Convert Dissimilarity Analysis Data Frames to Spatial Dissimilarity Networks

Given an sf data frame with the coordinates of a time series list transforms a data frame resulting from distantia() to an sf data frame with lines connecting the time series locations. This can be used to visualize a geographic network of similarity/dissimilarity between locations. See example for further details.

Usage

distantia_to_sf(df = NULL, xy = NULL)

Arguments

df

(required, data frame) Output of distantia() or distantia_aggregate(). Default: NULL

xy

(required, sf POINT data frame) Sf data frame with the coordinates of the time series in argument 'df'. It must have a column with all time series names in df$x and df$y. See [eemian_cooordinaes] example. Default: NULL

Value

sf data frame (LINESTRING geometry)

See also

Other dissimilarity_analysis: distantia_aggregate(), distantia_boxplot(), distantia_cluster_hclust(), distantia_cluster_kmeans(), distantia_matrix(), distantia_plot(), distantia_stats(), distantia_time_shift(), momentum_boxplot(), momentum_stats(), momentum_to_wide()

Examples

#three time series
#climate and ndvi in Fagus sylvatica stands in Spain, Germany, and Sweden
data("fagus_dynamics")
data("fagus_coordinates")

#load as tsl
#center and scale with same parameters
tsl <- tsl_initialize(
  x = fagus_dynamics,
  name_column = "name",
  time_column = "time"
) |>
  tsl_transform(
    f = f_scale_global
  )

#example using distantia()
#--------------------------------------------------
distantia_df <- distantia(
  tsl = tsl
)

#transform to sf
distantia_sf <- distantia::distantia_to_sf(
  df = distantia_df,
  xy = fagus_coordinates
)

#mapping with tmap
# library(tmap)
# tmap::tmap_mode("view")
#
# tmap::tm_shape(distantia_sf) +
#   tmap::tm_lines(
#     col = "psi",
#     lwd = 3
#   ) +
#   tmap::tm_shape(fagus_coordinates) +
#   tmap::tm_dots(size = 0.1, col = "gray50")


#example using momentum()
#--------------------------------------------------
importance_df <- momentum(
  tsl = tsl
)

#transform to sf
importance_sf <- distantia::distantia_to_sf(
  df = importance_df,
  xy = fagus_coordinates
)

names(importance_sf)
#>  [1] "edge_name"                  "y"                         
#>  [3] "x"                          "psi"                       
#>  [5] "most_similar"               "most_dissimilar"           
#>  [7] "importance__evi"            "importance__rainfall"      
#>  [9] "importance__temperature"    "psi_only_with__evi"        
#> [11] "psi_only_with__rainfall"    "psi_only_with__temperature"
#> [13] "psi_without__evi"           "psi_without__rainfall"     
#> [15] "psi_without__temperature"   "id.x"                      
#> [17] "id.y"                       "geometry"                  

# #map contribution to dissimilarity of evi
# #negative: contributes to similarity
# #positive: contributes to dissimilarity
# tmap::tm_shape(importance_sf) +
#   tmap::tm_lines(
#     col = "importance__evi",
#     lwd = 3,
#     midpoint = NA
#   ) +
#   tmap::tm_shape(fagus_coordinates) +
#   tmap::tm_dots(size = 0.1, col = "gray50")
#
# #map variables making sites more similar
# tmap::tm_shape(importance_sf) +
#   tmap::tm_lines(
#     col = "most_similar",
#     lwd = 3
#   ) +
#   tmap::tm_shape(fagus_coordinates) +
#   tmap::tm_dots(size = 0.1, col = "gray50")
#
# #map variables making sites less similar
# tmap::tm_shape(importance_sf) +
#   tmap::tm_lines(
#     col = "most_dissimilar",
#     lwd = 3
#   ) +
#   tmap::tm_shape(fagus_coordinates) +
#   tmap::tm_dots(size = 0.1, col = "gray50")