Episode 21: How to Gain Insights by Identifying Intervals in Your Line Chart - Featured Data Visualization by Ben Jones
 
 Ben Jones. Image via  LinkedIn .

Ben Jones. Image via LinkedIn.

 

Welcome to episode 21 of Data Viz Today. How can segmenting your line chart into intervals help you gain insights into your data? Host Alli Torban dives into specific ways you can visually break up your line chart into intervals as a way to quickly see your data from a different perspective. Example questions you could answer: Which marketing campaign had the greatest effect on sales? Does varying the basketball shot clock length change how many points are scored? Featured data visualization by Ben Jones perfectly models how this technique can visually communicate a relationship between two metrics.

Listen on Apple Podcasts, Google PlayStitcher, SoundCloud & Spotify.

"Monthly US Unemployment Rate by Presidential Term" by Ben Jones

  • Welcome! I'm Alli Torban.

  • 00:25 - Today’s episode is about visually breaking up your line chart into intervals to help see how your metric’s variation coincides with something else, so that you can gain insights and possibly take action. What are some examples where this would be helpful and how can you create your own?

  • 01:20 - Today’s featured data viz project is called “Monthly US Unemployment Rate by Presidential Term” by Ben Jones. Ben is a technical evangelism director for Tableau Software and teaches data visualization at the University of Washington.

  • 01:55 - Ben created his viz because he noticed that Political leaders often claim credit or assign blame for economic indicators, such as the seasonally adjusted unemployment rate, but he wanted to understand how this particular indicator has changed over time in the United States, relative to the presidential administrations that have coincided with the periodic swings. His goal was to plot the unemployment rate over time, and see how the presidential terms coincided with rise and fall of the unemployment rate.

  • 02:25 - Ben grabbed the unemployment rate from the Bureau of Labor and Statistics website. He added columns for president and party.

  • 02:50 - He used Tableau, and visually showed us the presidential intervals by using color, vertical lines, and labels at the top.

  • 04:30 - You can quickly look at this chart and visually comprehend how the presidential terms coincided with rise and fall of the unemployment rate, which was Ben’s goal.

  • 05:15 - Lots of use cases for plotting metrics over time and identifying intervals of change in power or leadership, like Ben did, whether it’s the president, or the CEO of a company.

  • 05:28 - You could also use it build a viz that will help you visually understand the impact of different marketing campaigns on a metric that’s important to you. Great way to visually assess periods of experimentation.

  • 06:15 - Other examples could be plotting sales and visually identify intervals where there was a tariff in place or not.

  • 06:26 - Or in sports, you can plot a particular metric and visually identify intervals where different rules were in place to see if that rule might have been affecting the game in some way.

  • 06:40 - My inspired viz: Showing the average points per game in Men’s College Basketball in the ACC from 1977 to 2017 and identifying the intervals by what kind of shot clock was in place at that time.

  • 07:25 - My goal was to plot the average points per game over time, and see how the change in the length of the shot clock coincided with the rise and fall of the scores. I used Tableau. See it here:

My inspired viz.

  • 09:35 - Final takeaway is that you can visually break up your line chart into intervals to help see how your metric’s variation coincides with something else. Then when you add small things like coloring the line by which interval it’s in, adding vertical markers, labeling each interval at the top and in the same color, and even adding trend lines or average lines, and then you have a powerful tool to assess what’s going on.

  • 10:15 - Listen for Ben’s 8 tips for data viz newbies!

  • 11:15 - Follow Ben on Twitter! @dataremixed

  • 11:42 - Get mapping right away with my free-mini course “Make Your First Custom Map in Under 30 Minutes”.


Episode 20: How to Use Isochrone Maps for Art or Advanced Analysis - Featured Data Viz by Topi Tjukanov
 
 Topi Tjukanov. Image via  Twitter .

Topi Tjukanov. Image via Twitter.

 

Welcome to episode 20 of Data Viz Today. What are isochrone maps and how can you use them for advanced analysis or data art? Host Alli Torban dives into specific ways you can create and use isochrones for personal or work challenges. Featured data visualization project by Topi Tjukanov perfectly models how isochrones can bring out interesting insights.

Listen on Apple Podcasts, Google PlayStitcher, SoundCloud & Spotify.

  • Welcome! I'm Alli Torban.

  • 00:25 - Today’s episode is all about isochrones! What they are and how you can use them for some interesting analysis and beautiful data art. The word isochrone comes from the Greek isos meaning equal and chrono meaning time. Equal time. It’s a line on a diagram or map connecting points relating to the same time or equal times. The useful aspect of isochrones is that the travel times are realistic because it takes into account where the roads are and even traffic conditions.

  • 01:40 - Today’s featured data viz project is called “Urban Forms” by Topi Tjukanov.

