Skip to contents

Analog similarity: best climate analogs within a geographic envelope

Source: R/analog_similarity.R
analog_similarity.Rd

Finds, for each focal location, the climate–nearest neighbor(s) in a reference dataset that satisfy a specified geographic distance threshold. This function is a wrapper that calls analog_search() using select = "knn_clim".

Usage

analog_similarity(
  x,
  pool,
  x_cov = NULL,
  y = NULL,
  weight = NULL,
  coord_type = "auto",
  max_geog,
  max_clim = NULL,
  k = 20,
  index_res = "auto",
  cell_area_weight = "auto",
  n_threads = NULL,
  downsample = 1,
  seed = NULL,
  progress = FALSE
)

Arguments

x

Focal locations for which analogs will be found. Should be a matrix/data.frame with columns x, y, and climate variables, or a SpatRaster with climate variable layers.

pool

The reference dataset to search for analogs. Either:

  • Matrix/data.frame with columns x, y, and climate variables, or SpatRaster with climate variable layers, OR

  • An analog_index object created by build_analog_index() (for repeated queries).

x_cov

Optional focal-specific covariance matrices for Mahalanobis distance calculations. Should be a matrix or data.frame with one row per focal location and one column per unique covariance component, or a SpatRaster with a layer for each component. For n climate variables, there are n*(n+1)/2 unique components, ordered as: variances first (diagonals), then covariances (upper triangle by row).

y

Optional vector, factor, matrix/data.frame, or SpatRaster giving values for each reference location (must have same number of rows/cells as pool). Required for stats "sum", "mean", "weighted_sum", "weighted_mean", "regression", and "tabulate". Numeric for continuous stats; factor or coercible-to-factor (character, integer, logical) for stat = "tabulate".

weight

Optional pool site weights for use in aggregation. Numeric vector, single-column matrix/data.frame, or single-layer SpatRaster, with one value per row/cell of pool. For aggregation stats like "weighted_mean", "regression", etc., weights multiply through the weighted aggregation alongside any kernel weighting and cell-area weighting; they do not influence which analogs are selected by knn_* modes (selection remains distance-only). They are reported in pair mode as a user_weight column. Values must be non-negative; NA is allowed and treated as 0 (the point is excluded from aggregation). Default NULL means no user-supplied weights.

If you want to exclude a static subset of pool sites entirely, masking pool (and any associated y / covariates) upfront is more efficient than passing weight = 0 for those sites, since the lattice index will not have to scan or distance-compute against them. Use weight = 0 for cases where the mask varies per query against a shared index, or where some sites have a continuous weight and others should be excluded.

coord_type

Coordinate system type:

  • "auto" (default): Automatically detect from coordinate ranges.

  • "lonlat": Unprojected lon/lat coordinates (uses great-circle distance; assumes max_geog is in km).

  • "projected": Projected XY coordinates (uses planar distance; assumes max_geog is in projection units).

max_geog

Maximum geographic distance constraint (default: NULL = no geographic constraint). When specified, only reference locations within this distance are considered. Radius units should be specified in kilometers if coord_type = "lonlat", or in projected coordinate units if coord_type = "projected".

max_clim

Maximum climate distance constraint (default: NULL = no climate constraint). Can be either:

  • A scalar: Euclidean radius in climate space (e.g., 0.5)

  • A vector: Per-variable absolute differences (length must equal number of climate variables)

Only reference locations within this climate distance are considered. When x_cov is provided, scalar thresholds are interpreted in Mahalanobis distance units.

k

Number of nearest analogs to return per focal location for kNN selection modes. Required when select is "knn_geog" or "knn_clim"; must be NULL for select = "all".

index_res

Tuning parameter giving the number of bins per dimension of the internally-used lattice search index. Either:

  • A positive integer.

  • "auto" (the default): Automatically tune the index resolution by optimizing compute time on a subsample of focal points. If focal has relatively few rows, auto-tuning is skipped and a default resolution of 16 is used. Auto-tuning is not supported when downsample < 1, because the speed-optimal resolution can sometimes result in higher uncertainty of stat results under downsampling. In that case set index_res explicitly; finer values (e.g. 32) generally give better accuracy at the possible cost of query speed.

Ignored if pool is an analog_index (uses index's resolution).

cell_area_weight

Controls cell-area weighting when pool is a raster. One of "auto" (default; on for raster pools, off otherwise), TRUE (force on; errors if pool is not a SpatRaster), or FALSE (force off). Cell-area weights correct aggregation statistics for non-uniform cell areas (e.g. lonlat grids near the poles, or projected grids on non-equal-area projections); they are computed via terra::cellSize() and normalized to mean 1. When pool is a pre-built analog_index, this argument must agree with the index's stored configuration: cell_area_weight = FALSE errors if the index was built with cell-area weighting on (rebuild the index instead).

n_threads

Optional integer number of threads to use for the computation. If NULL (default), the global RcppParallel setting is used (see RcppParallel::setThreadOptions).

downsample

Optional downsampling rate (0-1) for the reference pool, indicating the proportion of points to retain. Values < 1 reduce memory and improve speed at some cost to precision. Default is 1.0 (no downsampling). Ignored if pool is a pre-built index. When downsample < 1, index_res must be set explicitly (auto-tuning is not supported in this case; see the index_res parameter for details).

seed

Optional random seed for reproducible downsampling. If NULL (default), uses current R random state. Ignored if pool is a pre-built index or downsample = 1.

progress

Logical; if TRUE, display a progress bar during computation. Progress tracking works by splitting the focal dataset into chunks and processing them sequentially. Useful for large datasets. Default is FALSE.

Value

A data.frame, or a SpatRaster when x is one and k = 1. Contains one row per focal-analog pair with index, x, y, analog_index, analog_x, analog_y, clim_dist, and geog_dist. See analog_search() for full column conventions and metadata() for attached metadata attributes.

Details

For each focal location, analog_similarity():

  1. Identifies all reference points within max_geog km (and optional climate filter).

  2. Selects the k closest in climate distance.

This is the natural "inverse" of analog_velocity: instead of finding where the focal climate moves geographically, it finds the closest climatically similar conditions that are geographically reachable.

Among other uses, this operation is often the first step in a traditional analog impact modeling (AIM) analysis – though see analog_impact() for a more complete AIM implementation.

See also

analog_search() for the underlying flexible analog search function; tiled_analog_search() for memory-safe searches on large raster datasets.

Examples

if (FALSE) { # \dontrun{
# One-shot query
im <- analog_similarity(
  x = clim$clim1,
  pool = clim$clim2,
  max_geog = 100,
  k = 20
)

# With pre-built index (for repeated queries)
index <- build_analog_index(clim$clim2)
i1 <- analog_similarity(x = sites1, pool = index, max_geog = 100, k = 20)
i2 <- analog_similarity(x = sites2, pool = index, max_geog = 50, k = 10)
} # }