{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Case: Employment rate map"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"**Goal:** plot an interactive map of employment rates across Finnish regions.\n",
"\n",
"**Required modules:**\n",
"- Folium for plotting interactive maps based on leaflet.js\n",
"- Pandas for handling tabular data\n",
"- Geopandas for handling spatial data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# Import required modules:\n",
"import folium\n",
"import pandas as pd\n",
"import geopandas as gpd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Employment rate data \n",
"\n",
"Employment rate refers to \"the proportion of the employed among persons aged 15 to 64\". Data from Statistics Finland (saved from .xml-file to csv in Excel..)."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" seutukunta | \n",
" seutukunta_nimi | \n",
" tyollisyys | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" SK011 | \n",
" Helsingin seutukunta | \n",
" 73.0 | \n",
"
\n",
" \n",
" 1 | \n",
" SK014 | \n",
" Raaseporin seutukunta | \n",
" 70.3 | \n",
"
\n",
" \n",
" 2 | \n",
" SK015 | \n",
" Porvoon seutukunta | \n",
" 74.3 | \n",
"
\n",
" \n",
" 3 | \n",
" SK016 | \n",
" Loviisan seutukunta | \n",
" 71.5 | \n",
"
\n",
" \n",
" 4 | \n",
" SK021 | \n",
" Åboland-Turunmaan seutukunta | \n",
" 72.9 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" seutukunta seutukunta_nimi tyollisyys\n",
"0 SK011 Helsingin seutukunta 73.0\n",
"1 SK014 Raaseporin seutukunta 70.3\n",
"2 SK015 Porvoon seutukunta 74.3\n",
"3 SK016 Loviisan seutukunta 71.5\n",
"4 SK021 Åboland-Turunmaan seutukunta 72.9"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Read in data\n",
"data = pd.read_csv(\"data/seutukunta_tyollisyys_2013.csv\", sep=\",\")\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Sub-regional units\n",
"\n",
"The spatial data for the [sub-regional units](http://www.stat.fi/meta/luokitukset/seutukunta/001-2018/index_en.html#_ga=2.189315043.975495398.1543480533-446957274.1543480533) (*Seutukunnat* in Finnish) can be retrieved from the Statistics Finland Web Feature Service `http://geo.stat.fi/geoserver/tilastointialueet/wfs`"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"# A layer saved to GeoJson in QGIS..\n",
"#geodata = gpd.read_file('Seutukunnat_2018.geojson')\n",
"\n",
"# Get features directly from the wfs\n",
"url = \"http://geo.stat.fi/geoserver/tilastointialueet/wfs?request=GetFeature&typename=tilastointialueet:seutukunta1000k_2020&outputformat=JSON\"\n",
"geodata = gpd.read_file(url)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" id | \n",
" seutukunta | \n",
" vuosi | \n",
" nimi | \n",
" namn | \n",
" name | \n",
" geometry | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" seutukunta1000k_2020.1 | \n",
" 011 | \n",
" 2020 | \n",
" Helsinki | \n",
" Helsingfors | \n",
" Helsinki | \n",
" MULTIPOLYGON (((409963.522 6681658.341, 409969... | \n",
"
\n",
" \n",
" 1 | \n",
" seutukunta1000k_2020.2 | \n",
" 014 | \n",
" 2020 | \n",
" Raasepori | \n",
" Raseborg | \n",
" Raasepori | \n",
" MULTIPOLYGON (((306616.919 6665438.489, 306668... | \n",
"
\n",
" \n",
" 2 | \n",
" seutukunta1000k_2020.3 | \n",
" 015 | \n",
" 2020 | \n",
" Porvoo | \n",
" Borgå | \n",
" Porvoo | \n",
" MULTIPOLYGON (((427108.141 6694151.025, 427175... | \n",
"
\n",
" \n",
" 3 | \n",
" seutukunta1000k_2020.4 | \n",
" 016 | \n",
" 2020 | \n",
" Loviisa | \n",
" Lovisa | \n",
" Loviisa | \n",
" MULTIPOLYGON (((444038.768 6703649.355, 444155... | \n",
"
\n",
" \n",
" 4 | \n",
" seutukunta1000k_2020.5 | \n",
" 021 | \n",
" 2020 | \n",
" Åboland-Turunmaa | \n",
" Åboland-Turunmaa | \n",
" Åboland-Turunmaa | \n",
" MULTIPOLYGON (((190999.717 6715878.622, 191021... | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" id seutukunta vuosi nimi \\\n",
"0 seutukunta1000k_2020.1 011 2020 Helsinki \n",
"1 seutukunta1000k_2020.2 014 2020 Raasepori \n",
"2 seutukunta1000k_2020.3 015 2020 Porvoo \n",
"3 seutukunta1000k_2020.4 016 2020 Loviisa \n",
"4 seutukunta1000k_2020.5 021 2020 Åboland-Turunmaa \n",
"\n",
" namn name \\\n",
"0 Helsingfors Helsinki \n",
"1 Raseborg Raasepori \n",
"2 Borgå Porvoo \n",
"3 Lovisa Loviisa \n",
"4 Åboland-Turunmaa Åboland-Turunmaa \n",
"\n",
" geometry \n",
"0 MULTIPOLYGON (((409963.522 6681658.341, 409969... \n",
"1 MULTIPOLYGON (((306616.919 6665438.489, 306668... \n",
