09 May WORKBOOK – INFOGRAPHIC
4th February – Today we started off the lesson by going over the homework that we had to do, which was writing about what stories we can extract from certain databases. We had quite a few examples, the ones we went over in class were the following: are MPs expenses, Wikileaks war diaries, Gapminder comparing income to life expectancy in different countries, and the one that I wrote about which was a scene by scene comparison of Hollywood films to their true-life events.
When talking about the one I wrote about, I discussed how I compared the site to fake news and how the data can help journalists stray away from it as well as how it can come in handy to journalists writing reviews. It gives them much more background information. These types of movies are supposed to teach those about historical events and if the movie is based off completely inaccurate facts then it gives people a whole different story compared to what really happened, making their historical knowledge mainly false.
We then moved onto discussing visualising data, we were given many different examples such as static infographics, standalone charts, scrolling stories and animation. We were given separate examples for each of these examples, thus website showing the type of data that was explained.
In relation to this we watched an animated video about the losses of WW2, which used data to comparing all the soldier’s deaths in separate countries as well as a comparison of all the civilian deaths. The use of data in this video gave a different outlook on the war, having already studied it white thoroughly, I knew most of the information however seeing it in bar graphs/ different infographics gave a very broad view. Showing how extreme the circumstances were.
An overview of different types of infographics. Taken from https://www.ft.com.
Going more thoroughly into infographics, discussing many different types. I learn that good ones to use for distribution are bubble and heat-maps and that ones I should never use in ranking are dot strip lines and bump. I also learnt that when it comes to spatial infographics, thus the use of maps, you shouldn’t immediately use maps when something is location/geographically based. There should be another reason for using the map such as population or correlation. The image above shows the types of infographics that we looked at, all having their own purposes for certain data.
Infographic on the ultimate data dog, taken from https://informationisbeautiful.net.
We took a look at some specific infographics on a website, which were all very interesting, however I took an interest in one in particular which was: ‘Best in Show: The Ultimate Data Dog’. Shown above. This compared all different dogs based on intelligence, costs, longevity, grooming, aliments and appetite. This was shown with the dogs facing different ways and the size and colour of the dogs. Although this was interesting to me because of my obsession for dogs (as well as making me sad for all the doggies that were ranked so low, they should all be in the ‘hot dogs’ section, in my opinion) the graph itself wasn’t that good, it was hard to understand, a bit messy.
Infographic on my weekly spending on the first week of September, by Georgina Blackwell.
We ended the day by creating our very own infographic, we could choose any one we liked from the table that we looked at (which I displayed earlier). We had to make it about anything in our lives that we though fit. In the end, I decided to make a pie chart about my weekly spending. Having always kept a record of the money I spend every week (as my money comes in weekly), which is split it into different categories, this was rather easy. I took the first week of September and used that as my guide. I came up with what is shown above.
11th February – Today we started off the lesson by discussing the homework, which was comparing infographics on the state of the union speeches. We discussed most of the graphs that were provided in the presentation. A few people gave their views and which ones they had compared and what they managed to extrapolate from it.
This week I didn’t discuss what I had wrote about but I spoke about an infographic which was displayed a list of words and how much each president had used these words, which was given in a colour coordinated infographic. Green showing the word being used often and red showing that the word wasn’t used at all. I then talked about an infographic which looked at the frequency of a series of different words, these were grouped into 7 different topics, therefore showing the difference between term frequency of different presidents in their speeches. The last one I discussed was an infographic that displays words presidents have said that no other presidents have used, this infographic is in lists and thus is very different from the other two that I talked about.
Continuing with a presentation about ‘visualizing data’, we learnt about data terminology such as data point, which is one piece of data and aggregated data which is grouped or combined from several measurements.
This is when we moved onto the math part of data journalism – YAY… We discussed how to calculate the average, mean, median and mode of datasets. This was followed up by doing a few exercises to practice. We also compared what percent and percentage point is.
