{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Interactive maps\n", "\n", "In this tutorial we will learn how to publish data from Python on interactive [leaflet.js](http://leafletjs.com/) maps. \n", "\n", "JavaScript (JS) is a programming language for adding interactive content (such a zoomamble maps!) on webpages. [Leaflet](http://leafletjs.com/) is a popular JavaScript library for creating interactive maps for webpages ([OpenLayers](https://openlayers.org/) is another JavaScript library for the same purpose). \n", "\n", "Here, will focus on two Python libraries -**mplleaflet** and **Folium** - that are able to convert our data in (geo)pandas into interactive Leaflet maps.\n", "\n", "
\n", "\n", "**Explore also...**\n", " \n", "Other interesting libraries for creating interactive visualizations from spatial data:\n", " \n", "- [mapboxgl](https://github.com/mapbox/mapboxgl-jupyter)\n", "- [Bokeh](https://docs.bokeh.org/en/latest/)\n", "- [Geoviews](http://geoviews.org/)\n", "\n", "
\n" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "## From matplotlib to leaflet using mplleaflet" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "We can also convert maptlotlib plots directly to interactive web maps using [mllpleaflet](https://github.com/jwass/mplleaflet). \n", "\n", "All you need to do is to:\n", "\n", "1. visualize your data using matplotlib (or geopandas `plot()`)\n", "2. convert the plot into a webmap using `mplleaflet`" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "Let's demonstrate this using a simple static map" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "import geopandas as gpd\n", "import matplotlib.pyplot as plt\n", "import mplleaflet" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- read in sample data (the locations of transport stations in Helsinki):" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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addressidgeometry
0Kampinkuja 1, 00100 Helsinki, Finland1001POINT (24.93017 60.16837)
1Kaivokatu 8, 00101 Helsinki, Finland1002POINT (24.94189 60.16987)
2Hermanstads strandsväg 1, 00580 Helsingfors, F...1003POINT (24.97740 60.18736)
3Itäväylä, 00900 Helsinki, Finland1004POINT (25.09196 60.21448)
4Tyynenmerenkatu 9, 00220 Helsinki, Finland1005POINT (24.92148 60.15658)
\n", "
" ], "text/plain": [ " address id \\\n", "0 Kampinkuja 1, 00100 Helsinki, Finland 1001 \n", "1 Kaivokatu 8, 00101 Helsinki, Finland 1002 \n", "2 Hermanstads strandsväg 1, 00580 Helsingfors, F... 1003 \n", "3 Itäväylä, 00900 Helsinki, Finland 1004 \n", "4 Tyynenmerenkatu 9, 00220 Helsinki, Finland 1005 \n", "\n", " geometry \n", "0 POINT (24.93017 60.16837) \n", "1 POINT (24.94189 60.16987) \n", "2 POINT (24.97740 60.18736) \n", "3 POINT (25.09196 60.21448) \n", "4 POINT (24.92148 60.15658) " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import geopandas as gpd\n", "\n", "# File path\n", "points_fp = r\"data/addresses.shp\"\n", "\n", "# Read the data\n", "points = gpd.read_file(points_fp)\n", "\n", "#Check input data\n", "points.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- plot the data and create an interactive map using mplleaflet:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# 1.Plot data:\n", "points.plot()\n", "\n", "# 2. Convert plot to a web map:\n", "mplleaflet.show()" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "*the code above opens up a new tab with the visualized map*" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "We can also render the map insite the notebook (following [this example](http://nbviewer.jupyter.org/github/jwass/mplleaflet/blob/master/examples/NYC%20Boroughs.ipynb)):" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Hyapp\\Anaconda3\\envs\\grading\\lib\\site-packages\\IPython\\core\\display.py:694: UserWarning: Consider using IPython.display.IFrame instead\n", " warnings.warn(\"Consider using IPython.display.IFrame instead\")\n" ] }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 1. Plot data:\n", "ax = points.plot(markersize = 50, color = \"red\")\n", "\n", "# 2. Convert plot to a web map:\n", "mplleaflet.display(fig=ax.figure, crs=points.crs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Folium\n", "\n", "\n", "[Folium](https://github.com/python-visualization/folium) is a Python library that makes\n", "it possible visualize data on an interactive Leaflet map.