Minding the Borderlands

Mark Koester (@markwkoester) on the art of travel and technology

What Do Your Photos Say About You? Exploring Your Photo Metadata With PhotoStats App

475.

That is roughly the number of photos that was taken by each smart phone user in 2017. That’s a lot of photos. That’s a lot of data.

But with this photo data, what insights and learnings are we able to get from our photos?

The photos on your mobile phone are one of the richest collections of data you have on yourself. If you regularly take photos with your phone (which most of us do), then your phone is collecting data on you.

Or to put it a more positive way, when you take photos, you also collect some additional data like where you were, what time and certain conditions at the time of the photo taking event.

Unfortunately, we aren’t leveraging our photo data to understand ourselves and our behaviors. While photo backup services might use our photo data, most actually strip the metadata on our photos and we lose it forever.

Fortunately there is a solution to start tracking, recording and leveraging your photo’s metadata. Using a new app called PhotoStats.io, you can are able to backup your photo metadata on your phone and, in turn, start leveraging it to understand a piece of data about your life.

In this post, I want to explore, visualize and start to understand my photo data. First, I want to share how to collect your metadata on your photos. We will get all of the metadata on your phone’s photos using an app called PhotoStats. Second, we will look into the information we can glean off that data as well as create a few simple visualizations and infographics. We will conclude with a bit of initial data exploration and visualization and some further areas for future research.

An Exploration of Mood Tracking: Can We Measure How We Feel?

What is a mood? Can we track it? Can we improve it?

After a few previous failed attempts, I decided to try another exploration into mood tracking.

CONFESSION: I’m not depressed and not particularly prone to the “blues” either. I do have waxing and waning motivation and drive though. In fact, I tend to be a good mood most of the time and quite productive too.

Instead, I’m curious self-tracker, and for this mood tracking experiment, I wanted to see if I could better understand what affects my mood by tracking it. Hopefully, eventually, I can avoid factors that create negative moods and optimize my life to be in a better mood more often.

Unlike other tracking metrics like productivity, money or even the somewhat ambiguous idea of measuring good heath, mood isn’t easy to quantify. It’s difficult to be objective about our moods or even “score” our current mood. Scientists also struggle with measuring our moods for both practical and even philosophical reasons.

In this post, we will explore what is mood tracking and some of the problems in measuring it. We will take a look at the psychological understanding of moods and different ways scientists measure it. In the conclusion, I’ll share some of the problems I see in mood tracking.

NOTE: In future posts, I’ll share my review of a few mood tracking apps as well as how I tracked my mood and what I learned.

My Year in Podcast Listening: 2017

In 2017, I listened to 298 hours of podcasts. To put it in perspective, roughly 3.4% of my total time during last year went to podcast listening.

Compared to my 2016 podcast listening, I increased my daily podcast listening by 31 minutes per day (from 18 min per day in 2016 to 49 in 2017).

I mostly consume podcasts during the week (less on weekends). I listen to podcasts more often while traveling, and but I also tune in while running and during workouts.

How do I track my podcast listening? A bit over a year ago, I decided to “scratch my own itch,” and I built one of the first podcast tracking web services called PodcastTracker.com. It remains a simple service that helps self-trackers log what they listen to and export a log for visualization like I have done.

What did I learn? In this post, I want to share my year in podcast listening. For example, how much listening did I do? What were my favorite podcasts? What periods did I listen to podcasts? Finally I’ll conclude with a note about what I’ve learned.

Let’s first check out the full infographic.

2017 My Annual Review

As I look back on this past year and the past couple years before that, I can’t help but think to feel gratitude. Again and again good years pile up. Each year I tell me myself that was a best years ever.

I consider myself incredibly fortunate in my projects, life and pursuits. At the same time I also have taken risks and pursued passions that have allowed me to find ways to turn each year into something better, different and evolving than the last.

In all honesty, no year is perfect. There are plenty of up’s and down’s, moments of self-doubt, serious disappointment and plenty of uncertainty. You get sick. You miss out on something. You fail. That’s life.

In spite of these setbacks, I’m happy to report that 2017 was another great year for me. Several things didn’t pane out or went sideways, but I achieved nearly all of what I had hoped for this past year and many new positive surprises too. This includes good health and fitness, interesting travel and adventures, new things learned and tried, and lots building and writing too.

Using A Year in Numbers: My Data From 2017, here is my year in review.

A Year in Numbers: My Data From 2017

2017 was a year of self-tracking and personal data. So inspired by all the data I collected and some new found abilities in data visualizations, here is my year in data

(NOTE: Lessons learned from all of this tracking will be covered in separate post. My formal annual review will also be in a unique post.)

Let’s look at my past year… in data.

My Year in Book Reading: 2017

2017 was another great year of reading for me.

Numbers: I read 21,687 pages across 62 books and across 14 different genres. In terms of novels, I read mostly science-fiction, but I also added a few classics too. My non-fiction reading spanned a number of topics with a slight focus on science, business, technology, and history. My biggest month of reading was September 2017 in which I finished 10 books and 3672 pages.

One other topical reading highlight for 2017 was health and fitness, especially long-distance running. Along with completing two marathons and two half marathons, I read several great books on running and you have view my ratings and review in Science and Stories of Running and Some Great Books on Running.

Below is my data visualization of my year in reading, the top 5 books I read, and a few conclusions from this year and year to come.

