Data collection doesn’t always mean graphs – sound recording, satellite imagery and offshore buoys are also considered as data collection tools just as much as spreadsheets and computers.
Every single bead is a weather band that plays out a musical instrument reflecting behavioural elements not shown in a 2D graph. Translating a new medium of weather data to musical scores bridges a gap between art vs science. It is not always about the differences between them but what can also make them similar.
The weather band instrument reads differently wherever it is placed, in an art museum or as a musical piece. It’s an alternative entry point into understanding science that may not be a traditional perspective.
The science of data collection does not always have to be reflective of graphs and statistical numerics. This example reflected by Dr Miebach exemplifies we can understand data visualisation through music and art sculptures. We understand language and communication through visual forms and sounds just as much as images. It is up to us as humans how we perceive and breakdown the information. If the consume data in an art museum we may not be aware that it could also be consumed in a science museum just as effectively.
Meaningless without context – needs relevance.
Purple is fighting, red money giving away, green profiteering
Allows connection towards money
Visualising the information turns it into a landscape, an information map.
Heights = Intensity of certain fears in the media
Patterns found – twin peaks everywhere in same month for video games
Christmas = resurgence
April = Columbine shooting, fear remains with media, retrospective and anniversary
Data is the new oil – ubiquitous resource to provide new resources
Data is the new soil – fertile creative medium, visualisations, graphics are flowers that bloom from this medium. Rather than just facts they are patterns.
“Peak break up times according to Facebook”
Spring break, post weekends, prior summer and Christmas.
Eye is sensitive to patterns and shape
Data as a language alters our perceptions and change our views.
Military budget with respect to GDP?
USA has biggest budget upwards of 600 billion yet Myanmar has biggest comparative to GDP.
Military size per 100,000?
China has the largest military due to it’s overwhelming population however South Korea has largest per 100,000.
Balloon Race Diagram
Bubbles correspond to popularity due to Google hits
Evidence are higher above line.
Visualisation is a form of knowledge compression, curating the data accordingly to space.
Filter out effects according to human health; natural ingredients, heart health.
This TED talk/lecture pod reimagined data visualisations by reshaping perceptions reflecting on patterns and adding context. Data is ultimately meaningless without context, a mere reflection on graphics and statistics unless it’s compared to create a narrative. We can parallel data with preexisting knowledge and change our views. With the Balloon Race Diagram we saw bubbles corresponding to popularity, equating data as a form of knowledge backed by scientific information. By altering the information we can then produce a real time change in data, allowing a graphic that becomes relevant with current
The Billion Dollar-o-Gram [Image] (2014, Jan). Retrieved Oct 10 2018, from http://www.informationisbeautiful.net/visualizations/billion-dollar-o-gram-2013/
Mountains Out of Molehills [Image] (2007). Retrieved Oct 10 2018, from https://informationisbeautiful.net/visualizations/mountains-out-of-molehills/
Charts, maps, diagrams with visual storytelling of 2013 according to the New York Times.
2013 New York Times
Data represented all over the world and the history of human civilisation.
Our World in Data
Daily routines of famous creatives across different eras.
Time Management by Creatives
A collection of different stories from 2013 by Washington Post using graphics as visual storytelling.
2013: The Year in Stories
A visualisation of all shots made in the NBA in a season.
Narrative Visualisation: Telling Stories With Data
Segel & Heer 2010
Data Visualisation for Human Perception, chapter 35 from The Encyclopedia of Human-Computer Interaction, 2nd Ed.
The screen time occupied by students compared to the screen time for entertainment and socialising reflects a greater amount of time dedicated for university study and attendance.
On average people are spending 8 hours and 41 minutes daily on electronic devices (Davies, 2015). According to the data calculated from students, 2.57 hours per day was dedicated to university study, with 0.94 hours per day of laptop usage at university and 0.461 of other screen use. The use of a desktop at university accounted for 0.78 hours per day.
Our daily engagement with social media has been climbing from 90 minutes to 135 minutes between 2012 and 2017 (Statista, 2018). Compared to these statistics of social media engagement, an average of 0.29 hours daily of social media on laptops was accounted and 0.51 hours via mobile phones. The most time spent on screens in respect to entertainment was Netflix or similar consumption, 0.46 hours on laptops and 0.55 on TV.
