2 Loading the Live Telemetry Route Data

The examples provided utilise data that has already been downloaded.

2.1 Spatial Utilities

The GPS data is provided as a set of latitude and longitude co-ordinates. For various forms of analysis, such as calculating and comparing distance in meters, we need to map from the latitude/longitude co-ordinate system to a projection such as UTM (Universal transverse Mercator).

The following function identifies the correct UTM region for managing the conversion.

library(sf)

lonlat2UTM_hemisphere <- function(lonlat) {
    ifelse(lonlat[1] > 0, "north", "south")
}

lonlat2UTMzone = function(lonlat) {
  utm = (floor((lonlat[1] + 180) / 6) %% 60) + 1
  if(lonlat[2] > 0) {
    utm + 32600
  } else{
    utm + 32700
  }
}

Create a simple latitude/longitude CRS (co-ordinate reference system) string to specify the CRS for use with simple lat/long data:

latlon_crs = 4326

We can also set the timezone. This might be done explicitly, or from a location co-ordinate using the R lutz package.

timezone = "Europe/Helsinki"

2.2 Loading in the Telemetry Route Data

The data is available in the form of a simple CSV data file.

The data is presented in a reverse chronological order, but it is more natural for us to present it in chronological (increasing time) order.

telem_df_min = read.csv(file.path(path, "df_telemetrydata_SS7_Evans.csv") )  %>%
                  map_df(rev)

kable(head(telem_df_min), format = "html") %>%
  kable_styling() %>%
  kableExtra::scroll_box(width = "100%")

X	accx	accy	altitude	brk	driverid	gear	heading	kms	lat	lon	name	rpm	speed	status	throttle	track	utx	X_rally_stageid	X_carentryid	X_telemetryID	X_name
1870	0	0	0	0	NA	0	144	0.6	61.83077	25.12109	33	0	166	Competing	0	NA	1.633164e+12	bcc5537d-d28a-40af-ba86-95dad7fb9cd1	b185c5df-8115-40cf-bd81-566f016f6bf5	/a3f5f3f5-3fb0-42ab-af90-24d91c0493d0/ss07eva_telemetry_js/js	Evans
1869	0	0	0	0	NA	0	150	0.7	61.82966	25.12240	33	0	140	Competing	0	NA	1.633164e+12	bcc5537d-d28a-40af-ba86-95dad7fb9cd1	b185c5df-8115-40cf-bd81-566f016f6bf5	/a3f5f3f5-3fb0-42ab-af90-24d91c0493d0/ss07eva_telemetry_js/js	Evans
1868	0	0	0	0	NA	0	172	0.8	61.82912	25.12291	33	0	60	Competing	0	NA	1.633164e+12	bcc5537d-d28a-40af-ba86-95dad7fb9cd1	b185c5df-8115-40cf-bd81-566f016f6bf5	/a3f5f3f5-3fb0-42ab-af90-24d91c0493d0/ss07eva_telemetry_js/js	Evans
1867	0	0	0	0	NA	0	220	0.9	61.82856	25.12200	33	0	107	Competing	0	NA	1.633164e+12	bcc5537d-d28a-40af-ba86-95dad7fb9cd1	b185c5df-8115-40cf-bd81-566f016f6bf5	/a3f5f3f5-3fb0-42ab-af90-24d91c0493d0/ss07eva_telemetry_js/js	Evans
1866	0	0	0	0	NA	0	210	1.0	61.82766	25.12067	33	0	149	Competing	0	NA	1.633164e+12	bcc5537d-d28a-40af-ba86-95dad7fb9cd1	b185c5df-8115-40cf-bd81-566f016f6bf5	/a3f5f3f5-3fb0-42ab-af90-24d91c0493d0/ss07eva_telemetry_js/js	Evans
1865	0	0	0	0	NA	0	208	1.1	61.82662	25.11919	33	0	159	Competing	0	NA	1.633164e+12	bcc5537d-d28a-40af-ba86-95dad7fb9cd1	b185c5df-8115-40cf-bd81-566f016f6bf5	/a3f5f3f5-3fb0-42ab-af90-24d91c0493d0/ss07eva_telemetry_js/js	Evans

