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Digital Tools and Methods: Data Viz & GIS

List of tools & services to support data visualisation, data cleaning & analysis, and digital publishing

Introduction to Data Visualisation

In today’s world, we are often flooded with information and encounter more data than we can make sense of.  Thus, the need to be able to present data which can be processed quickly is more acute now than ever. Humans are predisposed to recognise patterns, colours, shapes, length etc   They can process complex information that has been presented as a visual more quickly than raw data organised in a table.  MIT neuroscientists have reported that a human brain can process an image within 13 milliseconds, that is in the blink of an eye. 

We can visualise various types of data eg. numerical, spatial, temporal, textual, etc using appropriate tools to convey and deliver our message.

There are two goals when presenting data: convey your story and establish credibility - Edward Tufte

Data Visualisation

Broadly speaking, a data visualisation may serve one of these 2 purposes: 

a) to explain something, or

b) to allow exploration of the data

In the academic context, visualisations can take these forms:

  • Scientific visualisations
  • Information visualisation / infographics

Fry (2008) suggested the following 7 steps in visualising data:

1. Acquire: Obtain the data

2. Parse: Provide a structure

3. Filter: Remove all but the data of interest

4. Mine: Apply mathematical / statistical methods or methods from data mining

5. Represent: Choose a visual model

6. Refine: Further improvements or refinements to the representation

7. Interact: Add methods that allow a user to explore / manipulate the data 

While these 7 stages are presented in a linear manner, the process, to a large extent, is iterative.  

There are multiple tools that support the visualisation of different types of data: 

  • Geo-spatial: ArcGIS, QGIS, Tableau
  • Data visualisation: Gephi, Excel, MATLAB, Tableau
  • Data cleaning: Open Refine, Excel, Tableau
  • Text visualisation: Word Clouds, network graphs

Open Refine and QGIS are open-source tools while ArcGIS & Tableau offer freemium models.



Data Analytics

The Seven C's of Data Analysis proposed by McDaniel & McDaniel (2012):

1. Choose your questions

2. Collect your data

3. Check out your data

4. Clean up your data

5. Chart your analysis

6. Customise your analysis

7. Communicate your results

Workshops & Seminars

NTU Library runs workshops (W) & seminars (S) on the use of data visualisation tools, text mining and data cleaning techniques under the Digital Scholarship Tuesdays series.  This series of workshops and seminars take place on Tuesday afternoons during the semester.

The venue, unless stated otherwise, is at StarGate, Level 3, Lee Wee Nam Library.  All NTU / NIE staff, students and alumni are welcome. 

Data visualisation and GIS workshops / seminars offered include: 

  • Cleaning Data with Open Refine (W): Dealing with messy survey data and large datasets? According to a Forrester Research report, up to 80% of an analyst's time is spent on data cleaning and preparation. Open Refine, an open source tool, simplifies the tedious task of cleaning data. This is an introductory workshop designed for beginners.
  • Data Visualisation with Tableau (W): An introduction to Tableau, one of the most widely-used data analytics and visualisation tool today.  Using an intuitive drag-and-drop interface, it enables users to analyse their data and build interactive visualisations without the need for any programming skills. 
  • An Introduction to QGIS (W): Discover QGIS, a popular open source software that allow you to create, edit, visualise, and analyse geospatial information. In this workshop, you will be taken through the steps of creating a map using freely available datasets, adding layers of data to enrich the map, and exporting the final product as image or PDF. This workshop is suitable for beginners who do not have prior knowledge of GIS. 
  • Mapping and Visualising your Spatial Research Data with ArcGIS (W): Tap on the power of digital mapping to present your research data in a visually appealing and engaging manner. Learn how to transform large datasets into an interactive online map by adding text, images, videos and external links. Finally share your map online via a unique URL or embed it on your website. 
  • Getting Started with Text Mining for the Humanities (S): In recent years, the use of computational text analysis to analyse large collections of ebooks, newspaper articles and online social media postings has been developing rapidly. Learn the steps and challenges of creating such projects a) building a content set b) applying techniques such as term frequency analysis, topic modeling, n-gram analysis and clustering. This is an introductory seminar designed for beginners. 
  • Applying Advanced Data Visualisation Techniques with Tableau (W): An important part of research is the ability to make effective visualisations to communicate the results and implications of your research in succinctly and persuasively. Using advanced Tableau features, this workshop will show you how to "tell stories with your data".  If you are new to Tableau, do sign up for "Data Visualisation with Tableau" first.



Data Analysis & Visualisation Software

The following are accessible from the 7 workstations in StarGate:





R and RStudio


Additional Resources

Books and Blog Posts

Ang, S. (2019, March 18).  From the text rose a cloud of words. Posted in NTU Library Blog. 

Berinato, S. (2016). Good charts: the HBR guide to making smarter, more persuasive data visualizations. Boston, MA: Harvard Business Review Press.

Fry, B. (2008). Visualizing data. Sebastopol, CA:O'Reilly Media Inc..

Knafic, C. N. (2015).  Storytelling with data: a data visualization guide for business professionals. Hoboken, NJ: Wiley.

McDaniel, E. & McDaniel, S. (2012). The accidental analyst:  Show your data who's boss. Seattle, WA: Freakalytics.