  • 02:40 - The first known isochronic map was created by Francis Galton in 1881.

  • 02:50 - So how do you make an isochrone map anyway? Mark your starting point, map out every possible route leaving that point, and then think about your assumptions - how fast are you traveling, and what’s the maximum amount of time that you want to travel. Then for every route, you use the classic formula: distance = rate * time. Solve for distance, and then you can mark a point on the map at the distance you can go when you’re going a certain speed for a certain amount of time. And you repeat that for every possible route. Connect all those points and you have a polygon around your starting point that shows how far you can travel in every direction during that amount of time.

  • 03:30 - As you can imagine, this is really labor intensive, but now there are  APIs you can use that can quickly grab all that route and travel distance information for you.

  • 03:40 - When Topi came across the HERE.com API and a python tutorial on how to convert the coordinates to polygons, he decided to try the code out for himself and experiment with what the different isochrone polygons looked like for different cities.

  • 04:15 - The Python scripts that can be run inside the QGIS Python Console.

  • 04:45 - He dropped starting points in 20 different european cities and ran generated 144 isochrones for each city - one for every 10 minutes during a 24 hour period and over-layed all those isochrones on top of each other so that you can get a realistic view of how far you can go in each city in an hour by car no matter what the traffic is like.

  • 06:15 - It’s fun thinking about the transportation infrastructure of the city that’s causing these different shapes, or the natural features like oceans, lakes, mountains that also affect the shape. It’s like a fingerprint for the city.

  • 06:55 - What are some ways you can use isochrones for geospatial analysis?

    1. You can do two isochrones - one around your work and another around your partner’s work and see where they overlap so you can find a home that’s in an area where you both have suitable commutes.

    2. Or you can map out potential store locations within a city and try to minimize the overlap of the isochrones around each location so your store locations cover as much of the city as possible.

    3. What kind of foot traffic can we expect for a store, at this location? What kind of delivery coverage can we offer? Where are our competitor’s delivery holes that we could fill.

    4. How many homes can a firehouse serve within a 10-minute drive?

    5. Where should we put each firehouse so that there’s a firehouse within 5 minutes of every house?

    6. Which workers are within a 15 minute drive of this outage?

    7. How far could this stolen vehicle have gotten by now?

    8. If we put a bus stop there, how many riders will live within a 5-minute walk?

    9. It’s also used to show how long it takes for runoff water within a drainage basin to reach a lake or outlet.

    10. Chris Slatt used an isochrone map to show how much time it took for residents in different parts of a Maryland city to take public transit to jury duty and showed that more than half wouldn’t be able to make it to the courthouse by the time it opens in the morning.

  • 08:40 - How do you generate an isochrone?

    1. Carto

    2. ARCGIS Online

    3. Mapbox isochrone plugin

    4. Google API

    5. HERE.com API

    6. Demo to try

  • 09:45 - My inspired viz - I used isochrones for analysis for an article that I wrote for a local magazine to see how much extra it costs to buy a house that’s within a 10 minute walk of a metro station compared to the rest of the city.

Example of my isochrone analysis of home prices around metro stations. Article about results.

 

  • 10:30 - My final takeaway is that isochrones are a super cool tool for art and analysis - and now there are more and more ways that for everyone to take advantage of them. Take a page out of Topi’s book and experiment with it a little, maybe make some data art with it, and then you’ll know how to do it when it comes time to use it!

  • 11:15 - Check out Topi’s website and follow him on Twitter!

  • 11:35 - Get mapping right away with my free-mini course “Make Your First Custom Map in Under 30 Minutes”.


Episode 19: [Mini] How to See Your Data Viz With Fresh Eyes
 
 Photo by  Bud Helisson  on  Unsplash

Photo by Bud Helisson on Unsplash

 

Welcome to episode 19 of Data Viz Today. How can you see your data viz with fresh eyes? Host Alli Torban dives into specific ways you can view your work from a new perspective when you're short on time!

  • Welcome! I'm Alli Torban.

  • 00:45 - This week’s mini-episode is about how you can hack the “fresh-eyes effect” and see your data viz from a new perspective immediately!

  • 01:25 - I put together a list of 5 of my favorite ways to quickly see your data viz from a new perspective. Here we go.

    1. Read it out loud. Read your title and accompanying text or annotations out loud. When you pull it outside of your head, the main message of your viz can feel different. Or, even better, record yourself reading it and then play it back with your eyes closed - and focus on what your words are making you envision.

    2. Print it out. You can print out your viz in a few slightly altered ways to give yourself a new perspective. Like, you can print it in black and white, or print it on a smaller scale. Then you can do other things with it like hold it up to a mirror or turn it upside down. Think about what stands out to you in your viz when looking at it in these different ways?

