momentum_spatial: Spatial Representation of 'momentum()' Data Frames

In distantia: Advanced Toolset for Efficient Time Series Dissimilarity Analysis

Spatial Representation of momentum() Data Frames

Description

Given an sf data frame with geometry types POLYGON, MULTIPOLYGON, or POINT representing time series locations, this function transforms the output of momentum(), momentum_ls(), momentum_dtw() to an sf data frame.

If network = TRUE, the sf data frame is of type LINESTRING, with edges connecting time series locations. This output is helpful to build many-to-many dissimilarity maps (see examples).

If network = FALSE, the sf data frame contains the geometry in the input sf argument. This output helps build one-to-many dissimilarity maps.

Usage

momentum_spatial(df = NULL, sf = NULL, network = TRUE)

Arguments

df	(required, data frame) Output of momentum(), momentum_ls(), or momentum_dtw(). Default: NULL
sf	(required, sf data frame) Points or polygons representing the location of the time series in argument 'df'. It must have a column with all time series names in df$x and df$y. Default: NULL
network	(optional, logical) If TRUE, the resulting sf data frame is of time LINESTRING and represent network edges. Default: TRUE

Value

sf data frame (LINESTRING geometry)

See Also

Other momentum_support: momentum_aggregate(), momentum_boxplot(), momentum_model_frame(), momentum_stats(), momentum_to_wide()

Examples

tsl <- distantia::tsl_initialize(
  x = distantia::eemian_pollen,
  name_column = "name",
  time_column = "time"
) |>
#reduce size to speed-up example runtime
distantia::tsl_subset(
  names = 1:3
  )

df_momentum <- distantia::momentum(
  tsl = tsl
)

#network many to many
sf_momentum <- distantia::momentum_spatial(
  df = df_momentum,
  sf = distantia::eemian_coordinates,
  network = TRUE
)

#network map
# mapview::mapview(
#   sf_momentum,
#   layer.name = "Importance - Abies",
#   label = "edge_name",
#   zcol = "importance__Abies",
#   lwd = 3
# ) |>
#   suppressWarnings()

distantia documentation built on April 4, 2025, 5:42 a.m.