Writing about visualization, demographics, dashboards, and spatial data science.

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I frequently come across criticisms of PowerPoint as a presentation tool, which is interesting to me given the ubiquity of its use across industries. When I worked as a data analyst prior to coming to TCU, I frequently prepared PowerPoints using a company template for my boss’s presentations or for talks of my own. In academia, we have considerable freedom in how we can communicate information; however, PowerPoint is still widely used in the classroom and is everywhere at professional conferences.

When covering Russia and the former republics of the USSR in World Regional Geography, a key part of my material addresses the issues that some of these countries have had in their transitions from centralized to market economies. Some of these countries experienced dramatic demographic shifts after the dissolution of the USSR, including a noticeable decline in life expectancy. I’d been using some static Excel charts to illustrate life expectancy declines in Russia, Belarus, and Ukraine in previous courses.

Please note: some NVD3 charts are performing very slowly in the latest version of Google Chrome at the moment; see this GitHub issue. As such, this post is best viewed in other browsers. I recently came across this really interesting post from Ben Jones that explores the history and future of world population change with Tableau. I haven’t used Tableau much, but I was impressed with the different ways in which Ben used the software to visualize various aspects of global population change.

I find population pyramids to be very effective teaching tools. In short, a population pyramid is a type of chart that shows the population size of different age cohorts on the x-axis, with gender usually displayed back-to-back to create the shape of a “pyramid.” It is used to illustrate a snapshot of the age and sex structure of a population, and can serve as a tool that aids in discussion of many thematic issues such as population growth, aging, and gender imbalance.

Over the last month or so, I’ve been working a lot with Plotly to visualize data for my World Regional Geography course. Plotly is a web-based service that allows for the creation of interactive, D3-based visualizations that can be easily shared. Visualizations are deployed and hosted on Plotly’s servers, so the service requires a connection to the internet to work. One of the great things about Plotly is that it has APIs for popular data programming languages like R, Python, and Julia.

Welcome! This is the first post of my effort to document my foray into developing interactive data visualizations for use in my teaching. Hopefully these examples will be of use to some readers who are interested in creating their own visualizations. I’ll first provide a bit of background. I’m a geography professor at Texas Christian University in Fort Worth, and started getting interested in data visualization when putting together materials for a course I taught in population geography last spring.