Visualising Google location history

Data Analyses
Published on
September 6, 2016


Since 2013, I have carefully kept my Google location history, fascinated by the fact that I could, in ten or twenty years from now, retrace every move I made in a day, be it a normal work day, or a holiday trip in Italy. Nathan Yau from Flowingdata.com has made a tutorial on how to plot location points with R, which gave me the idea of plotting my own location history for 4 years.



Full Code

Load and format data

The first step is to download your file with Google Takeout. You have the choice between JSON and KML, and I chose JSON, which I’m more familiar to work with.

### Load libraries
### Load JSON data
raw <- fromJSON("location-history.json")

The import of the JSON files is straightforward with the jsonlite package, although it can take several minutes if you upload several years of location history. The imported data is a list that includes a great number of nested dataframes in the activities column, which we drop as it won’t be needed.

We actually end up with a very simple dataframe with only 4 columns: time, year (computed from the timestamp), latitude and longitude.

In addition, we can filter the data on a delimited area of latitude and longitude, to focus the map on a specific zone – Paris in this example.

### Clean and format data
routes <-
    raw[[1]][1:4] %>%
    transmute(time = as.POSIXct(as.numeric(timestampMs) / 1000, 
							origin = "1970-01-01"),
              year = year(time),
              latitude = latitudeE7 / 1e+07,
              longitude = longitudeE7 / 1e+07) %>%
    filter(year >= 2013, year <= 2016)

### Filter on Paris area
zone <- c(48.825, 48.903, 2.250, 2.350)
routes <-
    routes %>%
    filter(latitude >= zone[1] & latitude <= zone[2],
           longitude >= zone[3] & longitude <= zone[4])


After having tried a number of plot styles, including with maps of Paris, I finally decided that a simple black background lead to the most impactful effect.

### Plot: define theme
theme_map <-
    theme(panel.background = element_rect(fill = "black", colour = "black"),
    panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
    axis.text.x = element_blank(), axis.text.y = element_blank(),
    axis.title.x = element_blank(), axis.title.y = element_blank(),
    axis.ticks = element_blank(), legend.position = "none")

Single year in points

This first map is the most basic, with a single year plotted, and each location datapoint plotted as a white dot with 80% transparency. As a results, the most frequented areas stand out: home, office, and home-office journey.

### Plot points only, for a specific year
ggplot(data = filter(routes, year == 2015),
           aes(x = longitude,
               y = latitude)) +
    geom_point(alpha = 0.2, size = 0.4, shape = 16, colour = "white") +

Facets with each year

We can also plot several years in a facet, with a different color for each year. You can see clearly different paths every year, as my home, office, and client’s office have changed between 2013 and 2016.

### Plot points by year
ggplot(data = routes,
           aes(x = longitude,
               y = latitude,
               colour = as.factor(year))) +
    geom_point(alpha = 0.2, size = 0.4, shape = 16) +
    theme_map +
    facet_wrap( ~ year)

Adding lines

Finally, the most beautiful effect in my opinon is achieved by drawing lines between each datapoint.

### Plot lines only, for a specific year
ggplot(data = filter(routes, year == 2015),
           aes(x = longitude,
               y = latitude)) +
    geom_path(alpha = 0.1, size = 0.4, colour = "white") +