This vignette shows how to prepare road-noise modelling inputs from
building height data, OSM-style roads, greenspace, canopy height, and
optional terrain data. The same input pattern used by svf()
is used here: pass building footprints as x, choose the
height column with height_field, and supply canopy/DEM
rasters directly or let the function retrieve them.
library(gloBFPr)
library(sf)
library(terra)The package includes a small building layer and companion raster
examples. For a real study area, replace this with
search_3dglobdf().
data(globfp_example)
data(globfp_example_dem)
data(globfp_example_canopy_height)
buildings <- globfp_example
dem <- rast(globfp_example_dem)
canopy_height <- rast(globfp_example_canopy_height)
names(buildings)By default, prepare_noisemodelling_inputs() and
get_noise_map() download OSM roads from the bounding box of
x. If measured traffic columns are not present,
infer_osm_traffic() fills screening-level speed and traffic
assumptions from the OSM highway class.
noise_inputs <- prepare_noisemodelling_inputs(
x = buildings,
height_field = "Height",
datasource_greenspace = "esri",
greenspace_zoom = 14,
canopy_height = canopy_height,
dem = dem,
receiver = "grid",
resolution = 25,
quiet = FALSE
)For measured traffic counts, pass a road layer with NoiseModelling
traffic columns directly through roads. The inferred
defaults are useful for screening or scenario comparisons, not
calibrated regulatory maps.
Use prepare_noisemodelling_inputs() when you want to
inspect or export the layers before running the external NoiseModelling
solver.
noise_inputs <- prepare_noisemodelling_inputs(
x = buildings,
height_field = "Height",
canopy_height = canopy_height,
dem = dem,
receiver = "grid",
resolution = 25,
quiet = TRUE
)
names(noise_inputs)
nrow(noise_inputs$receivers)The prepared object contains:
buildings: building polygons with PK,
HEIGHT, and optional POP.roads: road lines with PK and
CNOSSOS-style traffic columns.ground: hard/green ground absorption polygons.receivers: 3D receiver points, defaulting to 4 m
height.dem: optional terrain raster aligned for later
export.You can write a GeoPackage for inspection.
noise_inputs <- prepare_noisemodelling_inputs(
x = buildings,
roads = roads,
canopy_height = canopy_height,
dem = dem,
out_dir = tempdir(),
write = TRUE,
quiet = TRUE
)
noise_inputs$gpkgThe noise functions can follow the same style as svf():
provide datasource_canopy_height,
datasource_greenspace, and key instead of
supplying rasters. Roads are downloaded internally from the building
extent unless you pass roads explicitly.
noise_inputs <- prepare_noisemodelling_inputs(
x = buildings,
height_field = "Height",
min_tree_height = 2,
datasource_canopy_height = "metachm",
datasource_greenspace = "esri",
opentopo_key = Sys.getenv("OPENTOPOGRAPHY_KEY"),
receiver = "grid",
resolution = 25,
quiet = TRUE
)opentopo_key is only needed when DEM retrieval is
requested. Canopy height is used to classify green ground absorption; it
is not treated as a hard acoustic barrier.
get_noise_map(run = TRUE) runs the official headless
NoiseModelling WPS scripts. This requires Java 11 or newer
(11<=version<=17). The first run can download the headless
NoiseModelling release into the R user cache, or you can preinstall it
with install_noisemodelling().
install_noisemodelling(version = "5.0.1")noise_result <- get_noise_map(
x = buildings,
height_field = "Height",
datasource_canopy_height = "metachm",
datasource_greenspace = "esri",
dem = dem,
receiver = "grid",
resolution = 25,
run = TRUE,
keep_files = TRUE,
quiet = FALSE,
java = 17
)
plot_noise_map(noise_result, period = "DEN", scalebar = TRUE)plot_noise_map(noise_result, period = "DEN")The result includes the prepared inputs, the raw
RECEIVERS_LEVEL output, a spatial noise_map
receiver layer with period-specific columns such as LAEQ_D,
LAEQ_E, LAEQ_N, and LAEQ_DEN, the
official NoiseModelling CONTOURING_NOISE_MAP polygons in
isophones, the exported GeoJSON paths, logs from each WPS
script, and the NoiseModelling runner path.
