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1508919f76
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1508919f76 | |||
28041df425 | |||
c8c5a086a6 | |||
755089821c | |||
17205a5c19 | |||
214ff525cd | |||
4e4576d93d | |||
4b33f2d71a | |||
529d3ae923 | |||
9cf982c483 |
2
.gitignore
vendored
2
.gitignore
vendored
@ -4,3 +4,5 @@
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.Ruserdata
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.Ruserdata
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data/*
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data/*
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figures/*
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@ -30,7 +30,6 @@ extent <- list(longitude_max = max(extent$longitude),
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latitude_max = max(extent$latitude),
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latitude_max = max(extent$latitude),
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latitude_min = min(extent$latitude))
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latitude_min = min(extent$latitude))
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# get watershed areas
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# get watershed areas
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extent_huc <- get_huc(AOI = extent_poly, buffer = 0, type = "huc04")
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extent_huc <- get_huc(AOI = extent_poly, buffer = 0, type = "huc04")
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@ -1,4 +1,5 @@
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# load libraries
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# load libraries
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library(nhdplusTools)
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library(sf)
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library(sf)
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# reload extent data so you don't need to run the download data script again
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# reload extent data so you don't need to run the download data script again
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@ -26,13 +27,18 @@ extent <- list(longitude_max = max(extent$longitude),
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# load and crop hydrologic data
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# load and crop hydrologic data
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data <- list(NULL)
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data <- list(NULL)
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sf_use_s2(FALSE)
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sf_use_s2(FALSE)
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layers <- c("NHDArea",
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"NHDFlowline",
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"NHDWaterbody",
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"NHDPlusLandSea")
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for (layer in layers){
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for (layer in layers){
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data[[layer]] <- st_crop(st_read(paste0(nhdplusTools_data_dir(),"/data.gpkg"), layer = layer), y = extent_bbox)
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data[[layer]] <- st_crop(st_read(paste0(nhdplusTools_data_dir(),"/data.gpkg"), layer = layer), y = extent_bbox)
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}
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}
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# load political boundaries
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# load political boundaries
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political <- list(NULL)
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political <- st_crop(
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political_boundaries <- list.files(path = paste0(data_dir, "/political_boundaries"))
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st_transform(
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for (boundary in political_boundaries) {
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st_read(
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political[[boundary]] <- st_read(paste0(data_dir, "/political_boundaries/", boundary))
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paste0(data_dir, "/political_boundaries/bound_p/boundaries_p_2021_v3.shp")),
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}
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crs=crs),
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y = extent_bbox)
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@ -7,47 +7,74 @@ ifelse(!dir.exists(file.path(getwd(), figure_dir)), dir.create(file.path(getwd()
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# set colors
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# set colors
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colors <- list(darkblue = "#062e57",
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colors <- list(darkblue = "#062e57",
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lightblue = "#b1dcf3")
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lightblue = "#b1dcf3",
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lightgrey = "#6D6D6D")
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# set dimensions of figures
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# set dimensions of figures
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width <- 8.5
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width <- 8
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height <- 11
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height <- 10
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units <- "in"
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units <- "in"
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res <- 600
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res <- 600
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margin <- 0
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margin <- 0
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# set state to highlight
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state <- c("Wisconsin")
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# plot map
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# plot map
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plot_map <- function () {
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plot_map <- function () {
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plot(sf::st_geometry(extent_poly),
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plot(sf::st_geometry(extent_poly),
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col = colors$darkblue,
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col = colors$lightblue,
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border = "black")
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border = NA)
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plot(sf::st_geometry(data$NHDWaterbody),
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plot(sf::st_geometry(political$geometry),
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col = colors$lightblue,
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col = colors$lightgrey,
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border = NA,
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border = NA,
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add = TRUE)
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add = TRUE)
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plot(sf::st_geometry(data$NHDArea),
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plot(sf::st_geometry(political[political$NAME_En %in% state, ]),
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col = colors$lightblue,
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col = colors$darkblue,
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border = NA,
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border = NA,
