Author: danielkrivacic

Nathalie Miebach uses sculpture and music to explain weather data through the beads and threads that make up the sculpture. Every bead and thread represents a weather element which can be played as a musical note and show a visual representation of weather data bringing art, music and science together. Miebach explains how data is invisible to most people so she uses sculpture and music to make weather data not just visible but audible as well.

Miebach work is based on collecting data from a specific environment and compiles numbers together on clipboards which she then translates the data. The translation of the data is in the medium of a simple basket made up of Horizontal and vertical elements and uses the changes of the data points over time to create form. She uses natural reed which has a lot of tension allowing the numbers to control the form where the forms of the sculptures are made up completely by weather data where every coloured beed and string represents a weather element. The form of the sculpture made up of the beed and strings reveals the behavioural relationships between the weather patterns that can only be shown in 3 dimensions and not in a 2 dimensional graph.

The Vertical elements show times of the day over 24 hours and shows temperature range. On the grid it shows tide readings, water temperature, air temperature and moon phases. The data is also translated into musical scores where she uses the scores to collaborate with musicians to use music to explore the data through sound.

Miebach explains how the work can be seen as a sculpture that can be displayed in an art gallery, a 3 dimensional visualisation of data when presented in a science museum and a musical score when placed in a music hall. Miebach challenges the idea of what visual language is apart of whether it is in art, music or science. Overall the works created by Miebach allows complex data to be understood in a visual and musical art form which goes beyond the boundaries of traditional data visualisation as graphs and charts. Nathalie Miebach shows that data can be interpreted in many forms and there is no boundaries to the way data can be presented.

David McCandless speaks about the beauty of Data Visualisation in his TED X talk and explains how Data Visualisation is about seeing patterns and connections that matter and visualising the patterns and connections so that the information makes more sense to an audience. McCandless explains how Data Visualisation can be used to tell a story or focus on information that is important.

McCandless talks about the $Billion Dollar o-gram and how it was developed due his frustration of reporting Billion dollar amounts to the Press as they are meaningless without context. McCandless tells the audience about how the only way to understand the data was to present the data Visually through the infographic he designed. The graphic breaks up the categories of money being given away as red, money for fighting as purple, green for profit and having the sizes of boxes to present the amount which allowed for comparison of data. The $Billion Dollar o-gram allows for the possibility to look deeper into the information and make comparisons of smaller figures such as looking into the African Nation Debt and comparing it against other nations.

Understanding Data Visualisation

Other examples McCandless used where graphs that showed trends over time over yearly period and spoke about how hidden patterns can be found through the visualisation of data recorded over time and the reason for certain trends occurring a certain periods of time. The visualisation of the trends allow for speculation behind the reason the data is showing those certain trends due to specific dates and events that occur at certain times throughout the year. McCandless iterates how Data is Beautiful and that everyone is exposed to grids, space, alignment and typography in their everyday lives and have become visual people that demand the understanding of information through visual means. Vision is the fastest sense and has the same bandwidth of a computer network which is why visualisation is important to explaining complex information for it to be interpreted. McCandless explains how the combination of the language of the eye which interprets visual codes and the language of the mind which understands numbers and concepts and combining the two allows for the understanding of information.

Data Visualisation and effect on perspective

McCandles explains that data can show different views and change a persons perspective towards a specific topic through the way the data is presented and uses the example of the United States Military budget in comparison to the rest of the world to help explain the concept. The United States military budget is the highest in the world creating the perspective of the US being a war machine. When comparing the size of United States Military budget to its overall wealth of the nation the nations Military budget as a percentage to the countries wealth is smaller than many other nations military budgets compared to the wealth of other countries. Another example used showed China as the most militarised Nation having the highest number of soldiers compared to the rest of the world but in comparison to the countries large population China isn’t considered as a highly militarised nation compared to most nations have a higher ratio of soldiers to their population. Through the examples used McCandle explains that there are many different perspectives that can be shown through data and the way the data is visualised can develop a certain perspective to the way the data is viewed by an audience. When only showing certain figures in a data visualisation the Audience will be unable to get a greater understanding of the data and could develop the wrong understanding of what is being shown.

