Tag Archives: pattern of life

How does movement influence your daily happiness?

Imagine commuting an hour to work, one way, grinding through miles of traffic to get from your suburban home to a desk job in the big city. Excited yet?

Ok, now imagine that you lead a life of leisure traveling the world. You fly coast-to-coast to see a concert, soak in some culture, and drink fine wine. Does this lifestyle seem more appealing?

Lets try to quantify the influence of these travel patterns on individual happiness. We do this using geolocated tweets, which we have previously used to reveal the happiness of cities, and to quantify patterns of movement.

Each point corresponds to a geo-located tweet from 2011. (A) USA (B) Washington, D.C. (C) Los Angeles (D) Earth

Each point corresponds to a geo-located tweet from 2011.
(A) USA (B) Washington, D.C. (C) Los Angeles (D) Earth

First, we find the average location of each individual’s tweets. We call this their expected location. Then we draw circles emanating from this spot, like rings on a dart board. Some messages are written close to home, others from very far away.

Then we collect all of the words written at each distance, roughly 500,000 tweets per ring. Averaging the happiness of words found at each distance, remarkably we find that happiness increases logarithmically with distance from expected location. Tweets authored far from home contain a smaller number of negative words.

Tweets are grouped into ten equally populated bins by the distance from their author's average location, and the average happiness of words written at each distance is plotted. Expressed happiness grows logarithmically with distance from home.

Tweets are grouped into ten equally populated bins by the distance from their author’s average location, and the average happiness of words written at each distance is plotted. Expressed happiness grows logarithmically with distance from home.

Home is where the hate is? What? No.

Below we look at the difference between the happiest and saddest distances from home. Words appearing on the right increase the happiness of the 2500km distance relative to the 1km distance. For example, tweets authored far from an individual’s expected location are more likely to contain the positive words `beach’, `new’, `great’, `park’, `restaurant’, `dinner’, `resort’, `coffee’, `lunch’, `cafe’, and `food’, and less likely to contain the negative words `no’, `don’t’, `not’, `hate’, `can’t’, `damn’, and `never’ than tweets posted close to home. Words going against the trend appear on the left, decreasing the happiness of the 2500km distance group relative to the 1km group.

Word shift graph comparing the lowest average word happiness distance group to the words authored farthest from home.

Word shift graph comparing the lowest average word happiness distance group to the words authored farthest from home.

Tweets written close to home are more likely to contain the positive words `me’, `lol’, `love’, `like’, `haha’, `my’, `you’, and `good’. Moving clockwise, the three insets show that the two text sizes are comparable, the biggest contributor to the happiness difference is the decrease in negative words authored by individuals very far from their average location, and the 50 words listed make up roughly 50% of the total difference between the two bags of words. For you visual learning folks, here is a short video explaining how these word shifts work.

Take home story: people tweeting far from home talk about food more, and they swear less than people tweeting close to home. These people are probably enjoying awesome vacations, and tweeting about it!

In summary, if you are a fellow with a daily commute that makes you feel a little bit sad, you are not alone! Try swearing less. Or ride your bike.

If you are lucky enough to travel often, then keep smiling…maybe send the rest of us some pictures to cheer us up!

For more details on our analysis, check our paper “Happiness and the Patterns of Life: A Study of Geolocated Tweets” recently published in Nature Scientific Reports.

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Filed under geohappiness, psychology, social phenomena

A data-driven study of the patterns of life for 180,000 people

Here at the Computational Story Lab, some of us commute by foot, some by car, and a few deliver themselves by bike, even in the middle of our cold, snowful Vermont winter.  Occasionally, we transport ourselves over very long distances in magic flying tubes with wings to attend conferences, to see family, or for travel.  So what do our movement patterns look like over time?  Are there distinct kinds of movement patterns as we look across populations, or are they variations on a single theme?

Inspired by an analysis of mobile phone data by Marta Gonzalez at MIT, James Bagrow at Northwestern, and colleagues, we used 37 million geotagged tweets to characterize the movement patterns of 180,000 people during their 2011 travels. We used the standard deviation in their position, a.k.a. radius of gyration, as a reflection of their movement. As an example, below we plot a dot for each geotagged tweet we found posted in the San Francisco Bay area, colored by the author’s radius of gyration.