\n", "\n", "**Resources:**\n", "\n", "- [Folium Documentation](https://python-visualization.github.io/folium/)\n", "- [Example Gallery](https://nbviewer.jupyter.org/github/python-visualization/folium/tree/master/examples/)\n", "- [Folium Quickstart](https://python-visualization.github.io/folium/quickstart.html)\n", "\n", "### Creating a simple interactive web-map\n", "\n", "Let's first see how we can do a simple interactive web-map without any data on it. We just visualize OpenStreetMap on a specific location of the a world.\n", "\n", "First thing that we need to do is to create [a Map instance](https://python-visualization.github.io/folium/modules.html#folium.folium.Map)\n", "\n", "- Create a map object and set the location to Helsinki:\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "deletable": true, "editable": true }, "outputs": [], "source": [ "import folium\n", "\n", "# Create a Map instance\n", "m = folium.Map(location=[60.25, 24.8], zoom_start=10, control_scale=True)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "The first parameter ``location`` takes a pair of lat, lon values as list as an input which will determine where the map will be positioned when user opens up the map. ``zoom_start`` -parameter adjusts the default zoom-level for the map (the higher the number the closer the zoom is). ``control_scale`` defines if map should have a scalebar or not." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "- Let's see what our map looks like: " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "- We can also save the map already now \n", "- Let's save the map as a html file ``base_map.html``:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "outfp = \"base_map.html\"\n", "m.save(outfp)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "
\n", "\n", "**Task**\n", "\n", "Navigate to the location where you saved the html file and open it in a web browser (preferably Google Chrome). Open the file also in a text editor to see the source script.\n", "\n", "
\n", "\n", "
\n", "\n", "**Task**\n", "\n", "Create another map with different settings (location, bacground map, zoom levels etc). See documentation of [class folium.folium.Map()](https://python-visualization.github.io/folium/modules.html#folium.folium.Map) for all avaiable options.\n", " \n", "``tiles`` -parameter is used for changing the background map provider and map style (see the [documentation](https://python-visualization.github.io/folium/modules.html#folium.folium.Map) for all in-built options).\n", "\n", "
" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's change the basemap style to 'Stamen Toner'\n", "m = folium.Map(location=[40.730610, -73.935242], tiles='Stamen Toner',\n", " zoom_start=12, control_scale=True, prefer_canvas=True)\n", "\n", "m" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Adding layers to the map\n", "\n", "Let's first have a look how we can add a simple [marker](https://python-visualization.github.io/folium/modules.html?highlight=marker#folium.map.Marker) on the webmap:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create a Map instance\n", "m = folium.Map(location=[60.20, 24.96],\n", " zoom_start=12, control_scale=True)\n", "\n", "# Add marker\n", "# Run: help(folium.Icon) for more info about icons\n", "folium.Marker(\n", " location=[60.20426, 24.96179],\n", " popup='Kumpula Campus',\n", " icon=folium.Icon(color='green', icon='ok-sign'),\n", ").add_to(m)\n", "\n", "#Show map\n", "m" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "As mentioned, Folium combines the strenghts of data manipulation in Python with the mapping capabilities of Leaflet.js. Eventually, we would like to first manipulate data using Pandas/Geopandas before creating a fancy map.\n", "\n", "Let's first practice by adding the address points onto the Helsinki basemap.\n", "\n", "- Check what data we have in the points layer:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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addressidgeometry
0Kampinkuja 1, 00100 Helsinki, Finland1001POINT (24.93017 60.16837)
1Kaivokatu 8, 00101 Helsinki, Finland1002POINT (24.94189 60.16987)
2Hermanstads strandsväg 1, 00580 Helsingfors, F...1003POINT (24.97740 60.18736)
3Itäväylä, 00900 Helsinki, Finland1004POINT (25.09196 60.21448)
4Tyynenmerenkatu 9, 00220 Helsinki, Finland1005POINT (24.92148 60.15658)
\n", "
