In SDCA-tool/sdca-package: Shared Digital Carbon Architecture

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Example of demand modelling

Step 1 Build some Example geometry

library(sdca)
library(sf)
library(dplyr)

# lines = sf::read_sf("../sdca-data-prep/multipart_example.gpkg")
# points = sf::read_sf("../sdca-data-prep/point_example.gpkg")
# 
# lines = lapply(lines$geometry, function(x){x})
# points = lapply(points$geometry, function(x){x})
# 
# #infra = sf::st_geometrycollection(c(lines, points))
# infra = sf::st_sfc(c(lines, points), crs = 4326)
# infra = data.frame(infrastructure_type = c("transport","transport","onward_travel","transport","transport","transport"),
#           mode_class = c("Rail","Rail","onward_travel","Rail","Rail","Rail"),
#           intervention_class = c("New construction","New construction","onward_travel","New construction","New construction","New construction"),
#           mode = c("High speed rail","High speed rail","onward_travel","High speed rail","High speed rail","High speed rail"),
#           intervention = c("Viaduct","Viaduct","onward_travel","Station - at grade","Station - at grade","Station - at grade"),
#           geometry = infra)
# 
# st_write(infra, "../inst/exdata/portishead.geojson")

Step 2: Simulate the API returning the necessary data

infra = read_sf("../data/map2.geojson")


flow <- geojsonsf::geojson_sf("../../../ITSleeds/NTEM2OD/data/NTEM/NTEM_desire_lines.geojson")
flow <- flow[flow$from != flow$to, ]
buff <- sf::st_buffer(sf::st_combine(infra$geometry), 3000)
desire <- flow[buff, op = sf::st_intersects]
#desire <- desire[desire$from != desire$to,]

interventions <- read.csv("../../sdca-data/data_tables/interventions.csv")
assets <- read.csv("../../sdca-data/data_tables/assets.csv")
assets_parameters <- read.csv("../../sdca-data/data_tables/assets_parameters.csv")
components <- read.csv("../../sdca-data/data_tables/components.csv")
carbon_factors <- read.csv("../../sdca-data/data_tables/carbon_factors.csv")

dir.create("tmp")
download.file("https://github.com/SDCA-tool/sdca-data/releases/download/map_data/materialsites.geojson.zip",
              "tmp/materialsites.geojson.zip")
unzip("tmp/materialsites.geojson.zip",exdir = "tmp")
material_sites <- sf::read_sf("tmp/materialsites.geojson")
unlink("tmp", recursive = TRUE)
cent <- sf::st_centroid(infra$geometry)
distance <- nngeo::st_nn(material_sites, cent, returnDist = TRUE)
distance <- distance$dist
distance <- unlist(distance)
material_sites$distance = distance
material_sites = material_sites %>%
  st_drop_geometry %>%
  group_by(Material_Types) %>%
  summarise(distance_km = round(distance[distance == min(distance)] / 1000,1))


assets = assets[assets$intervention %in% infra$intervention,]
assets_parameters = assets_parameters[assets_parameters$asset %in% assets$asset,]
components = components[components$asset %in% assets$asset, ]
carbon_factors = carbon_factors[carbon_factors$cf_name %in% components$cf_name, ]

# Build example_json
infra_json <- geojsonsf::sf_geojson(infra)
desire_json <- geojsonsf::sf_geojson(desire)

path_dem = "dem.tif"
path_landcover = "landcover.tif"
path_bedrock = "bedrock.tif"
path_superficial = "superficial.tif"

json = list(infra_json, assets, assets_parameters, components, carbon_factors, desire_json, path_dem, path_landcover, path_bedrock, path_superficial, material_sites)
names(json) = c("user_input", "assets", "assets_parameters", "components", "carbon_factors", "desire_lines", "path_dem","path_landcover","path_bedrock", "path_superficial", "material_sites")

jsonlite::write_json(json,"../tests/testthat/overbridge_example.json", 
                     pretty = TRUE, simplifyDataFrame = FALSE, simplifyVector = FALSE, na = "null")

Step 3: Parse the API's data

args = readLines("../tests/testthat/portishead_example_r_input.geojson")
args = paste(args, collapse = "")
fls = list.files("../R", full.names = TRUE)
for(fl in fls){source(fl)}

Step 4: Make Calculations

dat = parse_json(args, FALSE)

# Test reading
# dat = jsonlite::read_json("../inst/exdata/portishead_example_r_input.geojson", simplifyVector = TRUE)
# dat = parse_json(jsonlite::toJSON(dat))
# 
# tmap_mode("plot")
# tm_shape(desire) +
#   tm_lines(lwd = 0.1) +
# tm_shape(infra) +
#   tm_lines(col = "red", lwd = 3)