Interactivity

Interactivity allows for information to be broken down and simplified and allows the user to chose, filter and explore the information they want to see making it easier for the user to get a clear understanding of the data. Technology has allowed data to be constantly updated and change over time as the data changes or evolves.

Information Visualisation outside of data

McCandless talks about how information visualisation can go beyond data and numbers and translate ideas and concepts as well. He uses an example of his own work which focuses on the political influences in society and culture and influences on the individual and their beliefs. The graphic consists of a left side and right side made up of illustrations and text to convey meaning in the information graphic. This type of visualisation approach is very different to data visualisation as it is not comparing numbers and compares views and beliefs. The idea shows that information visualisation can be used for anything convey information to an audience. Information visualisation is not limited and anything can be visualised to make information more understandable.

Information design and data Visualisation are focused on solving information problems so that an audience can understand the information better more more easily. Like every other form of graphic design information visualisation is about communicating and transferring data, concepts and ideas in a way that can be understood by a general audience. it is import to engage the audience through the use of aesthetics and consider the basic design principles to ensure that the information graphic can be decoded by properly.

Data Journalism is journalism which uses statistical analysis to convey and support information through numerical data. The purpose of data journalism is to provide evidence to prove the truth behind a story. In this day in age people question how valid and the truth behind what is told, data journalism ensures the audience they are receiving the truth with valid facts through data. Data visualisation allows journalists to provide more depth to a story which otherwise cannot be shown without the data and also allows for audiences opinions and discussions to occur due to being able to go deeper with the facts.

The Guardian are pioneers for data journalism as they were the first to introduce data evidence for their stories. Overtime Data Visualisation has changed from the very first visualisations back in Manchester Guardian 1821 when the Guardian recorded data on every school in Manchester through a table to show the number of students in each school, costs etc. The data recorded in a table is the most basic presentation of data journalism and was hard to compare the data easily. As years went on the visualisations improved and moved beyond the basic charts evolving into more visual representations of data. The evolution of bar, line and geographical charts were introduced and have led to more sophisticated, clearer and easy to interoperate visualisations. The Visualisations at current day are now interactive and allow for the user engagement to make the easier and more effective.

Data Presentation styles consists of graphs to present information for the reader to easily interpret. Graphs come in many different forms and are designed for specific data sets and to convey the information the way it wants to be interpreted. Designers tend to use the wrong graphs to present data for the aesthetic purpose but fail to present the information to be interpreted the right way.

Example of a poor Data Presentation style – Bubble charts – Market Capitalisation of the worlds Biggest Banks

Bubble charts use area to show the fall in stock price from the year 2007 to the year 2009 where the area shows the decrease in the billions of dollars lost in stocks over the years. The reason why the graph fails to show accurate comparisons is because human brains are only good at comparing single dimensions like lengths. Our brains cannot compare complex things such as length and height to work out area. The circles make it harder to calculate the area when looking at it with the naked eye due to having the dimensions, also the shape of circles make it more challenging to work out the area as it requires the use of a formula to calculate it. Circles make the comparisons harder compared to squares as the flat sides allow for people to compare the size of the squares along other ones due to having simple dimensions and being able to line up the length and height with every square.

Example of a successful Data Presentation Style – Billion Dollar 0 Gram

The Graph consists of squares to compare the billions of dollars through the use of area between each square. Having the simple dimensions of length and height where each square can be stacked up against each other to compare size makes it easier for accurate comparisons.

How we use Graphs?

Different graphs are used to express information in different ways as some graphs show more accurate judgement and others show more generic judgements to make other information to stand out and be clearer. For example colour saturation and shading are used for more generic comparisons and are used on maps to show things such as elevation above and below sea level, heat, rainfall etc. Generic graphs usually consist of colour saturation and shading to provide vague comparisons of data. Graphs used for more accurate comparisons are positioned along a common scale for example bar charts and line charts where bar charts compare data in categories and line charts compare changes over time.

