Data Storytelling for the Environmental Movement

Political Science

Data Storytelling for the Environmental Movement

By Vanessa Gordon

Cohort 2020-2021

360-401-DW 01

Course Description

N.B.: This course was developed by the Faculty of Political Science for the Environmental Studies profile.

We have lots of data, and we鈥檝e got lots of problems to solve. This course explores how we can find and read climate related data to understand what needs to change, and then vulgarize what we鈥檝e learned from the data for the general public.

To this end, students will learn how to read, work with, analyze, and argue with data, using tools that are focused, guided, inviting and expandable.聽 This is not a lecture-based course: it is built on a large number of workshops that show students how to use technological tools to make things that help us think.

INTRODUCTION: THE CLIMATE STRIKE MOVEMENT

First, students are brought up to speed on the latest climate movement news and its global demands.

Understanding the school strike for the planet. Guided viewing of .

WORKSHOP: from movement actors worldwide ().

MODULE 1: FINDING AND TELLING STORIES WITH DATA

This module takes an in-depth look at what we mean when we talk about 鈥榙ata鈥�. Then we examine how new technologies have turned data from something precious, to ubiquitous. We look at how and why data gets organized into datasets, and the emerging baseline standards for academic peer review of datasets. From there, we look at the inherent subjectivities in data, and we begin to examine how data can be used to tell a story.

Note on PowerPoints: in order to see the script associated with the slides, you need to open them on the PowerPoint app or PowerPoint for the web. (Once open, click on View and select 鈥榥otes鈥�).

What is data? (), The dataset? ()

WORKSHOP: the analog spreadsheet ()

What is the peer review process for datasets? ()
General peer review ()听

WORKSHOP: write a blog post on about a data presentation you saw recently ().听

misconceptions about data storytelling ), telling stories with data ()

MODULE 2: ETHICAL PROBLEMS

This module begins by looking at how data can be used to influence others to act against their own interests. It searches out the most egregious examples and tells the story about what happened.

Then it switches course, to discuss how people are working to make data serve the interests of even the most marginalized members of society. Students will learn about current regulation, innovation and best practice for the effective use of data.

Create an activity log of all the types of data you create during the course of one day ().
WORKSHOP: data log pair and share ()
Guided viewing of 鈥�()
Reading comprehension ()

Joel Gurin. 2014. . SAIS Review of International Affairs 34, 1 (2014), 71鈥�82.
Michael B. Gurstein. 2011. First Monday 16, 2 (January 2011)

MODULE 3: GETTING AND CLEANING DATA

This module explores how getting data isn鈥檛 only about finding it, it鈥檚 about making it. In this spirit, we look at data scraping, and some of the requirements for proper data storage with structured and unstructured data, relational and non-relational databases. We then examine one of the most important topics in data analytics: data cleaning. Data cleaning is the bulk of the work.

Where do we find data? ()

WORKSHOP: Finding data for your questions ()

Scraping data (), data storage strategies (), cleaning data: consistency and completeness (), cleaning data: usability and metadata (),听

WORKSHOP: Tidying data ()

Bad spreadsheets are bad (), tools for cleaning ()

Brian Burghart. 2014. . Gawker (August 2014).

Noah Veltman. 2013. . School of Data (November 2013).

Hadley Wickham. 2014. . Journal of Statistical Software 59, 10 (August 2014)

MODULE 4: ANALYSING DATA

This module endeavours to link back to Quantitative Methods and then beyond to help students understand some of the issues with interpreting results from a data set.

TEAM LIGHTNING TALKS: ()

Talk 1: Basic statistics recap
Talk 2: Exploratory data analysis
Talk 3: Common misunderstandings in statistics
Talk 4: the Null Hypothesis
Talk 5: Statistics and persuasion: the MAGIC criteria
Talk 6: Lying with statistics: P-hacking
Talk 7: Big data and inequity
Talk 8: Approaches to building data literacy
Talk 9: Small data
Talk 10: workshop – sketch a story
Talk 11: wordshop – deconstructing data visualizations

Gonick and Smith, Cartoon guide to statistics, William Morrow, 1993聽

Sameer Bhatnagar workshop
Abelson, Robert. 鈥淢aking Claims with Statistics鈥� from Statistics as Principled Argument Lawrence Eribaum Associates, 1995.

Aschwanden, Christie. 鈥淪cience isn鈥檛 broken: it鈥檚 just a hell of a lot harder than we give it credit for.鈥� FiveThirtyEight, Aug 19, 2015

鈥�鈥�. Ford Foundation, 2015聽

Bhargava, Rahul and Catherine d鈥橧gnazio. 鈥淎pproaches to Building Big Data Literacy鈥� Bloomberg data for good exchange conference, Sept. 2015.
Catherine D鈥橧gnazio, Warren, Jeffrey, Blair, Don 鈥淟ess is more: the role of small data for governance in the 21st 肠别苍迟耻谤测鈥�.
Bhargava, Rahul 鈥淪ketch a Story鈥� Data Culture Project, Databasic.io
Bhargava, Rahul 鈥淒econstruct a Data Viz鈥�, Data Culture Project, Databasic io

MODULE 5: TELLING STORIES WITH DATA

The great moment we鈥檝e all been waiting for! We start by examining what makes persuasive data visualizations, then carefully, with the aid of worksheets and aide-memoires, students choose one from a number of small datasets, learn to 鈥榬ead鈥� the data with the assistance of wtfcsv and then they draw a data visualization, using traditional charts, creative charts, or mapping techniques.聽

They then learn to make dashboards to read larger datasets, after which they might use google studio or move straight to making data sculptures and more complex creative data stories. It feels very much like waiting to see if your birds take flight!

How not to (), How to ()