    3. Change your scenery. Try to re-read your assignment or goal and then grab your print out and look it over while you’re walking or ideally outside. Changing your physical location can help get you out of all the minutiae of designing your viz and help you see it from the perspective of someone who’s looking to learn something. Think about whether you’re clearly communicating your main point and desired action?

    4. Imagine the best compliment. Think about the moment you send your viz to your client or boss. What is the most meaningful compliment you could receive about your viz? Are you hoping to hear something like “this viz is so engaging!” or “wow, we really need to take action” or “this is beautifully designed” - think about the compliment you’d want to receive the most, and that can help you re-frame how you’re seeing your viz. Ask yourself if your viz offers enough to be complimented in that specific way.

    5. Pretend to start over. Imagine that you just lost all of your work and you need to quickly recreate the viz in one sketch. You can think about your specific assignment or goal again, and then sketch out on a piece of paper a viz that solves that problem. I find that I sometimes go in a particular direction when creating a viz for whatever reason, and with this exercise, I get the chance to re-make the decisions that I made at the beginning, which can help alert me to problems or give me a new idea. So try to recreate your viz with a quick sketch with your final goal in mind, and notice if you’re drawn to creating anything differently.

    6. BONUS tip: Ask someone else. This one is obvious but worth mentioning - you can try to use someone else’s fresh eyes - give it to someone else and let them look at it for an appropriate amount of time (like if your goal is for someone to understand something quickly, then give them a handful of seconds, but if your viz is meant to be more exploratory, give them more time), and then ask them what they took away from the viz. If they seemed to miss the mark, maybe there’s something you could change to help.

  • 04:50 - My final takeaway is that real, true fresh eyes are the best way to give you some perspective on your data viz, but if you’re short on time, there are a few ways to hack it - you can read it out loud, print it out, change your scenery, think about your most desired compliment, or sketch out a redo.

  • 05:30 - Nominate a data viz to be featured on the show!

  • P.S. Get mapping right away with my free-mini course “Make Your First Custom Map in Under 30 Minutes”.


Episode 18: How to Start a Passion Project That Hones Your Skills & Opens New Doors - Featured Data Viz by Amy Cesal
 
  Amy Cesal . Image via Amy.

Amy Cesal. Image via Amy.

 

Welcome to episode 18 of Data Viz Today. How can you start your own data viz passion project that hones your skills and opens the door to new opportunities? Host Alli Torban dives into specific ways you can find and execute a passion project that brings you joy and growth. Featured data visualization project by Amy Cesal perfectly models how a passion project can be fun, bring new opportunities, and grow your skills!

Listen on Apple Podcasts, Google PlayStitcher, SoundCloud & Spotify.

 "Day Doh Viz" project by Amy Cesal. Image via her  website .

"Day Doh Viz" project by Amy Cesal. Image via her website.

  • Welcome! I'm Alli Torban.

  • 00:25 - This week’s episode is about how you can start a passion project that brings you happiness, hones your data viz skills, builds your portfolio and opens new doors.

  • 01:00 - Check out artist Heidi Horchler’s Instagram - she’s creating a watercolor version of my cover art, which came from her passion project.

  • 01:35 - Featured data viz called “Day Doh Viz” by Amy Cesal.

  • 01:40 - Amy is a graphic designer who specializes in data visualization.

  • 02:00 - Amy wanted to do a 100-day project that included data viz, 3-D objects, and was away from the computer.

  • 02:35 - She thought about her nostalgia around play-doh, so she decided to create a data viz made out of play-doh every day.

  • 03:00 - Her 100-day project’s goals:

    1. Get away from the computer and create more data visualizations for fun.

    2. Expand her network on social media - reaching and interacting with more people.

    3. Highlight data that she found interesting - mostly around personal finance.

  • 03:40 - Lessons learned: creating a play-doh viz every day was too cumbersome for her schedule so she scaled back to a few a week.

  • 04:15 - Skills learned: improved data collection and cleaning, how to use a clay extruder gun, photography, lighting, social media, copywriting, and kicking her perfectionism.

  • 05:15 - Has she achieved her goals? She’s had fun, moved away from the computer screen, gained hundreds of twitter followers, inspired teachers and others to explore play-doh vizzes, was asked to write guest blog posts for prominent blogs, more clearly defined her creative point of view, and it has given her more confidence.

  • 06:15 - How can you get started with your own passion project?

    1. Write a list of skills you want to improve upon or want to show off.

    2. Write another list of your unique experiences, things you’re passionate about, and things that bring out emotion in you (nostalgia, joy, anger, frustration).

    3. Combine things from both of these lists, think about the experience you want to have when doing this project, which will help lead you to the right project.

