Data science, machine learning & data visualization consultant and lecturer
Need an advice in machine learning, data science or career? Write to boris@gorelik.net and I promise to answer. It looks like you visited this…
Read more Boris Gorelik, Ph.D. Data science, machine learning, data visualization
On Sunday, I wrote about bootstrapping. On Monday, I wrote about visualization uncertainty. Let’s now talk about bootstrapping and uncertainty visualization.
Robert Grant is a data visualization expert who wrote a book about interactive data visualization (which I should read, BTW).
Robert runs an interesting blog from which I learned another approach to uncertainty visualization, bootstrapping.
Read the entire post: Data visualization with statistical reasoning: seeing uncertainty with the bootstrap — Dataviz – Stats – Bayes
The maximum data-ink ratio principle implies that one should not use colors in their graphs if the graph is understandable without the colors. The fact that you can do something, such as adding colors, doesn’t mean you should do it. I know it. I even have a dedicated tag on this blog for that. Sometimes, however, consistent use of colors serves as a useful navigation tool in a long discussion. Keep reading to learn about the justified use of colors.
Pew Research Center is a “is a nonpartisan American fact tank based in Washington, D.C. It provides information on social issues, public opinion, and demographic trends shaping the United States and the world.” Recently, I read a report prepared by the Pew Center on the religious divide in the Israeli society. This is a fascinating report. I recommend reading without any connection to data visualization.
But this post does not deal with the Isreali society but with graphs and colors.
Look at the first chart in that report. You may see a tidy pie chart with several colored segments.
Aha! Can’t they use a single color without losing the details? Of course the can! A monochrome pie chart would contain the same information:
In most of the cases, such a transformation would make a perfect sense. In most of the cases, but not in this report. This report is a multipage research document packed with many facts and analyses. The pie chart above is the first graph in that report that provides a broad overview of the Israeli society. The remaining of this report is dedicated to the relationships between and within the groups represented by the colorful segments in that pie chart. To help the reader navigating through this long report, its authors use a consistent color scheme that anchors every subsequent graph to the relevant sections of the original pie chart.
All these graphs and tables will be readable without the use of colors. Despite the fact that the colors here are redundant, this is a useful redundancy. By using the colors, the authors provided additional information layers that make the navigation within the document easier. I learned about the concept of useful redundancy from “Trees, Maps, and Theorems” by Jean-luc Dumout. If you can only read one book about data communication, it should be this book.
If you speak Arabic or Farsi, I need your help. If you don’t speak, share this post with someone who does.
Right-to-left (RTL) languages such as Hebrew, Arabic, and Farsi are used by roughly 1.8 billion people around the world. Many of them consume data in their native languages. Nevertheless, I have never seen any research or study that explores data visualization in RTL languages. Until a couple of days ago, when I saw this interesting observation by Nick Doiron “Charts when you read right-to-left“.
I teach data visualization in Israeli colleges. Whenever a student asks me RTL-related questions, I always answer something like “it’s complicated, let’s not deal with that”. Moreover, in the assignments, I even allow my students to submit graphs in English, even if they write the report in Hebrew.
Nick’s post made me wonder about data visualization do’s and don’ts in RTL environments. Should Hebrew charts differ from Arabic or Farsi? What are the accepted practices?
If you speak Arabic or Farsi, I need your help. If you don’t speak, share this post with someone who does. I want to collect as many examples of data visualization in RTL languages. Links to research articles are more than welcome. You can leave your comments here or send them to boris@gorelik.net.
Thank you.
The image at the top of this post is a modified version of a graph that appears in the post that I cite. Unfortunately, I wasn’t able to find the original publication.
See my recap of the recent EuroSciPy, published on https://data.blog
In which Boris Gorelik shares his favorite talks and workshops from EuroSciPy 2018.
My colleague, Chares Earl, pointed me to this interesting 2010 post that explores different ways to visualize categories of drastically different sizes.
The post author, Tom Hopper, experiments with different ways to deal with “Data Giraffes”. Some of his experiments are really interesting (such as splitting the graph area). In one experiment, Tom Hopper draws bar chart on a log scale. Doing so is considered as a bad practice. Bar charts value (Y) axis must include meaningful zero, which log scale can’t have by its definition.
Other than that, a good read Graphing Highly Skewed Data – Tom Hopper
I wonder how this analysis of remained unnoticed by the social media
The recent election of Doug Jones […] got me thinking: What if the Black populations of Southern cities were to experience a dramatic increase? How many other elections would be impacted?
via Back to Mississippi: Black migration in the 21st century — Charlescearl’s Weblog
Uncertainty is one of the most neglected aspects of number-based communication and one of the most important concepts in general numeracy. Comprehending uncertainty is hard. Visualizing it is, apparently, even harder.
Last week I read a paper called Value-Suppressing Uncertainty Palettes, by M.Correll, D. Moritz, and J. Heer from the Data visualization and interactive analysis research at the University of Washington. This paper describes an interesting approach to color-encoding uncertainty.
Uncertainty visualization is commonly done by reducing color saturation and opacity. Cornell et al suggest combining saturation reduction with limiting the number of possible colors in a color palette. Unfortunately, there the authors used Javascript and not python for this paper, which means that in the future, I might try implementing it in python.
Visualizing uncertainty is one of the most challenging tasks in data visualization. Uncertain
via Value-Suppressing Uncertainty Palettes – UW Interactive Data Lab – Medium