We went over a few examples of how fake data can be posted and how data can be posted and perceived in a whole different way if it isn’t displayed correctly. Something interesting that we were shown, is a more creative way of creating infographic, such as how Mona Chalabi from the Guardian creates her infographics. Using a more ‘self-created’ infographics with drawings.
Starting off after the break, we went over the homework which was about two podcasts that we had to listen to. The first one was ‘Mona Chalabi: How can we tell the good statistics from the bad ones?’ and ‘Alan Smith: Why do we trust intuition over even the most reliable numbers?’. A few students discussed their opinions on the podcasts. I shared one of my thoughts which was Mona talking about a survey that the US did on how many people support Jihad, it turned out as a high percentage however Mona was saying when approaching this information, she would look at the specifics such as questions and definitions. She came to see that in this survey there were two completely different definitions of jihad, one about the violence and one more leaning towards peaceful religion, which the majority defined it as. This changes the entire perspective on the situation.
Every table was handed out a number of infographics and we had to choose two of them to talk about. Discussing whether we would know what it is about without the title, we had to talk about how it looks, why certain things have been used and placed where they have been placed. Also discussing the statistics and where they are from. The first infographic we talked about is about ‘Drinking culture in Italy’ and the second infographic is ‘People in the UK spend more time communicating than sleeping’.
18th February – In the morning session with Micheal, we started by going over the homework, as we always do. We had to write a story on a discussion between Sarah O’Connor and Tim Harford. Talking abut what they discussed, whether we agreed with what was said and if there were any reservations. As well as using this to consider how we approach statistical claims and where they could have misled us. We then had to use this knowledge to analyse a current story.
A few students discussed what they had talked about. Me not being confident when it comes to statistics, decided to stay quite.
After going over the homework, we moved onto business data. We talked about stock exchange and FTSE, currencies. Looking at some real statistics and using excel to create an infographic. Where we could view how things have changed and increased/decreased.
Next we focused on finding data stories, where we were shown a few interesting websites. An example being as ‘Strava’ which is a massive heat-map. Shown below. Micheal also showed stories from BBC news and explained how they could be made into data stories.
We finished the morning session off by doing an exercise. We had to find three different articles on BBC news. Talking about how we can develop the story using data sets.
For the afternoon session we had an excel workshop. We went over all the basics such as percentages and addition. We were given a few data sets and shown how to make infographics. Shown what the best one is to use for certain data sets. Having never been very good with excel, this was a really helpful class. It is always good to know the basics.
25th February – This week I was not present for the morning session with Micheal as I was helping a friend out at the hospital. However after reviewing the presentation on Moodle , I saw that they discussed ‘Finding data sets’. They started off with going over the homework and then later on went over examples of finding data sets in sources. They ended the session by going over next weeks homework.
For the afternoon session we had another Excel lesson, where we went through the rest of the examples that we did in the previous lesson. We went over one called ‘Box and Whisker Plot’ which I found particularly interesting as I had to hand draw these quite often when studying in IB. Moving on, we used real data sets from the police about different types of crimes in many areas around the United Kingdom. We learnt how to gather the main data we would need for whatever we would be doing, from the data provided as well as how to make graphs from this data. Being shown which graph would be the best to use in this scenario.
11th March – We only had the morning session considering that LJ was ill and thus meaning we did not need to come in for the afternoon session.
We started off the lesson by discussing the data that we had to find and collect to make our infographics with. We spent a lot of time on this, with Michael going over each student’s data thoroughly and providing everyone with in depth feedback. After this we went back to discussing fight hoax, we were put back into the groups that we were split into the previous week and each group was given a specific social media on which we had to find any single article about Brexit.
Moving on from this, we had a speech by Ryerson University about doing an exchange in Canada, where they also handed out free pizza. They were trying to encourage students to do an exchange with them because their university really focuses specifically on the creative industries.
18th March – During the afternoon session with LJ we focused on a program called ‘In design’ which could help us make the layout of our infographic. We learnt how to use the program, being able to split it into columns, add text, add images, titles etc. We were given an assignment to create our own article layout using an article we found online, done in order to practice using the program.