"2 MULTIPOLYGON (((427108.141 6694151.025, 427175... \n",
"3 MULTIPOLYGON (((444038.768 6703649.355, 444155... \n",
"4 MULTIPOLYGON (((190999.717 6715878.622, 191021... "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"geodata.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Join attributes and geometries\n",
"\n",
"We can join the attribute layer and spatial layer based on the region code (stored in column 'seutukunta'). The region codes in the csv contain additional letters \"SK\" which we need to remove before the join:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"0 011\n",
"1 014\n",
"2 015\n",
"3 016\n",
"4 021\n",
"Name: seutukunta, dtype: object"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[\"seutukunta\"] = data[\"seutukunta\"].apply(lambda x: x[2:])\n",
"data[\"seutukunta\"].head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can join the data based on the \"seutukunta\" -column.\n",
"Let's also check that we have a matching number of records before and after the join:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Count of original attributes: 70\n",
"Count of original geometries: 70\n",
"Count after the join: 70\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" id | \n",
" seutukunta | \n",
" vuosi | \n",
" nimi | \n",
" namn | \n",
" name | \n",
" geometry | \n",
" seutukunta_nimi | \n",
" tyollisyys | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" seutukunta1000k_2020.1 | \n",
" 011 | \n",
" 2020 | \n",
" Helsinki | \n",
" Helsingfors | \n",
" Helsinki | \n",
" MULTIPOLYGON (((409963.522 6681658.341, 409969... | \n",
" Helsingin seutukunta | \n",
" 73.0 | \n",
"
\n",
" \n",
" 1 | \n",
" seutukunta1000k_2020.2 | \n",
" 014 | \n",
" 2020 | \n",
" Raasepori | \n",
" Raseborg | \n",
" Raasepori | \n",
" MULTIPOLYGON (((306616.919 6665438.489, 306668... | \n",
" Raaseporin seutukunta | \n",
" 70.3 | \n",
"
\n",
" \n",
" 2 | \n",
" seutukunta1000k_2020.3 | \n",
" 015 | \n",
" 2020 | \n",
" Porvoo | \n",
" Borgå | \n",
" Porvoo | \n",
" MULTIPOLYGON (((427108.141 6694151.025, 427175... | \n",
" Porvoon seutukunta | \n",
" 74.3 | \n",
"
\n",
" \n",
" 3 | \n",
" seutukunta1000k_2020.4 | \n",
" 016 | \n",
" 2020 | \n",
" Loviisa | \n",
" Lovisa | \n",
" Loviisa | \n",
" MULTIPOLYGON (((444038.768 6703649.355, 444155... | \n",
" Loviisan seutukunta | \n",
" 71.5 | \n",
"
\n",
" \n",
" 4 | \n",
" seutukunta1000k_2020.5 | \n",
" 021 | \n",
" 2020 | \n",
" Åboland-Turunmaa | \n",
" Åboland-Turunmaa | \n",
" Åboland-Turunmaa | \n",
" MULTIPOLYGON (((190999.717 6715878.622, 191021... | \n",
" Åboland-Turunmaan seutukunta | \n",
" 72.9 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" id seutukunta vuosi nimi \\\n",
"0 seutukunta1000k_2020.1 011 2020 Helsinki \n",
"1 seutukunta1000k_2020.2 014 2020 Raasepori \n",
"2 seutukunta1000k_2020.3 015 2020 Porvoo \n",
"3 seutukunta1000k_2020.4 016 2020 Loviisa \n",
"4 seutukunta1000k_2020.5 021 2020 Åboland-Turunmaa \n",
"\n",
" namn name \\\n",
"0 Helsingfors Helsinki \n",
"1 Raseborg Raasepori \n",
"2 Borgå Porvoo \n",
"3 Lovisa Loviisa \n",
"4 Åboland-Turunmaa Åboland-Turunmaa \n",
"\n",
" geometry \\\n",
"0 MULTIPOLYGON (((409963.522 6681658.341, 409969... \n",
"1 MULTIPOLYGON (((306616.919 6665438.489, 306668... \n",
"2 MULTIPOLYGON (((427108.141 6694151.025, 427175... \n",
"3 MULTIPOLYGON (((444038.768 6703649.355, 444155... \n",
"4 MULTIPOLYGON (((190999.717 6715878.622, 191021... \n",
"\n",
" seutukunta_nimi tyollisyys \n",
"0 Helsingin seutukunta 73.0 \n",
"1 Raaseporin seutukunta 70.3 \n",
"2 Porvoon seutukunta 74.3 \n",
"3 Loviisan seutukunta 71.5 \n",
"4 Åboland-Turunmaan seutukunta 72.9 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#print info\n",
"print(\"Count of original attributes:\", len(data))\n",
"print(\"Count of original geometries:\", len(geodata))\n",
"\n",
"# Merge data\n",
"geodata = geodata.merge(data, on = \"seutukunta\")\n",
"\n",
"#Print info\n",
"print(\"Count after the join:\", len(geodata))\n",
"\n",
"geodata.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"## Create a static map"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Now we have a spatial layer with the employment rate information (in column \"tyollisuus\").\n",
"Let's create a simple plot based on this data:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
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