The Bay Area is shown with a dot for each tweet, colored by the radius of gyration of its author.

The Bay Area is shown with a dot for each tweet, colored by the radius of gyration of its author. The color scale is logarithmic, so we can compare people with very different habits.

You can see from the picture that there are many people with a radius near 100km tweeting from downtown San Francisco. This pattern could reflect a concentration of tourists visiting the area, or individuals who live downtown and travel for work or pleasure. Images for New York City, Chicago, and Los Angeles are also quite beautiful.

In the image below, we rotated every individual’s movement pattern so that the origin represents their average location, and the horizontal line heading to the left represents their principle axis (most likely the path from home to work). We also stretched or shrunk the vertical and horizontal axes for each individual, so that everyone could fit on the same picture. Basically, we have a heatmap of collective movement, with each individual in their own intrinsic reference frame.  The immediate good news for these kinds of data-driven studies is that we see a very similar form to those found for mobile phone data sets.  Apart from being a different social signal, Geotagged Tweets also have much better spatial resolution than mobile phone calls which are referenced by the nearest cellphone tower.

Movement pattern exhibited by 180,000 individuals in 2011, as inferred from 37 million geolocated tweets. Colormap shows the probability density in log10. Note that despite the resemblance, this image is neither a nested rainbow horseshoe crab, nor the Mandelbrot set.

Movement pattern exhibited by 180,000 individuals in 2011, as inferred from 37 million geolocated tweets. Colormap shows the probability density in log10. Note that despite the resemblance, this image is neither a nested rainbow horseshoe crab, nor the Mandelbrot set.

Several features of the map reveal interesting patterns. First, the teardrop shape of the contours demonstrates that people travel predominantly along their principle axis, with deviations becoming shorter and less frequent as they move farther away. Second, the appearance of two spatially distinct yellow regions suggests that people spend the vast majority of their time near two locations. We refer to these locations as the work and home locales, where the home locale is centered on the dark red region right of the origin, and the work locale is centered just left of the origin.

Finally, we see a clear horizontal asymmetry indicating the increasingly isotropic variation in movement surrounding the home locale, as compared to the work locale. We suspect this to be a reflection of the tendency to be more familiar with the surroundings of one’s home, and to explore these surroundings in a more social context. The up-down symmetry demonstrates the remarkable consistency of the movement patterns revealed by the data.

We see a clear separation between the most likely and second most likely position.

We see a clear separation between the most likely and second most likely position.

Looking just at the messages posted along the work-home corridor, the distribution is skewed left, with movement from home in a heading opposite work seen to be highly unlikely.

The isotropy ratio shows the change in the probability density's shape as a function of radius.

The isotropy ratio shows the change in the probability density’s shape as a function of radius.

Above we see that individuals who move around a lot have a much larger variation in their positions along their principle axis, exhibiting a less circular pattern of life than people who stay close to home. Remarkably, the isotropy ratio decays logarithmically with radius.

Finally, we grabbed messages from the most prolific tweople, those 300 champions who had posted more than 10,000 geotagged messages in 2011. We received 10% of these messages through our gardenhose feed from Twitter. Below, we plot the times during the week that they post from their most frequently visited location. These folks most likely have the geotag switch on for all messages, and exhibit a very regular routine.

A robust diurnal cycle is observed in the hourly time of day at which statuses are updated, with those from the mode location (black curve) occurring more often than other locations (red curve) in the morning and evening.

A robust diurnal cycle is observed in the hourly time of day at which statuses are updated, with those from the mode location (black curve) occurring more often than other locations (red curve) in the morning and evening.

Peaks in activity are seen in the morning (8-10am) and evening (10pm-midnight), separated by lulls in the afternoon (2-4pm) and overnight (2-4am) hours.  As we and our friend Captain Obvious would expect, people tend to tweet more from their home locale than any other locale (red curve) in the morning and evening.

Bottom line: Despite our seemingly different patterns of life, we are remarkably similar in the way we move around. Our walks are a far cry from random.

Next up: We’ll examine the emotional content of tweets as a function of distance.  Is home where the heart is?

For more details on these results, see our paper Happiness and the Patterns of Life: A Study of Geolocated Tweets.

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Filed under networks, physics, prediction, social phenomena