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library(sf)
library(dplyr)
library(readr)
library(tidyr)
library(tidygraph)
library(sfnetworks)
library(stplanr)
library(stringr)
library(purrr)
library(mapview)

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# prelim functions

# download oemextract and clean up ----
get_oemextract <- function(wsx = "D:/data_store/Boundaries/county/county.shp"){
  message("Warning this will take 5-10 mins")
  wsx <- sf::read_sf(wsx) |> dplyr::select(geometry)
  wsx_roads <- osmextract::oe_get(
    wsx |> st_transform(crs = 4326),
    query = "SELECT * FROM 'lines' WHERE highway IN ('footway', 'unclassified', 
    'service', 'track', 'bridleway', 'path', 'residential', 'tertiary', 

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# data available from DfT website
library(mapgl)
library(sf)
library(dplyr)
library(osmdata)
library(magrittr)

geodata <- "./data/west sussex_tss_review_20250714a_esuid.gpkg"
st_layers(geodata)
dft <- read_sf(geodata, layer = "DfT Counts")

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library(sf)
library(dplyr)
library(mapgl)
library(units)

d <- read_sf("./data/STRLIGHT/STRLIGHT.shp")

maplibre(style = carto_style("dark-matter-no-labels")) |> 
  fit_bounds(d) |> 
  add_circle_layer(id = "Streetlighting2",

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# https://wikishire.co.uk/lookup/
# https://wikishire.co.uk/wiki/Sussex
# https://en.wikipedia.org/wiki/Rape_(county_subdivision)
# https://britishcountyflags.com/2020/09/14/_the-rapes-of-sussex/

library(sf)
library(dplyr)
library(mapgl)
library(units)

szechno

library(mapgl)
library(sf)
library(stringr)
library(dplyr)
library(readr)
library(lubridate)
library(tidyr)

# read in data and tidy up
locations <- read_csv("./data/afghanistan.csv") |> 

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library(rnaturalearth)
library(rnaturalearthdata)
library(mapgl)
library(mapview)
library(sf)
library(dplyr)

antartic10 <- ne_download(scale = 50, type = 'antarctic_ice_shelves_polys', 
                       category = 'physical')
antartic10l <- ne_download(scale = 50, type = 'antarctic_ice_shelves_lines', 

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library(mapgl)
library(sf)
library(stringr)
library(dplyr)
library(magrittr)
library(osmdata)

map_start <- c(-0.404549247616387, 50.9365654134795)
initial_zoom <- 10

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library(rnaturalearth)
library(rnaturalearthdata)
library(mapgl)
library(sf)
library(dplyr)
library(rmapshaper)

# https://www.naturalearthdata.com/features/

rivers50 <- ne_download(scale = 50, type = 'rivers_lake_centerlines', 

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library(sf)
library(shiny)
library(dplyr)
library(readr)
library(units)
library(mapgl)
library(stringr)

# d |> filter((end >= set_units(2020, "year")) & (end <= set_units(2029, "year"))| is.na(end)) |> View()
#