    4. Then write out what success looks like to you.

    5. Start creating and sharing!

    6. Or base you passion project on your dream job! If you want to be a data journalist for a big news outlet, pick items from your “passion” list and create vizzes in the style of that news outlet so you end up with a portfolio that has your point of view that looks like it’s already apart of their team.

  • 07:45 - Be open to tweaking the project if it’s not fun or achieving your goals.

  • 08:00 - THIS podcast is my passion project! Listen for my story.

  • 10:30 - I created a new passion project by going through the exercise above - listing out skills I wanted to improve and things that I care about.

  • 10:45 - It’s “Mini Data Reporter” - brings together my desire to improve children’s data literacy, serve the low-vision community, and improve my design skills. It’s a one-page assignments that leads kindergarten-aged kids.

  • 11:30 - My goal is to have these fun assignments for my daughter and I to do together, allow other low-vision kids/parents to use these worksheets to have fun learning data literacy, and improve my design/formatting skills.

 My passion project.

My passion project.

  • 12:00 - My final takeaway is that I really believe that growth brings happiness, and of course it’s possible to grow within your day job, but a passion project allows you to grow in ways that you choose while also having fun.

    1. Write a list of things that are unique to you, things that bring out an emotion in you, and skills that you’re looking to improve or show off. Combine these things, talk to your family and friends about it, and you will think of something fun to try. Then wrap some constraints around it and share what you create.

    2. The worst thing that can happen is that you discover something that you don’t actually like to do and you try another idea! The best that can happen is that you learn a new skill, connect with like minded people, build your portfolio and find new opportunities.

    3. Give it a try and tag me on twitter or instagram with your passion project because I’d love to see it and share it!

  • 12:45 - Amy’s advice to designers just starting a passion project: just do it, create something every day and let go of perfectionism.

  • 13:10 - Follow Amy on Twitter and check out her website! Thanks, Amy!

Get mapping right away with my free-mini course “Make Your First Custom Map in Under 30 Minutes”.


Episode 17: 3 Ways to Amp-Up Your Scatter Plot! Featured Data Viz by Maarten Lambrechts
 
 Maarten Lambrechts. Image via his  Twitter .

Maarten Lambrechts. Image via his Twitter.

 

Welcome to episode 17 of Data Viz Today. How can you take your scatter plot to the next level? Host Alli Torban dives into 3 ways you can layer onto a scatter plot to enhance your reader’s understanding of the data.

Listen on Apple Podcasts, Google PlayStitcher, SoundCloud & Spotify.

 "Lazy MP's" by Maarten Lambrechts. Image via his  website .

"Lazy MP's" by Maarten Lambrechts. Image via his website.

  • Welcome! I'm Alli Torban.

  • 01:00 - This week’s episode is about 3 things that you can layer onto a scatter plot to enhance your reader’s understanding of the data.

  • 01:15 - Check out artist Heidi Horchler’s Instagram - she’s creating a watercolor version of my cover art!

  • 02:00 - Today’s featured viz is called Lazy MP’s by Maarten Lambrechts. Maarten is a freelance data journalist and visualization consultant from Belgium.

  • 02:30 - He began this project because as a data journalist, he wanted to visualize who the lazy MP’s are in the Flemish Parliament.

  • 03:20 - He scraped the data on the Parliament’s website using R script.

  • 04:00 - He needed to pitch the story to his editors and thought a visual would help sell the story so he decided to use a scatter plot since he’s comparing two variables to see if there were any trends.

  • 04:38 - He used ggplot2 in R to create the initial scatter plot and in order to help readers quickly orient themselves to where each MP fell in relation to their colleagues, Maarten added a line to represent the Median for the x-axis and another Median line for the y-axis. The median is so easy for people to understand in a scatter plot - half the dots are above and half the dots are below. This is #1 on my list of 3 ways to amp-up your scatter plot - add lines that help orient your reader quickly - this can be median lines like in this case, or a trend line so readers can quickly see what kind of correlation there might be, or you can add a line that’s specific for your data - like here’s a line representing minimum wage, and then you can easily identify the dots below or above that line.

  • 05:35 - #2 - after Maarten added the Median lines, he then labeled each quadrant. So the bottom top right quadrant was called the busy bees (they had lots of documents filed, lots of things said), then there were the silent forces (lots of documents, few things said), the talkers (few documents, lots of talking) and, of course, the lazy MP's (few documents, few things said). Labeling the quadrants in a way that basically sums up what the implication is of being in that position. I feel like it’s such a smart way to assist your reader with interpreting the chart.

  • 06:25 - Another layer that Maarten added to this scatter plot was his use of color to introduce a third variable, which was which party the MP is a part of. This is #3 on ways to amp-up your scatter plot - using color, size or small multiples to represent a third variable. Be careful adding a fourth variable - that could get a little complicated, and in the book Data Points by Nathan Yau, he suggests using color and size to represent the same third variable so it’s a redundant encoding to really drive home the point. Maarten used small multiples to show the party in another way.

