Research Process: Data visualisation

1D/Linear

• lists of data items, organized by a single feature (e.g., alphabetical order)
(not commonly visualised)

2D/Planar (incl. Geospatial)

• Choropleth

• Cartogram

• Dot distribution map

• Proportional symbol map

• Contour/isopleth/isarithmic map

• Dasymetric map

• Self-organizing map

3D/Volumetric

• 3D computer models

• Surface and volume rendering

• Computer simulations

Temporal

• Timeline

• Time series

• Connected scatter plot

• Gantt chart

• Stream graph/ThemeRiver

• Arc diagram

• Polar area/rose/circumplex chart

• Sankey diagram

• Alluvial diagram

Multidimentional

Tree/Hierarchical

•General tree visualization

•Dendrogram

•Radial tree

•Hyperbolic tree

•Tree map

•Wedge stack graph (radial hierarchy)/sunburst

•Icicle/partition chart

Network

•Matrix

•Node-link diagram

•Dependency graph/circular hierarchy

•Hive plot

•Alluvial diagram

Sources:

https://guides.library.duke.edu/datavis/vis_types (Permission received from Angela Zoss, the creator of this guide, to reuse some of the content)

https://uark.libguides.com/dataviz

https://datavizcatalogue.com/

https://datavizcatalogue.com/blog/chart-selection-guide/ 

Data cleaning

• OpenRefine
• DataWrangler
• Microsoft Excel 

Statistical analysis 

• R
• RStudio    
• Notepad++ 
• NppToR     
• SAS       
• SPSS       
• Statistica        
  (Campus licenses for SAS, SPSS, Statistica, Atlas.ti, etc)

Qualitative analysis

• Atlas.ti
• Dedoose

Data Visualisation tools and applications

• Tableau Public 
• Microsoft Power BI 
• Google Data Studio   
• RAWGraphs
• Flourish

Code help

• D3.js
• Exhibit  
• Google chart tools 
• JavaScript InfoVis Toolkit

GIS/Mapping

• Quantum GIS
• Carto  

• OpenStreetMap

• Grass GIS  

Temporal data analysis

• D3.js         
• Observable
• Timeline JS

Text/Word clouds

• WordClouds
• IBM Word Cloud generator

Infographics

• Canva
• Venngage 
• ​Infogram
• Piktochart

Social and other network analysis 

• Vosviewer
• Gephi    
• NodeXL

Know your audience

Consider the audience’s level of:

• Literacy: You can use symbols, illustrations, animations and other universally understood graphics
• Numerical literacy: Even educated audiences are not always comfortable with data and stats. Do they understand ratios, complex formulas or statistics. Or do they need simplified data?
• Education/Level of technical expertise: Simplify content and define terms for less technical audiences but provide more detail for those with expertise
• Job function: What is the purpose of your data visualisation? Academics will want to know how data fits into existing literature. Funders will want to see results compared to money spent. An executive will want high-level results to guide decisions. A programme manager may only be interested in data relevant to their specific topic/area.

Find the story

Data Visualisation as storytelling

• Think of your data visualisation message as a thesis statement which needs a summary in a few concise sentences
• The ability to create a compelling, visual argument will be greater if you begin with a clear and focused message
• The type of story you tell affects the platform you will use.
• Infographics might be more useful for persuading the audience of your point of view, where dashboards leave the interpretation to the audience.
• Animations may be effective for college students, but not amongst older adults.

Use colour effectively

How to choose colours:

Step 1: Decide what the colours will represent

Decide which aspects of your data you want to represent with colour.

Step 2: Understand your data scale

The ColorBrewer tool defines three types of scale:

• Sequential – when data values go from low to high
• Divergent – when data has data points at both ends of the scale, with an important pivot in the middle.
• Qualitative – when the data does not have an order of magnitude.

Step 3: Decide how many hues you need

Based on the scale you chose in step 2, you can decide how many hues you need in the palette:

• Sequential data usually requires one hue, using luminance or saturation to define scale.
• Divergent data requires two hues, decreasing in saturation or luminance towards a neutral (usually white, black or gray).
• Qualitative data requires as many hues as values, but remember the limitations of the human brain. Try to not use more than seven or eight colours, otherwise the brain cannot recall what each one represents.

Step 4: Look for obvious options

Before getting too creative, take a look at your data to see if there’s an obvious set of colours.

Your application or corporate style guide might be a good starting point. 

Step 5: Create your palette

Use one of the many web resources. ColorBrewer is one of the best for picking schemes for sequential, diverging and qualitative data. Or if you have a starting point in mind, Adobe Color creates palettes from a single colour.

There are several groups of colours that work well together. You can identify them by their relative positions on the colour wheel:

• Monochromatic – shades of a single hue (sequential data).
• Analogous colours – colours that sit beside each other on the colour wheel (varied alternative for sequential data).
• Complementary colours – from opposite sides of the colour wheel (diverging data).
• Triadic colours – 3 colours equally spaced around the wheel (good starting point for a qualitative palette).

Source: https://cambridge-intelligence.com/choosing-colors-for-your-data-visualization/