{
"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": 1,
"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": 2,
"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": 2,
"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": 3,
"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_2018&outputformat=JSON\"\n",
"geodata = gpd.read_file(url)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" id | \n",
" vuosi | \n",
" seutukunta | \n",
" nimi | \n",
" namn | \n",
" name | \n",
" geometry | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" seutukunta1000k_2018.1 | \n",
" 2018 | \n",
" 011 | \n",
" Helsinki | \n",
" Helsingfors | \n",
" Helsinki | \n",
" MULTIPOLYGON (((409963.522 6681658.341, 409969... | \n",
"
\n",
" \n",
" | 1 | \n",
" seutukunta1000k_2018.2 | \n",
" 2018 | \n",
" 014 | \n",
" Raasepori | \n",
" Raseborg | \n",
" Raasepori | \n",
" MULTIPOLYGON (((306616.919 6665438.489, 306668... | \n",
"
\n",
" \n",
" | 2 | \n",
" seutukunta1000k_2018.3 | \n",
" 2018 | \n",
" 015 | \n",
" Porvoo | \n",
" Borgå | \n",
" Porvoo | \n",
" MULTIPOLYGON (((427108.141 6694151.025, 427175... | \n",
"
\n",
" \n",
" | 3 | \n",
" seutukunta1000k_2018.4 | \n",
" 2018 | \n",
" 016 | \n",
" Loviisa | \n",
" Lovisa | \n",
" Loviisa | \n",
" MULTIPOLYGON (((444038.768 6703649.355, 444155... | \n",
"
\n",
" \n",
" | 4 | \n",
" seutukunta1000k_2018.5 | \n",
" 2018 | \n",
" 021 | \n",
" Åboland-Turunmaa | \n",
" Åboland-Turunmaa | \n",
" Åboland-Turunmaa | \n",
" MULTIPOLYGON (((190999.717 6715878.622, 191021... | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" id vuosi seutukunta \\\n",
"0 seutukunta1000k_2018.1 2018 011 \n",
"1 seutukunta1000k_2018.2 2018 014 \n",
"2 seutukunta1000k_2018.3 2018 015 \n",
"3 seutukunta1000k_2018.4 2018 016 \n",
"4 seutukunta1000k_2018.5 2018 021 \n",
"\n",
" nimi \\\n",
"0 Helsinki \n",
"1 Raasepori \n",
"2 Porvoo \n",
"3 Loviisa \n",
"4 Åboland-Turunmaa \n",
"\n",
" namn \\\n",
"0 Helsingfors \n",
"1 Raseborg \n",
"2 Borgå \n",
"3 Lovisa \n",
"4 Åboland-Turunmaa \n",
"\n",
" name \\\n",
"0 Helsinki \n",
"1 Raasepori \n",
"2 Porvoo \n",
"3 Loviisa \n",
"4 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": 4,
"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": 5,
"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": 5,
"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": 6,
"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",
" vuosi | \n",
" seutukunta | \n",
" nimi | \n",
" namn | \n",
" name | \n",
" geometry | \n",
" seutukunta_nimi | \n",
" tyollisyys | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" seutukunta1000k_2018.1 | \n",
" 2018 | \n",
" 011 | \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_2018.2 | \n",
" 2018 | \n",
" 014 | \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_2018.3 | \n",
" 2018 | \n",
" 015 | \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_2018.4 | \n",
" 2018 | \n",
" 016 | \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_2018.5 | \n",
" 2018 | \n",
" 021 | \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 vuosi seutukunta \\\n",
"0 seutukunta1000k_2018.1 2018 011 \n",
"1 seutukunta1000k_2018.2 2018 014 \n",
"2 seutukunta1000k_2018.3 2018 015 \n",
"3 seutukunta1000k_2018.4 2018 016 \n",
"4 seutukunta1000k_2018.5 2018 021 \n",
"\n",
" nimi \\\n",
"0 Helsinki \n",
"1 Raasepori \n",
"2 Porvoo \n",
"3 Loviisa \n",
"4 Åboland-Turunmaa \n",
"\n",
