What’s Going On in this Graph? Environmental Data Visualization Literacy Workshop With R

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What’s Going On in this Graph? Environmental Data Visualization Literacy Workshop With R
Jajwalya Karajgikar

Data journalism is increasingly used to help the public understand complex scientific and social issues, but what makes an effective data visualization?  What techniques should we employ to create compelling graphs and tell better stories?  Using environmental data, we examine a set of real-world figures and graphs to identify techniques commonly used in data journalism and explore how to thoughtfully incorporate them into our work. Then we learnt to recreate them responsibly and reproducibly using the R Programming language.  

While this themed session focuses on environmental data, the concepts here are transferrable and applicable to a wide range of subjects where data plays an important role in understanding research outcomes, information diffusion, media literacy, etc.

The participants were given the space to brainstorm and discuss visualizations amongst themselves. The discussion points for each of the visualizations demonstrated were:

  • How does the visualization perform? 
  • What do you notice? 
  • What do you wonder? 
  • What’s going on in this graph? What story can it tell?
  • What could be improved upon? If anything?

Workshop Materials, including R worksheet, and National Parks Visitation dataset

Workshop Recording

 

Environmental Data Literacy Tools: 

Readings:

  • Bahlai, C et al. (2019). Open science isn’t always open to all scientists. American Scientist 107 (2): 786        
  • Ch 14 in Indigenous Data Sovereignty, Building a data revolution in Indian Country by Dr. Desi Rodriguez-Lonebear
  • Cheruvelil, KS and PA Soranno (2018). Data-intensive ecological research is catalyzed by open science and team science. BioScience 68 (10): 813 - 822
  • Hampton et al. (2015). The Tao of open science for ecology. Ecosphere 6 (7): 1 - 13C   
  • Lowndes et al. (2017): Our path to better science in less time using open data science tools
  • Mah, Alice. (2016) Environmental justice in the age of big data : challenging toxic blind spots of voice, speed, and expertise. Environmental Sociology.doi: 10.1080/23251042.2016.1220849
  • Martha C. Monroe, Richard R. Plate, Annie Oxarart, Alison Bowers & Willandia A. Chaves (2019) Identifying effective climate change education strategies: a systematic review of the research, Environmental Education Research, 25:6, 791-812, DOI: 10.1080/13504622.2017.1360842
  • Nyman, M., Ellwein, A. L., Daniel, M., and Connealy, S., Using Data-Rich Instruction for Climate Change Education: Road Blocks and Pathways, vol. 2011, 2011.
  • The Next Generation of Environmental Scientists are Data Scientists by Jenny Seifert and Kathryn Meyer
  • Wilke, C. (2019). Fundamentals of data visualization: A primer on making informative and compelling figures.
  • Wilson et al. (2017): Good enough practices in scientific computing

 

Examples of Data Visualization Literacy:

People:

 

About the Author

Jaj Karajgikar
Jajwalya Karajgikar
Applied Data Science Librarian
Jaj engages with researchers across the disciplines interested in employing techniques for data storytelling, natural language processing, computational social sciences, data visualization, network analysis, and text mining. She works with campus partners to establish foundational programming in research computing, data literacy, and data ethics.

With extensive experience in data storytelling, natural language processing, computational social sciences, data visualization, network analysis, and knowledge mining (text/data/etc), Jaj engages with researchers across the disciplines interested in employing these techniques. She teaches Carpentry workshops, provides learning opportunities, heads the R User Group, collaborating on digital scholarship projects, and working with the graduate center and other campus partners to establish foundational programming in research computing, data literacy, and data ethics.

Jaj has a  Masters Degree in Computational Sciences from George Mason University. She has completed internships in digital humanities at the National Park Services’ American Battlefield Protection Program and museum data science at the National Gallery of Art’s Department of Analytics and Enterprise Architecture. She is a researcher with the PrincetonDH New Languages for NLP Institute and has worked as a Graduate Research Assistant at the Digital Scholarship Center Lab of George Mason University.

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