Using raster vs vector

Rasters and vectors

Rasters are essentially matrices - grids of cells at a fixed resolution. Vectors are essentially polygons - shapes drawn by a line. See below an example of Luxembourg as a raster on the left, where the colour values correspond to elevation. On the right is a vector, showing the 12 different cantons.

urbioconnect uses rasters that work on SpatRaster (Spatial Raster) objects. Buffering is performed using a focal window (terra::focal()), and connected patches are identified using terra::patches().

The vector approach works on sf objects. Habitat and barriers are represented as polygons. Buffering uses sf::st_buffer(), and connectivity is determined by spatial intersection (sf::st_intersects()).

Both approaches expose convenience functions: habitat_connectivity() for rasters, and sf_habitat_connectivity() for vector. These run the full workflow in a single call.

Running raster and vector models on the same data

# Raster inputs
habitat_rast <- example_habitat()
barrier_rast <- example_barrier()

# Vector inputs
# Convert the habitat raster to polygons for the vector approach
habitat_sf <- terra::as.polygons(example_habitat(), dissolve = TRUE) |>
  sf::st_as_sf()

barrier_sf <- example_barrier_shp()
#> Reading layer `lizard_barrier' from data source 
#>   `/tmp/RtmpTHr54j/Rinst14dd1b6ec0cf/urbioconnect/ex/lizard_barrier.shp' 
#>   using driver `ESRI Shapefile'
#> Simple feature collection with 1 feature and 1 field
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 326089.6 ymin: 5820342 xmax: 327662.5 ymax: 5821909
#> Projected CRS: GDA94 / MGA zone 55

barrier_sf_clean <- clean(barrier_sf)
habitat_sf_clean <- clean(habitat_sf)
interpatch_dist <- 10
buffered_radius <- interpatch_dist / 2

library(tictoc)
#> 
#> Attaching package: 'tictoc'
#> The following objects are masked from 'package:terra':
#> 
#>     shift, size
tic()
raster_result <- habitat_connectivity(
  habitat = habitat_rast,
  barrier = barrier_rast,
  species = "Blue-tongued Lizard",
  interpatch_distance = interpatch_dist,
  verbose = FALSE
)
rast_time <- toc()
#> 2.594 sec elapsed

raster_result
#> # patch_connectivity:  data.frame
#> # Species:             Blue-tongued Lizard
#> # Patches:             703
#> # Resolution:          2x2
#> # Interpatch Distance: 10 m
#>   patch_id    area
#>      <dbl>   <dbl>
#> 1        1    60.0
#> 2        2  1648. 
#> 3        7    12.0
#> 4        8  1200. 
#> 5       10 92034. 
#> # ℹ 698 more rows

tic()
vector_result <- sf_habitat_connectivity(
  habitat = habitat_sf_clean,
  barrier = barrier_sf_clean,
  species = "lizard",
  interpatch_distance = interpatch_dist
)
vect_time <- toc()
#> 10.513 sec elapsed

vector_result
#> # patch_connectivity:  data.frame
#> # Species:             lizard
#> # Patches:             483
#> # Resolution:          NA
#> # Interpatch Distance: 10 m
#>   patch_id  area
#>      <dbl> <dbl>
#> 1        1 1536.
#> 2        2  287.
#> 3        3  292.
#> 4        4  850.
#> 5        5    8 
#> # ℹ 478 more rows

Comparing raster vs vector

Both approaches produce one row per connected habitat patch, with patch area and squared area.

nrow(raster_result)
#> [1] 703
nrow(vector_result)
#> [1] 483

Small differences in patch counts and areas are expected. The raster approach discretises space into cells, so patch boundaries are approximated at the chosen resolution. The vector approach preserves exact polygon geometry, so it typically produces slightly different (and arguably more precise) patch boundaries, particularly along curved or irregular barrier edges.

#> [1] 2.594

Timings for the methods are also important to consider. The raster approach took 2.594 seconds, and the vector approach took 10.513 seconds.

Summarising connectivity metrics

summarise_connectivity() works with output from either approach. You simply pass the appropriate area columns as vectors.

# Raster approach output uses the `area` column
summarise_connectivity(
  connectivity = raster_result$area,
  interpatch_distance = interpatch_dist,
  data_resolution = 10,
  species = "Blue-tongued Lizard (raster)"
)
#> # A tibble: 1 × 8
#>   species     interpatch_distance n_patches effective_mesh_ha prob_connectedness
#>   <chr>                     <dbl>     <int>             <dbl>              <dbl>
#> 1 Blue-tongu…                  10       703                 4           0.000015
#> # ℹ 3 more variables: patch_area_mean <dbl>, patch_area_total_ha <dbl>,
#> #   data_resolution <dbl>

# Vector approach output uses the `area` column
# Strip units before passing to summarise_connectivity
summarise_connectivity(
  connectivity = vector_result$area,
  interpatch_distance = interpatch_dist,
  data_resolution = NA,
  species = "Blue-tongued Lizard (vector)"
) 
#> # A tibble: 1 × 8
#>   species     interpatch_distance n_patches effective_mesh_ha prob_connectedness
#>   <chr>                     <dbl>     <int>             <dbl>              <dbl>
#> 1 Blue-tongu…                  10       483                 4           0.000016
#> # ℹ 3 more variables: patch_area_mean <dbl>, patch_area_total_ha <dbl>,
#> #   data_resolution <lgl>

The metrics — effective mesh size, probability of connectedness, mean patch area — will be close but not identical between the two approaches, reflecting the geometric differences described above.

Which approach should you use?

One factor to consider is what format your input data is already in. The other factor to consider is whether the data is large and computational time is important. For many situations, the raster based approaches will be much faster.

# Raster to vector
habitat_sf <- terra::as.polygons(example_habitat(), dissolve = TRUE) |>
  sf::st_as_sf()

# Vector to raster (requires a template grid)
rasters <- prepare_rasters(
  habitat = habitat_sf,
  barrier = barrier_sf,
  data_resolution = 10,
  target_resolution = 500
)
habitat_rast <- rasters$habitat_raster
barrier_rast <- rasters$barrier_raster

Analysis step-by-step

Both approaches expose individual functions if you need finer control — for example, to inspect intermediate outputs or to substitute a custom step.

# Raster step-by-step
barrier_mask <- create_barrier_mask(barrier_rast)
remaining <- drop_habitat_under_barrier(habitat_rast, barrier_mask)
buffered <- habitat_buffer(remaining, interpatch_distance = 10)
fragmented <- fragment_habitat(buffered, barrier_mask)
patches <- assign_patches_to_fragments(remaining, fragmented) |>
  add_patch_area()
areas <- aggregate_connected_patches(patches)

# Vector step-by-step
buffered_sf <- sf_habitat_buffer(habitat_sf_clean, interpatch_distance = 10)
fragments_sf <- sf_fragment_habitat(buffered_sf, barrier_sf_clean)
remaining_sf <- sf_drop_habitat_under_barrier(habitat_sf_clean, barrier_sf_clean)
patches_sf <- sf_assign_patches_to_fragments(remaining_sf, fragments_sf) |>
  sf_add_patch_area()
areas_sf <- sf_aggregate_connected_patches(patches_sf)

References

Kirk, H., Soanes, K., Amati, M., Bekessy, S., Harrison, L., Parris, K., Ramalho, C., van de Ree, R., & Threlfall, C. (2023). Ecological connectivity as a planning tool for the conservation of wildlife in cities. MethodsX, 10, 101989. https://doi.org/10.1016/j.mex.2022.101989