" namn \\\n",
"0 Helsingfors \n",
"1 Raseborg \n",
"2 Borgå \n",
"3 Lovisa \n",
"4 Åboland-Turunmaa \n",
"\n",
" name \\\n",
"0 Helsinki \n",
"1 Raasepori \n",
"2 Porvoo \n",
"3 Loviisa \n",
"4 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": 6,
"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": 7,
"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": 8,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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v6TXGS+7fvew932Ru4QQXElFa71l57pktkWCUS6f681JXPlnVEKWUXVLKfVLKfaSk50IsUoyFK2qynwB+le9GXmuEQlF++bNTy94fG/JgzDFctGubY32NmsePP/dI3urKF/lSjAX4S+AHwNYJlt0k/L4I5zuWD+VMJlXqrbltJE8lE+hN+ckw+uuvPc5zv8zPUJ8v8qIYK4SoBl4DfGHJEwvLXdOKsdkyODC1ahmLkvs60VGePw/qz/7FVwj58yMalQ/ypRj7GeBvpJQrRt+8UBRjV8M7s3ooqG4N2yeW0vwdx7mHpnnokz/JW33rJV+KsYeA7wgh+oHXAZ8XQrx6/c27NhkZXt0XUSNzN0SNPr/C7D9/8Lf4PeuPw84HeVGMlVI2SikbpJQNwPeB90gpf7z+5l17HDvayzf/8+lVyylryISrbMvvKOKfCfD1f7iGAuxXU4wtkGJkeIZPfOynG1b/M7oEO161NpnkyuYKXNVLQxh+/bXHCQfzE4+9HvKmGDvv+tvW36xrE/ekD69n9flhSZmNM6HcQwkAninRcfjWVnqezs0ptnhvAye9UepubKMkkcDfP4l1WyVSI3jy953c+8D+NbUnXxROVvKITrf691qn0yBbbPhia9NATAJn211UtmafcXTHXTs56U1FBw7ORjgRTNBdWsxJX5RTngg//NXy8nVXi4Ih5pHRFRYpWp2GlrYKdh2oYza2PqfWgKoyedc2qrZnp1Crcaycz+XCxTHGxr3ratN6KRhiHqmqWTgHKyu3076rmvb2anSKoLtzjJPP9qI97eVWZzmadTjATCTjFO3NLiA+0DeBeZUV909+vjRJ+tWk4H2TR1paK6ipLcZiNYCES93jTI4t9bKJROJceLyX3U0lhKr1jIdDqBK0QjAbiyKzTJYx5sjupKX/9AD6rlFK7z2Ae5kQg+/+8Hluv6WVne1L/SCvBoUeMY/oDVo+9+V30NpWSdf5URLxlfdohnqnmH5yFN0xL4bjXjTHPOycMXC4uDSr3rI0kv0ekJpUSa7gdaOqko//318SjeZHvztXCoaYZywWA9vb155dYNrtxxBUselW7+1GjYL6fdllp9/+wCFmQisb2dDIDL/41ZkVy2wUBUPcAHbsrGEtDtBanYb43iKeik7jzWJBM5CIc/xgBRXNFatXrmTXoGeO9GRVLt8UDHEDqK13cfim5pyfS8STNJptOT+nriCwqSiColI7F395nCaHadW6zpwd3hTH2YIhbhANTbkfxxlNOnrDq2tfz0cgiYaiGMyZh/L2lx/Cu78Z3YsP0Otd3dsmFkvw1DPdObUhHxQMcYN4+WtyP4prOFTDdDi347YbK8qpP9xK6a2Zc+31P3Eeg0aDJ5z93uUTOZ7a5IOCIW4QzmJrThJzQM4ZTkvsZkaeGKJjJMiIO0hTBn3DgDdIsyW3XboLXVc/2XhhH3GDSMSTVNcW4yqxcfJYdpLCnU/2cfiuBp6fcdNa4eLi+MrKYI0RPf3+OWFPx8EWdjWUQTxBMhAhGUsQqXJxypObA+zImIdwJIbJmB+P8GwoGOIGYbYY+PtP/AEVlQ6+8m+/4+c/Ok4kknn7xGI1EIsmiMeT6Cai7E2aiJyZgRXS+u2rKaf/JwvncmGjntO+9BCs0YNJDzkaIaT2FHt73Vd1c7tgiBuEogjKyovQaBRe+4bDlJXb+fxnfr2k3O7d1Zw7MQiARhF0n0nFuhhtem7eVU9cL0giSUoIJeIYtVrM03Emn1gaExObXb87V3mZnTtva2PWd3XDCAqGuIHodBqEEJjMBvbsr+W1b7iR0nIbX/3C70kkk7Rvr7pihABynixdxB+j5+Glq9eVwvLdQzNo9ArJ5Bq8boE3vu4G3vGW29Hrr75ZFAxxA5EypWVosRrZ1lJJ0/sqSCZVtu+s5vvffJZnHu3M6+uFQzFU7doTUl64OI6ap+xZuVIwxA1EWXSaIYRAo1HYubsW3Vs0uEe9dHfOrVAtVgO1+6tAI4hPR7jUMZrT6zlLbQTXkf2qp3cCfyCC0Zi/xOXZUti+ucpcFj9qba/izz5wHzrdnHtW7aFqHmOax5JTzFTn3kfoczAgRcC2sUkaBsc4oJPsCgd5y4vbKS3J/WQnHxQMcRPZta+Olh0pT2tFEVzQzgl8riXKb+SSG6tlZYH3KpuBvQ4Du5MxBjsGGbkwQscvjtP1ZCfWdSYrXw+Fofkq4/eF0WgUzBYD504PMTyQ2its3FnJk9G50M7heIi9N9fTe3yYeCz7ZI1NZQ5iGrjQszDq127U0uAP0PWj47iXmQeGNzHgvtAjXmWsNiPmdK81Mea5kmlK51jYk83Eo/xO68F1T11O9XcdG6D/+UF2LwojaDBq6Pzd2RUXI2b76k4RG0XBEK8y8wUydTrtla2WoVNjWDQLByiHTo+uJ/cAeKlKIlNz0YQOow7cK+sxHrx3L/e9456cXytfFIbmTaSk2UnNA9vQJUCXALecTYXppdmrFNHdcynneq1mHc5omN3RMJMXR5kZ87DaRpF7aIqv/d132XFTKzfcf/VDSwuGuIkc7RvlqG95MapjCQ/ba4sZG8otBro0OMvJH+QWDDXYOcI3PvZ9bn7loU0xxMLQvEmEo3FOD6zs5RJMJuhqhqZd2ccwazWCwXNDa26XZpNWzoUe8SqQVFW+/OjzDE55uW9fK7fvaCQST/Cuew6jqpJHO5Z3zw8kEhxzSQ7vrsq4wd1WbUMRkHB78E/OUlxfxtk1BEAZTHr+5uvv48CLduX8bD5Y1RCFEG3AQ/MuNQEflVJ+Zl6ZNwN/k/41APy5lPJ0Htt5TXOqf5TPPfwMZUVWbm2r59GOHl60uxmHxcgD+1v53dlLqCu450fUJNqSpSvaqhIznT99dsG10Z61+RLWbq/m9v9y45qezQf5ki7uA+6UUu4BPgY8mO+GXsvsra/iZQe2o6oqO2vLedHuZmYCIf7yKz/BYjJgNqx+InJEeqhtXhh+4J6NULujJi9t3GyB91yH5ozSxVLK+amQjgD5eXdeIGg1Cp/4o/vxBMLYTAZ6xqYosVuwm4189KHfEI2vvmEdTiYwl9hg3igej6uYmmuhc3mZ5GyZGfOsu471kKshZpQuXsQ7gYcz3RBCvBt4N0BdXW4btdc6PcNuvvXrE0x6ApzzuDGZ9Hzm7a+kf9LDhDfzXqFOKMSlSpPJRq1b0HVyqXS5Nk/LTc/ELKcfP8feOzcnB0vWhjhPuvgjK5S5m5Qh3pbpvpTyQdLD9qFDh7Zeso8N4ldHLvDQoyfpuDRGqdPKtAwhQ2E+/M2H6R7LrLddrDOwXWdjmjgcmaJ7duHxW1WJGaNOQZvHIfVb//RD6ttrmBqewWQzUt2c/Wp9veTSI64kXYwQYg/wZeB+KWV2adivEyY9fjoupRYRbk+AwzurOToyuqwRQuqIT+c1Yp0KMja79Aw4EksSONmFd3J9efnmc+I3Z/iD8ncBqZOWjz/yP/NW92rkRbpYCFFHSk32j6WUVz8WcQszMDbDb44ufEvOnBvhcGkl2lXUF/xlGnzLCMPP+KLU3bwjb+1cTMATuKoLmHxJF3+UlKLs59PZqZbPE3sdIaXk777yCJ0DSweRju5R2m0unKblU9WeCM5QWutcvv6VXnydSR+7nr/E0YevnlRdXqSLpZTvAt6V36Zd+4xN+zjbu3w20YsDbprqXbTWlHKsd5hkcqFp3W4qJRz2YjTpiWQIkJ+NSmzFVvwzCxc7DffuIb6rBg0w+8OjzPavnvdlMW/6yGuu6lFf4YhvgwhH41wYyCIJl13Lkd4h9jVWodUo6OYF5Z+IeXC3GLA7M7tnDbuDNLzipgW9X8vrbuRssZmu0RnOj87gvbsd23tfQtsf355T+9tvbruqqXQLhrhB/O5ENx/+/M9WLWfVpDazL45PUV/upKzYxqHmahAQVZNsC+iZXCGl7qhTR8v776f+JbvZ9oEHOGtceFYcDMcYm/ZzPB6n6C/upfm12Z2ePPvT57Mqly8KZ80bwC+ePs///vpvyWauH4+mNrN94Si+cMpJdnhmlp115RTrDPR9b/m1X9O9LZzqG2MAwGWB0ZU3K0amfDgaSrL6G5792TH+OquS+aHQI+aZXz57nk9841GisQQAFpMeTXp1XGw3LymvWUYa9tzwBE/2DaK/vxplGQ2dmJp9CMFlJgLZhQOE/RHGejPu1G0IBUNcJ/O3OCb8AT5z9ijNu8opdVrZvbOa0p1Owtu1lB1w0VcVZveBGqrLUskdy0pteNWVVbouuKdxNjgy3jMqubtslViWX6XPJxKK8u69H+Qf3/hpfNO5SeWthcLQvE4uT+iTqsq/PHOEXo+HXjzoKxTGRBx/IGVoXekEP0+ERlCK4a7WBjr6xvBPrB4KEDlQRONOF6O/7ad6ZwVTfR40OoXuidyTBplGsz9TjgSjPP7dZ5ke9fC+f30Xjbuzk0leC4UeMQ+M+3x88bnn+c7pucQ5ManiT2Tu7VRgajaIP5KdZuFF9zTHwtM0P9DGKa+HmUodw3aJPxRd/eFFBCodOT9z9qkLfPrdX8z5uVwoGOI6Saoqx0dG+fSTz6xeOM2txTVcHM1tby8ST+ATqXlnKBInuUZpkO4xD85t5Tk/552cJRFPrOk1s6FgiOvEG47wxeeyO0iqNFu5tbiG0z25SYlcZiXn2VywN62gd7cM432T/PYbT+bl9TNRMMR18L+OPcZk2E+/xwvAtgonB9uXV/gfDwYIhWIkMqh1NZY6OdBQhVZZ/iNZg/hDRvS7sstYtRhL0dJVf74oGOIaSaoq/3nhBC/75X/QWptKfWYr1vHczCDLnQJLASarnsON1dS6iq5ctxh0COBk3yh2o4EDDZnztHjiERy29QfBnxyboeV1uYUFWB0WDr4ks053PigYYhbEkhFiidT8KJlM4omE8URT+3ESGFBn2LnDRURNBS211y506RcS9jrLOFBcQU/vJMd6Ryi3W6/cb68qo28ytZr1BMN0DIxzqHGhWmu9y4FDZySqUdnXXkNrQxn1VcUoazyG63eaaX3zbSi67LaA3v6Pb8Kchy/BchS2b7LAHRzlR/3jvHPHIY65R/jBpbP0+TxXNpTdkSDuyJy7lkcfRBFweT3R4ihmdiK0wBP7eO8Ih5uq6Z6YWbJ6TqgqpwfGKLaYmAmGOVxbzfmTI7hlyliP94xcKetwmthWUUwwGKV7YPkY6cV4/GE8QENLBVPnR1Ysu+u27bzyPS/Nuu61UDDEVRgPDXLS9whxWc3P+s7ziVNPMh0JLVveZTRTY7dS1mjmdG/qyO3i7AwNxUVYI3oC84zuWG/KAGaDS087EkkVs0FHi7OYjhPLx6R4A2GO94ygEdDUWErIH2V8yregjBBQ5bIzNu1fsODR6zRMXxhF0ShUt1TiKHcwNTzF2KWFJyq3vOqGFd6h/FAYmlcglPDTGzzFef+zBJKjdPouXRmSM6FTFJw2OBW4xHlNH4cPOK7c6w/MIsq1GLIMYNdqFMoVMx0nswuMSkroHpkikIyzY9vCBVOJw0LwoROUHx9hPzoOV7g47HSwKyqo3d+CqaqcsekonecnKG1YuLVz6KV7uesNt2TVhvVQ6BFXQCd1lOqqiMsYSf0jAPzhjlt4bsTBgcoISPjdoMJkONVDmrV6xiJzJxcdgSFcthKm0ykoJsMh2hwOBqe8q752IqkyMpx7ZN1sMEIgHKW9uYLzPSlfyAZFRzcQ9oToW0UuWU0vzet31fL2f3gjt75643tDKPSIKzIW7+OXEw8SV+fkgLXGZ7i16VFM5scwWR7jgbYuaq0pldXZWIQ9tqYrZZNS0lJvTz0nBAow4c3+3LaoaG2Lg6Qq6Z30UOq0YjbqMUxmDjfIxGDvFHtfso+RiTBdHSvPHfNJwRAzEEh4mYgM8L3BT+KJZ/BAEakeTqi7CcdbaC7VcaisEkUInh0dYa99LgPUhDLNzSVVlAWM3OysJrpCAsfFmExrT7gTjMRoCsMuZxGdZ0ZpubEFe7Fl1edCwSjnTqWmA9/73K/57XePrLkNuVAYmjMwFrpEJBkglPQtX0gq+GMVPDUeZDbuBeCmym08MzpC97QXm9GIPxlhOOzF4jShG1E40TXXw2gVhYS6choKnWHtgkitNhtdj8ylx+juHKe8yLhsMNZy/L/3f53SKid7b2tbc1uyodAjzkNKiaomeXb6J7ijIyTlCmerQsVq/A27i+1XLp30X2JvZTFGjQ6PT2GXLeWt0sUYsV0BbKaUKmx7eSn1fXCocvlTmCKzkUh47dnki5SlfYyrevlArOVQkyp//9Yv8JuHnl298Dq4bgzRHfEuuZZIJnh+4vyV38OJAD8d/RyzcTfPzfx82brsmgqc8YNohZ5a60IJuIuBURJ6PwfKKpFyrsebwk/TntRc0hFW8E+HGPxFP7c4KthbUU5tcdGCelpKXFy6lP2+4GIiLO1tNbq1DYDhQITPfuhbDGxgssjrxhCdeis/H3mKxydPMhNNDbn+RIgdjnSv5RtESi2HnPczHRsjqmYewgQKv39kF1/6lo1nf3U7gqXDpy8R5kzwEucCC430uLaXW3bVMNExZ2A9j/Uz9ssBor+d4IbKecoKsbVlj7pMx+wslYvCAhKLPHYW54FZiUQswUde+xm+9vGfMjGYf/2E68IQpZR0B4YJJ2P8anxu8h2JRzHpjISTUY5MdRBIhHHoa7mj9I1oRCqoyaBY0AkDZo0Dp66BIm6idzQ1ZA+MJYmNl6HPwlNaKxRuHaij52s9+Kczb4iPPjbMLa5KDotiIuPLb5pnQ7nJyMzEwqCr0WEvdpeV4ooiWl65H/mi7dS/aj+aLAV0PG4f3/5/D/O2w/+TBz/6fSKh7HNAr4bYLDmyQ4cOyWPHNi4OP5aMo9fMyb19d/BR/IkQJfoidju2IaXKSc9F2qz1aDU6nps+y2Pu4zxQcQs9wWGOTZ/l7vIYTw7Vc34mSo1Nx4AvjhYNdQNljE75riw2drzExtngylsdtwUa6f337LZDWnZUciKYu/f1fHYcrsY8GiZ51oNGr6E/vRJuu6WJcUVlaF7syj6nDWXES//pQRJZKJNdee72Nv7xO3+ZtcqsEOK4lPJQpnsv2B5xvhECbLNW87PRp/jW4K/pD4zxlb5fYNYa+XjXN/hfnf9OOBnl7tID/HT0SZ5wnyKkJnh8sI0TUxEiqqRnNkZcSsIyQVfdKHv2zJ1A+M5Jak1ONCs5IASz/8K7x2YRK+s44HSZadpemvFeY1spZ6PT/NbqwXODnY6Dc3NDWWxeYIQApzx+Tpg1NNyfm3fNqSe7eMdNH+X887kLzi9mVUMUQrSlZUQu//MJId6/qIwQQnxWCNEjhDgjhDiw7pblgfm9/cHi7Xyw7U38ffs72W6rp9PXxz93f4+pmBeb1syRmXN8Z+i3TMXmhrMiU+bJfbXGwXDf3NbOyGiA0UeTFHeWst2SeSUcupR9jjyvN0SJdfk9P71eobK1mDNiBqdroY9g3TYXHZoZfNHUXucpxceEjFJS5UBv0jG8Qq6+oVgMZ5l92fuZmBya4X++4V/oOtWf03OLyZdi7P1AS/rfu4F/W1er8kBSqkQSC2M6bi7eRb21inAiwnZ7w5Xr/cEx3NGlx2kWfeYFQ1wmUDI4sM7MRBh6MkalceEKWK9omO7MTbWr2mZd9p5Op+XpmRHiqkp5vQNIBXGV31LKWZ2HeAbH27H7XLS9vJ3xqeVPdtyhKJW7s9dYNVkNtL5iH4lbm3niwvpOYXIdmjMqxgKvAr4mUxwBHEKIqyeulwGNUJZsYEgkT06e4kJggDZbHTqR6vFUJMl5Wy2lBge7i5rQkflkY1INUNmYuceKxRLUqiXYdUb26au5RW1gl7aSeDi3eA9vdPnAqJoWF2WW1OtPG2K03V5D022V9HhmSC4z5x8gxI9sk+w8vLJ3dtC6ci6/y9TuqMJ1zw6OzQaYjkQ5fnYwo+d5tuRLMbYamL9XMZy+tmDj6WorxvqifsxaA0mZ5KcjT3FryS5moj5OzHZxztdHQibRCg07i+rp8PbRaqslpoYZCU1Soi3jscHl9/GimsyGpapw/PdubnXU0vPo/O9rbg6slXoT/RnShLc3lOM3JZlMn5BcnMluK2WPtFF5Pkpwsp/mnWX0DGd+rsPjp721guGLmcWjarZXEmku5ZwvBPP8Kzu6Rnn/P3yPj33gFTjXEFKQL8XYTO/ykq/m1VaMrbCUoKKiCAWb1kypsZgKk4Oh8UkSMrU6bLHV0OnrZndRC9OxGSaj0xTpbfyy10NyhRZ2MYFG0WaMpqsvdnDp0cEMT2XP8EU35dUWJvwpgyuyGGmqcuH1h7FM6LAYdQST2Z281AgT6rf7uZRWFKs063C4THh9mV3a/C2lNOyoxDQbBouBhEYQ758mGogy1VDMpC/z1tLJ88P84+ce5lMf+S857VFCbkPzSoqxw8D8Pr8GWFuoWh4RQqARGuLJBEatgdHwFBcDo8zEUgsNm9bMaDjVzE5/N5PRVC9RJOpWNEKACmFbNqQzEIthMK8v+bZi0KLRaTjQWkNNWREKglNdI/SPzjA0PMMus2v1StIMyzAl9XPHe2PnxtCfHGd3Q+ZoviF/mDMeP8+pCZ7zBznuDXDOaaS7xs7kKrHUR07189unL2TdtsvkRTEW+CnwlvTq+SZgVkq5cedBOeJLBOnxDfHe4/+Hn409deV6vbmCcHLpKrJzamV9GIHAqbdS4ci8oJgOhChtzt5QFmNzmrAWm6i2Wzl5YZiR8Vm86RS27W2V+FsUnvMvr7s4HzMKd4WdDF9Y2H/oLTr6cpA9TkqZ4dAwM6fO557lIF+Ksb8EekklX/gS8J6cW7KBxGWSx6dOEVFjxNS54ax7SqHetHCu2mRuYjS0siEeMNdztHeCgDOJ1Zh5QdNpDmKyZTfxr6goYu/OGhxFZoxGHZZWJ+f9Xs4OL9VXHB+fZXYZBYnFCCmpxsTwt88vuWcuMhPboID51963L+dn8qUYK4H35vzqV4mpiJdgIkyx3kKz1YEQGiYiIU7Phun0xHhp/U7iyhQ6YeDZ4STxFVQUbBojw+OpedtkNMQtTTWcyhB8tM/qosffl1X7LFotZ5/vQyiCkjI7/ufHqdlbwnCG1LZ2mxHITtHLJvS4fjXFYk0JrUHLeNcEu1+8neNT+c2vsndHNdvqM2+0r8R14Y94wd9FlclOkV5Ld6DryvVmxx7cYzEe7p8GBIqIsZqSR6OhlNOBuY/2Ge8wt22v4ULfJKF5OfA6I15Meg2JLLLPS31qYJKqxD2eMr5idwyLUU84Gl8Q8DQwMkP7wRLOB1dfLfuIE7i3jMrHtYz1ptpcXOck3OKiQWroDOSeC3o1bju0bU3PvWCP+C6jSpWp2ATT8f4FRlhvbOb8dHBR2ZXrKtPZ6B1auiH8lG+Y6eIYB7ZXU1Vs50BbyhO77mDmQPn5VFU5sFuWDuGjF9206i1sV8yYjXMLHynBFdRxu7U6K1m6ASV85YRJaBSmamxMTPs5MeMlFFm7v+NyrKU3hOvAEEPJMA+U34tJM6cLWKOv59QJF9uNNey2V7HXXo0uiw+1SufAv8xGsxQpg4w4Vc6EJ7HUmtA1WZYV2byMEUHHc5mH8N5jQ4x0TtBesdCd61znGOeeH2anr4g7TdXcZa+h1VmMSadFqyg4jXN/q18msO5OGYdMqmwrd6z6d64Vk0FHyxoN8QU/NEeTUU7OniGQmOv9tEkLvWM+esd8CAEmvQ5Xi5nxyMqBTSNxD7sry+gYW16kfTicqsOXiOFwGWjbXUXnqeVXkT7v6vO96GjmIbRvaPrKMUJNWEelL4ylxETIE6CuxIy1xkpXeQQxO9fz6Sx6mutL6ckhGD9bbj7QSLFj9biYTLzgDdFlKCYmF64yZybNQMowpQSNAnJIz8HKenrEBLPxzI4B7niAOl32SlqhZILZ9L7+zgN1RIMxVFUl6I8ykZ4Lmsx65i8XFI2CuuioLDQbhhUC+nbUleG7MEMilmQ2bbSeIR+eIR8thyqIOhR2395KUgvPdA2h12nY3VZFR1d+t3rvuql1zc++4A0RYJdtB49qn8SX8KMTOp7sWHgy4A/H8YfjTM2Gaal10VKm4VxgjHCGkwtNDvvU5UkjXZMj7L+hlmPDE1f24fbvrsbe4iAyE0FbZaKp0U7v433U7K9EV2sjPuRHbzcS80VwX5ymaEcp9GfellWEwKrRMjSd2aN85Ng4Wq2CttFOJJ76e2LxJB39Y+xoLqezJz862W2NZdx6sGn1gsvwgp8jQmrBcpvrRgyKgSKtg8QKxybdQx5OHp9it6U64/2L8bEF88k2p4ubtHOuXzcXV3ODq4qDARcDR1Mf8snhyQWbwRGN5LmpCU6rsxwfHueSEqb6lc2c0QY5OjJGoFJPokjLSSVA4qYSwoa59hr0WiwmPW2NZRgNOvbsrEb6V150VFQ6mAmEFqzqVVUyNDVLzRoUZDPx2vv3Y8wi7/RyXBc9YpOtHqvWzGPuJ/EmPLz8fj2/fMS+4ipZJDKflba5S5i2q5RoTGhi0HN0DJ+ioDQLDhZXcOnXK7tDNW8rpXN04RxzyhdkyjfXo83Xxh73+nH7AuzfW0N0NsZYLIheq6VjepJil5mTI2NoUDh4Wz0Xnh5YklKjrMzOqDYOGfaufcEIxWYTDpvpysmNTlGIqyo1DivD3gBGnYYiowGnSc+FSW/Gv+m+O9t54O5dK/7dq3FdGKJZa0aSOntOqAkiMoQqV3YAFepSQzxkq+Xib1L7cbNahUTicj+nckOsjKHfrzzM6XQKQyF/VonC55NUJaOzfmYCIcKxyxYlmL7iaa3ylGeC22+qp/PIQmOcmvJTVOoiEMm82lc14E9EOWC0Mj4whd1pwV5SROdz/Rw+1MBozyQ+7xSOehc31pcQV0DMRojaDJydmMZs0vOhd704p78nE9eFIQJUmsupMVXRHeglkAhwx14LT5zOPK9qLnEx2D3LDe31hIhii5mY7A7SPTZDbWkRI+7ZeUaYYmx4FovRgDe2/Co4HldxGQ341iDCbjHqQcDI9PJB/0/6JjjUXEZf91yPq6oSZSLCnsZyOobHkYscpQbHU0slVUj83jB+b5iRvtSX7cKx/ivlRgemGR2Y20RXFEHzbY389XvvxWRcn4MHXCdzRIDJ8BSDodQ2iktXsqwROk1GLGiZ9oQ5/bQbzmg58/gE46MBElJicWbOU+IPRamsLmJbw/L7aHq9hnHP2nKWFJdYVjTCy7iLJa27F26kT08F6Hl+iMNVy/sq+5zanHLvqarkj+8/yL4d2Xt0r8R1Y4iPTDxGNJ1cRxfOrKqv0yiUaEycG0wNsS6zaYljwNmxScqcmb1uOvrGuTjhxllkAgGLowlKXDZMOU7oK4ptbG8tJyklBn0WJykBP8+qM9TeVsPug/XUNZTQsr0SnU7DuRODbK/MnAKtZ2aW+tbssw18+BOv58UP7M26/GpcN4bojs6dD3f2LPzmC+BQdRV7SsqvSAgDNDmKGZlZ2Avtba/G1WTHqM88q5ESHKUWdmyvpKV1bjW9o7UCjxqlpaqE8mUMeTE7t1cyQoDTkxMcGxmhrSllKFWldvbtqlnR6dunJDkxOcmFqI/Ts9NEK/XsPFC/Ymyg3pKd6FNJmZ1b7slv0vLrYo4YTUa5FOynTrcNoeoY0FjRKrErccmHa6o5cXHharfSbssY4xsQcc55JmnfUUJkIMSUd+kQ3z2cMnohYE9bJecujjESCDAbiHD6YmoTeU9zJWeW2RsE2NlYwYnxufvRZJJTE+Mc2FnNObeb/iEfN+yozuj5AzA666PIoCOeVh+LxJM8PzqKZoWsBaIoO7e19/6PV6Bfx1ZNJq4LQ9SgYadyK198+PJJwhQ1BivVZQ6klJzoXvhh3lhdw/HuYdwsnc+Zk6nh8bx/it0NpcycDi2b/0RK0Oo1aDUKFoOeqfRpjkGnYSawspLDpMePUaclMm9qkFBVjg7PtfXk+BjbaorpHV4YjK/XKLQXueicXOg8KxFLZEfm0x8OYjTpVhR/uvnuHdx8d357Q7hOhmatRss7d7wCsz71LXaaTIx7Axy/OJwywkWfzWjQj2OZ5IlR7dxqucPnZvsqWZziUsVcZmZg3pAfjScpL8o8PB9urKa9uoxqm40bXCsHQsaTKsKydN54oLKSzu7sPLjnMxkIE93porSyKON9V5mNv/771+RcbzZcF4YIYDMa2V+d+mDbnMUrlh3yztJQlbmMCS1W/dxcasi4vN7gnuYqTvWNMTWbvSbh5LSf7r5JOnrGuNgziVm38hDYOe6mpnyh4ZybcrN3Vw0NNS621WWXn/ky3kiU0sbMYQ7v+sB92B0bk/TnujHEpJTUO1MfmDsLwzg3OUlZ0VJPktNnR2gvmduiGQ8FKFqm91wpku3ExRH2Ny08RtxbV8Gwe/bKUN/UWEIovrrPoKt0Ye/qj8Q4MjrKtCa2trRpGdp9z8v3cvfL8rdKXvKSG1bzFkOnKJRaUx9YkdmIUaulymmnxmnHZV36LQ/HE9SUOpZcVxSYHgtwuDK1V2fW6heM7PO34s71jrO3ZRnnWAkdl0bZ3VBOa6WLg/VVXFiUqDvkz27jWywT1eQ0GelfJn55JTrn5Yxxldm56fUHeM9/f0XO9eTCdbFYgVRCxcP1NZh1Os5MTYAeBiM+bHo9rWUl1JQV0TXoJpLOMNVcUsykZ6EfoMmgpa61hNPj41waS7lx+RMxnI1F7BUl9KizGBUN+jGVsSkfdZVOuoSHqhI7o1NLN6OTSYleajjfn5rP6Rapas36I2j1gsQqvVrvrAe9TiEWX2iRZp1uydlzNmgUBcWgZdvNjZzwe3nLPTuwWrNLOL5WrhtD7BqfRKsIoonEAlkOfyzG2ckJoskkbRUu7FoDSalisGkZ6JqmpaEURREoFg2dU25Ojy9dBAyGZhmcp8qwo9pFi7kUvUuPPZlEp8s8RDssJvpGUj1WmdNKaZWdsSEvM+kA9okZPze0V/PM9Mp+g1OBEDe21XDy7EIH3J7pGWornAyNZx8gpVEEu8qcnD8keMY7zf62au65Ye1+htly3RiiSW/A6/dxuK6aIwMLP7BoMrXX1jWdMoryIitFViMV252c6E0bQQ4nc53+adCC3qdg0ukoCmSeATmsRgZHZ9i7vRqhFTw7NkJbhYtoPEEwrcpw7uI4xdUmZiIre3KfHhtne1MZ3QNukmk3N5NWi1mf3Sb1ZZpqS/jdcGr/sr7SySff/6olPfVGcN