C355_L08_Data_Visualisation (PS)_I

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Republic Polytechnic *

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C355

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Psychology

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Nov 24, 2024

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RP School of Infocomm C355 Data Visualisation I Lesson 08 Data Visualisation I
2 C355 – Data Visualisation 1 L08 Learning Objectives Describe the history of Data Visualisation, important contributors to the field, and contemporary practitioners. Explain two human perception principles (Preattentive Attributes, Gestalt Principles) used in Data Visualisation. Produce tabular and graphical charts to visualize information from a large dataset with the aid of a Visualisation software.
3 C355 – Data Visualisation 1 History of Visualisation Travel back in time on how Visualisation helps to explain information to the public. C001 Getting Insights through Data
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4 C355 – Data Visualisation 1 Claudius Ptolemaeus – Almagest (astronomy treatise) Claudius Ptolemaeus, known as Ptolemy, publishes the Almagest around 150 CE in Egypt, providing a thorough treatise on astronomy, solar, lunar, and planetary theory Earliest preserved use of a table; held detailed astronomical information A copy of the Almagest from the 9 th century, in Greek, on parchment
5 C355 – Data Visualisation 1 Florence Nightingale – coxcomb diagram Florence Nightingale is famous for her work as a nurse during the Crimean War, but she was also a data journalist. She realized soldiers were dying from poor sanitation and malnutrition, so she kept meticulous records of the death tolls in the hospitals and visualized the data. Her "coxcomb" or "rose" diagrams helped her fight for better hospital conditions and ultimately save lives. Blue - death from preventable diseases, pink – death from wound, black – others
6 C355 – Data Visualisation 1 Dr John Snow – Cholera Map of London (1854) In 1854, London was gripped by cholera; many thousands were to die in the ensuing epidemic. Most doctors at the time believed that the disease was caused by foul smelling mist "miasmas" a view contested by Dr John Snow who suspected that contaminated drinking water was the cause. It uses small bar graphs on city blocks to mark the number of cholera deaths at each household in a London neighborhood. The concentration and length of these bars show a specific collection of city blocks in an attempt to discover why the trend of deaths is higher than elsewhere. The finding: the households that suffered the most from cholera were all using the same well for drinking water.
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7 C355 – Data Visualisation 1 Charles Minard – map of Napoleon's disastrous Russian campaign of 1812 Charles Joseph Minard (1781 - 1870) was a French civil engineer; produced an array of graphics that combine a many data points into a compelling visual story One of the most cited examples of statistical graphics occurred when Charles Minard mapped Napoleon’s invasion of Russia. The map depicted the size of the army as well as the path of Napoleon’s retreat from Moscow – and tied that information to temperature and time scales for a more in-depth understanding of the event. 7 Visualisation Elements • Colors – the going and returning path • Thickness of line – strength of army • Timescale • Map
8 C355 – Data Visualisation 1 Data Visualisation What it is and why it matters C001 Getting Insights through Data
9 C355 – Data Visualisation 1 Data Visualisation Data Visualisation is the representation and communication of data in visual mode using graphs and charts and various formats. The underlying belief is that human minds process images better than text. By using visual elements such as charts, graphs, and maps, data Visualisation turns large and small datasets into visuals that are easier for the human brain to understand and process Patterns, trends and correlations that might go undetected in text-based data can be exposed and recognized easier when presented in visual format, thus, allows decision-makers to actually grasp the meaning and insights.
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10 C355 – Data Visualisation 1 Why is Visualisation Important? Data is growing at an extremely fast rate – With large datasets, a way to understand vast amount of data is needed – It is a challenge to manage and present the data without losing key and relevant information – Incorrect analysis of the data may occur without Visualisation Human Minds work well with Visuals – Visualisation presents information that is more easily interpreted for the human minds – Reading charts is easier than going through pages of rows and columns of numbers
11 C355 – Data Visualisation 1 Why is Visualisation Important? Example : Anscombe's quartet comprises four data sets that have identical simple descriptive statistics Summary statistics are all identical for all the four datasets: However, the four datasets vary considerably when graphed even though their statistics are identical Graphs reveal data that statistics may not
12 C355 – Data Visualisation 1 Understanding Human Visual Perception (HVP) How human read and view things C001 Getting Insights through Data
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13 C355 – Data Visualisation 1 Understanding Human Visual Perception ( HVP ) Visualisations are subject to interpretation. Therefore, it is important to understand some aspect of HVP. Specifically: – Preattentive Attributes – Gestalt Principles A knowledge of each enables us to craft good Visualisations.
