My husband and I have binoculars and a bird guide on a window ledge in our dining room. Sounds dorky, right? Well, I’m old enough to not care much about being a dork these days so onward.
We have a few bird feeders in our backyard and if I was home about this time of year and through the winter, we would be watching for birds and trying to identify them. Sometimes we would use our trusty guide book or use Cornell’s Merlin Bird ID app. There was also a time when we would send information about which birds showed up in our backyard.
So when I came upon this story in the New York Times about the loss of 3 billion birds since 1970 I was shocked, then deeply saddened, then alarmed.
Now, when I say shocked, I wasn’t surprised that there was a population loss. Climate change, pollution, deforestation, agricultural practices, invasive species … the list goes on. But really … danger, Will Robinson!
3 billion birds since 1970.
3 billion birds. 50 years. Gone.
Now, this graphic is beautiful. The colors, the structure, the information is clear and helpful. This is the online version in the Science Section:
Clearly, hit hardest are the Grasslands. The article mentions that even Robins and Sparrows, and Starlings (those invasive species) experienced significant declines.
Robins and sparrows.
a full-blown crisis.
David Yarnold, president and chief executive of the National Audubon Society
Isn’t everyone shocked and alarmed?
Can you imagine a world without birds? I don’t know about you but that gives me the willies. In fact I recall a moment while walking in our neighborhood when I realized there were no bird sounds. It was the eeriest feeling. I went out every morning listening specifically for a chirp or a call. What a relief when I finally heard one.
Conjuring a world without birds is a thing I don’t dare imagine, like the death of a child. Their fate is our own.
So, while writing this post, I realized that I wanted to show you what losing 3 billion birds looks like. Can you picture it? I can’t. If I had to explain to my 7-year-old niece what a loss of 3 billion birds looks like what would I show her?
It is insane to see the relationship of plastic bottles to iconic structures like the Eiffel Tower.
Would the story have greater impact and meaning among non-bird experts and fans if there was a way to see scale; a tasteful rendering and relationship of the number of birds to something familiar like the Statue of Liberty or covering an area the size of Texas or ?
I’m thinking yes. Do I have an exact solution? No, but I’d love to explore it some time in the future. In the meantime, I’m going to continue to help conservation efforts and take every day I see birds (even those pesky Starlings) as a gift.
I signed up for a poetry class during summer break while an undergrad at The University of the Arts (UARTs) pursuing my BFA in Photography. I had no idea what I was getting into but I went with it. The experience turned out to be one of the unexpected “bests” of my life. That summer was also the year I learned, in great detail what happened to my grandfather , a man I never knew because someone decided to take his life long before I was ever imagined.
Writing poems gave my anger and loss (can you mourn someone you never knew?) a place to rest. It was also the first time I recited a poem in public. Granted, it was a room of my classmates but for someone who honestly prefers to sit in the audience, this was big. Huge.
It’s been awhile since I’ve read her poems and this idea of a project has been a wonderful way to reacquaint myself with her and her work. It’s been, well, decades. Does her work resonate with me now as it did then? I believe this project will be an emotional journey as much as it is a learning experience. What will I discover?
I’m proposing a visualization that slightly petrifies me.
Self-learning: Python, D3, scraping data, build a corpus… oh my.
Blogs, tweets, StackOverflow, GitHub, Codecademy, Lynda … you name it, I’m searching for “How to …” often.
Learning python: New jargon. Decisions about IDEs. Pandas… they aren’t cute black and white furry animals?
Learning how to properly analyze and clean text data: Pre-processing? Does my data need to be organized in a csv file or like a massive dump in a txt file? Looking for tutorials on how to prep text with Jupyter notebook) Soooo many questions.
Learning D3 is not going to be easy though I am hopeful Amelia Wattenberger’s book, Full Stack D3 and Data Visualization will be a huge help. (She is based in Rochester, NY – cool.)
Some classmates have suggested that I use R instead of Python. I’ve gone back and forth on this. Maybe I’m crazy but I prefer to learn Python because it seems a language that crosses many disciplines for many applications. Then, there’s the fact that python was named in honor of Monty Python’s Flying Circus!
Part of this is also recognizing that I know what is good but my abilities to execute are far below what it probably takes to meet my own expectations. Ira Glass has something to say about this and I’m trying to find some comfort. One would think familiarity of this mind space would make it easier each time. But no…
Still, I have some bright sides.
Mindy McAdams, a data journalism professor at the University of Florida was helpful in getting me setup with Python. I didn’t know it at the time but miniconda was the right way to go and given the number of IDE options, I’m so glad I went with Jupyter Notebook. (Seriously Mindy I cannot thank you enough.)
The bright side wouldn’t be complete without mad props to Lenny Martinez, one of UM Interactive Media’s data journalism professors. His positivity is all goodness when I feel like I’m drowning.
And, to end on a positive note, I do know how to install packages and I know kernals and cells, virtual environments and I’m not afraid of Terminal so much anymore. Not bad. Perhaps I need to just get over the fear that I’ll break something if I type incorrect syntax and also not worry so much about creating a properly formatted text file.
It’s always inspiring to hear from people who love what they do. Our Data Visualization Studio class was fortunate to welcome three (remote) guest speakers: Luís Melgar, Julia Wolfe and Maarten Lambrechts. I want to share my notes and takeaways both as a reminder for myself and to share with anyone learning more about data visualization like me. Warning: This is a long post.
