadsheet from the stats agency, the data looks at different characteristics of the workforce (by province, age, gender, and demographic group). Even if it’s this is your first time and you’re following along, throw a quick glance at each of the sheets. It’s part of developing that methodical work ethic that will become invaluable as you progress in this type of journalism.

As an important sidenote, you’ll need to have only a basic working knowledge of Excel. I won’t be wielding any sort of magic on the worksheets, so anyone can follow along. For the sake of brevity (and so you don’t drop into a catatonic stupor from me detailing every single step), I will leave you to figure out how to do the basic manipulations in Excel after I explain them.

Now the journey begins

We’ve talked about what it really means to produce a work of data journalism, how we start considering an idea that will lead towards a piece, and some introductory remarks about how to look at a dataset. Finally. The process, the good stuff. How does it work?

Step I: Take a bite out of the data

For this guide, I want to see the size of the workforce in all the provinces in South Africa and how it has fared between 2013 and the second quarter of 2015. That data is in the very first worksheet. (You’re welcome to look through all the others and see what other interesting insights you can mine from them.)

So we went from an original spreadsheet of more than 20 worksheets:


… to just this one entitled Table 1: Population of working age (15-64 years):


Let’s copy ’n’ paste the bottom part of into a new sheet, since that’s the view of the data we want to work with. To move towards a clean dataset, I took out the heading and “thousands” rows, and the cell labelled “South Africa”. I also took out the totals row, so it doesn’t come up later to confuse us. (I will adjust all the values, to reflect millions, in a minute.) It now should look like this:


Now, let’s change all the cells to show values in millions. I created columns next to each original column and multiplied the value by 1000. It now will look like this:

I also removed all borders, decimal places, and made the thousand separator a comma; this will help us make our charts readable and accessible later. At this point, you’d (and I did, too, at some point) be ready to take this table and analyse it. Not quite yet. Although it is indeed cleaner, the data structure we need is not there. Why does this matter? Because the data needs to be organized in a way that we can aggregate or group them. The wise old sages of data journalism say, if your data is not summarized [or aggregated], it is not ready for analysis.

Step 2: Transform the data into an analysis/visualization-ready structure

What factors are we ultimately looking to expose from this data set? They are province, year and the total number of workers. But, before that, we’re going to create this new data structure with the following columns:

If you studied database design or are a working programmer, you would have failed your database design test or received the chiding of your life if you proposed this dataset design. And your lecturer (or boss) would have been right; it’s not a normalized (computer science speak for optimised) dataset. However, this is data analysis for a piece of data journalism, so you may scorn those rules! We need to have duplicate rows in order to aggregate the data later (remember?).

Step 3: Produce the final dataset

In the screenshot above, I put in the structure to be followed for all years. So, copy in the totals for 2013, 2014 and 2015. You will then have a dataset that should look this. You should have 91 rows and only Q1, Q2 for 2015.

We’re almost there! The last step is actually aggregating the data. So, take a deep breath and create a PivotTable in a new sheet. Your summarized data should look like this:


Clean up the table: put in thousands separator, remove decimal places, and take out that cell labelled “Row values”. It should now look like this:


Step 4: Produce the visualisation

Congratulations! You have a dataset that is ready to be visualised.

We’re going to use Infogr.am to produce a infographic. This guide won’t cover how to sign up and use Infogr.am, so (as with Excel) you’ll have to become acquainted with the tool. I do assure you that it’s straight-forward and intuitive; you’ll use it like a professional in no time! You shall see.

Create a new infographic, choose any template you like, and look at the blank work area. It will look like this:


Give the infographic a title like “Total workforce in provinces, 2013 – 2015” or something similar, as you see fit. Then, add a grouped bar chart from the popup wizard. You’ll see the chart show up on the work area. (Delete the existing chart that comes with the template, that is now below the one you just created.)
Double-click on the chart and you’ll see an interface appear, not too different from Excel. Delete all the data you see, copy the data in your Excel worksheet from the last step we created (the PivotTable), and paste it into the Infogr.am spreadsheet interface. It should look like this:


When you pasted the data in, the graphic should have automatically updated itself. It’s starting to look great!

