Tackling the Problems of a Changing World with Data
If you feel like the past few years have been particularly hectic and stressful, you're not alone. We doubt any period in human history could be described as uneventful, but the confluence of global crises, technological progress, and the proliferation of media has given our current moment a very specific—and occasionally overwhelming—texture. Everything! Everywhere! All at once!
We wanted to offer a constructive outlet for the many data practitioners who'd like to think about solutions and pragmatic approaches to the (many) problems our global community is facing, so we recently put together a wide-ranging collection of 30 excellent articles that tackle topics like population analytics, sustainable agriculture, and urban planning. We also included a few public datasets in case you wanted to jump into the fray and start tinkering.
This week, we highlight some of the contributions you'll find in "Our Shifting Global Village," the special feature we shared earlier this month. We hope you'll explore a few of our other picks, too, and that you'll leave your reading inspired to learn more—and maybe even contribute to meaningful projects down the line. Enjoy!
- Crafting stories at a human scale. When we talk about planet-wide issues like population growth or climate change, it's easy to lose track of the actual people who will consume (and be affected by) these conversations. Emily A. Halford‘s overview of artist Norwood Viviano's work does a great job connecting the dots between data, visual storytelling, and communicating complex messages.
- Computer vision meets wildlife. Designed well, emerging technologies come with massive potential to help humans live more sustainably in their environments. Abhay Kashyap presents a compelling case study: a project that leverages AI-powered systems to support a nonprofit organization dedicated to preserving wild cats in California.
- Assessing the environmental impact of a key industry. With so much public data available for researchers, the challenge often lies in finding the right dataset and defining a reasonable scope for your project. Aine Fairbrother-Browne provides a great example of how to accomplish both, with a study of UK airline data that highlights the aviation sector's effects on the environment and the areas where it has the greatest potential to improve.
- How to push transportation planning forward. If you're interested in geospatial data, urban planning, or graph theory, don't miss Sutan Mufti‘s hands-on primer, which introduces us to the fascinating topic of network analysis for transportation planners. (Sutan uses Python tools you might already be familiar with, so the learning curve is likely to be flatter than you think!)
Before we let you go to explore these essential topics, we wanted to offer a few more reading recommendations for those of you looking to explore other themes this week:
- For a hands-on approach to monitoring NLP models in production, don't miss Elena Samuylova‘s latest tutorial.
- What is simulated annealing and why should you care? Follow along Hennie de Harder‘s introduction to learn about this powerful optimization technique.
- Managing data teams comes with its own unique challenges; Rebecca Vickery outlines six approaches to implement best practices in your organization.
- In his debut TDS article, Diogo Leitão tackled a crucial question for ML practitioners: when to use early stopping when working with gradient-boosted trees.
- From the forefront of graph machine learning, Michael Galkin and coauthors Hongyu Ren, Michael Cochez, and Zhaocheng Zhu present their latest work on neural graph databases.
Thank you for your time and your support this week! If you enjoy the work we publish (and want to access all of it), consider becoming a Medium member.
Until the next Variable,
TDS Editors
