{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from topojson import Topology\n",
"import geopandas as gpd\n",
"\n",
"africa = gpd.read_file(gpd.datasets.get_path(\"naturalearth_lowres\")).query('continent == \"Africa\"')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Comparison simplification without Topology and with Topology"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
""
],
"text/plain": [
"alt.HConcatChart(...)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l = Topology(\n",
" data=africa, \n",
" topology=False\n",
").toposimplify(4).to_alt().properties(title='WITHOUT Topology')\n",
"r = Topology(\n",
" data=africa, \n",
" topology=True\n",
").toposimplify(4).to_alt().properties(title='WITH Topology')\n",
"l | r"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Comparison no Topoquantization and with Topoquantization"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
""
],
"text/plain": [
"alt.HConcatChart(...)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l = Topology(africa).to_alt().properties(title='NO Topoquantization')\n",
"r = Topology(africa).topoquantize(100).to_alt().properties(title='WITH Topoquantization')\n",
"l | r"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Comparison between Douglas-Peucker simplification and Visvalingam-Whyatt simplification"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
""
],
"text/plain": [
"alt.HConcatChart(...)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"topo = Topology(africa) \n",
"\n",
"l = topo.toposimplify(\n",
" epsilon=4, \n",
" simplify_with='shapely', \n",
" simplify_algorithm='dp'\n",
").to_alt().properties(title=['Douglas-Peucker simplification'])\n",
"\n",
"\n",
"r = topo.toposimplify(\n",
" epsilon=4, \n",
" simplify_with='simplification', \n",
" simplify_algorithm='vw'\n",
").to_alt().properties(title='Visvalingam-Whyatt simplification')\n",
"l | r"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Comparison datasize of raw GeoJSON and optimized TopoJSON"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"africa.to_file('africa.geo.json', driver='GeoJSON')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"topo = Topology(\n",
" data=africa, \n",
" simplify_with='simplification', \n",
" simplify_algorithm='vw', \n",
" toposimplify=4, \n",
" topoquantize=200\n",
")\n",
"topo.to_json('africa.topo.json')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"africa.geo.json: 102.724 KB\n",
"africa.topo.json: 11.916 KB\n",
"factor smaller: 8.620678079892581 X\n"
]
}
],
"source": [
"import os\n",
"size_geojson = os.stat('africa.geo.json').st_size\n",
"size_topojson = os.stat('africa.topo.json').st_size\n",
"\n",
"print(f'africa.geo.json: {size_geojson/1000} KB')\n",
"print(f'africa.topo.json: {size_topojson/1000} KB')\n",
"print(f'factor smaller: {size_geojson/size_topojson} X')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
""
],
"text/plain": [
"alt.HConcatChart(...)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l = Topology(\n",
" data=africa, \n",
" topology=False, \n",
" prequantize=False\n",
").to_alt().properties(title=['Raw GeoJSON', '102.72 KB'])\n",
"r = topo.to_alt().properties(\n",
" title=['Optimized TopoJSON', f'{size_topojson/1000} KB ({round(size_geojson/size_topojson, 2)}X smaller)']\n",
")\n",
"l | r"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Showcase multi-layer Topology"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
""
],
"text/plain": [
"alt.HConcatChart(...)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"continent = africa.dissolve(by='continent', as_index=False)\n",
"\n",
"a = Topology(\n",
" data=[africa, continent], \n",
" object_name=['africa_countries', 'africa_continent']\n",
")\n",
"\n",
"l = a.toposimplify(1).to_alt(object_name='africa_countries').properties(title='Object `africa_countries`')\n",
"r = a.toposimplify(1).to_alt(object_name='africa_continent').properties(title='Object `africa_continent`')\n",
"\n",
"import altair as alt\n",
"alt.data_transformers.consolidate_datasets = False\n",
"l | r"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Showcase computing Topology using Jupyter Widgets"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6a5ea2b9b99942c1b85c007e87fd0a89",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(FloatSlider(value=0.01, description='Toposimplify Factor', max=10.0, step=0.01, style=Sl…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t = Topology(africa, prevent_oversimplify=False)\n",
"t.to_widget()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.13"
}
},
"nbformat": 4,
"nbformat_minor": 4
}