{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Edinburgh Living Landscape Pollinator Pledge\n", "\n", "This notebook illustrates how to plot the location data collected by the [Edinburgh Pollinator Pledge](https://edinburghlivinglandscape.org.uk/pollinatorpledge/) initiative.\n", "\n", "It uses the [folium](https://github.com/python-visualization/folium) library, which is a Python interface to [leaflet.js](https://leafletjs.com)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preliminary Steps\n", "\n", "We start off by importing a few libraries " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "from os import path\n", "\n", "import folium\n", "import geopandas as gpd\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "from shapely.geometry import Point" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we fetch the main pledge data which was provided for this challenge. We've stored it on GitHub for convenience." ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'https://raw.githubusercontent.com/prewired/workshops/master/data/processed/swt_pollinator_data-2019-02-15.csv'" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "url = 'https://raw.githubusercontent.com/prewired/workshops/master/data/processed/'\n", "fn = 'swt_pollinator_data-2019-02-15.csv'\n", "fn = path.join(url, fn)\n", "fn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The [pandas](https://pandas.pydata.org) library is powerful tool for loading tabular data into a structure called a `DataFrame`. The `read_csv()` method takes file-like object and returns a `DataFrame`. Although it's probably not required, we can make sure that the date column in the input is parsed correctly. We also shorten one of the column labels, which is inconveniently long.\n", "\n", "The `head()` method allows us to look at the first few rows of the `DataFrame`." ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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latitudelongitudetypePlant for pollinatorsMake space for natureExpand the networkWhat is your first step going to be?Entry IdEntry Date
055.938051-3.217624Communal greenspaceNaNMake space for natureNaNNaN7672019-12-02 13:43:00
155.925941-3.277307Small gardenPlant for pollinatorsMake space for natureNaNCreate an insect house7602019-11-02 07:36:00
255.943268-3.288348Large gardenPlant for pollinatorsMake space for natureNaNPlant native flowers, make wildlife pond7562019-10-02 13:09:00
355.956412-3.290882Small gardenPlant for pollinatorsMake space for natureNaNPlanting flowers7502019-09-02 09:10:00
455.899452-3.218028Small gardenPlant for pollinatorsNaNNaNDecide on types of pollinators7462019-08-02 07:57:00
\n", "
" ], "text/plain": [ " latitude longitude type Plant for pollinators \\\n", "0 55.938051 -3.217624 Communal greenspace NaN \n", "1 55.925941 -3.277307 Small garden Plant for pollinators \n", "2 55.943268 -3.288348 Large garden Plant for pollinators \n", "3 55.956412 -3.290882 Small garden Plant for pollinators \n", "4 55.899452 -3.218028 Small garden Plant for pollinators \n", "\n", " Make space for nature Expand the network \\\n", "0 Make space for nature NaN \n", "1 Make space for nature NaN \n", "2 Make space for nature NaN \n", "3 Make space for nature NaN \n", "4 NaN NaN \n", "\n", " What is your first step going to be? Entry Id Entry Date \n", "0 NaN 767 2019-12-02 13:43:00 \n", "1 Create an insect house 760 2019-11-02 07:36:00 \n", "2 Plant native flowers, make wildlife pond 756 2019-10-02 13:09:00 \n", "3 Planting flowers 750 2019-09-02 09:10:00 \n", "4 Decide on types of pollinators 746 2019-08-02 07:57:00 " ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pollinator_data = pd.read_csv(fn, parse_dates=['Entry Date'])\n", "pollinator_data = pollinator_data.rename(columns={'What type of space do you have?': 'type'})\n", "pollinator_data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Version 1: Import location using CircleMarkers\n", "\n", "This approach pulls the data from the `DataFrame` directly.\n", "\n", "We will ignore all columns apart from the first three — this is just for cosmetic reasons, we could skip this step." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(226, 3)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pollinator_data[['latitude', 'longitude', 'type']]\n", "df = df.dropna(subset=['latitude', 'longitude']) # drop any rows that have missing geo-coordinates\n", "df.shape # rows x columns" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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latitudelongitudetype
055.938051-3.217624Communal greenspace
155.925941-3.277307Small garden
255.943268-3.288348Large garden
355.956412-3.290882Small garden
455.899452-3.218028Small garden
\n", "
" ], "text/plain": [ " latitude longitude type\n", "0 55.938051 -3.217624 Communal greenspace\n", "1 55.925941 -3.277307 Small garden\n", "2 55.943268 -3.288348 Large garden\n", "3 55.956412 -3.290882 Small garden\n", "4 55.899452 -3.218028 Small garden" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To make it easier to import data into a map, we will create a list of triples with the data we need. The Python `zip()` method creates an iterable of n-tuples from *n* input iterables." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(55.93805129999999, -3.2176237000000003, 'Communal greenspace'),\n", " (55.9259405, -3.2773065000000003, 'Small garden'),\n", " (55.943267500000005, -3.2883483, 'Large garden'),\n", " (55.9564125, -3.290882, 'Small garden'),\n", " (55.899451899999995, -3.2180276, 'Small garden')]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "locations = zip(df.latitude, df.longitude, df.type)\n", "list(locations)[:5] # we convert the iterable to a list before indexing into it" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have an iterable of locations, it is straighforward to feed them into a folium map. In this approach, we can style the markers in various ways, so we've chosen to represent them as a red `CircleMarker`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tiles = \"openstreetmap\"\n", "edinburgh_centre = (55.953251, -3.188267)\n", "\n", "m = folium.Map(location=edinburgh_centre, tiles=tiles, zoom_start=12)\n", "\n", "for loc in locations:\n", " point = [loc[0], loc[1]]\n", " folium.CircleMarker(location=point, \n", " radius = 5,\n", " popup= loc[2],\n", " color = 'red',\n", " weight = 1,\n", " fill='true',\n", " fill_color='red',\n", " fill_opacity=0.25).add_to(m)\n", "\n", "# m.save('pollinators_circlmarkers.html') # do this if you want to save the map as a standalone html file.\n", "m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Version 2: Import locations as GeoJSON \n", "\n", "This alternative approach uses [GeoPandas](https://geopandas.readthedocs.io/en/latest/) to help organise the data as GeoJSON.\n", "\n", "We start off by creating a list of shapely `Point` objects." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(226, 4)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "points = [Point(x, y) for x, y in zip(df.longitude, df.latitude)]\n", "\n", "polli_gdf = gpd.GeoDataFrame(df, geometry=points) # create a GeoDataFrame\n", "polli_gdf.crs = {\"init\": \"epsg:4326\"} # set a the Coordinate Reference System\n", "polli_gdf.shape" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tiles = \"openstreetmap\"\n", "edinburgh_centre = (55.953251, -3.188267)\n", "\n", "m = folium.Map(location=edinburgh_centre, tiles=tiles, zoom_start=12)\n", "\n", "style_function = lambda x: {\"fillColor\": \"#00FFFFFF\", \"color\": \"#000000\"}\n", "\n", "polli_geo = folium.GeoJson(\n", " polli_gdf,\n", " tooltip=folium.GeoJsonTooltip(\n", " fields=[\"type\"], \n", " labels=True,\n", " sticky=False,\n", " ),\n", " style_function=style_function\n", ")\n", "\n", "m.add_child(polli_geo)\n", "\n", "m.save(\"pollinators_markers.html\")\n", "m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Adding a new layer: Edinburgh Green Space Audit\n", "\n", "One of the nice things we can do with this kind of map is add a new layer. It would be interesting to see how the pollinator locations relate to other green spaces in the city. So let's use data from the Council's Green Space Audit. You can find out some more information about it on the Council's [Mapping Portal](http://data.edinburghcouncilmaps.info/datasets/223949a6212f4068b30aa6ed8fc2e1ef_15)\n", "\n", "We can fetch the data in GeoJSON format from the Mapping Portal using the GeoPandas `read_file()` method." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "edinburgh_greenspaces = gpd.read_file(\n", " \"http://data.edinburghcouncilmaps.info/datasets/223949a6212f4068b30aa6ed8fc2e1ef_15.geojson\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we look at the columns in the `GeoDataFrame`, there is quite a lot of information:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['OBJECTID_1', 'OBJECTID', 'Id', 'Ownership', 'Access', 'ry_Use',\n", " 'Shape_Leng', 'PAN65', 'Shape_Le_1', 'NP_Name', 'Np_NO', 'CLASSIFICA',\n", " 'NAME', 'YEAROPEN', 'Comments', 'PF_Quality', 'PF_CF', 'AuditScore',\n", " 'Area_ha', 'OS_Quality', 'PQA_Grade', 'OLD_OS_Ref', 'OS_Ref',\n", " 'Shapearea', 'Shapelen', 'geometry'],\n", " dtype='object')" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "edinburgh_greenspaces.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "However, we are mainly interested in the `geometry` column, which consists of a series of shapely `Polygon` objects." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 (POLYGON ((-3.191305985701971 55.9798613048917...\n", "1 (POLYGON ((-3.191575981920173 55.9736176672753...\n", "2 POLYGON ((-3.192261702385649 55.9744905596996,...\n", "3 POLYGON ((-3.189952463388474 55.97719496687115...\n", "4 POLYGON ((-3.189397958243322 55.97560093581826...\n", "5 POLYGON ((-3.179811420034048 55.97683150098928...\n", "6 POLYGON ((-3.186472913977041 55.97290840346809...\n", "7 POLYGON ((-3.180550860742277 55.97340911991013...\n", "8 POLYGON ((-3.180143933366065 55.9741876890718,...\n", "9 POLYGON ((-3.180884988531127 55.97478681461671...\n", "Name: geometry, dtype: object" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "edinburgh_greenspaces['geometry'][:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Folium's `GeoJson()` method makes it simple to read in this data and add it to the map `m` that we created earlier." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "style_function = lambda x: {\"fillColor\": \"#00FFFFFF\", \"color\": \"#000000\"}\n", "\n", "greenspace_geo = folium.GeoJson(\n", " edinburgh_greenspaces,\n", " tooltip=folium.features.GeoJsonTooltip(\n", " fields=[\"NAME\", \"PAN65\", \"AuditScore\"],\n", " labels=True,\n", " sticky=False,\n", " ),\n", " style_function=style_function,\n", ")\n", "\n", "m.add_child(greenspace_geo)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bee Tracks\n", "\n", "SWT has supplied us with polygon data which shows bumblebee movement across the city. The following `.shp` file is just a big polygon which we can again read in using GeoPandas." ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "# the url that we used before!\n", "# url = 'https://raw.githubusercontent.com/prewired/workshops/master/data/processed/'\n", "fn = 'Bombus_HSI_0.5_dissolve.geojson'\n", "fn = path.join(url, fn)\n", "bombus = gpd.read_file(fn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The default `plot()` method in GeoPandas uses the matplotlib library." ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "bombus.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To make this look a bit bigger, and get rid of the axes, we need to use a bit more of matplotlib." ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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