mirror of https://github.com/apache/superset.git
feat(maps): Add Italy regions code to the map generator notebook (#27542)
This commit is contained in:
İSKENDER ÜLGEN OĞUL
GitHub
parent
36290ce72f
commit
86aa8bde8b
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@ -40,6 +40,27 @@
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"Download datasets (_Admin 0 - Countries_ in [1:10](https://www.naturalearthdata.com/downloads/10m-cultural-vectors/), and _Admin 1 – States, Provinces_ in 1:10 and [1:50](https://www.naturalearthdata.com/downloads/50m-cultural-vectors/)) from Natural Earch Data:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Dependencies\n",
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"\n",
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"import os\n",
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"import requests\n",
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"import geopandas as gpd\n",
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"import matplotlib.pyplot as plt\n",
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"import shapely\n",
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"import pandas as pd\n",
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"import shapely.geometry\n",
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"import shapely.ops\n",
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"import shapely.affinity\n",
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"from shapely.geometry import Polygon, MultiPolygon\n",
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"import shutil"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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@ -52,9 +73,6 @@
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import requests\n",
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"\n",
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"data_dir = os.path.expanduser(\"~/Downloads\")\n",
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"if not os.path.exists(data_dir):\n",
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" os.mkdir(data_dir)\n",
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@ -106,15 +124,14 @@
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import geopandas as gpd\n",
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"import matplotlib.pyplot as plt\n",
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"import shapely\n",
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"\n",
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"# Read Natural Earth data files into GeoDataFrames\n",
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"df_admin0_10m = gpd.read_file(f\"{data_dir}/ne_10m_admin_0_countries.zip\")\n",
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"df_10m = gpd.read_file(f\"{data_dir}/ne_10m_admin_1_states_provinces.zip\")\n",
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"df_50m = gpd.read_file(f\"{data_dir}/ne_50m_admin_1_states_provinces.zip\")\n",
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"\n",
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"# Convert column names to lowercase\n",
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"df_admin0_10m.columns = df_admin0_10m.columns.str.lower()\n",
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"\n",
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"# Download and load the GeoJSON file for India\n",
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"india_geojson_url = \"https://github.com/geohacker/india/raw/bcb920c7d3c686f01d085f7661c9ba89bf9bf65e/state/india_state_kashmir_ladakh.geojson\"\n",
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"\n",
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@ -126,31 +143,6 @@
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" print(\"Please download the file from the URL and try again.\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "_PKEg8s1t1LT",
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"outputId": "9937eacd-7a05-491f-f356-790ad75a819f"
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},
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"outputs": [],
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"source": [
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"df_50m.columns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "w8OD4nq2t1LT",
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"outputId": "b7164440-d6e0-4074-b357-47fd8a8d2884"
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},
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"outputs": [],
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"source": [
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"df_admin0_10m.columns = df_admin0_10m.columns.str.lower()\n",
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"df_admin0_10m.columns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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@ -178,18 +170,10 @@
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"source": [
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"df_50m[df_50m.adm0_a3 == 'USA'].plot(figsize=(20,10))\n",
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"plt.show()\n",
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"\n",
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"india_gdf.plot(figsize=(20, 10))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "VvpqxOjMt1LU"
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},
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"source": [
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"Use 1:50m geometry for some large countries:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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@ -199,7 +183,9 @@
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},
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"outputs": [],
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"source": [
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"df_50m['admin'].unique()"
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"# Use 1:50m geometry for some large countries:\n",
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"\n",
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"print(*df_50m['admin'].unique(), sep='\\n')"
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]
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},
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{
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@ -210,8 +196,6 @@
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"\n",
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"df = pd.concat([df_10m[~df_10m['admin'].isin(df_50m['admin'].unique())], df_50m])"