Commonly used charts

Line chart: Records values over a period of time recording the changes at each time increment.

Bar chart: Displays values of each category and allows for comparisons between the values in each category.

Scatterplot: Shows each variable as a single dot plotted across an X and Y axis.

Pie Chart: Displays the relative percentage of the information as a section of the chart.

It is important that designers consider the right visualisation approach for a specific data set as choosing a more aesthetic approach may result in the failure to convey the correct information in a data visualisation. Using simple visualisation methods are usually the most effective in conveying data effectively and having simple dimensions that can easily compare data all need to be considered.

The Functional Art – Alberto Cairo

The visualisation looks at the worlds population and shows the percentage increase in world population showing a drop in the increasing fertility rates. The visualisation shows that the fertility rates will stabilise eventually as increase is reducing. The data presents how from 1970 the trend has been negative but doesn’t show the multiple patterns of rich and developing countries.

Comparison of Spain and Swedens Fertility rates

• Compares the Countries rates through a double line graph.
• Shows how Spains fertility rates are in decline.
• Swedens fertility rates are stabilising.

Fertility rates of all countries

The Data Visualisation showing all of the countries through the multiple lines in the chart makes the data look messy and hard for the viewer to understand what is being told through the chart.

Data Visualisation of all countries fertility rates only highlighting a select few of rich and poor countries.

• Shows how the rich countries are increasing in fertility.
• Fertility rates in developing countries are decreasing.
• The Graphic highlights a few rich and poor countries and clearly identifies what is going on.

The use of line charts has evolved and improved overtime to make it easier for an audience to understand. Successful line charts consists of minimal lines for data comparison as each person is unable to decode loads of data all at once. Earlier charts would only consists of only a few variables to compare data but as technology has become more advanced allowing for the ability of interactivity more data can be shown as long as certain variables can be filtered through keeping the design minimal and easy for the

Data Visualisation has been dated back for hundreds of years and has been used more a more as time progresses. The first early innovative visualisations have had an effect on the future of data visualisation where designers have drawn inspiration to previous methods to develop effective visualisations to communicate the data the way they want it to be communicated.

Napoleons invasion of Russia 1812

• This early visualisation shows the start of where the Polish Soldiers began their trip at the Polish border and their trip towards Moscow.
• The thickness of the line shows the armies strength at critical points on the graphic.
• The graphic shows how the army started with 422 000 men at the river and how the number dropped to 100 000 when arriving at Moscow. The men retreated and returned to the Polish border with 10 000 men left.
• The graphic shows the numbers of soldiers at each point and location in the top part of the Visualisation.
• The bottom part of the visualisation shows the temperature at each point of time reading from left to right.

The Graphic presents the data in an easy way to understand for the viewer to comprehend the complex data. The Graphic provides tools for the reader to analyse and make comparisons for themselves.

Florence Nightingale’s Crimean war data visualisation 1858

• The data visualisation records the death rates of soldiers in hospitals due to malnutrition and lack of sanitation proving the bad conditions in hospitals during the period.
• The graph shows the real the real threat of the disease.
• The graph uses area to present the number of deaths.
• Bar charts would not be effective in showing the changes in death rates overtime.

Otto Neurath 1882 – 1945

• Visualised the invisible economic factors that underlie the functioning of society.
• Used multiples of the same image instead of large images to show size and quantity.

Different forms of visualisations in the past have paved the way for the future of data visualisation as new innovative approaches to visualising data have allowed designers to use research to explore past methods the visualisation which inspirs more innovative contemporary design. As Data Visualisation progresses, designers work out ways to make their data easier and simple for an audience to interpret.

In Data Visualisation there are 4 main data types which are Nominal, Ordinal, Interval and Ratio data types.