Have a look at the infographic. Everything is in there, but it may not be immediately understandable. You have to scroll doarrowswn to the legend to see which colors denote which provinces. So, instead of having to re-format the data, click on the two-directional arrows icon in the top right-hand corner of the spreadsheet interface. This nifty feature will switch together the rows and the columns, so that the provinces are now the rows and the years are now the columns.


Always aim to show the values on the chart (where appropriate, obvs), so click the “Show values” switch and the totals will reflect on the chart. Also, click on the Settings button and scroll down to add “total (in millions)” in the X-axis textbox. This will help the reader (and you) understand further the chart.

If you click the “Publish” button, you can give your graphic a title and then choose whether you want it to be an interactive or image. This is how the final image would look like:


And you have produced your first visualisation. Pat yourself on the back, have a coffee or beer, and get ready because you’ve just started the process. 🙂

Before we look at the rest of the work needed, let’s review what we’ve done:

  1. We looked at a data source and extracted a view of the data that we want to look at. In this case, we asked the question, what was the size of the workforce in all of South Africa’s provinces between 2013 and 2015?
  2. We followed a basic process of cleaning, formatting, transforming, and summarizing the data until we produced a table showing the data we need to answer our question.
  3. We then inserted the data into our visualisation tool and produced an infographic, shown above.

At this point, you’re so excited that you jump on Twitter or email, and send out your work to everyone you know. Hold on! Not yet.

What do your findings really mean?

Yes, you analysed the data and you answered your question. Gauteng province has had the largest workforce within the time period we chose, but it’s been decreasing in size since 2013. The Northern Cape has been consistently below 5 million since the same year. Why is this?

That’s why the second part of the title for this guide has the disclaimer: “start of a story”, because now starts the work of journalism that you know or were trained to do. At this point, you would:

  • contact analysts, experts, academics to interpret and comment on the data
  • depending on the scope of the story or your editor’s instructions, you’d look at other data sets or speak to experts to explain the context behind the findings
  • even analyse/visualise other datasets to test and refine your findings
  • and, do anything else required to make sure the piece is balanced and fair.

Once you’ve done any or all of these steps, you write the final article, include the infographic we produced above, and submit it for publication. If you run your own blog or website, you would just publish it live.

There’s no place like the end!

And the end, it is. I hope that you’ve come this far and your appetite has been whet to do further (and more sophisticated) work in data journalism.

If anything hasn’t worked for you or you’d like some help with a certain section, follow me on Twitter @minaddotcom and we can figure it out together. Please also check out the Johannesburg chapter of Hacks/Hackers @HacksHackersJHB for more information and resources on data journalism.


I’ve included below all spreadsheets, tools, and links, so you can pick up this guide any time and see how I arrived at the final infographic.

Police raid on #Alexandra Wednesday morning uncovers little

inside hostel room
inside hostel room

Members of the South African police, along with units of Tactical Response Team and Bomb Disposal, entered the Alexandra Men’s Hostel around midnight Wednesday morning.

SANDF soldiers and armoured cars were stationed close to the hostel, while the police walked through and searched rooms. SAPS spokesperson Colonel Noxolo Kweza explained that the purpose of the raid was to uncover illegal firearms.

police before search

Some residents were asked to vacate the rooms. Although visibly perplexed by the raid, there were no instances of backlash.


inside hostel room

One officer was overheard saying, “the criminals are here”, but only two crates of beer were brought out by the end of the raid. Later reports said that one arrest was made.


#Jeppestown raid targeted ‘criminal elements’ of xenophobic attacks

The raid on Jeppestown Men’s Hostel Tuesday night was about the ‘criminal elements’ from the xenophobic attacks in the area. 11 suspects were arrested.

SAPS provinicial spokesperson Lt Kay Makhubela explained that the police seized stolen goods, ‘believed to may have’ come from lootings.

Reports have also mentioned large amounts of dagga received.