We note quite a few null columns in there, so let’s clean those out of the dataframe too:

telem_df_min = telem_df_min %>%
                  select(-c(accx, accy, altitude, brk, driverid,
                            gear, rpm, throttle, track)) %>%
                  select(-c(X_rally_stageid, X_carentryid, X_telemetryID))

kable(head(telem_df_min), format = "html") %>%
  kable_styling() %>%
  kableExtra::scroll_box(width = "100%")

X	heading	kms	lat	lon	name	speed	status	utx	X_name
1870	144	0.6	61.83077	25.12109	33	166	Competing	1.633164e+12	Evans
1869	150	0.7	61.82966	25.12240	33	140	Competing	1.633164e+12	Evans
1868	172	0.8	61.82912	25.12291	33	60	Competing	1.633164e+12	Evans
1867	220	0.9	61.82856	25.12200	33	107	Competing	1.633164e+12	Evans
1866	210	1.0	61.82766	25.12067	33	149	Competing	1.633164e+12	Evans
1865	208	1.1	61.82662	25.11919	33	159	Competing	1.633164e+12	Evans

2.3 Preparing the Telemetry Route Data

We can perform a small amount of cleaning on the data to remove any rows where there are missing latitude or longitude values before before generating a spatial features data object with a column containing the geometry information with the lat/long co-ordinate reference system (CRS) specified.

To better manage the timing components, we can cast the universal time stamp (in milliseconds) to a datetime stamp, ensuring we also set the correct time zone along the way. A delta_s columns is also used to capture the time difference (in seconds) between sample points.

X	heading	kms	name	speed	status	utx	X_name	geometry	delta_s	utc
1870	144	0.6	33	166	Competing	1.633164e+12	Evans	POINT (25.12109 61.83077)	0.0	2021-10-02 11:37:23
1869	150	0.7	33	140	Competing	1.633164e+12	Evans	POINT (25.1224 61.82966)	3.2	2021-10-02 11:37:26
1868	172	0.8	33	60	Competing	1.633164e+12	Evans	POINT (25.12291 61.82912)	6.4	2021-10-02 11:37:30
1867	220	0.9	33	107	Competing	1.633164e+12	Evans	POINT (25.122 61.82856)	9.6	2021-10-02 11:37:33
1866	210	1.0	33	149	Competing	1.633164e+12	Evans	POINT (25.12067 61.82766)	12.8	2021-10-02 11:37:36
1865	208	1.1	33	159	Competing	1.633164e+12	Evans	POINT (25.11919 61.82662)	16.0	2021-10-02 11:37:39

For convenience, let’s also generate a UTM projection of this spatial features dataframe:

# Generate a UTM projection of the route telemetry
telem_df_min_utm = telem_df_min %>%
                      st_transform(crs = st_crs(utm_proj4_string))

2.4 Visual Preview of the Telemetry Route Data

We can preview the route on an interactive map using the leaflet package, casting the route data to a set of paired co-ordinates:

library(leaflet)

## Warning: package 'leaflet' was built under R version 4.0.5

tmp_route <- telem_df_min %>% st_coordinates() 

leaflet(tmp_route) %>% 
  addProviderTiles("OpenTopoMap", group = "OSM") %>% 
  addPolylines(color = "red", weight = 5)

The “live” telemetry includes the road section data. To access the data for just the stage we need to filter it somehow. There are two ways we might do this:

• based on time: if we know the stage start time for the driver, and their stage time, we can filter the data based on timestamps;
• based on location: if we have route data for the stage, we can create a buffered area around the stage route, and then filter telemetry data based on points that lay inside that buffered area.