Filter and rank detected trajectories

Applies post-processing filters and ranking to trajectory data based on score, length, and other criteria. This function helps refine the output from detect_main_trajectories() by keeping only the most relevant trajectories according to user-specified constraints.

Usage

filter_trajectories(tr_tbl, top_n = 3, min_score = NULL, min_length = NULL)

Arguments

tr_tbl

A tibble of trajectories from detect_main_trajectories()$trajectories. Must contain at least traj_id, score, and length columns.

top_n

Maximum number of trajectories to keep after filtering and sorting (default: 3). If NULL, keeps all trajectories that meet the filter criteria.

min_score

Minimum score threshold for trajectories (default: NULL). Trajectories with score less than min_score are discarded. Useful for removing weak or noisy paths.

min_length

Minimum trajectory length in distinct years (default: NULL). Trajectories shorter than min_length are discarded. Ensures only trajectories spanning a meaningful temporal horizon are kept.

Value

A filtered and sorted trajectory tibble with the same structure as input, containing only trajectories that meet all criteria, sorted by descending score. Returns an empty tibble if no trajectories meet the criteria.

Details

This function provides a straightforward way to refine trajectory detection results by applying quality filters and ranking. The filtering process occurs in three steps:

1. Quality Filtering: Remove trajectories that don't meet minimum quality standards

   • min_score: Filters by the dynamic programming path score (higher = better)

   • min_length: Filters by temporal span in distinct years

2. Ranking: Sort remaining trajectories by descending score to prioritize the most significant paths

3. Selection: Keep only the top top_n trajectories after filtering and sorting

Typical Use Cases

• Focus on strongest signals: Use min_score to remove low-confidence trajectories

• Ensure temporal significance: Use min_length to require multi-year evolution

• Limit visualization complexity: Use top_n to focus on the most important paths

• Progressive refinement: Chain multiple calls with different criteria

See also

detect_main_trajectories() for generating the trajectory data, plot_group_trajectories_lines_2d() and plot_group_trajectories_lines_3d() for visualizing filtered trajectories

Examples

if (FALSE) { # \dontrun{
# Get trajectories first
traj_data <- detect_main_trajectories(
  groups_cumulative_trajectories = my_data,
  group = "component1_g01"
)

# Basic: Keep top 3 trajectories by score
top_trajectories <- filter_trajectories(traj_data$trajectories)

# Keep top 5 trajectories with minimum quality standards
quality_trajectories <- filter_trajectories(
  tr_tbl = traj_data$trajectories,
  top_n = 5,
  min_score = 10,
  min_length = 4
)

# Keep all trajectories meeting minimum length (no top_n limit)
long_trajectories <- filter_trajectories(
  tr_tbl = traj_data$trajectories,
  top_n = NULL,
  min_length = 5
)

# Very strict filtering for high-quality, long trajectories
strict_trajectories <- filter_trajectories(
  tr_tbl = traj_data$trajectories,
  top_n = 3,
  min_score = 15,
  min_length = 6
)

# Use filtered trajectories for visualization
plot_group_trajectories_lines_2d(
  traj_data = traj_data,
  traj_filtered = quality_trajectories
)
} # }