lakefetch 0.1.3

Bug fixes

• Fixed commented-out code in @examples for add_lake_depth() and get_lake_boundary(). Examples now use runnable code throughout.

lakefetch 0.1.1

New features

• Maximum fetch location: fetch_calculate() gains a find_max_fetch parameter that identifies the location in each lake with the highest possible fetch. Uses an efficient longest-internal-chord algorithm (sub-second per lake) rather than brute-force grid search. Returns the point location, chord length, bearing, and optionally full directional fetch profile.
• Relative exposure classification: Sites are now classified by both absolute fetch thresholds (exposure_category) and lake-relative proportional thresholds (exposure_relative). The proportional method classifies sites based on the ratio of effective fetch to the lake’s maximum possible fetch (longest internal chord), providing lake-size-aware exposure context. Default thresholds: Sheltered < 25%, Exposed > 50%. New output columns: fetch_proportion, lake_max_chord_m, exposure_relative.

Improvements

• Optimized OSM downloads for spread-out sites: download_lake_osm() now handles geographically spread datasets (e.g., GLEON’s 429 global sites). When site spread exceeds 0.5 degrees, sites are grouped into spatial clusters (~0.1 degree grid) and each cluster gets a small bounding box query. This replaces the old single-bbox approach that would cover the entire globe and timeout. Tested with 50 globally-spread GLEON sites (48/50 matched, 26 min download).
• Robust Overpass API handling: Each cluster query retries up to 3 times across 3 different Overpass servers, with 1-second rate limiting between queries. Failed clusters are reported at the end so users know which lake boundaries may be missing.
• Minimum area filter: Water bodies smaller than 0.0001 km² (100 m²) are automatically filtered out after download, removing garden ponds, fountains, and other tiny features.
• Download progress bar: OSM cluster downloads now display a progress bar and report elapsed time when complete.
• Custom column names: load_sites() now accepts lat_col, lon_col, site_col, and lake_col arguments to explicitly specify column names when auto-detection doesn’t match your data format.
• Progress bars: Long-running fetch calculations now display progress bars in interactive sessions, so users can see that computation is proceeding. Progress is shown for site buffering, directional fetch calculation, and multi-lake sequential processing.
• Shiny app performance: fetch_app() and fetch_app_upload() now use a hybrid approach for large datasets. For small datasets (<=50 sites), rose diagrams are pre-rendered in popups as before. For large datasets (>50 sites), rose diagrams and rays are generated on demand when a marker is clicked, preventing the app from freezing or crashing at startup.
• Marker clustering: Shiny apps automatically cluster markers when there are more than 30 sites or when sites span a wide geographic area (>5 degrees), preventing browser slowdowns and overlapping markers.

Bug fixes

• Invalid (0,0) coordinates: load_sites() now detects and removes rows where both latitude and longitude are 0, which typically indicates missing data rather than a real location.
• Geometry processing crash: Fixed st_is_valid() crash when processing corrupted or empty geometries from large OSM downloads. Empty geometries are now skipped and invalid ones are repaired automatically.
• Buffer match count: Fixed negative match count display in assign_sites_to_lakes() when st_join produced duplicate rows.

lakefetch 0.1.0

Initial CRAN release. ## Features

• Fetch calculation: Ray-casting algorithm to measure directional fetch (open water distance) from sampling points to shoreline
• Automatic boundary download: Downloads lake boundaries from OpenStreetMap with multi-server fallback for reliability
• Multi-lake support: Process multiple lakes in a single analysis with automatic site-to-lake assignment
• Name-based matching: Falls back to lake name matching when spatial intersection fails
• NHD integration: Optional integration with National Hydrography Dataset for US lakes (outlets, inlets, watershed area, connectivity classification)
• Weather integration: Historical weather data from Open-Meteo API for wave energy calculations
• Depth estimation: Empirical depth estimation from lake surface area
• Visualization: Static plots (maps, bar charts, rose diagrams) and interactive Shiny app
• Exposure classification: Automatic classification into Sheltered/Moderate/Exposed categories

Validation

• Analytical validation against synthetic lakes with known geometry (0% error)
• Literature validation against lakes with published morphometry (100% pass rate)
• Edge case validation for islands, complex shorelines, and multiple islands (100% pass rate)
• 79 unit tests via testthat (now 112)

Documentation

• Getting started vignette with complete workflow example
• Validation vignette documenting test methodology and results
• Example datasets: adirondack_sites, wisconsin_lakes, example_lake