14 C355 – Data Visualisation 1 The Value of Data Visualization Reference: https://vimeo.com/29684853
15 C355 – Data Visualisation 1 Activity How many times does the digit 7 appear?
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16 C355 – Data Visualisation 1 How many times does the digit 7 appear?
17 C355 – Data Visualisation 1 Is it easier now? (with colour)
18 C355 – Data Visualisation 1 And now? (with colour and bold)
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19 C355 – Data Visualisation 1 How about now? (with underline)
20 C355 – Data Visualisation 1 Checkpoint 1 What have we done here to make the digit seven easier to see? Colour Colour & Bold Colour, Bold and Underline
21 C355 – Data Visualisation 1 Improve Visualisation further – Ordering
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22 C355 – Data Visualisation 1 Order & change other numbers to gray colour
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23 C355 – Data Visualisation 1 Use Bar Chart to Visualize the Data (is it even easier?) # of times digit 7 appears: 17
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24 C355 – Data Visualisation 1 Use Bar Chart to Visualize the Data (with ordering) # of times digit 7 appears: 17
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25 C355 – Data Visualisation 1 Checkpoint 2 What have we done to find out how many times does the digit 7 appear? Ordering Charting Sorting the bar
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26 C355 – Data Visualisation 1 Preattentive Attributes Things like color, size, orientation and texture are preattentive. Preattentive attributes are visual properties that human notice without using conscious effort to do so. These attributes are: – Perceived in less than 10 milliseconds – Unconsciously processed If we can harness Preattentive Attributes, we can craft intuitive, rapidly- understood visualisations.
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27 C355 – Data Visualisation 1 Preattentive Attributes
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28 C355 – Data Visualisation 1 Preattentive Attributes of Visual Perception 4 categories of preattentive attributes: – Form – Color – Position – Motion
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29 C355 – Data Visualisation 1 Preattentive Attributes – Visual Variables Information is usually encode in shapes, color, position, etc. Decades of research has taught us certain visual attributes more accurately represent certain data to the human brain Cleveland and McGill* studied what people are able to decode most accurately and ranked them in the following list. * William S. Cleveland and Robert McGill’s paper Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods
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30 C355 – Data Visualisation 1 Example Preattentive Attributes – Visual Variables Example 1: What if we use Hue to represent Quantitative data? – Can you tell which color is greater than the other? Hue is less accurate for Quantitative data, but more accurate for Nominal data (e.g. represent Female, Male)
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31 C355 – Data Visualisation 1 Example Preattentive Attributes – Visual Variables Example 2: What if we use Length to represent Nominal data (x-axis: Nation) This is incorrect use of bar chart as if the USA cars were longer or better than other cars Length is more accurate for Quantitative data, but less accurate for Nominal data (e.g. countries)
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32 C355 – Data Visualisation 1 Gestalt Principles Reference: https://youtu.be/dk7cXdjX2Ys
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33 C355 – Data Visualisation 1 Gestalt Principles A set of visual perception theories developed by three German psychologists to describe how humans organize and perceive groups of objects. Our brains organise and group visual elements into groups or unified wholes when certain principles are applied. – Objects will be perceived in their simplest form – Humans naturally follow lines or curves – The human mind will attempt to fill in "missing" details We can use these principles to highlight patterns that are important, and downplay other patterns. Gestalt Principles are: – Similarity – Continuation – Closure – Symmetry – Figure & Ground – Proximity 33
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34 C355 – Data Visualisation 1 Gestalt Principles of Design
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35 C355 – Data Visualisation 1 Example – Gestalt Law of Continuity The chart on the left, ordered alphabetically by product name is not following the Gestalt Principle of Continuity. The chart on the right is rearranged to better follow the Gestalt Principle of Continuity. The chart is ordered in descending by sales amount. Our human eye tends to follow a continuous path be it lines, curves, or intersections. The law of continuity make it easier to read this graph because the eye can follow an orderly, continuous path.
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36 C355 – Data Visualisation 1 Example – Gestalt Law of Similarity Chart rearranged to follow the Gestalt Principles of Continuity and Similarity. The multi-coloured graph is harder to read causes extra cognitive overload. The color of the bars didn't have any special meaning, and the entire chart used the same categorical axis and measure. Hence, using the same colour follows the Gestalt Principle of Similarity. Instead of seeing individual bars, we now see them more as a group of bars. Applying the law of similarity by using the same colour for the bars