  • 07:45 - The whole process, from scraping to the visualisation was done in R (scraping with Rvest, visualising with ggplot2). Final touches for print were done in Illustrator.

  • 08:18 - So what are the three ways you can amp up your scatter plot?

  • 1. Add lines to help orient your reader to your data - this can be median lines, trend lines, or specific values that matter to your story

  • 2. Name the quadrants - this helps your reader interpret the scatter plot quickly and understand the implications of a point being in a certain position.

  • 3. Add a third variable by using color, size or small multiples - this helps your reader parse through the points a bit better to really understand the story. Just be careful not to encode too much - using color, size or small multiples on the same variable can be a good thing - the redundancy can be helpful to your reader.

  • 09:00 - Maarten’s advice to designers just starting out - Although reading some books about data visualisation obviously helps to learn the basics and get inspired, the most important thing to start with visualisation is just making visualisations. Grab a visualisation you like and try to plug in your own data, or try to remake it with the tool of your choice. What can also be very helpful is getting feedback on your work. Not only from people from the data visualisation community, but also from people with no background in visualisation. It can be really insightful to hear what others see in your work, and what they don't see.

  • 09:40 - If you want to see more of Maarten’s work check out how website www.maartenlambrechts.com and follow him on twitter at @maartenzam

  • 10:10 -  Get mapping right away with my free-mini course “Make Your First Custom Map in Under 30 Minutes”.


Episode 16: How to Persuade & Set Goals Using Simulations - Featured Data Viz by Gabrielle LaMarr LeMee
 
 Gabrielle LaMarr LeMee. Image via her  LinkedIn .

Gabrielle LaMarr LeMee. Image via her LinkedIn.

 

Welcome to episode 16 of Data Viz Today. How can visualizations help people make decisions and set goals? Use a simulation/what-if analysis and visualize the results! Host Alli Torban dives into why simulations are so powerful in persuading people and how to know if your client could benefit from a what-if visualization!

Listen on Apple Podcasts, Google PlayStitcher, SoundCloud & Spotify.

 "Educate Your Child" by Gabrielle LaMarr LeMee. Image via her  website .

"Educate Your Child" by Gabrielle LaMarr LeMee. Image via her website.

  • Welcome! I'm Alli Torban.

  • 01:15 - Check out artist Heidi Horchler’s work at daydreamodyssey.com and follow her on Instagram!

  • 01:30 - Follow #SoDS18 on Twitter for Summer of Data Science goal inspiration! Renee’s blog post about setting goals.

  • 02:15 - Today’s inspired viz “Educate Your Child” by Gabrielle LaMarr LeMee

  • 02:45 - The purpose of her thesis was to explore patterns of segregation in the Chicago school system in order to highlight the systemic and individual choices that create and maintain the problem.

  • 03:15 - Data used: Census data and Chicago Public Schools Data

  • 03:45 - Used R and ArcMap to analyze the data

  • 04:00 - The simulation was first a board game

  • 04:15 - Inspired by Spent interactive simulation

  • 04:25 - Final visualization coded in D3

  • 04:40 - Describing the viz. See it here.

  • 06:15 - She really used the “near-view” by making this simulation about one individual child

  • 06:50 - Benefits of simulations/what-if analysis: no risk, can assess the weaknesses and opportunities quickly, and when you can visualize opportunities, it makes it so much easier to make decisions and to set goals.

  • 07:10 - What do you need to create a what-if analysis? Decide on the factors that you want to vary, the factors you want to see affected, and the formula that combines these factors

  • 07:50 - Examples of what-if analysis: forecasting sales, assessing the impact of increased minimum wage, home buying affordability.

  • 08:55 - What kinds of things should you listen for that would hint that a simulation/what-if analysis would be useful? If you hear a lot of “what if we do this…”, if there’s a big change coming or if you want to propose your own big change, or if you’re in a siutation where it’s impossible or difficult for people to experience someone else’s point of view, or if people are having the same conversation over and over!

  • 09:50 - My inspired viz gives you the opportunity to change various inputs and see how expensive of a house you can afford and when. *I’m not a financial planner - This is for demonstration purposes only. :) Interact with it here.


Episode 15: 3 Things I Learned From The Economist Data Team - Featured Data Viz by The Economist Data Team
 
 The Economist. Image via their  website .

The Economist. Image via their website.

 

Welcome to episode 15 of Data Viz Today. What can we learn from the top-notch Data Team at the Economist? Host Alli Torban dives into the 3 things that she learned from their team as they put together their predictive model for the U.S. midterm elections. (Hint: the complexities of predictive model building, visualizing uncertainty, and annotations!)