Nominal: Nominal data is data based on named categories and are unordered. This data can be counted but cannot have an average but can be used to calculate a percentage. When there is only 2 sets of data it is called Dichotomous data.

Ordinal: Ordinal data is data which is ordered where numbers are assigned to the categories to make the data easy to analyse. For example scores between 1-5 where 1 is bad and 5 is great or vice versa.

Interval: Interval data is the measuring of periods of time. The value of 0 doesn’t measure the absence of something and could mean the start of a new interval.

Ratio: Ratio data is the amount of something. 0 means an absence of something where there is nothing in that section or period of time.

These groups branch under the 2 data types which are Qualitative and Quantitative data.

Qualitative data is non numerical consisting of ordinal and nominal data.

Quantitative data is numerical consisting of interval and ratio data.

The different data types need to be considered before designing a visualisation depending on the approach of the visualisation as each data type has a different different outcome and visual approach to

The 4×4 model is essential to data visualisation ensuring that you capture the right audience through overcoming the three main challenges that designers encounter in visual communication.

Three main challenges

• There is loads of data making it hard for your data to stand out.

• People want fast and efficient content and want to understand the information quickly without working hard for it.

• People demand to be edutained

Solution to the challenge

• Understand user eye flow and create a guide for the eye to travel and not get lost in the information
• Capture the attention of those who are interested in that type of content.
• Lead the audience through levels: the water cooler, Cafe content, Library research, Lab content.

Levels of Content

Water cooler: Capture the audience attention if it is the right audience they will engage in the content those not interested will leave.

Cafe content: Goes into detail of the content but not overly detailed.

Library content: For those who are really interested in the content they will dig deeper into the information to learn more.

Lab content: Interactive data allowing users to engage and work with the content and become apart of it. Users can help update and improve the information.

Visualisation

• 30-50% is visual processing
• 70% of sensory receptors are in the eyes
• It only takes 1/10th of a second to make sense of a scene
• People are visual, Visual communication is 10 times more effective then non visual content
• Interactivity: People want information to relate to them.

4×4 model for winning knowledge content

It is important for Data Visualisation Designers to consider the 4×4 model in order to engage the right audience and use certain levels in the visualisation to to break down the visualisation for people who have different levels of interest in

Data Visualisation is the representation of data information in the form of a graphic to visualise the information making it easy to understand and be interpreted by an Audience. The amount of Data that is available is growing where up to 23 Exabytes (23 000000 Terabytes) of information which was the amount of data recorded up until the year 2002 is now recorded every 7 days. Data visualisation designers must engage with the aesthetics, form, political, environmental and social aspects in order for the audience to understand what the data is saying.

Data: Data is the Quantity or qualitative variables belonging to a set of items where the results of measurements are visualised through graphs and images. Data on its own has no meaning and must be interpreted to have a purpose.

Data Visualisation: Data visualisation is the creation and understanding of the visual presentation of the data. The goal of the visualisation is communicate the information clearly and efficiently using statistical graphics, plots and infographics.

Infographics V Data Visualisation

Infographics: Infographics visualise information to be interpreted and don’t identify a range of numbers.

Data Visualisation: Data Visualisations visualise data to be interpreted which consist of a range of numbers.

Data visualisation must focus on helping the audience/User to analyse and reason about the data and provide evidence. It should make complex information more understandable and easy to interpret. When looking at a certain type of data set it is important to consider the right form of visualisation to correctly convey the information that you want to be understood. The right type of visualisation is important in conveying the story that you want to tell through the data.

Simple charts are common and are easily understood which it is important to consider basic methods first as they could be the most effective.

The two main Basic charts that should be considered first are:

Bar Charts: Used for comparing variables.

Line Charts: Used to compare data over time.

Overall it is essential to ensure that a data Visualisation effectively communicates the message that needs to be conveyed through the way the data is presented and ensure that the audience can interpret the information easily and efficiently. It is important as without an effective visualisation to communicate the right message the Audience would not be able to understand the data and what it means.