Running Your Best: A Comparison of Race Predictors, Calculators and Models

Running your best race requires a number of good things to happen. You need to have done solid training and gotten yourself as ready as possible. You need to be healthy and well-rested. You need to know the conditions and have the necessary gear for the race. But arguably one of the most important factors in running your best is having a good prediction.

There are a number of tools and methods online to to estimate and predict your next race time. These race predictor calculators looks at factors like weekly running distance and previous time at a short distance (like the half marathon or 5k) to guess your expected next race time.

While there is a segment of runners who just want to finish and don’t care much about performance or improvement, I don’t really include myself in that group. Like the vast majority of runners I know, we are trying to run our best at races. We want hit a target and get a Personal Best (PBs).

Later this week I’ll be running my second marathon in Chiang Mai, Thailand. I’m happy to say that my training has gone quite well. I’ve been using an smart, adaptive training program from TrainAsOne in order to push my different capacities, avoid injury and get myself ready. At the time of writing, my conditions are good. I’m not sick, and I’ve been getting good sleep and decent nutrition.

In this post, I want to look at several of the predictors, calculators and models used to estimate run times. We will be using my recent half marathon score (1:45:00) and training log to see what these models predict from my next race. Unfortunately, there are a lot of models and predictors out there and there is some degree of variance. Several of the older models were purely mathematical and their predictions are dangerously optimistic. More recent models are based on statistical data and algorithms to make their predictions.

A Year in Self-Tracking: Q4 2017 Update

2017 has been my year of tracking and personal data exploration. For the past year I’ve been meticulously tracking about 20 data points. Not only have I been tracking but I’ve been “optimizing” my life too.

As a followup to my Q2 check-in and as we head into the end of 2017, I wanted to share an update and attempt to dissect some of this tracking. Unlike my normal writing, this post is a mix of vinettes, i.e. observations and some of the things I’ve learned during my year of “tracking everything.”.

(NOTE: At the end of the post, I’ve included a full breakdown of what I’m currently tracking and the tech or process I use.)

Data-Driven Run Training With TrainAsOne: Observations From a Tracking Guy Who Runs

“We don’t rise to the level of our expectations, we fall to the level of our training.” - Archilochos (6th Century BCE Greek Poet)

Much in life is about our training and preparation. This is especially true with running. Whether it’s a shorter distance or a marathon, how well you train has a huge impact on how well you run a race.

When it comes to running, there is a lot to digest on the topic of training. There is both a lot of science as well as a lot of long-held beliefs, traditions and even folklore. As a new or even moderately experienced runner, one of the most important yet confusing areas to navigate are training plans.

Run training plans have been around a long time, and they remain a popular topic online and in fitness magazines. There are hundreds of running programs and training plans out there.

Whether it’s your first 5k or your next marathon, these plans are intended to help you prepare. Some are more geared towards just completing the race distance, while others focus on helping you improve. There are plans for pure beginners and even plans for elite athletes.

If you have no idea how or what to train, then these training plans can be a huge help. I followed a run-walk plan to complete a 5k and used a plan with a coach when I did my first marathon.

In this post, I want to share my take on run training, training plans and my experience using an adaptive run training system called TrainAsOne. I’ve used TrainAsOne to successfully achieve Personal Best’s (PB) at races ranging from a 5k to two Half Marathon’s, and I’m currently following this plan to prepare for my second marathon.

To give you a bit of background, I’m a 34-year old male. I consider myself a tracking, data guy who runs. I’ve been running consistently for about a year and half.

Journaling for Self-Trackers and Quantified Self Enthusiasts

Journaling is a great exercise for your mind. It can help you deal with emotions, record a memory, capture a lingering thought, or clear your mind for the real work of your day. Writing a journal is a highly recommended habit for artists, entrepreneurs and pretty much anyone. It is also a great way to start your day. According to several research studies, regular journaling can even make your happier and more productive.

At its most basic, journaling is the act of spending some time to write something. Journaling is moment to write anything.

For self-trackers and quantified self enthusiasts, journaling also offers an opportunity to capture some personal data too. Depending on the tool you use (more on this later), when you create an entry, you also collect various metadata on that moment like date, time, location and even the weather. You could also note your mood too. These data points can be used for data analysis and data visualization.

Along with the metadata, when you journal, you are creating a piece of text. You have the word count and frequency of word usage. This text and its words can be analyzed with more sophisticated data processing techniques. For example, natural language processing (NLP) is a branch of machine learning and artificial intelligence that is capable of gathering statistics, deriving meaning, building models and understanding the sentiment of the text.

Furthermore, if you looking to build a positive habit into the start or end of your day, then journalling is one of the best. For example, you can use a few minutes of journaling at the beginning of the day to prepare your mind and feelings. This is often referred to as “morning pages,” and this is how I journal.

You can also use journalling at the end of the day as a way to express what you did, how it went and project plans for the day to come. This kind of journaling can be a great way to close out your day and building positive feelings too.

There are tons of ways you can journal. Some people like keeping a diary of events and memories. Others like journaling while traveling or keeping a log of their children’s lives. The format of journaling is wide open and highly individualized.

In this post, I want to look briefly at journaling with a particular focus on how it can be used for self-tracking. We will look at the benefits of journaling, various tools you can use to capture your words, and how you can capture and use data you can get.