3MEeucdr5x7DQuy+qrPkVAl2eK0aif6mI7zmUWI6sRkyqzsSh1DZlXoUlUyrc5OeIe5chY6svRNT2N02aiyGLEZTej12lwGFbfaI4kEpyZnsRWaaa1oZTqsiIsRUY6mKH5YCXtbatrYu1vr+HiyFwIwd/+yUuxr/Fvz5XrwhCjyQTeSJSb6mv5XffKscbNlcWYnXoSqsqQb5b+uJemKlfWqcsyIqGqZKnbmclioN/rTReZG77DFhg3RqltLSFeqmEksLqzw2Um/UHMVj0lZVYiepVwMsnxmQnO4WH3vlq0ywRz7Wmr4vjFkSvtuG1fE7ubV49CzBfXxdCsQSA0Kbf6lbZD2qpKuBCeIhFaOOk/Mj7E4YZqTvWsTUXlWHCMgyXlSxYsJoseMigUD/t82E0Gzk+58UazF/i8TCiRYNzvZ2re6Y0vFuOp2Ahtu12UCgMTA16sZgN6vRa9QUvf2Nw8sshq5MNvX7+PYS5cF4YYV1WGAl6+N36KQy1VHOvOPPkfcHuoqrAx6MugCbMOdY64mkSkFyyKAjqNhiK7acUsp06LicHZ3MQ9L3N2bKmDbqPLiUWn5+z4BG6LCbte0DO+9FtwuL2O977+NkqzdMzIF9lq3ziEEN8XQlwQQnQKIW5edL9ICPEzIcRpIcQ5IcTbN6a5a8Ok01FisBAgytFEH81lmU9NIvEk5drMH4BmmZVvNtgNJvzJOHWtLoyVJgJWlaFkgEvuFQTbhVjbZnQGNIqgXDExfGKClqSd+IUQ01NLN/UVIXjbK29gR9PyiYg2imzniP8MPCKl3A7sBRanuHwvcF5KuRe4C/i/aT3FLYGUkpHQLL5YlCSSZHGcHVWl6DLMl5bLsjC9Qnrc1fBEw/iiUbonp/EEw1kZmCcQwm7KzhtmNQ5XVHPuxAjJpGR8dPle9k33HeBQ+8YLqGZi1aFZCGEH7gDeBiCljAGL5agkYBMpF18rqZnPxkhNrQFfOMS5mXEMmtSfezHqBgNsaynBFbFysn9ub7B3fAa9WUMsudAFbDoSwmLQEYwunWPeUFtDUqocH15+vy9XWd9gNMbOigpOD2U/L20sdaIIgdOaCoJOSMmIz4cSW93wK1w2/vwPbsupjfkkmzliE+AG/l0IsRc4DvyVlHJ+3/45UhqJo4ANeIOcn/8rzdWWLr7ML0a6+P1oLxOhhZuBl6JTXBJTHKipoyMtAeePxGgvLcNs0nHRN810OhfzTDhMld52xRA1QlBqtVBus/L84DBlVivt5aWcn3DjMBlpqi9CkQpCFcSiSYyKQigawxfO7tguqUrODYyzvbKUvukZBIJIIoFRq71y+jOfKoedkbCfcDzBYsXjcQLc0lbJ+RVSmL3l5Tdclf3C5Vg14Y8Q4hBwBLhVSvmcEOKfAZ+U8m/nlXkdcCvwAWAb8Btgr5Ry2X2HjU74cxkpJVNBP7f//ItEk5k7aZ1QsGuMbIuXLegdt1eX0BFIGWilxcr0WIjDdaneT0HQOe4mnIgvUI69YXcFpyIjhNSlGoYt2lLKYjZOnMm+lztcV835wQm2NZSgkwqKRnB0cF6WU43C3vpKArEY593Ly4joFIUbdeX0pVXBLm+YCwHvevXNvOs1Ny/7bL5YKeFPNj3iMDAspXwu/fv3gQ8vKvN24ONpncQeIUQfsB04usY2540jk/1MBAPLzv0g5TM4nQih6NxohLhyBKjM82AYCwa4ubmWo93L69hY9DpGE7MZjRCgO+GmW3Gzp6qG7tHVnRHqios4eWkEKSHoi9I/7qG+fGGGUaNWy6nxMWKrDP1xVeWp6Bj1rUXUK1bOnBxGp9XwT3/xcu44sDYpuXyyqiFKKceFEENCiDYpZRepFBeLJUgH09efFEKUA22kFGQ3HSnhB/1niKlJWopKcIeDRJJxIhl6R3ciyM6yai5OTLO9siQlAWcyMx0Osa+skgtDS3scraKwt6GchCnBifAgs1mIuSZXyNNs1GnZVVmOAM72j19xWuhPnxcPTHjYWVuKTqfBZjNybHyUWA4JJAeCsziLDGxvKOev33wX+9oyK1pcbbLdR/xL4JvplXAv8PbLssVp5diPAf8hhOggFdLzN1LKxUKlV5WkqjIVDnBsahB31E+t3UqMKGVWE8W6Eo5ODmdcvdrtBpgAm03HlMFLXcJGsdlIUlUJRBZa2YGmSi5px3ku2QsRsspgcVBTy/nx5d+aKoedzoGJBSECi+lKfyF2tFYQiOWemNGTjPC5d76C1vrsBaU2mmyli08Bi8f2+dLFo8C9+WvW+lGE4MT0MI+OdVNqsvLsZP+Ve5dws9dVy8mphavcaosdr9HLnt1OepKjeOJBwI3DbKYhUMXB2iqmgyEcJiN6rYbZQASjSw/J7DKJHvLWc65/eUk7gF73DLsqyugcWrmc1ajnlDv3cACA+/dt31JGCC/Qk5VwIk4kHufTZ39PXyDzpnEwGeb28kbCaoykSDAemWUyMsNUQqAuCmLxJkJE7SE6z/owajVXzofrnUXURlyEdFE8ydX3GYNZpJUttZqJxlbf+Yonkhwur+L5idyl5W5qWDn71GbwgnR6MGl1hJNRPth+J6+qzSwOVKyxcqxzApU4Z2dHmIoGQLDECAEOabeRdKdiRyKJuf3FAc8sxwZG2a5Z3bPFpTdhyyK9mMVgyGobRafVEM6wjbMS//2ld/LkX/8JNzUWDPGqoEqJ02il1VnGPVUtWLQLD3luKGrk6PkJIokk0VkNRs3KA4NA0jO5/HGcukJYiV5RqLXaaSy2IUtX7xH7pz0rxh5fJhCJEZyJcHNZDcYsDPedNx/krTceoMxmzUla5GrxgjTESDKOSavDpjPQ6R1nm30ukm2/o5Yj5yeuuDudH56hKlqOU7+8JKuialY8lpsNRLmlqpobK6opNy0MuGorceCXPs55R9FrNRzeuXL4qVGrWXGraT6jMz48EwFut6688n35rjb+20vuyKrOzeIFN0dUZSIV0CQlz04OoErJOc/cBrJOLv2TL455ubm9gudjmX0Vk6YYeysqMAgNCDg6ujAgX69VOD6T2q0qM9q4qagqLYksGIt4rijPTptmiZgnOcx2NFqJf1blXP/CFXQkkeTc5CRWg26BsOZiXCYjJp0e76VZJuMz7K1yEonEMTSbGY+FmIymFlC3NtXxdy97URbv3OZyTRqilDLj8KKqKoqiRUrJBc8Ex6eGuKO8ia9fOn7FGKqNRcBSN6lYNNVHZuqLBhMz+M5JpASdVuHGtmrOed0EomlRJ+3cwDIZ8TO5jCh8kd6AmtTQXXkagHbjTuiHGpedihIDiQSMuEN4AmHKbzAyeVQQjGQezuvsRVw8PfcFGxpN+xMeDdDWUs6kNkRDsYNPvPo+bMb8OE9sJNekIS43x4nLEEpSy2xsFItip8xk5T8vHeOv2u/gs51P8Nftd5IMaoGLS