SANDF spokesperson Xolani Mabanga wouldn’t comment on who carried out or led the raid, saying only that ‘the military is in support of the SAPS’.

Makhubela clarified that the police moved in, while the military secured the perimeter: ‘Military backs up while police do their job.’

Further questioning to Lt Makhubela didn’t establish direct links betwern the raid and the overall violence against non-South Africans. When asked if raid was legal and the police had a court order: “… The police have the right to enter premises, when they see an immediate threat, for a raid without a search warrant. ”

There were related reports Tuesday that journalists were forced by police to delete photos taken around the hostel. Makhubela declined to confirm whether orders were given to officers to do so. He added that it’s not ‘illegal’ for journos and photographers to take photos as part of their jobs. He invited affected members of the press to open a case against the offending officer.

Some government uptake from TomTom data and insights

The TomTom Traffic Index released earlier this month reveals insight about congestion and traffic patterns in South Africa.

Cape Town, in 55th position on the global index, remains the most congested city in the country, with morning commutes adding up to 72% to commuting time. Johannesburg listed at 77 fares only slightly better, with morning travel adding up to 59% to commuting time.

‘The data suggest that coastal cities are more congested,’ explained Carey Dodd, Marketing Manager for TomTom Consumer in South Africa and Sub Saharan Africa, at a recent roundtable discussion of the index.


Pretoria, East London, Durban, and Bloemfontein don’t appear on the rankings because their inner central business districts (CBD) don’t meet the criterion of having a population larger than 800,000.


Jaap Schaapherder, TomTom Africa Key Account Manager, mentioned that the cities of Johannesburg, Durban, Cape Town and Tshwane are redesigning roads based on the congestion data from TomTom. The extent of this use will be the subject of further stories.

Etienne Louw, TomTom Africa General Manager, stated that there are 700,000km of roads in South Africa and 9 million overall in Africa. The company boasts that the quality of their maps is ‘better than competitors’ and is able to send out map updates to 400 million GPS units within 2 minutes through its network.

Louw did state that SANRAL is using TomTom data for road design and to alleviate congestion, but that the company is not consulted on policy at the roads agency.

When asked if TomTom’s traffic data has been used by RTMC to understand trends in road accidents, Louw would not comment on why RTMC hasn’t used the data. He cited that there was a ‘long selling cycle’.

Deep in South Africa has contacted the RTMC for confirmation and comment.

[interactive map] Top hijacking and smash ‘n’ grab hotspots in Gauteng

infographic on top hotspots

Deep in South Africa has produced an interactive map below of the top hijacking and smash ‘n’ grab hotspots in Gauteng, based on data from the Gauteng Department of Community Safety and eBlockwatch.

The data from the Department of Community Safety comes from a question-and-answer session with Gauteng Community Safety MEC Sizakele Nkosi-Malobane. Nkosi-Malobane mentioned problems with the data, citing “system errors”.

The map will be updated with more data points.

[webdoc] Protest Chants of the EFF

in the thick of a EFF protest
in the thick of a EFF protest

Deep in South Africa is experimenting with new ways of telling a story and leveraging powerful new web technologies that facilitate that.

Protest Chants of the EFF is a short webdoc (web documentary), powered by Interlude’s Treehouse, on a protest held by the EFF last year May during the general elections. It’s  designed for both desktop/laptop and mobile.

This is a first foray into online documentary making with this app, so please let us know in the comments or contact us about any technical problems faced.

Following the Jeppestown evictions

301 Marshall Street Jeppestown

One of the buildings at the center of last week’s Jeppestown protest has been identified as 301 Marshall Street, owned by Rilagraph Pty Ltd.

Deep in South Africa has seen and confirmed an eviction notice served to the residents. Rilagraph’s attorney Greg Vermaak of Vermaak & Partners confirmed that the eviction notice was served July 2014 and that the City of Johannesburg was notified ‘as per their formal process

The residents applied earlier this month on 5 March to have the eviction order rescinded.

The updated map from the full recap of the protests is below:

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