Listen on Apple Podcasts, Google PlayStitcher, SoundCloud & Spotify.

 "Who’s ahead in the mid-term race" image via their  website .

"Who’s ahead in the mid-term race" image via their website.

  1. Building a predictive model is both really complicated and really exciting.

  2. Conveying uncertainty is crucial both visually and when reporting results.

  3. Annotation and clean formatting go a long way in making visualizations that anyone can understand.

  • 01:30 - Thanks to Heidi Horchler for my new cover art! Check out her Daydream Odyssey Project and her Instagram!

  • 02:14 - Workshops by Jon Schwabish and Stefanie Posavec:  Chicago and DC.

  • 02:25 - Alberto Cairo’s online free data viz course.

  • 03:30 - I describe how they put together the predictive model. Read the full methodology here.

  • 07:53 - Conveying uncertainty is crucial both visually and when reporting results.

  • 08:10 - Try conveying a probability as 2-in-3 rather than 66% to keep your reader from doing any mental rounding.

  • 09:15 - Try adding a shaded confidence interval.

  • 09:55 - Annotation and clean formatting go a long way in making visualizations that anyone can understand.

  • 10:08 - Consider adding an annotation to a histogram that sums up what the graph is saying.

  • 10:50 - Consider showing the same information in two different ways (e.g. spatially and then categorized by color).

  • 11:40 - Try website testing tools to help you test the understandability of your data viz, like the 5-second test on www.useabilityhub.com

  • 12:30 - Alex told me that the design mockups were done using ‘R’ for prototyping and Adobe Illustrator for design. The final visualizations in the story were built using D3 and React so that they can be updated dynamically when they rerun the model each day, and this also allows them to make the visuals responsive for mobile users.

  • 12:50 - Full story, full methodology

  • 13:00 - James’ Quora Q&A

  • 13:15 - My final takeaways are:

    1. Predictive models are super fascinating, but we should also keep in mind that the real world is really complex, which a model can’t fully capture.

    2. Think about the ways that you can show uncertainty - not just visually but also in the text, like how they convey a probability as 2-in-3 rather than an exact percentage.

    3. And lastly, really prioritize adding annotations and clean formatting so that your charts are understandable to anyone.

  • 13:45 - What’s their advice to designers just starting out? Matt McLean said to force yourself to look at your designs from the perspective of a reader who knows nothing about the subject. Overcoming the problem of assumed knowledge is key to creating visualisations that actually show what you think they are conveying. Also, learn to code.

  • 14:15 - If you want to see more, follow the team on twitter at @ECONdailycharts.

  • 14:27 -  Get mapping right away with my free-mini course “Make Your First Custom Map in Under 30 Minutes”.


Episode 14: How to Use Visual Metaphor to Evoke Emotion - Featured Data Viz by Valentina D'Efilippo
 
 Valentina D'Efilippo. Image via her LinkedIn.

Valentina D'Efilippo. Image via her LinkedIn.

 

Welcome to episode 14 of Data Viz Today. How can you use visual metaphor to evoke emotion? Host Alli Torban dives into specific ways to find a visual metaphor that's right for your data! Featured data viz by Valentina D'Efilippo models how to use visual metaphor to create a beautiful, meaningful, and memorable data viz.

Listen on Apple Podcasts, Google PlayStitcher, SoundCloud & Spotify.

"Poppy Field" by Valentina D'Efilippo. Image via her website.

  • Welcome! I'm Alli Torban.

  • 00:25 - “How can you use visual metaphor to evoke emotion?”

  • 01:00 - Enroll in my free mapping mini-course here!

  • 01:25 - Featured viz “Poppy Field” by Valentina D’Efilippo. She’s an award-winning information designer, illustrator, and author based in London. In 2013, she published The Infographic History of the World and she also leads creative workshops attended by students and professionals alike, including a series of Masterclasses with The Guardian.

  • 02:00 - James Ball found the data source: the Polynational War Memorial – an independent study which attempts to compile all the conflicts since the beginning of the 1900.

  • 03:00 - She conducted some preliminary analysis in excel to see how to best present the data variables and insights. At the same time, she considered the art direction that was most suitable to the subject. Valentina decided that a scatter bubble chart was the best viz to display the data, and then she began looking for a visual metaphor that could illustrate and humanise the data. She created mood boards about war and was looking for patterns, density, length and size, and she thought of creating a field of commemoration. Then she had the idea of using Poppies because they’re used as a commemorative symbol for lives lost in war.

  • 03:45 - The final version was created using Adobe Illustrator

  • 05:45 - Check out the interactive version of her data viz here

  • 06:11 - Thought Co. defines a visual metaphor as the representation of a person, place, thing, or idea by means of a visual image that suggests a particular association or point of similarity.

