{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## QuadkeyPandas key features\n", "\n", "You can try out vgridpandas by using the cloud-computing platforms below without having to install anything on your computer:\n", "\n", "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/opengeoshub/vgridpandas/blob/main/docs/notebooks/16_quadkey.ipynb)\n", "[![image](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/opengeoshub/vgridpandas/HEAD?filepath=docs/notebooks/16_quadkey.ipynb)\n", "[![image](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/opengeoshub/vgridpandas/blob/main/docs/notebooks/16_quadkey.ipynb)\n", "[![image](https://jupyterlite.rtfd.io/en/latest/_static/badge.svg)](https://jupyterlite.gishub.vn/lab/index.html?path=notebooks/vgridpandas/16_quadkey.ipynb)\n", "\n", "Full VgridPandas DGGS documentation is available at [vgridpandas document](https://vgridpandas.gishub.vn).\n", "\n", "To work with Vgrid in Python or CLI, use [vgrid](https://pypi.org/project/vgrid/) package. Full Vgrid DGGS documentation is available at [vgrid document](https://vgrid.gishub.vn).\n", "\n", "To work with Vgrid DGGS in QGIS, install the [Vgrid Plugin](https://plugins.qgis.org/plugins/vgridtools/).\n", "\n", "To visualize DGGS in Maplibre GL JS, try the [vgrid-maplibre](https://www.npmjs.com/package/vgrid-maplibre) library.\n", "\n", "For an interactive demo, visit the [Vgrid Homepage](https://vgrid.vn)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Install vgridpandas\n", "Uncomment the following line to install [vgridpandas](https://pypi.org/project/vgridpandas/)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# %pip install vgridpandas" ] }, { "cell_type": "markdown", "id": "4740c9f5", "metadata": {}, "source": [ "### Import quadkeypandas" ] }, { "cell_type": "code", "execution_count": 3, "id": "a10c4a1d", "metadata": {}, "outputs": [], "source": [ "from vgridpandas import quadkeypandas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Latlon to Quadkey" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Pandas" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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lonlathousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximityquadkeyquadkey_res
0-122.2337.8841880129.03221268.3252452600NEAR BAY023010212210
1-122.2237.862170991106.0240111388.3014358500NEAR BAY023010212210
2-122.2437.85521467190.04961777.2574352100NEAR BAY023010212210
3-122.2537.85521274235.05582195.6431341300NEAR BAY023010212210
4-122.2537.85521627280.05652593.8462342200NEAR BAY023010212210
.......................................
20635-121.0939.48251665374.08453301.560378100INLAND023010031310
20636-121.2139.4918697150.03561142.556877100INLAND023010031310
20637-121.2239.43172254485.010074331.700092300INLAND023010031310
20638-121.3239.43181860409.07413491.867284700INLAND023010031210
20639-121.2439.37162785616.013875302.388689400INLAND023010031310
\n", "

20640 rows × 12 columns

\n", "
" ], "text/plain": [ " lon lat housing_median_age total_rooms total_bedrooms \\\n", "0 -122.23 37.88 41 880 129.0 \n", "1 -122.22 37.86 21 7099 1106.0 \n", "2 -122.24 37.85 52 1467 190.0 \n", "3 -122.25 37.85 52 1274 235.0 \n", "4 -122.25 37.85 52 1627 280.0 \n", "... ... ... ... ... ... \n", "20635 -121.09 39.48 25 1665 374.0 \n", "20636 -121.21 39.49 18 697 150.0 \n", "20637 -121.22 39.43 17 2254 485.0 \n", "20638 -121.32 39.43 18 1860 409.0 \n", "20639 -121.24 39.37 16 2785 616.0 \n", "\n", " population households median_income median_house_value \\\n", "0 322 126 8.3252 452600 \n", "1 2401 1138 8.3014 358500 \n", "2 496 177 7.2574 352100 \n", "3 558 219 5.6431 341300 \n", "4 565 259 3.8462 342200 \n", "... ... ... ... ... \n", "20635 845 330 1.5603 78100 \n", "20636 356 114 2.5568 77100 \n", "20637 1007 433 1.7000 92300 \n", "20638 741 349 1.8672 84700 \n", "20639 1387 530 2.3886 89400 \n", "\n", " ocean_proximity quadkey quadkey_res \n", "0 NEAR BAY 0230102122 10 \n", "1 NEAR BAY 0230102122 10 \n", "2 NEAR BAY 0230102122 10 \n", "3 NEAR BAY 0230102122 10 \n", "4 NEAR BAY 0230102122 10 \n", "... ... ... ... \n", "20635 INLAND 0230100313 10 \n", "20636 INLAND 0230100313 10 \n", "20637 INLAND 0230100313 10 \n", "20638 INLAND 0230100312 10 \n", "20639 INLAND 0230100313 10 \n", "\n", "[20640 rows x 12 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "df = pd.read_csv('https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/housing.csv')\n", "resolution = 10\n", "df = df.quadkey.latlon2quadkey(resolution, lat_col='lat', lon_col='lon')\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### GeoPandas" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximitygeometryquadkeyquadkey_res
0-122.2337.8841.0880.0129.0322.0126.08.3252452600.0NEAR BAYPOINT (-122.23 37.88)023010212210
1-122.2237.8621.07099.01106.02401.01138.08.3014358500.0NEAR BAYPOINT (-122.22 37.86)023010212210
2-122.2437.8552.01467.0190.0496.0177.07.2574352100.0NEAR BAYPOINT (-122.24 37.85)023010212210
3-122.2537.8552.01274.0235.0558.0219.05.6431341300.0NEAR BAYPOINT (-122.25 37.85)023010212210
4-122.2537.8552.01627.0280.0565.0259.03.8462342200.0NEAR BAYPOINT (-122.25 37.85)023010212210
..........................................
20635-121.0939.4825.01665.0374.0845.0330.01.560378100.0INLANDPOINT (-121.09 39.48)023010031310
20636-121.2139.4918.0697.0150.0356.0114.02.556877100.0INLANDPOINT (-121.21 39.49)023010031310
20637-121.2239.4317.02254.0485.01007.0433.01.700092300.0INLANDPOINT (-121.22 39.43)023010031310
20638-121.3239.4318.01860.0409.0741.0349.01.867284700.0INLANDPOINT (-121.32 39.43)023010031210
20639-121.2439.3716.02785.0616.01387.0530.02.388689400.0INLANDPOINT (-121.24 39.37)023010031310
\n", "