For production work, start with a building layer from
search_3dglobdf() and replace OSM-inferred traffic with
local speed and volume observations when available.
get_noise_map() exposes the main acoustic controls used
by Noise_level_from_source.groovy. The defaults are
deliberately moderate for screening maps; increasing propagation
distance, reflection order, diffraction, or ray export can make the run
much slower.
noise_result <- get_noise_map(
x = buildings,
height_field = "Height",
canopy_height = canopy_height,
dem = dem,
receiver = "grid",
resolution = 25,
run = TRUE,
java = 17,
reflection_order = 1,
max_src_distance = 500,
max_reflection_distance = 350,
diffraction_horizontal = TRUE,
diffraction_vertical = FALSE,
wall_alpha = 0.1,
humidity = 75,
temperature = 31,
favourable_occurrences = rep(0.5, 16),
max_error = 0.1,
export_source_id = FALSE,
frequency_field_prepend = "HZ"
)plot_noise_map(noise_result, period = "DEN", scalebar = TRUE)Key controls:
reflection_order: maximum number of specular
reflections on vertical surfaces. Higher values are more realistic in
street canyons but much slower.max_src_distance: maximum source-receiver search
distance in meters. Larger values include farther roads.max_reflection_distance: maximum distance used when
searching walls for reflected paths.diffraction_horizontal and
diffraction_vertical: enable diffraction over horizontal
edges or around vertical edges. NoiseModelling recommends horizontal
diffraction for many propagation studies; vertical diffraction is mainly
for rail and industrial sources under CNOSSOS-EU guidance.wall_alpha: wall absorption coefficient.
0.1 is a common reflective facade assumption.humidity, temperature, and
favourable_occurrences: atmospheric absorption and
meteorological propagation settings.max_error: pruning threshold in dB for negligible
source contributions. A smaller value can be more complete but
slower.export_source_id: keeps receiver levels by source id,
useful for source contribution diagnostics.rays_name: exports propagation rays or attenuation
diagnostics to a table or file URL. This is mainly for debugging and can
be very large.noise_wps_args: passes named raw arguments to the
NoiseModelling WPS script for advanced options not yet represented by a
dedicated R argument.The OSM traffic defaults used by infer_osm_traffic()
mirror the category values embedded in NoiseModelling’s
Import_OSM.groovy, including the cited Good Practice Guide
assumptions. Import_OSM.groovy itself works from a local
.osm, .osm.gz, or .osm.pbf
extract; the current R workflow instead downloads roads from the
building bounding box and applies matching traffic defaults in R.
If you already have a local OSM extract and want NoiseModelling to
create the ROADS table itself, pass osm_file
and leave roads = NULL.
You can download a regional .osm.pbf extract directly in
R. osmextract is a convenient option when the area is
available from a provider such as Geofabrik:
install.packages("osmextract")
osm_file <- osmextract::oe_get(
place = "Detroit, Michigan",
provider = "geofabrik",
download_directory = tempdir(),
force_download = FALSE
)You can also download a known extract URL with base R:
osm_file <- file.path(tempdir(), "michigan-latest.osm.pbf")
utils::download.file(
"https://download.geofabrik.de/north-america/us/michigan-latest.osm.pbf",
osm_file,
mode = "wb"
)noise_result <- get_noise_map(
x = buildings,
height_field = "Height",
canopy_height = canopy_height,
dem = dem,
osm_file = osm_file,
receiver = "grid",
resolution = 25,
run = TRUE,
java = 17
)This uses your x buildings and ground preparation from
R, but asks NoiseModelling’s Import_OSM.groovy to create
the road network and traffic defaults from the OSM file.