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Inline exercises for notebooks 1, 2, 3, and 4 #15

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Inline exercises for notebooks 1, 2, 3, and 4 #15

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61da92c
Adding exercises to notebook 1
darribas Jul 9, 2018
7ff6e22
Adding missing image
darribas Jul 9, 2018
ba74dd1
Inline exercises for notebook 2
darribas Jul 9, 2018
591beee
Exercises for notebook 3
darribas Jul 10, 2018
7a29420
Adding minor edits and exercises to notebook 4
darribas Jul 10, 2018
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127 changes: 126 additions & 1 deletion 03-spatial-joins.ipynb
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Expand Up @@ -239,6 +239,92 @@
"joined['continent'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"**Exercise**: Which rivers pass within 1 degree of Paris? And for Hong Kong?\n",
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I would maybe do it in meters (that's often problem that you need to convert the crs for that)

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I tried first but I ran into issues trying to get a world CRS in metres that worked and the rivers layer was acting up so, for the sake of the example, I left it on degrees.

"\n",
"Tip: `rivers` contains empty geometries, make sure to get rid of them before anything else\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!--\n",
"paris = cities.query('name == \"Paris\"')\\\n",
" ['geometry']\\\n",
" .squeeze()\\\n",
" .buffer(1)\n",
"\n",
"river_lines = rivers[rivers.geometry.notna()]\n",
"\n",
"river_lines[river_lines.crosses(paris)]\n",
"\n",
"hk = cities.query('name == \"Hong Kong\"')\\\n",
" ['geometry']\\\n",
" .squeeze()\\\n",
" .buffer(1)\n",
"\n",
"river_lines = rivers[rivers.geometry.notna()]\n",
"\n",
"river_lines[river_lines.crosses(hk)]\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"**Exercise** [PRO]: Find all European cities within 1 degre of at least one river\n",
"\n",
"\n",
"This requires:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Obtaining a list of European cities\n",
"1. Creating a `GeoDataFrame` with all river lines\n",
"1. For every European city, checking whether at least one river \"crosses\" the area within 1 degree\n",
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Isn't this an exercise for notebook 2 ?

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A list of European cities requires a spatial join. It's a compound exercise.

"1. If so, saving the record of a city"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!--\n",
"eu_cities = joined.query('continent == \"Europe\"')\n",
"river_lines = rivers[rivers.geometry.notna()]\n",
"\n",
"answer = []\n",
"for id, city in eu_cities.iterrows():\n",
" buf = city['geometry'].buffer(1)\n",
" rivers_crossing = river_lines.crosses(buf).sum()\n",
" if rivers_crossing > 0:\n",
" answer.append(city)\n",
"answer = geopandas.GeoDataFrame(answer, \n",
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I would personally do it with a function and apply. It is maybe more complex in the end, but a good pattern to show IMO.
Or first with loop, and then as exercise to convert it to apply

" crs=eu_cities.crs)\n",
"answer\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand Down Expand Up @@ -300,6 +386,45 @@
"\n",
"</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"**Exercise**: if we define rural as those areas that are not within 50Km of the centre of a city, what is the extention of rural North America? What is the percentage that it covers?\n",
"\n",
"Tip: make sure your projection allows you to calculate distances in metres ([wink wink](http://epsg.io/3083))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!--\n",
"namerica = countries.query('continent == \"North America\"')\\\n",
" .to_crs(epsg=3083)\n",
"urban = geopandas.GeoDataFrame(\n",
" {'geometry':cities.to_crs(epsg=3083)\\\n",
" .buffer(50000)}, \\\n",
" crs = namerica.crs)\n",
" \n",
"rural = geopandas.overlay(namerica, urban, how='difference')\n",
"\n",
"print('Rural: ', rural.unary_union.area * 1e-6, ' Km^2')\n",
"print('Rural is %.2f%% of the total extent'%\n",
" (rural.unary_union.area * 100 / \n",
" namerica.unary_union.area))\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
}
],
"metadata": {
Expand All @@ -318,7 +443,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.5"
"version": "3.6.5"
}
},
"nbformat": 4,
Expand Down