Spatial Representation of momentum() Data Frames

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
  )
#> distantia::utils_prepare_time():  duplicated time indices in 'Krumbach_I':
#> - value 6.8 replaced with 6.825.

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()