" ], "text/plain": [ " address id \\\n", "0 Kampinkuja 1, 00100 Helsinki, Finland 1001 \n", "1 Kaivokatu 8, 00101 Helsinki, Finland 1002 \n", "2 Hermanstads strandsväg 1, 00580 Helsingfors, F... 1003 \n", "3 Itäväylä, 00900 Helsinki, Finland 1004 \n", "4 Tyynenmerenkatu 9, 00220 Helsinki, Finland 1005 \n", "\n", " geometry \n", "0 POINT (24.93017 60.16837) \n", "1 POINT (24.94189 60.16987) \n", "2 POINT (24.97740 60.18736) \n", "3 POINT (25.09196 60.21448) \n", "4 POINT (24.92148 60.15658) " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "points.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- conver the points to GeoJSON features using folium:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# Convert points to GeoJSON\n", "points_gjson = folium.features.GeoJson(points, name=\"Public transport stations\")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Check the GeoJSON features\n", "#points_gjson.data.get('features')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Now we have our population data stored as GeoJSON format which basically contains the\n", "data as text in a similar way that it would be written in the ``.geojson`` -file." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "- add the points onto the Helsinki basemap" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create a Map instance\n", "m = folium.Map(location=[60.25, 24.8], tiles = 'cartodbpositron', zoom_start=11, control_scale=True)\n", "\n", "# Add points to the map instance\n", "points_gjson.add_to(m)\n", "\n", "# Alternative syntax for adding points to the map instance\n", "#m.add_child(points_gjson)\n", "\n", "#Show map\n", "m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Layer control\n", "\n", "We can also add a `LayerControl` object on our map, which allows the user to control which map layers are visible. See the [documentation](http://python-visualization.github.io/folium/docs-v0.5.0/modules.html#folium.map.LayerControl) for available parameters (you can e.g. change the position of the layer control icon)." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create a layer control object and add it to our map instance\n", "folium.LayerControl().add_to(m)\n", "\n", "#Show map\n", "m" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Heatmap\n", "\n", "[Folium plugins](https://python-visualization.github.io/folium/plugins.html) allow us to use popular plugins available in leaflet. One of these plugins is [HeatMap](https://python-visualization.github.io/folium/plugins.html#folium.plugins.HeatMap), which creates a heatmap layer from input points. \n", "\n", "Let's visualize a heatmap of the public transport stations in Helsinki using the addresses input data. [folium.plugins.HeatMap](https://python-visualization.github.io/folium/plugins.html#folium.plugins.HeatMap) requires a list of points, or a numpy array as input, so we need to first manipulate the data a bit:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "deletable": true, "editable": true }, "outputs": [], "source": [ "# Get x and y coordinates for each point\n", "points[\"x\"] = points[\"geometry\"].apply(lambda geom: geom.x)\n", "points[\"y\"] = points[\"geometry\"].apply(lambda geom: geom.y)\n", "\n", "# Create a list of coordinate pairs\n", "locations = list(zip(points[\"y\"], points[\"x\"]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check the output:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/plain": [ "[(60.1683731, 24.9301701),\n", " (60.1698665, 24.9418933),\n", " (60.18735880000001, 24.9774004),\n", " (60.21448089999999, 25.0919641),\n", " (60.1565781, 24.9214846),\n", " (60.23489060000001, 25.0816923),\n", " (60.2033879, 25.042239),\n", " (60.2753891, 25.035855),\n", " (60.2633799, 25.0291078),\n", " (60.22243630000001, 24.8718598),\n", " (60.1711874, 24.94251),\n", " (60.2306474, 24.8840504),\n", " (60.240163, 24.877383),\n", " (60.22163339999999, 24.9483202),\n", " (60.25149829999999, 25.0125655),\n", " (60.2177823, 24.893153),\n", " (60.2485471, 24.86186),\n", " (60.2291135, 24.9670533),\n", " (60.1986856, 24.9334051),\n", " (60.22401389999999, 24.8609335),\n", " (60.2436961, 24.9934979),\n", " (60.24444239999999, 25.040583),\n", " (60.20966609999999, 25.0778094),\n", " (60.20751019999999, 25.1424936),\n", " (60.225599, 25.0756547),\n", " (60.2382054, 25.1080054),\n", " (60.18789030000001, 24.9609122),\n", " (60.19413939999999, 25.0291263),\n", " (60.18837519999999, 25.0068399),\n", " (60.1793862, 24.9494874),\n", " (60.1694809, 24.9337569),\n", " (60.16500139999999, 