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]
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},
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@ -273,7 +257,7 @@
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" 'indonesia',\n",
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" 'iran',\n",
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" 'italy',\n",
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" 'italy_regions', # this one is custom - see below\n",
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" 'italy_regions', # this one is derived from italy - see below\n",
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" 'japan',\n",
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" 'jordan',\n",
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" 'kazakhstan',\n",
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@ -345,7 +329,7 @@
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"\n",
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"# CSV files that exist specifically on the repo, rather than in the dataset\n",
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"custom_countries = [\n",
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" 'italy_regions',\n",
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" \n",
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"]\n",
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"\n",
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"# Make sure all country names are covered:\n",
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@ -373,8 +357,6 @@
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},
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"outputs": [],
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"source": [
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"import geopandas as gpd\n",
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"\n",
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"alt_maps = dict()\n",
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"\n",
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"def get_gdf(country):\n",
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@ -393,9 +375,13 @@
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" for i, country in enumerate(countries):\n",
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" # create subplot axes in a 3x3 grid\n",
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" ax = plt.subplot(len(countries) // 5, 6, i + 1) # nrows, ncols, axes position\n",
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" get_gdf(country).plot(ax=ax)\n",
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" gdf = get_gdf(country)\n",
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" if not gdf.empty: # check if GeoDataFrame is not empty\n",
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" gdf.plot(ax=ax)\n",
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" ax.set_aspect('equal', adjustable='datalim')\n",
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" else: # if GeoDataFrame is empty\n",
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" ax.text(0.5, 0.5, country, ha='center', va='center') # add country name to the center of the subplot\n",
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" ax.set_title(country)\n",
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" ax.set_aspect('equal', adjustable='datalim')\n",
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"\n",
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" plt.tight_layout()\n",
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" plt.show()\n",
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@ -411,7 +397,7 @@
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"source": [
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"### Handle countries with flying islands\n",
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"\n",
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"#### USA"
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"- For countries with flying islands, we need to move the islands closer to the mainland."
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]
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},
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{
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@ -420,7 +406,7 @@
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"id": "Z4y46Zuot1LU"
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},
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"source": [
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"For countries with flying islands, we need to move the islands closer to the mainland."
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"#### USA"
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]
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},
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{
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@ -513,14 +499,15 @@
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"\n",
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"china = df[df.admin == \"China\"]\n",
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"china_copy = pd.concat([china, china_sars], ignore_index=True)\n",
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"\n",
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"# Combine the 'name_zh' columns\n",
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"china_copy[\"name_zh\"] = china_copy[\"name_zh\"].combine_first(china_copy[\"name_zh_y\"])\n",
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"\n",
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"# Drop the extra 'name_zh_x' and 'name_zh_y' columns, if they exist\n",
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"china_copy = china_copy.drop([\"name_zh_x\", \"name_zh_y\"], axis=1)\n",
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"\n",
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"# Plotting the DataFrame\n",
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"china_copy.plot(figsize=(12, 12))"
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]
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@ -542,7 +529,7 @@
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"source": [
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"#### Finland\n",
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"\n",
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"The Åland Islands (ISO country code AX) is an autonomous region of Finland, and carries the ISO-3166 code FI-01."
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"- The Åland Islands (ISO country code AX) is an autonomous region of Finland, and carries the ISO-3166 code FI-01."