March 26th – Worked on my prison idea, found datasets from 2005 until 2018 with male and female prison amounts in the England, Ireland and Wales.
| Male Pop | Female Pop | Overall Pop | total male % | total female % | ||
| Jan-10 | 80,489 | 4,236 | 84,725 | |||
| Jan-11 | 80,079 | 4,066 | 84,145 | 95% | 5% | |
| Jan-12 | 83,483 | 4,185 | 87,668 | 95% | 5% | |
| Jan-13 | 79,919 | 3,918 | 83,837 | 95% | 5% | |
| Jan-14 | 81,045 | 3,932 | 84,977 | 95% | 5% | |
| Jan-15 | 81,271 | 3,812 | 85,083 | 96% | 4% | |
| Jan-16 | 81,629 | 3,832 | 85,461 | 96% | 4% | |
| Jan-17 | 81,240 | 3,947 | 85,188 | 95% | 5% | |
| Jan-18 | 80,618 | 3,919 | 84,537 | 95% | 5% | |
| Jan-19 | 78,675 | 3,797 | 82,472 | 95% | 5% | |
| UK Pop | male | females |
| 63,306,843 | 49.13% | 50.87% |
| 63,306,843 | 49.13% | 50.87% |
| 63,306,843 | 49.13% | 50.87% |
| 63,306,843 | 49.13% | 50.87% |
| 63,306,843 | 49.13% | 50.87% |
| 65,397,080 | 49.29% | 50.71% |
| 65,788,574 | 49.31% | 50.69% |
| 66,181,585 | 49.34% | 50.66% |
| 66,573,504 | 49.37% | 50.63% |
| 66,959,016 | 49.39% | 50.61% |
March 27th – MEETING WITH MICHEAL, gave prison idea, men vs women amount of people in prison, no story behind it as very obvious. Gave me an idea of using the Oscars data and focusing on the number of women directors. Same day I got all the data of how many of the directors were male and female. He suggested talking to someone from WFTV to get a quote about why they think so little women directors are winning or being nominated.
data from: http://individual.utoronto.ca/shehroz/
| name | year | nominations | Director | lead role |
| Green Book | 5 | Male | Male | |
| The Shape of Water | 2017 | 13 | Male | Female |
| Moonlight | 2016 | 8 | Male | Male |
| Spotlight | 2015 | 6 | Male | Male |
| Birdman | 2014 | 9 | Male | Male |
| Gravity | 2013 | 10 | Male | Female |
| Life of Pi | 2012 | 11 | Male | Male |
| The Artist | 2011 | 10 | Male | Male |
| The King’s Speech | 2010 | 12 | Male | Male |
| The Hurt Locker | 2009 | 9 | Female | Male |
| Slumdog Millionaire | 2008 | 10 | Male | Male |
| No Country for Old Men | 2007 | 8 | Male | Male |
| The Departed | 2006 | 5 | Male | Male |
| Brokeback Mountain | 2005 | 8 | Male | Male |
| Million Dollar Baby | 2004 | 7 | Male | Male |
| The Lord of the Rings: The Return of the King | 2003 | 11 | Male | Male |
| The Pianist | 2002 | 7 | Male | Male |
| A Beautiful Mind | 2001 | 8 | Male | Male |
| Traffic | 2000 | 5 | Male | Male |
| the Shape of Water | 1999 | 8 | Male | Male |
| Male nominations | Female nominations | Best Director | |||||
| 6 | 1 | 5 men | |||||
| 8 | 0 | 4 men 1 fmela | |||||
| 7 | 0 | 5 men | |||||
| 6 | 1 | 5 men | |||||
| 8 | 0 | 5 men | |||||
| 7 | 1 | 5 men | |||||
| 8 | 0 | 5 men | |||||
| 7 | 2 | 5 men | |||||
| 9 | 0 | 5 men | |||||
| 4 | 0 | 4 men 1 female | |||||
| 4 | 0 | 5 men | |||||
| 4 | 1 | m&f for one | 5 men | ||||
| 4 | 0 | 5 men | |||||
| 4 | 0 | 5 men | |||||
| 3 | 1 | 4 men 1 woman | |||||
| 4 | 0 | 4 men 1 woman | |||||
| 4 | 0 | 5 men | |||||
| 4 | 0 | 5 men | |||||
| 4 | 0 | 5 men | |||||
| 4 | 0 | 5 men | |||||
| Male nominations | Female nominations | ||||||
| 2018 | 8 | 1 | |||||
| 2017 | 9 | 0 | |||||
| 2016 | 8 | 0 | |||||
| 2015 | 7 | 1 | |||||
