Quantified Savagery

Where Personal Data Runs Wild

Persistent Location Tracking: Looking for a Few Good Data Points

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.

Persistent Location Tracking: Picking the Right Tool

In this post, I compare Google Latitude and InstaMapper, two popular services for persistent location tracking. I walk through installation and data extraction via API for each service, then provide some subjective first impressions as to which one better suits my location-tracking needs.

Don’t Hate, Cross-Correlate

In this post, I discuss cross-correlation. Although commonly used in signal processing, cross-correlation can be useful in a Quantified Self context. I’ll present a bit of the mathematics behind cross-correlation, demonstrate a quick example, and briefly explain where you might use this in analyzing your personal data.

Self-Tracking for Panic: Another Dataset

In this post, I perform the same analyses presented in my last post using data from my second panic tracking period. I then test whether my average alcohol and sugar consumption changed measurably between the two tracking periods.

During the second tracking period, I gathered data using qs-counters, a simple utility I built for reducing friction in the recording process.

Self-Tracking for Panic: A Deeper Look

In this post, I apply three statistical and machine learning tools to my panic recovery journal data: linear regression/correlation, the Fast Fourier Transform, and maximum entropy modelling.

Panic!

In this post, I’ll tell the story of how I got started with self-tracking and talk briefly about my first experiment.