571+KO0lpSDX4+qSqaNM8zEUr1KKBlDpzUQi6vEEyqnzo1QWWKjxeHEWikwmBQMUS1RdW7xUGdxUm6ypBNTQlQGgTD+dJ0NFidhSwd337WNSfspAiKJkwPMjM1QrtjpVMc4VLuNs91zX5p6RxED3lm2u1xoV/CXvdgzwf23buNDb76bUuvaVP6vNlvaEFVVhfgIKAnQlEPSA7hAhlD0xQt6xngyQjweRSFJKD5NkamSUCJOjaUIXzzMiytbiQc0dEyM884DB/jqiRMLer/BKT/2oIGZYIDmcgczsRB2nZHt2gri03A+vnAIHZvyMzblZ1eDhguaESwmwX5LKwJ4dnKIaquFruBSg7/MdDREqVllrOjMlWuKdhqTXstALHX8d6z8EreYW5DjEl1EAgKHUUvPuZVTUxj0Wv7s/pupcWSWDtmKbElDlFIikz6QSYgegcinwfR60NwHejO+eBQDfow6C9FkAIPGhqpK4moIf2wSk64YRai8b8fteGJBdELLmD/Apelppqw2Gh0OmpzF9Hs9V86VE6pkJpjqZvrds9xS3cyJJ8Y5vcIB0bYqJ0PadEpYIekP95KQSW4pb2Y2sXIIZ0xNUm4opy/UD0CJtpFgxIknttAh4ritj70Xq+jun6K20sngqAchUkeXlaV2xtwL/Upu3FPPjm2VbG/KXutwK7AlDVEIgZQCErOgaMFwDwQfBOV7YPsHVFmDNzZFhW4nGozE4hEi6gxC1SFJIjAw6HcTTcYJywT7HU0kVMm2Yhc/6rpIKB7nD3a2Mx4M8nB3NxOLctIlVIl7MLpicPqhXeWoZQHcYq5QTKbmoRdW6Akvc9BVTm9oLq+xN1TEyemlXjnbo2VcSksJD6bngXccauauG1oIBKM8cayHUxdG0Ok02C1Gbjuwjdfeu2/V199qbDlDlDKJjA1A9EcggyAMIEpAKQYEBD+HyfZvJGJB4skwGmEkrE4RTfhIJJOYtC6iagK7wcpIaJpaUwkajSCuJrEbTTQ6HZyZmOCdBw/w3Y6zvKKllSHfLKUWC88MDTHg9WLR6/GMryIbNxnBW7N2v45Lfi/FRgeeuBcAi07hUEkdHTOjRNUEekVDTE1yg7EepUow7vbxopvasJn1vPzuXTTXleGe8fOSm9t4+Knz7GmpxBeKsa2mZEvuE67GljJEVVVBRhD6OqT/UdBsg+QAqDOgqYRED6gq3tggQ5Ez7DG8Cb1eQyzqZyLcSXvxK3AHeygy1DMT8FNvKiEqY3TPjlJpsyOlwitbWrDpUicvWALUmIs4r+/jsPMQM+ESej0eHAYj0wEfxVYzqqriDS1dGTgdBrwiu/2+xWiFQltREcNh75Vrir4XvdzGB3few0+HOtheVE7n7Di3t22jQmNnW20xu1qqOdc7zuSUH4fNRGlxakry0tvaMRl1GPX5Fc+8mmwpQ1QUBbAgZSw1HGOH6K8AAbpdYHkxqhQkZJRK/QF0GiNxNYQnOoRD14BG0VFsqkevNePXhzg3O5gKQlJVEkaVSqMDl9XKrfX1CG2SCTFBIhmk0VLG+FSM3/Wl9hHHgwHefNseDIqWrz9xImNb9ZW5h3leRiLxxKeIJKMcdu5hJDzBdMzDvqJy3FE/f7H9dvY4q+gLerjoneCWGxqpcTnw+sPcvLeRKW8Agy4lvq4oYNRrMei21EeZM1uy9ULoEbYPocYHIPQlEpp6tDKOYv0zROS32JI6VKWUSMxPUolQZ7kZnSZ1jKfXppQcpiIBjsz0sK+okQZzKU6TladnujAJPaVaK4OBIPuK6jnm6eWBykNETII7mxrQ6+BgdRlDlwJ8+8lTS9qmCMHBO2xcNHWv+e9TJQjVya0lTVSbyhgMjVJuLMEgLAQTMcpMNnyxCIdL69jvqkaraFBVicuRijAsSf+fTKpoNApGg+6aHI7nsyUN8TKKrh7V9Sgz/rOUac+iet4LSjUawx+iwcR4pAOTrohyYxkaTeq8NRAPYdGaqLeV8sGiV9DpHeG/n/sm91bu47fjp3lR2R5+HZjgnrLdbDNXoFd0oEi+M/kYE4ZZwmqUMU8JofMlGQOeLou4SyFTuXFzHJ7vLN1Js6WcqXiQO0raeXr6OW4vPYxDY0OrOthd3IBZa8BuSB05KkJJ5UpRBMo8h8d4IgkyNafWbmKsSb7Y0oYIoGjtlOkbU5Hp0YfB+AakMHBh9udohI4KwyuY9g3hKqpGEVqsulSPaNIaCCciVJiLeEvdHdh0ZvZuq8GqN9NireBQSQskVYw6AzKRxKzVo6Yz5hkVPS5XEeMeP5F4gh0NZeiKNUgBx9URknYbDepOvOEEXkNXxnabNHrCyRgttipUqdJmq8aiNXJfxQFqTcWgCGbCHl5afgcttgaemDxKvbEEl9GKbp4ThiJERofbzQx02ghWDZ7aKHIJnlKTMUiqkLhITGg5Nfs0JfpmEsRodb4YNSmJqxH0GiOa9IcopUQiCcejaFHQ6/QE4+GUgUYjaBQNJoORpJrkT47+G+NhD554EJ3Q8MbSu5jt0xIQMYY8Xp5U+pFpY7icqw5S9nFjZSX+aBynLUFfbG4VfcDZRKXRyeuqb8aTCCMl1FtLKDMUkUAlFA/hMNiv+SE1F9YbPLXpKBo9aEDqdqOXKjeUbV/wAWq1gmgswWx8mGJNAwDheBSz3ohFbyKhJpFSYtIYkIDZYEovjCCixknIJG32alRVMhKZpk1TT6xB5aHpDkKGOI3RYkaDPupsDmw6A8fdKacHCRwZS+39Wfx67m1uZyI+xVDQTaOlnD+qu4tScxFCCGLJOPp0jl0NGgyaa+fU42pwTRjiZYQQCJF5SBoOPIceO3ZtFVqtHpMuddCvqirhRJRoMkk0EcVhsGDWGZFSoqoqo0EP72p4EXE1iUmjp8bs4uSEG5NGR2nIgjcWJhCPEVOTOA0mTk3NbTrfUFbLDeXVOHUmthUVU2U18/Oxo1g1Bt7ccCdlJseVsvpcEj1fh1wThiilihAru07WWA5j0hVdKXe5x1SRGDU6fIkwdr0ZnZLa9kiqydS2jprALLSU2EqRQKnJTkxM8MmTj3KorJr9JVUE4v3scVXg0BvpmB7nYGk1r6rfwUjAy33VLTQ5XBi0OhQhuDO5k9fW3UK5yblCawss5pqYI2bLYvewy7/PxkMUpRcxkUQUAQTiUYw6A96Qn57gOI3mUmrspShCIZpI4A766fHP8JO+TupsDipMZqbDYZpsTqpNVuwWC3opsOkM2C0pDxdVqsSTCQzaLSP7s6W45ueI2SKEQFWTKIqGhJoEJFqhvWKEAEZtasjWKToURcGr8XO4eBtmw5yHdlKqPDLSw8/7O7HrjXxo721MhYI0VBeh1+vRa7UoQixZaChCKRjhGnlB9YhrQZUqSno4v9yDqlISTyYxaFNOtqqUV+JIIok4Rm1hvrcWrpsecS0o8+ael