| 2014 | 9 | 0 | |||||
| 2013 | 8 | 1 | |||||
| 2012 | 9 | 0 | |||||
| 2011 | 8 | 2 | |||||
| 2010 | 10 | 0 | |||||
| 2009 | 4 | 1 | |||||
| 2008 | 5 | 0 | |||||
| 2007 | 5 | 1 | |||||
| 2006 | 5 | 0 | |||||
| 2005 | 5 | 0 | |||||
| 2004 | 4 | 1 | |||||
| 2003 | 5 | 0 | |||||
| 2002 | 5 | 0 | |||||
| 2001 | 5 | 0 | |||||
| 2000 | 5 | 0 | |||||
| 1999 | 5 | 0 | |||||
April 2nd -We started working with our datasets and used online programs to start making a design. I choose a theme of black, blue and yellow. LJ and I talked about colour scheme, changed it from black to white. Told me that I should change my graph to make it much more creative and artistic.
April 3rd – Called Liz Tucker, the chair of WFTV. Was told to send an email asking what I would like her to talk about. On the same day got a reply to one of the BBC articles she did about the exact same topic.
Liz Tucker: Liz@wftv.org.uk , +44 (0)2072871400
Website: https://wftv.org.uk/
April 12th – Called Liz again and then was told to email Liz to ask for personal quotes as not able to use from BBC.
April 14th, 15th, 16th – Brainstormed different ways that I could portray that it wasn’t even possible for women to win because they weren’t even nominated. Making graph more creative. Used a weight scales and more men going on the scale where the women all falling off the scale. Found it too messy and thus didn’t use. Tried a director chair with two circles and the percentages on each circle, didn’t find clear enough and didn’t like the look of it. Try movie reels with certain amounts of men and women, was too similar to designs already have and thus scrapped. Couldn’t figure anything out so took a break.
Original graph idea:
April 17th -Started writing the infographic story, wrote up about all the data that was found and all about the Oscar awards themselves.
About oscars: https://en.wikipedia.org/wiki/Academy_Awards
Tweet used: https://twitter.com/yaayandnaay/status/1087776488454348802
gender in management source: https://www.researchgate.net/publication/242336344_Boards_of_directors_and_gender_diversity_in_UK_companies
April 25th – Added more to the story, edited what I had already written, wrote a better first line.
April 26th – Still no reply from Liz so I called and explained that I needed direct quotes to me, was told to send an email and she answered immediately. Sent me the quotes she said for the BBC but directly to me. Added the quotes she had given to me and a broad context of the director industry itself.
April 27th – Finished the story by adding a title and finishing off the end, added pieces in and edited what was already done. Added a few things to the infographic itself, from data that was added on the 26th.
Emma stone incident at recent Oscars:
https://www.insider.com/emma-stone-best-director-oscar-sexism-2018-3
April 29th – Had a meeting with Michael and LJ, showing them my infographic so far and the story that I have written. They both supplied me with feedback.
April 30th – Edited all of the feedback that LJ and Michael gave to me, I emailed the draft to Michael to get the last bits of feedback before the hand in.
May 7th – Got feedback from Michael and edited it all and hence finished the assignment.
