Using evacpath in a new region

For a new region, evacpath expects three core spatial inputs:

  1. a hazard or inundation zone,
  2. a road/pathway network, and
  3. a DEM or elevation raster.

The most important requirement is that distance calculations should use a projected coordinate reference system in meters.

The workflow builds on open-source least-cost path methods for evacuation planning (Cordero et al. 2025) and uses leastcostpath for least-cost path and movement-potential modeling (Lewis 2023; Lewis 2021).

Regional checklist

Item Review before modeling
Hazard or inundation input A defined hazard footprint and its intended threshold.
Road or pathway input A connected, suitable pedestrian network with documented attributes.
DEM or elevation input Coverage across the study area, elevation sign convention, and resolution.
Target CRS A projected CRS in meters for distance calculations.
Walking speed A documented assumption appropriate to the planning scenario.
Local exclusions Piers, private roads, closures, or other locally unsuitable features.
Output directory An explicit directory when using write_evac_outputs().
QA/QC review CRS, geometry, extent, origins, exits, and reachability checks. 
library(evacpath)

Minimal workflow

result <- run_evacpath(
  hazard_zone = "path/to/hazard_zone.gpkg",
  roads = "path/to/roads.gpkg",
  dem = "path/to/dem.tif",
  target_crs = "EPSG:XXXX",
  region_name = "New region",
  walking_speed_mps = 1.22
)

For context, this is the model result returned by a compact run using the packaged inputs. A new region should be interpreted only after its own input and QA/QC review.

Modeled evacuation time to safety for the compact packaged example.
Modeled evacuation time to safety for the compact packaged example.
Modeled output Example value
Minimum modeled time (minutes) 0.31
Median modeled time (minutes) 10.09
Mean modeled time (minutes) 9.65
Maximum modeled time (minutes) 22.84
Origins 40.00
Destinations 12.00
Walking speed (m/s) 1.22

Common customizations

result <- run_evacpath(
  hazard_zone = "path/to/hazard_zone.gpkg",
  roads = "path/to/roads.gpkg",
  dem = "path/to/dem.tif",
  target_crs = "EPSG:XXXX",
  region_name = "New region",

  # Road and escape-point geometry assumptions
  road_buffer_m = 2,
  escape_buffer_m = 5,
  final_road_buffer_m = 3,

  # Origin-point density
  grid_resolution = 50,
  max_origins = NULL,

  # Travel-time assumption
  walking_speed_mps = 1.22,

  # Output clipping
  clip_mode = "road_hazard"
)

Case-specific preprocessing

Keep region-specific choices outside the package. For example, if your DEM uses negative elevation for land, correct it before calling run_evacpath(). If your road layer contains piers or other unsuitable features, remove them with clean_roads().

roads <- clean_roads(
  roads = "path/to/roads.gpkg",
  exclude = list(field = "road_type", values = c("pier", "private")),
  target_crs = "EPSG:XXXX"
)

References

Cordero, E., Ruiz Vélez, R., Huérfano Moreno, V., Sherman, C., 2025. Enhancing tsunami evacuation strategies in Puerto Rico using open-source least-cost path analysis. J. Disaster Sci. Manag. 1, 18. https://doi.org/10.1007/s44367-025-00018-y

Lewis, J., 2023. leastcostpath: Modelling Pathways and Movement Potential Within a Landscape.

Lewis, J., 2021. Probabilistic Modelling for Incorporating Uncertainty in Least Cost Path Results: a Postdictive Roman Road Case Study. Journal of Archaeological Method and Theory 28, 911-924. https://doi.org/10.1007/s10816-021-09522-w