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37 C355 – Data Visualisation 1 Example – Gestalt Law of Proximity This law states that things that are close to one another are perceived to be more related than things that are spaced farther apart. If the audience would like to compare the sales of a region by quarter to other regions, then it would be best to group them by quarter (chart on the left chart). However, if the audience would like to track the sales of each region across the four quarters, it would be much easier to do so when the law of proximity is applied to focus on region (chart on the right), rather than quarter.sa UHJH BB 0 Applying the law of proximity to track sales by region.
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38 C355 – Data Visualisation 1 Example – Gestalt Law of Figure-Ground The chart on the left has additional cognitive load due to the lower contrast between the bars and background, meaning it takes the viewers’ brains more time to assess – which elements are figures (carrying data and requiring immediate attention) and – which are ground (not so important right now) This chart reduce cognitive load and allow viewers to process the figures faster .
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39 C355 – Data Visualisation 1 Tableau Desktop A data Visualisation tool C001 Getting Insights through Data
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40 C355 – Data Visualisation 1 Tableau Tableau is a business intelligence and data Visualisation tool allowing users to make sense of their data through interactive charts, graphs, and diagrams Tableau has a suite of products: – Tableau Desktop (using for this module) – Tableau Prep – Tableau Online – Tableau Server Tableau’s offering is primarily deployed on-premises, either as: – a stand-alone desktop application or – integrated with a server for sharing content; Tableau Online is the cloud-based SaaS offering.
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41 C355 – Data Visualisation 1 Strengths of Tableau Great Visualisations – Allows anyone to connect to data, visualize and create interactive, sharable dashboards in a few clicks Ease of use – It's easy enough that any Excel user can learn it, but powerful enough to satisfy even the most complex analytical problems Fast – We can create parallelized dashboards, quick filters and calculations
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42 C355 – Data Visualisation 1 Hands-On Practice C001 Getting Insights through Data Tableau Desktop
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43 C355 – Data Visualisation 1 Problem Solution C001 Getting Insights through Data Tableau Desktop
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44 C355 – Data Visualisation 1 BQ1. How many customers are there in each country in their current market share? Insights: US top country Approach: Filled Map and Saturated colors by customers count (Alternative: Colored table or Vertical Bars) Pre-attentive : Saturated Colors (intensity denotes volume) Gestalt : Principle of Foreground/Background (highlighted with blue)
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45 C355 – Data Visualisation 1 BQ2. Who are their top 10 customers who spent on their products? Insights: Top spender Brosina Approach: Order by customer total retail spending in descending where top spender appears at top and filter for top 10 Pre-attentive : Saturated Colors (intensity denotes volume)
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46 C355 – Data Visualisation 1 BQ3. What is the average amount spent by a male and female customer? Insights: Female spent more than male on average by about $70. Approach: Vertical bars, sorted by length/ avg total retail Measure aggregate by Average Pre-attentive : Length for comparison Gestalt: Principle of Continuity (bars are ordered by descending height)
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47 C355 – Data Visualisation 1 BQ4. What mode of order purchase (ie, order type) is more popular with male and female customers? Pre-attentive : Colors (use of different colors to highlight and for grouping) Length (use of length/height to denote quantum) Gestalt Principle : Principle of Proximity (same group cluster together by gender) Principle of Continuity (bars are ordered descending height) Principle of Foreground/Background (use different colors to highlight – orange & blue) Insights: Most popular mode of order is Retail Sale for both genders. Approach: Interleave bar, group by gender, sorted by order count descending
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48 C355 – Data Visualisation 1 Insights: Orion Gold members give most profit for female customers while Orion club members generate most profit for male customers BQ5. Which customer groups tends to bring the most profit among the male and female customers? Approach: Interleave bar, group by gender. Sorted descending Pre-attentive : Colors (use of different colors to highlight and for grouping) Length (use of length/height to denote quantum) Gestalt Principle : Principle of Similarity (same color denotes same customer group) Principle of Proximity (same group cluster together by gender) Principle of Continuity (bars are ordered by descending height)
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49 C355 – Data Visualisation 1 What we have learnt History of Data Visualisation and important people in the field Understand human perception, how human process the world, and relate them to Data Visualisation – Preattentive Attributes – Gestalt Principles Use Tableau to create Data Visualisation – One-Way Table, Two-Way Table, Bar Chart, Pie Chart, Pie Chart on a World Map, Relationships, Calculated Field, Line Chart, Filter
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