Luís Melgar: learn something new & a great way to Quiet Imposter Syndrome
What I particularly loved about Luís’s talk was the mix of professional and personal advice. It was positive and endearing, a talk that makes you feel good and ok exactly where you are.
Keep a data journal
Log your data, track your sources to maintain sanity. Truth, this. If you’ve ever felt like you were drowning in data or hunting for sources in the middle or near the end of a project, this is your life jacket.
Write down what you did with the data, how you came up with your conclusions. This is accountability.
Track your path to the very end.
Data is imperfect
I love how Luís phrased this tip: interview your data.
Questions to ask:
Where is it coming from?
How it is defined?
What questions does it bring to mind?
How was the data collected?
Who collected the data?
Get clarity. What can you really see from the data? Dig into it.
Advice about coding
Is your code reproducible?
Make it easy to track mistakes
Comment your code
Use another language to double-check your calculations
Test, test, test
Review, review, review
Check, check, check.
Rinse and repeat.
Learn something new
Pick a project, set a goal. Do it.
Expect the unexpected
Some people may give you data in a PNG. No, really.
Coding is hard. So, for those who aren’t natural coders, it’s OK if you can’t execute what you have in your mind. Luís shared his experience working on his Master’s capstone and some of the hurdles he came across and how he learned to adapt and work around his own limitations.
Know your technical limitations
Different companies and organizations use a wide range of publishing tools. Understand what is possible within those constraints.
Always keep your audience in mind
Make your visualizations responsive and usable on mobile. This is a must.
Don’t ask for too much advice.
This tip made me think of some photographers I know who seem to ask too many people to give them feedback about their work. Let me clarify: I think it is OK to ask as many people as you want but take advice from just a few— the people you trust most. It was nice to hear Luis same the same. Basically everyone has an opinion but whom do you trust?
Love your Classmates
Yes indeed. Luís shared a funny photo of some of his classmates (with some random dude cute out – lol) and how he continues to stay in touch. He shared a couple of memories about music and food which was sweet and funny.
Life is about relationships. The relationships you build or burn in graduate school will have a big impact on your life moving forward. Take the time to learn something new about the people you see every day. They will enrich your life in so many ways.
Keep a notebook of success stories
This. I was surprised to hear him talk about imposter syndrome and it was refreshing to hear someone with so much more experience share that even he suffers from it from time-to-time. He shared how women and minorities tend to experience it more. Interesting.
His solution? Keep a notebook close by where you write down your success stories —what you have learned or accomplished. Big or small. When you are feeling like an imposter, or perhaps just feeling a bit low or unsure, refer to your notebook. It’ll make you feel better; that you are making progress; that you are learning; that you are exactly where you need to be and where you are supposed to be.
Thank you Luís. It was wonderful and inspiring to hear from a graduate of our program.
Her advice aligns with much of what Alberto has shared as well as what I’ve read in books and online. It helps to hear similar advice but in a different way.
Analyze data: Find the story. What are your questions?
Be skeptical of the data
Talk with a subject matter expert or know enough about the subject to either spot something surprising or identify a problem.
Understand basic design sensibilities: Color, hierarchy, white space, alignment, typography.
Edit and proof: Layer your workflow and establish a process for self-editing.
Comment your code so you can remember it later. Save every step.
Keep your data organized
Do rigorous spot checking
Make sure your source aligns with your visualizations
Check your outliers. Are they reflected in the source?
Provide documentation. As you work, make notes or comments about what you found and how. You may need it to back up your work, your conclusions.
Bring context to data and moments.
Learn how to get and scrape data. What kind of unique data can you get; something that no one else has?
Freedom of Information Act (FOIA). Luís Melgar mentioned this as well. Understand how to file a FOIA request. (See links below)
Excel. Ramp up your skills.
Reporting. Hit the streets if needed.
Interview. Yes, talk to people!
Understand state laws. What are you legally within right to request and what are institutions required to give you? I loved this tip. I learned state laws and policy is important secondary research for product design, too.
Understand different data formats: PDF? Excel? I took this to also mean, understand how to read them and how to extract data from PDFs (Tabula to the rescue …)
I’m glad he did because he made a story I would probably pass over into a story I wanted to read. Even more, I wished for a follow-up. What more could I learn? For example, when he shared a brief look at his sketches and a sentiment analysis he had done, I wanted to learn more. What patterns are developing in Europe?
Details from Why Budapest, Warsaw, and Lithuania Split Themselves into Two
Honestly, Maarten’s talk was a good inside look at what it takes to be a data journalist/ data visualization designer. It was a bit of a wake up call, too. It takes a certain level of tenacity, a doggedness, if you will. Perhaps this is why I latched on to a quote he made during an interview with Open Belgium 2016:
If you want to become a data journalist, you should start thinking like a programmer.
There’s just something about programming that is empowering. Perhaps because it forces you to look at a problem in many different ways. It changes how you think about things, look and interact with the world. Hmm…
Like many other professions, what most people see is the finished, polished work. Viewers/readers don’t see what goes on behind the curtain. That’s what I enjoyed most about Maarten’s talk. He showed us his sketches, his thinking; what worked, what didn’t. It was a comfort to see the mess.
Thank you Maarten for the incredible resources and sharing your story with us. Consider me a new fan of your blog.
Wow, you made it to the bottom. Nice.
Thank you data visualization community for your generosity. Ya’ll are icing on the cupcake.