Data Vis

Odyssey Years

"Quantified self" visualization of all Foursquare/Swarm check-ins between 2010-2025

Context

In 2007, I read David Brooks' article, "The Odyssey Years" and it left a deep impression on me: "There used to be four common life phases: childhood, adolescence, adulthood and old age. Now, there are at least six: childhood, adolescence, odyssey, adulthood, active retirement and old age. Of the new ones, the least understood is odyssey, the decade of wandering that frequently occurs between adolescence and adulthood."

I've always loved the bildungsroman coming of age genre, and dreamt of what kind of story I'd live myself. Alone, adrift, and navigating a figurative sea of change, I entered my 20s and early 30s with my heart open to the world. This map marks the past 15 years of my life, with all of its wanderings and wonderings.

Impact

Influenced early on by a somewhat more innocent era of the quantified self (before privacy became monetized and bartered by Web 2.0 social media companies), I started tracking my check-ins on Swarm (then Foursquare). Over the years, the heatmap of where I've been on the planet became somewhat of a living map of the adventures I had been on. In 2020 (10 year marker) and again in 2025, I scraped my own data from Foursquare's API and started to visualize the map of where I'd been.

Given the extremely private nature of the data, the content is not hosted online but a video is included here. The data can be filtered by date range and location (city/country). While the data is rich (and ongoing), I will be continuing to improve the design in future days (years?) to add more personal storytelling to the data.


In 2020: This project was an experiment to get more comfortable working with APIs and map-based visualizations. I wrote many of the data-cleaning scripts by hand, and started playing around with the web interactions in Observable.

In 2025: Newly empowered by Cursor, I was able to finish cleaning up the data (given a major API change in 2019), and generate many of the basic map interactions.

To explore using an LLM, the app also includes an early version of a chat AI that you can ask questions of from my data, using the check-ins for RAG (retrieval augmented generation).

And although I've gone many places, NYC will always be home :)