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]
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},
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{
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@ -575,17 +562,19 @@
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"\n",
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"finland = df[df.admin == \"Finland\"]\n",
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"\n",
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"# Concatenate the 'finland' DataFrame with 'finland_aland' DataFrame\n",
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"finland_copy = pd.concat([finland, finland_aland], ignore_index=True)\n",
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"\n",
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"# Combine 'name_fi' columns. However, since both columns are named 'name_fi', this might be redundant\n",
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"# If you have two different columns for 'name_fi' values in each DataFrame, specify them as 'name_fi_x' and 'name_fi_y'\n",
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"finland_copy[\"name_fi\"] = finland_copy[\"name_fi\"].combine_first(finland_copy[\"name_fi\"])\n",
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"\n",
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"# Drop the 'name_fi' column, if that's intended. This will remove the 'name_fi' data entirely.\n",
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"# If you meant to drop other columns (like 'name_fi_x' and 'name_fi_y'), update the column names accordingly\n",
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"finland_copy = finland_copy.drop([\"name_fi\"], axis=1)\n",
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"\n",
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"# Plotting the DataFrame\n",
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"finland_copy.plot(figsize=(12, 12))\n"
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]
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@ -598,7 +587,7 @@
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"source": [
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"#### Norway\n",
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"\n",
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"Remove NO-X01~ (The uninhabited Bouvet Island) and move Svalbard closer to mainland"
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"- Remove NO-X01~ (The uninhabited Bouvet Island) and move Svalbard closer to mainland"
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]
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},
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{
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@ -624,6 +613,7 @@
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"outputs": [],
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"source": [
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"norway_copy = norway.copy()\n",
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"\n",
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"norway_copy = norway_copy[norway_copy[\"iso_3166_2\"] != \"NO-X01~\"]\n",
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"reposition(norway_copy, norway.name == 'Svalbard', -12, -8, 0.5, 0.5)\n",
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"#reposition(norway_copy, norway.name == 'Nordland', 10, 0, 2, 2)\n",
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@ -663,8 +653,10 @@
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"outputs": [],
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"source": [
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"portugal_copy = portugal.copy()\n",
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"\n",
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"reposition(portugal_copy, portugal.name == 'Azores', 11, 0)\n",
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"reposition(portugal_copy, portugal.name == 'Madeira', 6, 2, simplify=0.015)\n",
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"\n",
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"portugal_copy.plot(figsize=(8, 8))"
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]
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},
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@ -700,7 +692,9 @@
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"outputs": [],
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"source": [
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"spain_copy = spain.copy()\n",
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"\n",
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"reposition(spain_copy, spain.name.isin(['Las Palmas', 'Santa Cruz de Tenerife']), 3, 7, 1, 1)\n",
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"\n",
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"spain_copy.plot(figsize=(8, 8))"
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]
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},
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@ -732,7 +726,7 @@
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"id": "IOuQ_OzMt1LW"
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},
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"source": [
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"Russia looks off because of Chukchi runs across E180. We need to move the parts on the other side of the map to the right."
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"- Russia looks off because of Chukchi runs across E180. We need to move the parts on the other side of the map to the right."
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]
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},
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{
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@ -745,10 +739,6 @@
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},
