Comparing evacuation scenarios with evacpath

evacpath can compare pedestrian-evacuation assumptions without changing the underlying study inputs. This article uses the packaged example data and clips all spatial inputs to a small focus area before modeling.

Load example inputs

library(evacpath)
library(terra)

focus <- ext(669000, 675000, 9223000, 9225000)

dem <- crop(rast(system.file("extdata/dem.tif", package = "evacpath")), focus)
inundation <- crop(
  rast(system.file("extdata/tsunami_inundation_depth.tif", package = "evacpath")),
  focus
)
roads <- crop(vect(system.file("extdata/rds.gpkg", package = "evacpath")), focus)

zones <- prepare_tsunami_zones(
  inundation = inundation,
  dem = dem,
  target_crs = "EPSG:32748"
)

roads <- clean_roads(
  roads,
  exclude = list(field = "man_made", values = "pier"),
  target_crs = "EPSG:32748"
)

Run a baseline model

walking_speed_mps controls conversion from route distance to evacuation time. The lcp_* arguments control the conductance surface and can change modeled route geometry.

baseline <- run_evacpath(
  hazard_zone = zones$hazard_zone,
  escape_zone = zones$escape_zone,
  roads = roads,
  dem = zones$dem,
  target_crs = "EPSG:32748",
  max_origins = 500,
  max_destinations = 100,
  walking_speed_mps = 1.22,
  keep_routes = TRUE
)

Compare walking speed and path assumptions

comparison <- compare_evac_scenarios(
  hazard_zone = zones$hazard_zone,
  escape_zone = zones$escape_zone,
  roads = roads,
  dem = zones$dem,
  target_crs = "EPSG:32748",
  max_origins = 500,
  max_destinations = 100,
  scenarios = list(
    baseline = list(walking_speed_mps = 1.22),
    slow_walkers = list(walking_speed_mps = 0.75),
    alternative_neighbours = list(lcp_neighbours = 8)
  )
)

comparison$summary

Scenario sensitivity in modeled evacuation time.

Scenario	Walking speed	Median min.	Mean min.	Maximum min.
baseline	1.22	7.27	9.75	22.84
slow_walkers	0.75	11.83	15.86	37.15
conservative_routing	1.22	7.48	10.26	23.96

Diagnose inputs

Diagnostics collect coordinate reference system, geometry, overlap, elevation, origin, destination, and optional reachability issues in one report.

diagnostics <- diagnose_evac_model(
  hazard_zone = baseline$hazard_zone,
  roads = baseline$roads,
  dem = baseline$dem,
  escape_zone = baseline$escape_zone,
  origins = baseline$road_points,
  destinations = baseline$escape_points,
  conductance = baseline$conductance
)

diagnostics
has_errors(diagnostics)

The generator also records a compact QA/QC result from the same packaged run. It is a snapshot of the supplied inputs and settings, not a general guarantee about a new study area.

QA/QC check	Result
Errors	0
Warnings	1
Informational checks	2
Road features	193
Sampled origins	40
Candidate safety exits	12
DEM resolution (m)	5.12 x 5.12

Map bottlenecks

Routes are retained only when keep_routes = TRUE. This keeps ordinary runs lightweight while allowing route-density analysis when it is needed.

bottlenecks <- map_evac_bottlenecks(evac_result = baseline)

plot(bottlenecks$density_raster)
plot(bottlenecks$high_density_polygons, add = TRUE)

Modeled high-use corridors from the compact packaged example.