# Visualisation of my Google Map History

## Abstract

Recently, I needed a few localisation points to create a test for a visualization.

To create a real effect, I decided to see if I could use the data I create everyday when I use Google map. Why I am not shy about these data? Simply because they are outdated due to the fact that I have recently moved. (It would be a shame to be robbed by a bugler expert in data because of a blog post :) ).

I ended to find a lot of interesting things:

• Over two years, Google had stored 12K points with an associated time stamps.
• These data tells a lot on me:
• Where I work
• Where I live
• My typical day
• How I move: by car, by bike or by foot.

All in all, this data set reflects pretty accurately my routine week and is perfect for advertisers and more. Well done, Google.

## Presentation of the Dataset

I went to the takeout manager of my Google account to see if I could use the data from my Google map account.

The process is fairly easy and fast. In my case, I was able to get 12K coordinates over 2 years of use of Google products.

In addition of the coordinates, Google also provide a time stamp and a bunch of variables poorly completed: velocity, altitude, accuracy and activity with a reliability score.

### Heat Map of my Historical Positions

The data set is included in my github account. I have limited the data set to London and 2 years.

The first thing to do with this data set is to create an heat map to visually assess the content.

Here, one difficulty is to interpret the density scale: What exactly are we representing?

The density is calculated at an exact point through a two-dimensional kernel density. It is estimated on a grid of, in that case, `100` * `100` points.

A small calculus allows us to define the area of a square:

A density of 6 could be read as exp(6) or 403 points per `53,290` square meter or `76` points per hectare.

## Heat Map per Time Period

Is it possible to define specific place at specific time period?

Let’s have a look at the full time period:

In a strange way, no data appear between November 2015 and March 2016.

The time stamp is mainly associated with hours between 6 and 10 in the afternoon. It seems logic to me, as it is the time of the day when I am the most active. More astonishing, there is points registered during the night. It may be nice to create a heat map per hour to see if we can predict where I will be at certain hours:

As we can see on that graph, I am pretty quiet during the night, wake up at Acton between 8 and 9 and run around London the rest of the day, coming back home between 7 and 10 p.m..

Is there a discretisation that can be done per day?

I have more data stored for Saturday and Sunday, which seems normal, as it is the days where I use Google map the most.

It reflects well a typical day: I am generally out in Monday and Wednesday, so I stay still in Tuesday. Saturday and especially Sunday are days where I move a lot around London.

## Activity

In the data set, we also can find a variable activity which is a list of activities, not really well define.

Over `4,072` rows are completed with a list of activity. It represents `38.8%` of the rows.

Activities have a time stamps of their own.

`79,392` activities are recorded, an average of 7 per location. Nevertheless, we can find a confidence variable. If we restrict to only the highly confident time stamps, over 90, we limit to `16,657` rows.

Most of the time stamps of the activities are a couple of minutes after the time stamp of the coordinates. Nevertheless, we can also observe a decreasing sinusoidal trend with peaks at 24h, 48h and 64h.

I don’t have a car nor a bike so I was astonished to see so much tags “inVehicle” and “onBicycle”. The reason of so much of these tags is that they are associated to a low confidence. When limiting to a confidence of 90 or above, the activities make finally sense.

Most of my activities are standing and tilting. I tilt when I use my unicycle so it makes sense to have so much and I am still mainly at work and at home.

Is it possible to predict that way if a person has a car or a bike? Yes, definitely.

I have created a graph by type that can be found here. In that graph, it is possible to check where I am standing still and where I am just passing.

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