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add = TRUE)
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add = TRUE)
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plot(sf::st_geometry(data$NHDFlowline),
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plot(sf::st_geometry(data$NHDWaterbody),
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col = colors$lightblue,
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col = colors$lightblue,
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lwd = data$NHDFlowline$TotDASqKM^0.3204*0.0446,
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border = NA,
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border = NA,
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add = TRUE)
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add = TRUE)
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plot(sf::st_geometry(data$NHDArea),
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col = colors$lightblue,
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border = NA,
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add = TRUE)
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plot(sf::st_geometry(data$NHDFlowline),
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col = colors$lightblue,
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lwd = data$NHDFlowline$TotDASqKM^0.3204*0.0446,
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border = NA,
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add = TRUE)
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plot(sf::st_geometry(extent_poly),
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col = NA,
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border = "black",
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lwd = 1,
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add = TRUE)
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}
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}
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# save figure
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# save figure
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tiff(filename = paste0(figure_dir,"/map.tiff"),
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# tiff(filename = paste0(figure_dir,"/map.tiff"),
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width = width,
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# width = width,
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height = height,
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# height = height,
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units = units,
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# units = units,
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res = res,
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# res = res,
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compression = "lzw")
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# compression = "lzw")
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par(mai=c(margin, margin, margin, margin))
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# par(mai=c(margin, margin, margin, margin))
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par(mar=c(1,1,1,1))
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# par(mar=c(1,1,1,1))
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plot_map()
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# plot_map()
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dev.off()
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# dev.off()
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#
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# jpeg(filename = paste0(figure_dir,"/map.jpg"),
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# width = width,
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# height = height,
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# units = units,
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# res = res,
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# quality = 93)
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# par(mai=c(margin, margin, margin, margin))
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# par(mar=c(1,1,1,1))
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# plot_map()
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# dev.off()
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png(filename = paste0(figure_dir, "/map.png"),
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png(filename = paste0(figure_dir, "/map.png"),
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width = width,
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width = width,
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@ -6,10 +6,10 @@ Downloads USGS data for a given location and makes a pretty map of the rivers. T
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1. Clone the repository
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1. Clone the repository
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2. Edit the extent of the map you want to produce in `extent.csv`. Include lat/long coordinates of two opposite corners (i.e. top-left and bottom-right).
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2. Edit the extent of the map you want to produce in `extent.csv`. Include lat/long coordinates of two opposite corners (i.e. top-left and bottom-right).
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3. Run the scripts:
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3. Run the scripts:
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- `01_download_data.R` - This will take the longest. The whole of Wisconsin downloads ~12GB of data
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- `01_download_data.R` - This will take the longest. The whole of Wisconsin downloads ~12GB of data. It downloads the data to a `data` directory in the repo
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- `02_process_data.R`
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- `02_process_data.R` - This loads and processes the data. After the initial run downloading the data for a given extent, you can start with this script.
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- `03_make_figures.R`
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- `03_make_figures.R` - This generates figures in a `figures` directory in the repo.
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## Example figure
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## Example figure
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BIN
example.png
Normal file
BIN
example.png
Normal file
Binary file not shown.
After Width: | Height: | Size: 15 MiB |
@ -1,3 +1,3 @@
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latitude,longitude
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latitude,longitude
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47.33402,-93.10419
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47.73208,-93.55154
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42.43754,-86.832
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41.49273,-86.64896
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3
extent_olympic.csv
Normal file
3
extent_olympic.csv
Normal file
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latitude,longitude
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48.50775,-124.91972
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46.88398,-122.07015
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3
extent_wa.csv
Normal file
3
extent_wa.csv
Normal file
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latitude,longitude
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49.17657,-125.43151
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45.23644,-115.81455
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3
extent_wi.csv
Normal file
3
extent_wi.csv
Normal file
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latitude,longitude
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47.73208,-93.55154
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41.49273,-86.64896
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