I found these results to be rather surprising considering the average typical usage of social media and general screen time. Due to the demanding nature of university and other commitments, students opted for alternative methods of occupying their free time.
Davies, M. (2015, March). Average person now spends more time on their phone and laptop than sleeping, study claims. Retrieved from https://www.dailymail.co.uk/health/article-2989952/How-technology-taking-lives-spend-time-phones-laptops-SLEEPING.html
Statista. (2018, January). Daily time spent on social networking by internet users worldwide from 2012 to 2017(in minutes). Retrieved from https://www.statista.com/statistics/433871/daily-social-media-usage-worldwide/
Journalism is about telling a story with the use of data. Using key information sets, key data, key reference elements to inform a story. Processing the data requires asking the right questions. It is not restricting you to text, rather telling a story visually. Trusting journalists can be recognised through data and statistics, being open and transparent.
Data journalism is the recognition of power and measurement in helping public discourse. Scrapping, mining and statistical data reveals patterns and trends. Not for mere entertainment however affecting public and political discourse.
Data journalism history
Graphics made of type – easily reproduced (The Manchester Guardian, 1901).
Diagram and representation of data made of type – alphabet, lines which represented where the front half of battalion was, firing lines, infantry.
The Somme Battle Achievement
Sections of the lands after the war
The Manchester Guardian Commercial 1938
Importance and freedom of colour gives us as opposed to the cross hatching used at the time – complicated data
Meteorite – 54,000 events. Latitudes and longitudes in the data
Interactive/able to search
The guardian came up with a code/algorithm which looked at the achievement of gold medals, and how much more value was derived from country’s who had less population and less expectation of winning compared to more favoured country’s.
A visual interpretation was utilised from a google spreadsheet and changed the graphics live with up to date information. Population adjusted figures – it allowed for greater discourse between the public, discussing why certain countries were able to do well or not so well (economic/population reasons), why one country is particularly well in one sport. Allowed for individual research, explore with data journalism through personal interest rather than focusing on the popular/main winners.
This lecture pod was an important reflection of journalism and reinforcing the ideas of visualisation through narrative. Telling a story through numbers allows the audience a clearer idea of comparisons rather than appealing to emotion or persuasive language. A narrative can be created which dissects the equality of statistics, for example in the olympics the use of data allowed the public to view population adjusted figures of gold, silver and bronze medals, adding greater weight to the achievements of nations who are less likely to succeed due to socioeconomic factors. Further discussion among the public can ultimately create change as issues are presented on a surface level with statistical analysis and supporting visualisations.
Why do we use graphs? To make comparisons easier
Designers choose what is aesthetic or fashionable – overuse of bubble charts. Our vision and brains struggle to measure surface area, better equipped at analysing length. Due to this we struggle to differentiate statistics that involve circles and tend to underestimate size, figures and values.
The more accurate and easier it is to make a judgment the more likely the reader will take away a perception of the presented patterns. This reflects the notion of the human brain finding it easier to compare simple measures, dollar values or figures. Lines or bar based charts on a single axis are the best visual representations for data.
The three most common types of charts used are time series charts (plotting changes over time, renowned in stock markets), bar charts are one dimensional which compares things and a scatter plot that has a variable on each axis.
Reimagined data can have serious implications on the wellbeing and health of professionals and public. In the case of the NASA launch, the rocket manufacturer’s graphics were inconclusive and poorly displayed. Despite this information the launch still went forward and ended in disaster. The mode of data presentation affects our mentality and understanding towards the issue.
Incredibly useful, easy to use and pre-exisiting understanding. Quick to compare information, highs and lows revealed at a glance. Trends are visible within the graph, good for comparing categories.
As popular as bar charts and frequently used. Line charts connect individual data points allowing us to understand a sequence. Their primary value is to understand trends over time, eg. Stock price change over a 5 year period.
Commonly used, and sometimes poorly executed. They should be used to show the relative proportions and percentages of information and only those things. Limit to 6 proportions. If using more than 6, consider a bar chart.
We use graphs as a tool of presenting information as a pattern or simplifying data so the human brain can easily measure size, figures and values. The lecture was important as it reflected the benefits of showcasing data for easily consumed purposes. In a professional setting graphs can even save lives as seen in the NASA example. The mode of data presentation affects our mentality and understanding towards the issue.