Spatial Representation of distantia() Data Frames

Given an sf data frame with geometry types POLYGON, MULTIPOLYGON, or POINT representing time series locations, this function transforms the output of distantia(), distantia_ls(), distantia_dtw() or distantia_time_shift() 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

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

Arguments

df

(required, data frame) Output of distantia() or distantia_time_shift(). 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 distantia_support: distantia_aggregate(), distantia_boxplot(), distantia_cluster_hclust(), distantia_cluster_kmeans(), distantia_matrix(), distantia_model_frame(), distantia_stats(), distantia_time_shift(), utils_block_size(), utils_cluster_hclust_optimizer(), utils_cluster_kmeans_optimizer(), utils_cluster_silhouette()

Examples

tsl <- distantia::tsl_initialize(
  x = distantia::covid_prevalence,
  name_column = "name",
  time_column = "time"
)

df_psi <- distantia::distantia_ls(
  tsl = tsl
)

#network many to many
sf_psi <- distantia::distantia_spatial(
  df = df_psi,
  sf = distantia::covid_counties,
  network = TRUE
)

#subset target counties
counties <- c("Los_Angeles", "San_Francisco", "Fresno", "San_Joaquin")

sf_psi_subset <- sf_psi[
  which(sf_psi$x %in% counties & sf_psi$y %in% counties), ]

#network map
# mapview::mapview(
#   distantia::covid_counties,
#   col.regions = NA,
#   alpha.regions = 0,
#   color = "black",
#   label = "name",
#   legend = FALSE,
#   map.type = "OpenStreetMap"
# ) +
#   mapview::mapview(
#     sf_psi_subset,
#     layer.name = "Psi",
#     label = "edge_name",
#     zcol = "psi",
#     lwd = 3
#   ) |>
#   suppressWarnings()

#one to many
sf_psi <- distantia_spatial(
  df = df_psi,
  sf = distantia::covid_counties,
  network = FALSE
)

#subset one county
sf_psi_subset <- sf_psi[sf_psi$x == "Los_Angeles", ]

#one to many map
#dissimilarity of all counties with Los Angeles
# mapview::mapview(
#   sf_psi_subset,
#   layer.name = "Psi with LA",
#   label = "y",
#   zcol = "psi",
#   alpha.regions = 1
# ) |>
#   suppressWarnings()