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"outputs": [],
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"source": [
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"import shapely.geometry\n",
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"import shapely.ops\n",
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"import shapely.affinity\n",
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"\n",
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"def shift_geom(geom, cutoff=0):\n",
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" border = shapely.geometry.LineString([(cutoff, -90), (cutoff, 90)])\n",
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" splitted_geom = shapely.ops.split(geom, border)\n",
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@ -815,8 +805,6 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from shapely.geometry import Polygon, MultiPolygon\n",
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"\n",
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"# NUTS - 1 Codes for Turkey and correspong region - city names\n",
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"\n",
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"region_dict = {\n",
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@ -896,7 +884,7 @@
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"id": "aYFQYe8-t1LW"
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},
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"source": [
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"#### France"
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"### France"
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]
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},
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{
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@ -978,123 +966,63 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"france_regions = france_copy.copy()\n",
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"# Define the mapping from the departments to their parent division\n",
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"# i.e. from departments to regions\n",
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"# Source: https://www.iso.org/obp/ui/#iso:code:3166:FR\n",
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"dep_to_reg = {\n",
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" \"FR-01\": \"FR-ARA\",\n",
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" \"FR-02\": \"FR-HDF\",\n",
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" \"FR-03\": \"FR-ARA\",\n",
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" \"FR-04\": \"FR-PAC\",\n",
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" \"FR-05\": \"FR-PAC\",\n",
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" \"FR-06\": \"FR-PAC\",\n",
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" \"FR-07\": \"FR-ARA\",\n",
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" \"FR-08\": \"FR-GES\",\n",
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" \"FR-09\": \"FR-OCC\",\n",
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" \"FR-10\": \"FR-GES\",\n",
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" \"FR-11\": \"FR-OCC\",\n",
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" \"FR-12\": \"FR-OCC\",\n",
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" \"FR-13\": \"FR-PAC\",\n",
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" \"FR-14\": \"FR-NOR\",\n",
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" \"FR-15\": \"FR-ARA\",\n",
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" \"FR-16\": \"FR-NAQ\",\n",
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" \"FR-17\": \"FR-NAQ\",\n",
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" \"FR-18\": \"FR-CVL\",\n",
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" \"FR-19\": \"FR-NAQ\",\n",
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" \"FR-21\": \"FR-BFC\",\n",
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" \"FR-22\": \"FR-BRE\",\n",
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" \"FR-23\": \"FR-NAQ\",\n",
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" \"FR-24\": \"FR-NAQ\",\n",
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" \"FR-25\": \"FR-BFC\",\n",
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" \"FR-26\": \"FR-ARA\",\n",
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" \"FR-27\": \"FR-NOR\",\n",
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" \"FR-28\": \"FR-CVL\",\n",
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" \"FR-29\": \"FR-BRE\",\n",
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" \"FR-2A\": \"FR-20R\",\n",
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" \"FR-2B\": \"FR-20R\",\n",
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" \"FR-30\": \"FR-OCC\",\n",
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" \"FR-31\": \"FR-OCC\",\n",
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" \"FR-32\": \"FR-OCC\",\n",
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" \"FR-33\": \"FR-NAQ\",\n",
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" \"FR-34\": \"FR-OCC\",\n",
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" \"FR-35\": \"FR-BRE\",\n",
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" \"FR-36\": \"FR-CVL\",\n",
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" \"FR-37\": \"FR-CVL\",\n",
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" \"FR-38\": \"FR-ARA\",\n",
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" \"FR-39\": \"FR-BFC\",\n",
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" \"FR-40\": \"FR-NAQ\",\n",
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" \"FR-41\": \"FR-CVL\",\n",
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" \"FR-42\": \"FR-ARA\",\n",
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" \"FR-43\": \"FR-ARA\",\n",