  • 06:50 - Do you even need “embellished” visual metaphors in data viz? I think i depends on your goals, like we talked about in episode 01.

  • 07:15 - A paper called Understanding Visual Metaphor by Dr. Matthew Peterson and others talks about how metaphor is foundational to human thought, and it takes advantage of existing knowledge to explain the unfamiliar.

  • 08:00 - Try some creative exercises that artists use to get their creative juices flowing: write two separate lists and then combining items from each list into different combinations. Or use this website to generate visual metaphors.

  • 09:30 - Create a simple chart first with your data, and then think about a visual metaphor that you can use.

  • 10:00 - My inspired viz is a physical dendrogram representing all the people served at a medical facility.

My inspired viz - a physical dendrogram!

 

  • 12:15 - Final takeaway is using visual metaphor in our data viz can help our readers use their existing knowledge to explain the unfamiliar. Metaphor is foundational to human thought, and using it creatively in data viz can help strengthen its impact and beauty. Try creating a simple and clear viz first, define your goals and then spark your creativity with the exercises stolen from the art world!

  • 12:45 - Valentina’s advice to designers just starting out: she recommends to be curious and use self-initiated projects to explore subjects you’re interested in and keep nurturing your ideas and creativity. In the world of data viz, we’re required to do so many things at once – don’t let your current skill-set limit your exploration.

  • 13:10 - Visit Valentina’s website and follower on Twitter or Instagram!

  • 13:45 -  Get mapping right away with my free-mini course “Make Your First Custom Map in Under 30 Minutes”.

  • 13:55 - No episode next week for Memorial Day. See you soon!


Episode 13: How to Enhance Your Portfolio with Maps - Featured Data Viz by Hyemi Song
 
 Hyemi Song. Image via her LinkedIn.

Hyemi Song. Image via her LinkedIn.

 

Welcome to episode 13 of Data Viz Today. How can you enhance your portfolio with maps? Host Alli Torban dives into specific ways to get started making your own maps! Featured data viz by Hyemi Song models how to use a beautifully styled map to show emerging spatial patterns.

Listen on Apple Podcasts, Google PlayStitcher, SoundCloud & Spotify.

"CityWays" by Hyemi Song. Image via her website.

  • Welcome! I'm Alli Torban.

  • 00:28 - If you’re a data viz practitioner, please consider taking the Data Visualization Community survey.

  • 00:55 - Edward Tufte will be in D.C. giving his 1-day workshop on June 12, 13, or 15.

  • 01:05 - Enroll in my free mini-course called “Make Your First Custom Map in Under 30 Minutes”.

  • 01:40 - Today, we’re diving into the question “How can you enhance your portfolio with maps?”

  • 02:25 - Featured viz called “CityWays” by Hyemi Song

  • 03:00 - Hyemi Song is an award-winning data visualization designer based in New York City.

  • 03:30 - The data was collected using people’s location data from activity tracking apps over the course of a year in Boston and San Francisco.

  • 03:38 - She used Python to clean the data.

  • 03:45 - She made prototypes in Tableau and QGIS.

  • 04:00 - She discovered that a lighter colored basemap didn’t show the density of the routes very well, so she went with a darker colored basemap. She used yellow and red to represent the routes in each city because they represented the warm feeling of the viz (like weather, activity).

  • 04:25 - She used Javascript, D3.js, Mapbox, Leaflet, HTML, CSS.

  • 05:20 - In Nathan Yau’s book Data Points, his chapter on Visualizing Spatial Data talks about how spatial data is easy to relate to because at any given moment, you have a sense of where you are. And the natural hierarchy of a map lets you explore at different granularities.

  • 06:10 - There was a really cool article published by Steven Bernard of the Financial Times called “Data Visualisation: how the FT newsroom designs maps” and he said that when he joined the team 22 years ago, they created maps by tracing over atlases, scanning it, and then retracing in something like Adobe Illustrator. Today, they use QGIS and R, which allows them to join data to maps instantly, so they can spend more time on the visual representation. He also noted that maps usually work best when there’s some sort of emerging spatial pattern. But it’s not necessary. You can use annotations or animations to draw the reader’s attention or tell an interesting story. Maps aren’t always the best way to represent spatial data - sometimes a bar chart will do, but definitely don’t let yourself be limited by not knowing how to map some points.

  • 07:35 - Options to get started: Try Datawrapper if you want some software guidance, Nathan Yau has a lot of tutorials on how to make maps in R, and Mapbox’s tutorials are top notch.

  • 07:56 - My inspired viz: I downloaded data from NYC Open Data site with all trees in NYC and their health.

