commit a56463a7c0e281e95d7e088b7c18d06fb0e26684
parent fb988f1c5d251856a86ef013e1e2d92ed87c5998
Author: Erik Loualiche <[email protected]>
Date: Mon, 24 Feb 2025 08:48:06 -0600
map doc
Diffstat:
5 files changed, 96 insertions(+), 367 deletions(-)
diff --git a/.gitignore b/.gitignore
@@ -1,5 +1,5 @@
# --------------------------------------------------------------------------------------------------
-/Manifest.toml
+**/Manifest.toml
.DS_Store
tmp
diff --git a/docs/Manifest.toml b/docs/Manifest.toml
@@ -1,365 +0,0 @@
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diff --git a/docs/Project.toml b/docs/Project.toml
@@ -1,4 +1,13 @@
[deps]
+CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
+DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+DataFramesMeta = "1313f7d8-7da2-5740-9ea0-a2ca25f37964"
Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
DocumenterVitepress = "4710194d-e776-4893-9690-8d956a29c365"
+GADM = "a8dd9ffe-31dc-4cf5-a379-ea69100a8233"
+GeoJSON = "61d90e0f-e114-555e-ac52-39dfb47a3ef9"
+GeoMakie = "db073c08-6b98-4ee5-b6a4-5efafb3259c6"
+GeometryOps = "3251bfac-6a57-4b6d-aa61-ac1fef2975ab"
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TigerFetch = "59408d0a-a903-460e-b817-a5586a16527e"
+ZipFile = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
diff --git a/docs/make.jl b/docs/make.jl
@@ -4,7 +4,7 @@
# --------------------------------------------------------------------------------------------------
# from within doc
-import Pkg; Pkg.develop(path="..")
+# import Pkg; Pkg.develop(path="..")
using TigerFetch
# --------------------------------------------------------------------------------------------------
diff --git a/docs/src/demo/simple_map.md b/docs/src/demo/simple_map.md
@@ -1 +1,86 @@
# Stata-like utilities
+
+
+```@setup simplemap
+import Pkg;
+Pkg.add(["CairoMakie", "DataFrames", "DataFramesMeta"])
+Pkg.add(["GADM", "GeoMakie", "GeometryOps", "GeoJSON", "Shapefile", "ZipFile"]);
+```
+
+You will need a bunch of libraries to plot these; maybe setup a temporary environment, because it is not fun waiting for Makie to recompile at every startup.
+```@example simplemap
+using CairoMakie, DataFrames, DataFramesMeta
+using GADM, GeoMakie, GeometryOps, GeoJSON, Shapefile, ZipFile
+using TigerFetch
+tmp_dir = mktempdir(); map_dir = joinpath(tmp_dir, "map");
+```
+
+## Plotting county subdivisions
+
+We are downloading the county shapefiles (which is a national file), subsets it to Minnesota and plot it.
+```@example simplemap;
+tigerdownload("county"; output=map_dir)
+isfile(joinpath(map_dir, "tl_2024_us_county.zip"))
+```
+
+First, we process the file which is national to only keep counties in the state
+```@example simplemap;
+df_shp_cty = Shapefile.Table(joinpath(map_dir, "tl_2024_us_county.zip")) |> DataFrame
+@rsubset!(df_shp_cty, :STATEFP=="27")
+```
+
+```@example simplemap;
+fig = Figure(size=(750,900));
+homerule_centroid = GeometryOps.centroid(df_shp_cty.geometry)
+ga = GeoAxis(fig[1, 1];
+ dest = "+proj=ortho +lon_0=$(homerule_centroid[1]) +lat_0=$(homerule_centroid[2])",
+ xticksvisible = false, xgridvisible = false, xticks=[0],
+ yticksvisible = false, ygridvisible = false, yticks=[0])
+poly!(ga, df_shp_cty.geometry;
+ color=:white,
+ strokecolor = :black, strokewidth = 0.5, shading = NoShading,
+ colormap = :dense, alpha = 0.5)
+save("p1.svg", fig); nothing # hide
+```
+
+
+
+## Addings water areas and roads
+
+Roads are at the state level and water areas at the county level, so this will complete the example.
+
+From simple to more complicated we start with primary and secondary roads
+```@example simplemap;
+tigerdownload("primarysecondaryroads"; state="MN", output=map_dir)
+df_shp_roads = Shapefile.Table(joinpath(map_dir, "tl_2024_27_prisecroads.zip")) |> DataFrame
+lines!(ga, df_shp_roads.geometry; color=RGBf(255/255, 203/255, 71/255), alpha=0.75, linewidth=0.2)
+save("p2.svg", fig); nothing # hide
+```
+
+
+
+
+And the water areas; we download them in a separate directory because there is one file per county:
+```@example simplemap;
+mkpath(joinpath(map_dir, "MN"))
+tigerdownload("areawater"; state="MN", output=joinpath(map_dir, "MN"))
+```
+
+Then we read all of the downloaded shapefiles in the dictionary and keep only the subset of the largest lakes or rivers:
+```@example simplemap
+df_shp_water = [ DataFrame(Shapefile.Table(joinpath(map_dir, "MN", f)))
+ for f in readdir(joinpath(map_dir, "MN")) ]
+df_shp_water = reduce(vcat, df_shp_water, cols=:union)
+@rsubset!(df_shp_water, :AWATER > 1_000_000) # only keep larger water
+```
+
+And now the plot:
+```@example simplemap
+poly!(ga, df_shp_water.geometry;
+ color=RGBf(170/255, 218/255, 255/255),
+ strokewidth=0.5, strokecolor=RGBf(144/255, 202/255, 249/255))
+save("p3.svg", fig); nothing # hide
+```
+
+
+