20640 rows × 13 columns

\n", "
" ], "text/plain": [ " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", "0 -122.23 37.88 41.0 880.0 129.0 \n", "1 -122.22 37.86 21.0 7099.0 1106.0 \n", "2 -122.24 37.85 52.0 1467.0 190.0 \n", "3 -122.25 37.85 52.0 1274.0 235.0 \n", "4 -122.25 37.85 52.0 1627.0 280.0 \n", "... ... ... ... ... ... \n", "20635 -121.09 39.48 25.0 1665.0 374.0 \n", "20636 -121.21 39.49 18.0 697.0 150.0 \n", "20637 -121.22 39.43 17.0 2254.0 485.0 \n", "20638 -121.32 39.43 18.0 1860.0 409.0 \n", "20639 -121.24 39.37 16.0 2785.0 616.0 \n", "\n", " population households median_income median_house_value \\\n", "0 322.0 126.0 8.3252 452600.0 \n", "1 2401.0 1138.0 8.3014 358500.0 \n", "2 496.0 177.0 7.2574 352100.0 \n", "3 558.0 219.0 5.6431 341300.0 \n", "4 565.0 259.0 3.8462 342200.0 \n", "... ... ... ... ... \n", "20635 845.0 330.0 1.5603 78100.0 \n", "20636 356.0 114.0 2.5568 77100.0 \n", "20637 1007.0 433.0 1.7000 92300.0 \n", "20638 741.0 349.0 1.8672 84700.0 \n", "20639 1387.0 530.0 2.3886 89400.0 \n", "\n", " ocean_proximity geometry quadkey quadkey_res \n", "0 NEAR BAY POINT (-122.23 37.88) 0230102122 10 \n", "1 NEAR BAY POINT (-122.22 37.86) 0230102122 10 \n", "2 NEAR BAY POINT (-122.24 37.85) 0230102122 10 \n", "3 NEAR BAY POINT (-122.25 37.85) 0230102122 10 \n", "4 NEAR BAY POINT (-122.25 37.85) 0230102122 10 \n", "... ... ... ... ... \n", "20635 INLAND POINT (-121.09 39.48) 0230100313 10 \n", "20636 INLAND POINT (-121.21 39.49) 0230100313 10 \n", "20637 INLAND POINT (-121.22 39.43) 0230100313 10 \n", "20638 INLAND POINT (-121.32 39.43) 0230100312 10 \n", "20639 INLAND POINT (-121.24 39.37) 0230100313 10 \n", "\n", "[20640 rows x 13 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import geopandas as gpd\n", "\n", "df = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/housing.geojson')\n", "resolution = 10\n", "df = df.quadkey.latlon2quadkey(resolution)\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Quadkey to geo" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = df.quadkey.quadkey2geo(quadkey_col='quadkey')\n", "df.plot(edgecolor='white')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### (Multi)Polygon to Quadkey" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import geopandas as gpd\n", "\n", "gdf = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/polygon.geojson')\n", "resolution = 18\n", "gdf_polyfill = gdf.quadkey.polyfill(resolution, compact=True, predicate='intersect', explode=False)\n", "gdf_polyfill = gdf_polyfill.quadkey.quadkey2geo(quadkey_col='quadkey')\n", "gdf_polyfill.plot(edgecolor='white')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Quadkey binning" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Pandas" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "\n", "resolution = 10\n", "df = pd.read_csv('https://raw.githubusercontent.com/opengeoshub/vopendata/main/csv/housing.csv')\n", "stats = 'max'\n", "numeric_col = 'median_house_value'\n", "df_bin = df.quadkey.quadkeybin(resolution=resolution,\n", " lat_col='lat',\n", " lon_col='lon',\n", " stats=stats,\n", " numeric_col=numeric_col,\n", " # category_col='ocean_proximity',\n", " )\n", "df_bin.plot(\n", " column=f'{numeric_col}_{stats}',\n", " cmap='Spectral_r',\n", " legend=True,\n", " linewidth=0.2,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### GeoPandas" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import geopandas as gpd\n", "\n", "resolution = 10\n", "df = gpd.read_file('https://raw.githubusercontent.com/opengeoshub/vopendata/main/shape/housing.geojson')\n", "stats = 'max'\n", "numeric_col = 'median_house_value'\n", "df_bin = df.quadkey.quadkeybin(resolution=resolution,\n", " stats=stats,\n", " numeric_col=numeric_col,\n", " # category_col='ocean_proximity',\n", " )\n", "df_bin.plot(\n", " column=f'{numeric_col}_{stats}',\n", " cmap='Spectral_r',\n", " legend=True,\n", " linewidth=0.2,\n", ")" ] } ], "metadata": { "kernelspec": { "display_name": ".venv (3.13.11)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.11" } }, "nbformat": 4, "nbformat_minor": 5 }