24.9250072),\n", " (60.159069, 24.9214046),\n", " (60.1719108, 24.9468514),\n", " (60.20548979999999, 25.1204966)]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "locations" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from folium.plugins import HeatMap\n", "\n", "# Create a Map instance\n", "m = folium.Map(location=[60.25, 24.8], tiles = 'stamentoner', zoom_start=10, control_scale=True)\n", "\n", "# Add heatmap to map instance\n", "# Available parameters: HeatMap(data, name=None, min_opacity=0.5, max_zoom=18, max_val=1.0, radius=25, blur=15, gradient=None, overlay=True, control=True, show=True)\n", "HeatMap(locations).add_to(m)\n", "\n", "# Alternative syntax:\n", "#m.add_child(HeatMap(points_array, radius=15))\n", "\n", "# Show map\n", "m" ] }, { "cell_type": "markdown", "metadata": { "editable": true }, "source": [ "### Clustered point map\n", "\n", "Let's visualize the address points (locations of transport stations in Helsinki) on top of the choropleth map using clustered markers using folium's [MarkerCluster](https://python-visualization.github.io/folium/plugins.html?highlight=marker%20cluster#folium.plugins.MarkerCluster) class." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "editable": true }, "outputs": [], "source": [ "from folium.plugins import MarkerCluster" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "# Create a Map instance\n", "m = folium.Map(location=[60.25, 24.8], tiles = 'cartodbpositron', zoom_start=11, control_scale=True)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "# Following this example: https://github.com/python-visualization/folium/blob/master/examples/MarkerCluster.ipynb\n", "\n", "# Get x and y coordinates for each point\n", "points[\"x\"] = points[\"geometry\"].apply(lambda geom: geom.x)\n", "points[\"y\"] = points[\"geometry\"].apply(lambda geom: geom.y)\n", "\n", "# Create a list of coordinate pairs\n", "locations = list(zip(points[\"y\"], points[\"x\"]))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create a folium marker cluster\n", "marker_cluster = MarkerCluster(locations)\n", "\n", "# Add marker cluster to map\n", "marker_cluster.add_to(m)\n", "\n", "# Show map\n", "m" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Choropleth map" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Next, let's check how we can overlay a population map on top of a basemap using [folium's choropleth method](http://python-visualization.github.io/folium/docs-v0.5.0/modules.html#folium.folium.Map.choropleth). This method is able to read the geometries and attributes directly from a geodataframe. \n", "This example is modified from the [Folium quicksart](https://python-visualization.github.io/folium/quickstart.html#Choropleth-maps).\n", "\n", "- First read in the population grid from HSY wfs like we did in [lesson 3](https://automating-gis-processes.github.io/site/notebooks/L3/spatial-join.html):" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " geometry index asukkaita \\\n", "0 MULTIPOLYGON Z (((25476499.999 6674248.999 0.0... 3342 108 \n", "1 MULTIPOLYGON Z (((25476749.997 6674498.998 0.0... 3503 273 \n", "2 MULTIPOLYGON Z (((25476999.994 6675749.004 0.0... 3660 239 \n", "3 MULTIPOLYGON Z (((25476999.994 6675499.004 0.0... 3661 202 \n", "4 MULTIPOLYGON Z (((25476999.994 6675249.005 0.0... 3662 261 \n", "\n", " asvaljyys ika0_9 ika10_19 ika20_29 ika30_39 ika40_49 ika50_59 \\\n", "0 45 11 23 6 7 26 17 \n", "1 35 35 24 52 62 40 26 \n", "2 34 46 24 24 45 33 30 \n", "3 30 52 37 13 36 43 11 \n", "4 30 64 32 36 64 34 20 \n", "\n", " ika60_69 ika70_79 ika_yli80 \n", "0 8 6 4 \n", "1 25 9 0 \n", "2 25 10 2 \n", "3 4 3 3 \n", "4 6 3 2 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import geopandas as gpd\n", "from pyproj import CRS\n", "import requests\n", "import geojson\n", "\n", "# Specify the url for web feature service\n", "url = 'https://kartta.hsy.fi/geoserver/wfs'\n", "\n", "# Specify parameters (read data in json format).