3s4RQgMWu2Va5p5PV/BCDeGF2TwVIFrj7woxqbL3CWEOJVWjH08/00t8EIm26H5smLs69L6NwtyRAghHMDngfuklINCiK2lZ1Fgy5Mvxdg/BH4opRxMl1lZuKVAgUVkMzTPV4w9KYT4shBicWhYK+AUQvxeCHFcCPGWTBUJId4thDgmhDjmXkFUssD1RzaGqAUOAP8mpdwPBFkq1KkFDgIPAC8F/lYIsSSBm5TyQSnlISnlodLS0sW3C1zHZGOImRRjD2Qo84iUMpjWRXyCVPaBAgWyYlVDlFKOA0NCiLb0pUyKsT8BbhdCaIUQZuBGlqbAKFBgWfKiGCul7BRCPAKcAVTgy1LKsxvS4gIvSDbtiE8I4QYGruJLlgCbKqecZqu0A65+W+qllBkXB5tmiFcbIcSx5c45r8d2wNZqS+GIr8CWoGCIBbYE15MhPrjZDUizVdoBW6gt180cscDW5nrqEQtsYQqGWGBrIKXcsv+AWuB3pE5pzpFKzwtQTCoDanf6f+e8Zz4C9ABdwEvnXT8IdKTvfZa5aYkBeCh9/TmgYd4zb02/Rjfw1vQ1DXAS+PkmtuXPSR21Xki/Nzdv5nuSl896s41tFUOsBA6kf7aREr9uBz4JfDh9/cPAJ9I/twOn029kI3AJ0KTvHU1/YAJ4GLg/ff09wBfSP78ReGiegfWm/3emf3aSSgX8rXmGuBlt8QPvS9/TA47NfE9e8IaYwTB/AryE1De7cp6xdqV//gjwkXnlf5V+oyuBC/Ouvwn44vwy6Z+1pE4axPwy6XtfBN4LPArcM88Qr2pbADvgA9606L3ZrPdkQTvW+u+amSMKIRqA/aSGinIp5RhA+v/LHuHVwNC8x4bT16rTPy++vuAZKWWCVP531zJ1/Snw34D5MmFXuy1NpAzxA4v8QzfrPclLnt1rwhCFEFbgB8D7pZS+lYpmuCZXuJ7LM62AX0p5fJXmbnRbtKR6s2fk8v6hV6Mdi59ZF1veEIUQOlJG+E0p5Q/TlyeEEJXp+5XA5dCEYVILnMvUAKPp6zUZri94RgihBYqAmQx17QK2CyH6ge8A9wghvrEJbRkGQsCR9O+X/UM34z2Z/8z62Ox53ypzQgF8DfjMouufYuHE/JPpn3eycGLey9zE/HngJuYm5i9LX38vCyfm303/XAz0kZqUO9M/F6fv3cXcHHEz2hIBbkjf+7t0Gzb1PVn3Z73ZxraKId5Gqus/A5xK/3sZqfnKo6S2EB6d/2YA/4PUyrCL9Cowff0QcDZ973PMbVUYge+R2qo4CjTNe+Yd6es9wNvnXZ9viJvRlo8Cx9Lvy4/TRrGp78l6/xWO+ApsCbb8HLHA9UHBEAtsCQqGWGBLUDDEAluCgiEW2BIUDLHAlqBgiAW2BP8/WX2etDvxh5UAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Define which variable to plot\n",
"geodata.plot(column=\"tyollisyys\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Adjusting the figure, we need to import matplotlib pyplot"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Adjust figure size\n",
"fig, ax = plt.subplots(1, figsize=(10, 8))\n",
"\n",
"# Adjust colors and add a legend\n",
"geodata.plot(ax=ax, column=\"tyollisyys\", scheme=\"quantiles\", cmap=\"Reds\", legend=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create an interactive map\n",
"\n",
"Next, we'll plot an interactive map based on the same data, and usign the folium library, which enables us to create maps based on the JavaScript library leaflet.js."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# Create a Geo-id which is needed by the Folium (it needs to have a unique identifier for each row)\n",
"geodata['geoid'] = geodata.index.astype(str)"
]
},
{
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"data": {
"text/html": [
"Make this Notebook Trusted to load map: File -> Trust Notebook
"
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""
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"source": [
"# Create a Map instance\n",
"m = folium.Map(location=[60.25, 24.8], tiles = 'cartodbpositron', zoom_start=8, control_scale=True)\n",
"\n",
"folium.Choropleth(geo_data = geodata, \n",
" data = geodata, \n",
" columns=['geoid','tyollisyys'], \n",
" key_on='feature.id', \n",
" fill_color='RdYlBu', \n",
" line_color='white',\n",
" line_weight=0,\n",
" legend_name= 'Employment rate in Finland').add_to(m)\n",
"m"
]
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"We can also plot \"tooltips\" on the map, which show the values for each feature."
]
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"cell_type": "code",
"execution_count": 12,
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"outputs": [
{
"data": {
"text/html": [
"Make this Notebook Trusted to load map: File -> Trust Notebook