  • 08:40 - I brought the data into Mapbox and styled the points’ color to correspond to the health of the tree.

  • 09:15 - I documented all my steps to create this map with videos and step-by-step guidelines in this free mini-course.

My inspired viz - mapping the health of NYC trees! 

 

  • 09:20 - Final takeaway: being able to visualize spatial data is a must because it allows people to instantly orient themselves and relate to the data, and nowadays with the barrier of entry being so low, there’s really no reason not to dive in and add a map to your portfolio today!

  • 09:40 - Hyemi’s advice for designers just starting out: teach yourself (both technically and conceptually) through diving into a personal project.

  • 09:55 - You don’t need anyone’s permission to make maps or data viz. Just pick a data set, ask yourself some questions and visualize it!

  • 10:10 - Check out Hyemi’s website at and follow her on Twitter!

  • 10:30 - Get mapping right away with my free-mini course “Make Your First Custom Map in Under 30 Minutes”.


Allison Torbanmap, song
Episode 12: How to Encourage Exploration Without Interactivity - Featured Data Viz by Krisztina Szucs
 
 Krisztina Szucs. Image via her website.

Krisztina Szucs. Image via her website.

 

Welcome to episode 12 of Data Viz Today. How can you encourage a reader to explore your data visualization without adding any interactivity? Host Alli Torban dives into specific ways to make an exploratory static data viz - no coding necessary! Featured viz by Krisztina Szucs models how to encourage exploration by visualizing the rating and profit of past box office hits.

Listen on Apple Podcasts, Google PlayStitcher, SoundCloud & Spotify.

"Spotlight on Profitability" by Krisztina Szucs. Image via her website.

  • Welcome! I'm Alli Torban.

  • 00:25 - Two announcements: Follow me on Instragram for fun behind-the-scenes stuff! And I’ll be launching a free mini-course this week on how to create and embed an interactive heat map so sign up for my newsletter to hear about its release!

  • 01:00 - Today we’re diving into the question “How can you encourage a reader to explore your data visualization without adding any interactivity?”

  • 02:12 - Featured viz:  “Spotlight on Profitability” by Krisztina Szucs

  • 02:30 - Krisztina is a Data Visualization designer coming from the field of Graphic Design based in Budapest, Hungary.

  • 03:45 - She always starts with a basic chart and makes sure it makes sense that way, and then adds more design elements. Check out the GIF of Krisztina’s design process!

  • 05:10 - Tools used Excel and Adobe Illustrator (she didn’t let her lack of tool knowledge stop her from creating!)

  • 06:55 - So how can we recreate this? How can we make an exploratory viz without relying on interactivity?

  • 07:25 - Tips from the book “Data Driven Storytelling” that just came out edited by Nathalie Henry Riche on exploratory viz:

    • Pros - you give the reader the power to ask their own questions and find their own answers. Personalize their reading experience. It can also help communicate the complexity of the data and mitigate some of the biases thats inherent when you provide the narrative.

    • Cons - especially in a static viz, it requires a lot of time and attention from a reader, which they may not be willing to invest. It also requires a lot of design effort, so it’s important to consider your audience - will your effort be worth the amount of effort your audience is willing to part with.

    • Consider giving the reader flexibility over the view, focus or sequence of the story.

    • View - is the ways in which the data is shown to the reader

    • Focus - is the subject of the story - the particular subset data shown

    • Sequence - is the order in which the information in the story can be viewed.

  • 09:45 - How Krisztina executes these three things in her viz

  • 11:00 -  For my inspired viz, I wanted to try to put all these ideas into practice, the idea of allowing flexibility in the view, focus and sequence. I wanted to emulate the 3 different relationships like I mentioned in Krisztina’s viz. One variable on the first line, the angles between that value and the variable on the other line, and the difference between the second variable. I immediately thought about balancing the different attributes of a restaurant when you’re deciding where to go to eat. You want to know the cuisine, you want to know the price range, the food rating, how’s the ambiance, do they take reservations.

My inspired viz / experiment. Would be interesting to do this with a much bigger sample size!

 

  • 12:37 - I hand-drew this viz - no fancy tools! :)

  • 13:00 - My final takeaway: while it’s amazing what we can do right now with code and interactivity, it’s interesting to take away the interactivity and think about how we can encourage exploration in a static viz. Try giving the reader flexibility in the view, focus or sequence. Also try freeing yourself of your tools, OR stop thinking about your lack of tools as a roadblock to creating cool dataviz.

  • 13:30 - Krisztina’s advice for a designer just starting out:

  • 13:55 - Try Krisztina’s project www.plotparade.com

  • 14:12 - Krisztina’s website and follower her on Twitter!

  • 14:40 - Sign up for my newsletter for weekly tips and details about my heat map mini-course coming out this week!