This will be a quick post: I’ve got another population-based map rendering to share, based on the work described in this post.
In this post, I show off some images from a project I’m working on (which will remain nameless for now!) These images visualize subdivisions of the Earth into Google Maps tile-sized regions with roughly equal population. I’ll also provide a brief and non-technical rundown of the process by which I generated these images.
To calculate your risk of various diseases, 23andMe scours the medical research literature for studies that correlate incidence rates for those diseases with mutations at specific locations in the human genome. The locations where these mutations commonly occur are referred to as single-nucleotide polymorphisms, or SNPs.
In this post, I show how I applied the findings of this study about caffeine-induced anxiety to discover more about myself. I have no genetic research background whatsoever, and my knowledge of genetics is minimal, so it’s amazing that this is slowly becoming accessible to a wider audience.
Over the holidays, I received a 23andMe genetic testing kit as a gift. In this post, I’ll take a look at my results through the lens of prospect theory, which aims to quantify our perception of risk. 23andMe results estimate your lifetime likelihood of various medical conditions, making them a great dataset for testing out these concepts in behavioral economics.
I spent a few weeks in the not-so-frozen Canadian northlands over the winter holidays. While there, I had the chance to visit an old childhood favorite: the Ontario Science Centre, six floors of science-based awesomeness. One of their current exhibits, the Amazing Aging Machine, uses a computer vision software package called APRIL to predict how your face will change over the next 50 years.
In this post, I explore my results from that exhibit alongside a customized aging I performed using the APRIL API.
In this post I introduce datafist, an in-browser tool for visually exploring your data.
In this post, I discuss my ongoing experiment with eliminating alcohol from my diet. I use this experience to address a question: why do we find some forms of habit change easier than others?
In this post, I revisit the question of whether Google Latitude meets my persistent location tracking needs. In my previous post, I compared Google Latitude to InstaMapper and concluded that the latter is too battery-intensive. By looking at maps and base-level insights from the data, I suggest that Google Latitude optimizes for battery life at the expense of data quality.