This function processes GPS track data into a spatial grid summary, calculating time spent and other metrics for each grid cell. The grid size can be specified to analyze spatial patterns at different scales.
Arguments
- data
A data frame containing GPS track data with columns: - Trip: Unique trip identifier - Time: Timestamp of the GPS point - Lat: Latitude - Lng: Longitude - Speed (M/S): Speed in meters per second - Range (Meters): Range in meters - Heading: Heading in degrees
- grid_size
Numeric. Size of grid cells in meters. Must be one of: - 100: ~100m grid cells - 250: ~250m grid cells - 500: ~500m grid cells (default) - 1000: ~1km grid cells
Value
A tibble with the following columns: - Trip: Trip identifier - lat_grid: Latitude of grid cell center - lng_grid: Longitude of grid cell center - time_spent_mins: Total time spent in grid cell in minutes - mean_speed: Average speed in grid cell (M/S) - mean_range: Average range in grid cell (Meters) - first_seen: First timestamp in grid cell - last_seen: Last timestamp in grid cell - n_points: Number of GPS points in grid cell
Details
The function creates a grid by rounding coordinates based on the specified grid size. Grid sizes are approximate due to the conversion from meters to degrees, with calculations based on 1 degree ≈ 111km at the equator. Time spent is calculated using the time differences between consecutive points.
Examples
if (FALSE) { # \dontrun{
# Process tracks with 500m grid (default)
result_500m <- preprocess_track_data(tracks_data)
# Use 100m grid for finer resolution
result_100m <- preprocess_track_data(tracks_data, grid_size = 100)
# Use 1km grid for broader patterns
result_1km <- preprocess_track_data(tracks_data, grid_size = 1000)
} # }