{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sujet 3 : L'épidémie de choléra à Londres en 1854" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous chargeons deux bibliothèques spécifiques au traitement des données spatiales :\n", "\n", "* [folium](https://python-visualization.github.io/folium/index.html), qui permet d'afficher des cartes avec python et Leaflet.js ;\n", "* [geopandas](https://geopandas.org/) qui permet de prendre en compte la nature géographique des données traitées. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import folium\n", "import pyproj\n", "import geopandas as gpd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Importation et prétrtaitements des données" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous lisons les fichiers géographiques (format Shape d'ESRI), et les convertissions en *dataframes* de la bibliothèque geopandas." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cas de choléras" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "cholera_deaths = gpd.read_file('Cholera_Deaths.shp',encoding='ANSI') " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'proj': 'tmerc',\n", " 'lat_0': 49,\n", " 'lon_0': -2,\n", " 'k': 0.9996012717,\n", " 'x_0': 400000,\n", " 'y_0': -100000,\n", " 'ellps': 'airy',\n", " 'units': 'm',\n", " 'no_defs': True}" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cholera_deaths.crs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous reprojetons ces couches géographiques dans un référentiel géographique (défini par le code EPSG) exploitable sous folium. Attention, la valeur de l'attribut 'crs' écrit sous la forme \"EPSG:4326\" ne fonctionnera pas sous Windows (version pyproj 3.0.0.post1) :\n", "\n", "* ligne 359-360 de la fonction \\_\\_init\\_\\_.py : *# EPSG only works on case-insensitive filesystems* \n", "* nous utilisons donc l'autre manière d'écrire cet argument (https://geopandas.org/projections.html), en minuscule\n", "\n", "Les deux écritures sont cependant possible sous Linux : cf. version pyproj, et ce que dit le fichier \\_\\_init\\_\\_.py ?" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "cholera_deaths = cholera_deaths.to_crs(epsg = 4326)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comme nous avons beaucoup de cas à cartographier, nous convertissons notre geopandas *dataframe* en un fichier **geojson**, un format de données géographiques courant, directement lisible par folium." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "cholera_deaths.to_file(\"cholera_deaths.geojson\", driver='GeoJSON')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous initialisons la carte produite par **folium**, en la centrant sur un point et en définissant un niveau de zoom." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "### Initialisation de la carte, et ajout direct des cas de cholera" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "carte_snow_1854 = folium.Map(location = [51.514 , -0.1365], zoom_start=17)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "folium.GeoJson(\"cholera_deaths.geojson\", name=\"Incidences\").add_to(carte_snow_1854)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pompes à eau" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "pumps = gpd.read_file('Pumps.shp')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "pumps = pumps.to_crs(epsg = 4326)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "pumps.to_file(\"pumps.geojson\", driver = 'GeoJSON')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[folium.CircleMarker([pumps.geometry.y[i], pumps.geometry.x[i]] , popup = \"Pompes\", color = 'red', radius = 100).add_to(carte_snow_1854) for i in range(len(pumps.geometry.x))]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La méthode *LayerControl* permet de gérer l'affichage de plusieurs couches géographiques" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "folium.LayerControl().add_to(carte_snow_1854)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Affichage de la carte" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Make this Notebook Trusted to load map: File -> Trust Notebook
" ], "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "carte_snow_1854" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 4 }