Heatmapping My New York City Marathon Training

Motivation

This post was inspired by my wife who used the GPS data from her Strava app to plot her running routes during 2020. Since I don’t run nearly as much as I used to, I need to go back to when I was training for the NYC marathon to find enough running to make such a map worthwhile. Also presenting a challenge is that I’m a bit of a luddite when it comes to running technology. I don’t have a GPS watch and I don’t run with a phone. To track my runs I manually enter my routes and workouts into MapMyRun and I time my runs with an ol’ fashioned sportswatch.

While this works for me on the road, it made the data gathering process for this visualization more difficult. And while MapMyRun does have TCX files for each workout, its not that useful if the data didn’t come from a GPS watch.

At the end of the day, my goal with this analysis is to make a cool looking heatmap of my training routes for the NYC Marathon… or at least to make a visualization that was cooler looking that my wife’s.

For those who can’t wait… this was final output:

Libraries Used

This analysis uses four main packages. Tidyverse for data manipulation, sf for modifying spatial data, tigris for getting the basemaps to plot my routes and extra to bring in new s for the plots.

library(tidyverse) # Data Manipulation
library(sf) # Manipulation Spatial Data
library(tigris) # Getting Tract and Roads Spatial Data
library(extra) # Better Fonts For GGPLOT

Gathering Data

If I had a GPS watch or used Strava, I could just download all my files which would contain Geo information and plot it directly. But because I do everything manually, I needed to jump through some hoops. From my MapMyRun account I was able to download:

1. user_workout_history.csv – Containing all of my workouts along with a column for route_id.
2. GPX files for each route that I had saved.

This led to the semi-painful manual process of using the first file to write down each route id that I had run, look up that route, and download the individual GPX file. Fortunately, I’m a creature of habit and and ran the same routes often, so there were only 24 to individually download.

runs <- read_csv('data/user_workout_history.csv') %>% 
  # Create Route ID column
  mutate(route_id = str_extract(RouteID, '\\d+') %>% as.integer)

all_routes <- map_dfr(
  #Get all gpx files in the directory
  keep(dir('data'), ~str_detect(.x, "gpx")),
  #Read them in
  ~read_sf(paste0('data/',.x), layer = "track_points") %>% 
    #keep the segment id and the geometry field
    select(track_seg_point_id, geometry) %>% 
    # create a route_id based on the file
    mutate(route_id = parse_number(.x))
) %>% 
  #Extract Lat and Long as Columns
  cbind(., st_coordinates(.))

#Join Routes to Runs to Duplicate 
runs_and_routes <- runs %>% 
  inner_join(all_routes, by = "route_id") 

###Download Roads Map from Tigris
nyc <- roads("NY", "New York")
ggplot() + geom_sf(data = nyc) + ggthemes::theme_map()

st_bbox(runs_and_routes$geometry)
##      xmin      ymin      xmax      ymax 
## -74.01880  40.70806 -73.93118  40.82113

### Construct Bounding Boxes and Expand Limits By A Delta
bbox <- map2_dbl(
  st_bbox(runs_and_routes$geometry),
  names(st_bbox(runs_and_routes$geometry)),
  ~if_else(str_detect(.y, 'min'), .x - .01, .x + .01)
)

bbox
##      xmin      ymin      xmax      ymax 
## -74.02880  40.69806 -73.92118  40.83113

#Set CRS for NYC to CRS for Running Routes And Crop to the Bounding Box
nyc2 <- st_transform(nyc, crs = st_crs(runs_and_routes$geometry)) %>% 
  st_crop(bbox)

ggplot() + geom_sf(data = nyc2) + ggthemes::theme_map()

Constructing the Heatmap

With the new basemap created and the route data in its own data frame. I can create the heatmap using stat_density2d with the route data and geom_sf with the map data. From the stat_density2d piece I pass in the routes data and set the fill value to be the count at each X and Y using the after_stat() option. The n parameter sets the number of grid points in each directions for the density.

The base map is very rectangular where it is tall but skinny. This made it difficult to add titles. To make things look better, I use ggdraw from the cowplot package to create a new drawing layer and add titles/captions to that layer.

p <- ggplot() + 
  #Construct the Heatmap Portion
  stat_density2d(data = runs_and_routes,
                 aes(x = X, y = Y, fill = after_stat(count)),
                 geom = 'tile',
                 contour = F,
                 n = 1024
                 ) +
  #Draw the Map of Manhattan
  geom_sf(data = nyc2, color = '#999999', alpha = .15) + 
  scale_fill_viridis_c(option = "B", guide = F) + 
  ggthemes::theme_map() + 
  theme(
    panel.background = element_rect(fill = 'black'),
    plot.background = element_rect(fill = 'black')
  )

cowplot::ggdraw(p) + 
  labs(title = "JLaw's Marathon Training Heatmap",
       caption = "**Author**: JLaw") + 
  theme(panel.background = element_rect(fill = "black"),
        plot.background = element_rect(fill = 'black'),
        plot.title = element_text(color = "#DDDDDD",
                                  family = 'Nirmala UI',
                                  #face = 'bold',
                                  size = 18),
        plot.caption = ggtext::element_markdown(color = '#DDDDDD',
                                    family = 'Calibri Light',
                                    hjust = 1,
                                    size = 12),
        

  )

Concluding Thoughts

I’m really happy with how this came out. It also provides some information about my running habits, mainly that I ran in Central Park a lot and that you can roughly tell where I worked at the time as that area is slightly hotter. There are some parts of Manhattan that I did run but don’t show up well in the map because I might have only run there once. An exploration of how much of Manhattan did I run will be covered in a follow-up post.