\n", "# Available feature types in this particular data source: http://geo.stat.fi/geoserver/vaestoruutu/wfs?service=wfs&version=2.0.0&request=describeFeatureType\n", "params = dict(service='WFS',\n", " version='2.0.0',\n", " request='GetFeature',\n", " typeName='asuminen_ja_maankaytto:Vaestotietoruudukko_2018',\n", " outputFormat='json')\n", "\n", "# Fetch data from WFS using requests\n", "r = requests.get(url, params=params)\n", "\n", "# Create GeoDataFrame from geojson\n", "data = gpd.GeoDataFrame.from_features(geojson.loads(r.content))\n", "\n", "# Check the data\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "from pyproj import CRS\n", "# Define crs\n", "data.crs = CRS.from_epsg(3879)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- re-project layer into WGS 84 (epsg: 4326)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'init': 'epsg:4326', 'no_defs': True}\n" ] } ], "source": [ "# Re-project to WGS84\n", "data = data.to_crs(epsg=4326)\n", "\n", "# Check layer crs definition\n", "print(data.crs)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "- Rename columns" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "# Change the name of a column\n", "data = data.rename(columns={'asukkaita': 'pop18'})" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "# Create a Geo-id which is needed by the Folium (it needs to have a unique identifier for each row)\n", "data['geoid'] = data.index.astype(str)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " geoid pop18 geometry\n", "0 0 108 MULTIPOLYGON Z (((24.57654 60.18042 0.00000, 2...\n", "1 1 273 MULTIPOLYGON Z (((24.58102 60.18267 0.00000, 2...\n", "2 2 239 MULTIPOLYGON Z (((24.58538 60.19391 0.00000, 2...\n", "3 3 202 MULTIPOLYGON Z (((24.58541 60.19166 0.00000, 2...\n", "4 4 261 MULTIPOLYGON Z (((24.58544 60.18942 0.00000, 2..." ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Select only needed columns\n", "data = data[['geoid', 'pop18', 'geometry']]\n", "\n", "# Convert to geojson (not needed for the simple coropleth map!)\n", "#pop_json = data.to_json()\n", "\n", "#check data\n", "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- create an interactive choropleth map from the population grid:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false, "deletable": true, "editable": true, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create a Map instance\n", "m = folium.Map(location=[60.25, 24.8], tiles = 'cartodbpositron', zoom_start=10, control_scale=True)\n", "\n", "# Plot a choropleth map\n", "# Notice: 'geoid' column that we created earlier needs to be assigned always as the first column\n", "folium.Choropleth(\n", " geo_data=data,\n", " name='Population in 2018',\n", " data=data,\n", " columns=['geoid', 'pop18'],\n", " key_on='feature.id',\n", " fill_color='YlOrRd',\n", " fill_opacity=0.7,\n", " line_opacity=0.2,\n", " line_color='white', \n", " line_weight=0,\n", " highlight=False, \n", " smooth_factor=1.0,\n", " #threshold_scale=[100, 250, 500, 1000, 2000],\n", " legend_name= 'Population in Helsinki').add_to(m)\n", "\n", "#Show map\n", "m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tooltips\n", "\n", "It is possible to add different kinds of pop-up messages and tooltips to the map. Here, it would be nice to see the population of each grid cell when you hover the mouse over the map. Unfortunately this functionality is not apparently implemented implemented in the Choropleth method we used before. \n", "\n", "Add tooltips, we can add tooltips to our map when plotting the polygons as GeoJson objects using the `GeoJsonTooltip` feature. (following examples from [here](http://nbviewer.jupyter.org/gist/jtbaker/57a37a14b90feeab7c67a687c398142c?flush_cache=true) and [here](https://nbviewer.jupyter.org/github/jtbaker/folium/blob/geojsonmarker/examples/GeoJsonMarkersandTooltips.ipynb))\n", "\n", "For a quick workaround, we plot the polygons on top of the coropleth map as a transparent layer, and add the tooltip to these objects. *Note: this is not an optimal solution as now the polygon geometry get's stored twice in the output!*" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Convert points to GeoJson\n", "folium.features.GeoJson(data, \n", " name='Labels',\n", " style_function=lambda x: {'color':'transparent','fillColor':'transparent','weight':0},\n", " tooltip=folium.features.GeoJsonTooltip(fields=['pop18'],\n", " aliases = ['Population'],\n", " labels=True,\n", " sticky=False\n", " )\n", " ).add_to(m)\n", "\n", "m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Rember that you can also save the output as an html file: " ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "outfp = \"results/choropleth_map.html\"\n", "m.save(outfp)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:grading]", "language": "python", "name": "conda-env-grading-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 4 }