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" \"FR-44\": \"FR-PDL\",\n",
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" \"FR-45\": \"FR-CVL\",\n",
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" \"FR-46\": \"FR-OCC\",\n",
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" \"FR-47\": \"FR-NAQ\",\n",
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" \"FR-48\": \"FR-OCC\",\n",
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" \"FR-49\": \"FR-PDL\",\n",
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" \"FR-50\": \"FR-NOR\",\n",
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" \"FR-51\": \"FR-GES\",\n",
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" \"FR-52\": \"FR-GES\",\n",
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" \"FR-53\": \"FR-PDL\",\n",
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" \"FR-54\": \"FR-GES\",\n",
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" \"FR-55\": \"FR-GES\",\n",
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" \"FR-56\": \"FR-BRE\",\n",
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" \"FR-57\": \"FR-GES\",\n",
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" \"FR-58\": \"FR-BFC\",\n",
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" \"FR-59\": \"FR-HDF\",\n",
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" \"FR-60\": \"FR-HDF\",\n",
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" \"FR-61\": \"FR-NOR\",\n",
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" \"FR-62\": \"FR-HDF\",\n",
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" \"FR-63\": \"FR-ARA\",\n",
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" \"FR-64\": \"FR-NAQ\",\n",
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" \"FR-65\": \"FR-OCC\",\n",
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" \"FR-66\": \"FR-OCC\",\n",
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" \"FR-67\": \"FR-GES\",\n",
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" \"FR-68\": \"FR-GES\",\n",
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" \"FR-69\": \"FR-ARA\",\n",
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" \"FR-70\": \"FR-BFC\",\n",
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" \"FR-71\": \"FR-BFC\",\n",
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" \"FR-72\": \"FR-PDL\",\n",
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" \"FR-73\": \"FR-ARA\",\n",
|
||||
" \"FR-74\": \"FR-ARA\",\n",
|
||||
" \"FR-75\": \"FR-IDF\",\n",
|
||||
" \"FR-76\": \"FR-NOR\",\n",
|
||||
" \"FR-77\": \"FR-IDF\",\n",
|
||||
" \"FR-78\": \"FR-IDF\",\n",
|
||||
" \"FR-79\": \"FR-NAQ\",\n",
|
||||
" \"FR-80\": \"FR-HDF\",\n",
|
||||
" \"FR-81\": \"FR-OCC\",\n",
|
||||
" \"FR-82\": \"FR-OCC\",\n",
|
||||
" \"FR-83\": \"FR-PAC\",\n",
|
||||
" \"FR-84\": \"FR-PAC\",\n",
|
||||
" \"FR-85\": \"FR-PDL\",\n",
|
||||
" \"FR-86\": \"FR-NAQ\",\n",
|
||||
" \"FR-87\": \"FR-NAQ\",\n",
|
||||
" \"FR-88\": \"FR-GES\",\n",
|
||||
" \"FR-89\": \"FR-BFC\",\n",
|
||||
" \"FR-90\": \"FR-BFC\",\n",
|
||||
" \"FR-91\": \"FR-IDF\",\n",
|
||||
" \"FR-92\": \"FR-IDF\",\n",
|
||||
" \"FR-93\": \"FR-IDF\",\n",
|
||||
" \"FR-94\": \"FR-IDF\",\n",
|
||||
" \"FR-95\": \"FR-IDF\",\n",
|
||||
" \"FR-GF\": \"FR-GF\",\n",
|
||||
" \"FR-MQ\": \"FR-MQ\",\n",
|
||||
" \"FR-GP\": \"FR-GP\",\n",
|
||||
" \"FR-RE\": \"FR-RE\",\n",
|
||||
" \"FR-YT\": \"FR-YT\",\n",
|
||||
"}\n",
|
||||
"# Apply the mapping departments => regions\n",
|
||||
"france_regions[\"iso_3166_2_region\"] = france_regions['iso_3166_2'].apply(lambda x: dep_to_reg[x])\n",
|
||||
"# Merge the departments together \n",
|
||||
"# https://geopandas.org/en/stable/docs/user_guide/aggregation_with_dissolve.html\n",
|
||||
"france_regions = france_regions.dissolve(by='iso_3166_2_region')\n",
|
||||
"# Fix the label\n",
|
||||
"france_regions['iso_3166_2'] = france_regions['iso_3166_2'].index\n",
|
||||
"# Fix the name\n",
|
||||
"france_regions['name'] = france_regions['region']\n",
|
||||
"france_regions.plot()"
|
||||
"france_regions = france_copy[['geometry','region_cod','region']]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"france_regions = france_regions.dissolve(by=['region_cod', 'region']).reset_index()\n",
|
||||
"\n",
|
||||
"france_regions = france_regions.rename(columns={'region': 'name', 'region_cod': 'iso_3166_2'})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"france_regions.plot(figsize=(10, 7), edgecolor='black', column='iso_3166_2', legend=False, cmap='tab20')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Italy"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Italy Regions"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"italy_regions = df[df.admin == 'Italy'][['geometry','region_cod','region']]\n",
|
||||
"\n",
|
||||
"italy_regions = italy_regions.dissolve(by=['region_cod', 'region']).reset_index()\n",
|
||||
"\n",
|
||||
"italy_regions = italy_regions.rename(columns={'region': 'name', 'region_cod': 'iso_3166_2'})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"italy_regions.plot(figsize=(10, 7), edgecolor='black', column='iso_3166_2', legend=False, cmap='tab20')"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -1205,6 +1133,7 @@
|
|||
" \"france\": france_copy,\n",
|
||||
" \"france_regions\": france_regions,\n",
|
||||
" \"turkey_regions\": turkey_regions,\n",
|
||||
" \"italy_regions\": italy_regions,\n",
|
||||
" \"netherlands\": netherlands_copy,\n",
|
||||
" \"norway\": norway_copy,\n",
|
||||
" \"uk\": uk_copy,\n",
|
||||
|
|
@ -1236,8 +1165,6 @@
|
|||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import shutil\n",
|
||||
"\n",
|
||||
"simplify_factors = {\n",
|
||||
" \"uk\": 0.005,\n",
|
||||
"}\n",
|
||||
|
|
@ -1368,13 +1295,6 @@
|
|||
"\n",
|
||||
"print(\"TypeScript code written to src/countries.ts\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
|
|
|||
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Loading…
Reference in New Issue