{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Map Trove newspaper results by place of publication\n", "\n", "In another notebook, I explored some ways in which you could [map Trove newspaper results using the `state` facet](Map-newspaper-results-by-state.ipynb). In this notebook we'll go a bit deeper and map the actual **locations** in which the newspapers returned by our search results were published.\n", "\n", "To do this, we'll use the `title` facet. This returns a list of all the newspapers in our results, and the number of matching articles in each.\n", "\n", "You can use this notebook to visualise your own search queries, just edit the search parameters were indicated.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
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If you haven't used one of these notebooks before, they're basically web pages in which you can write, edit, and run live code. They're meant to encourage experimentation, so don't feel nervous. Just try running a few cells and see what happens!.

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\n", " Some tips:\n", "

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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Add your API key\n", "\n", "You need an [API key](http://help.nla.gov.au/trove/building-with-trove/api) to get data from Trove." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Your API key is: \n" ] } ], "source": [ "# This creates a variable called 'api_key', paste your key between the quotes\n", "# <-- Then click the run icon \n", "api_key = ''\n", "\n", "# This displays a message with your key\n", "print('Your API key is: {}'.format(api_key))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setting things up\n", "\n", "You don't need to edit anything here. Just run the cells to load the bits and pieces we need." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RendererRegistry.enable('notebook')" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Import the libraries we need\n", "# <-- Click the run icon \n", "import requests\n", "import pandas as pd\n", "import os\n", "import altair as alt\n", "import json\n", "import folium\n", "from folium.plugins import MarkerCluster\n", "from folium.plugins import HeatMap\n", "import numpy as np\n", "alt.renderers.enable('notebook')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Set up default parameters for our API query\n", "# <-- Click the run icon \n", "params = {\n", " 'zone': 'newspaper',\n", " 'encoding': 'json',\n", " 'facet': 'title',\n", " 'n': '1',\n", " 'key': api_key\n", "}\n", "\n", "api_url = 'http://api.trove.nla.gov.au/v2/result'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Construct your search\n", "\n", "This is where you set your search keywords. Change 'weather AND wragge date:[* TO 1954]' in the cell below to anything you might enter in the Trove simple search box. For example:\n", "\n", "`params['q'] = 'weather AND wragge'`\n", "\n", "`params['q'] = '\"Clement Wragge\"'`\n", "\n", "`params['q'] = 'text:\"White Australia Policy\"'`\n", "\n", "`params['q'] = 'weather AND date:[1890-01-01T00:00:00Z TO 1920-12-11T00:00:00Z]'`\n", "\n", "You can also limit the results to specific categories. To only search for articles, include this line:\n", "\n", "`params['l-category'] = 'Article'`" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Enter your search parameters\n", "# This can be anything you'd enter in the Trove simple search box\n", "params['q'] = 'weather AND wragge date:[* TO 1954]'\n", "\n", "# Remove the \"#\" symbol from the line below to limit the results to the article category\n", "#params['l-category'] = 'Article'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get the data from Trove\n", "\n", "Everything's set up, so just run the cells!\n", "\n", "### Make an API request" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# <-- Click the run icon \n", "response = requests.get(api_url, params=params)\n", "data = response.json()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reformat the results" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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title_idtotal
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" ], "text/plain": [ " title_id total\n", "0 840 4840\n", "1 16 4815\n", "2 508 1688\n", "3 10 1550\n", "4 74 1513" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# <-- Click the run icon \n", "def format_facets(data):\n", " facets = data['response']['zone'][0]['facets']['facet']['term']\n", " df = pd.DataFrame(facets)\n", " df = df[['display', 'count']]\n", " df.columns = ['title_id', 'total']\n", " df['total'] = pd.to_numeric(df['total'], errors='coerce')\n", " return df\n", "df = format_facets(data)\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load location data\n", "\n", "I've previously created a [CSV file](data/trove-newspaper-titles-locations.csv) that provides geolocated places of publication for newspapers in Trove. Some newspapers are associated with multiple places (for example a cluster of nearby country towns), so the CSV file can contain multiple rows for a single newspaper title. Note also that any newspapers that were added to Trove since I last harvested the locations in April 2018 will drop out of the data.\n", "\n", "We're going to merge the facets data with my geolocated titles file, matching on the `title_id`. We'll only take the first matching row from the geolocated data." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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title_idtotaltitlestateplace_idplacelatlon
08404840The Telegraph (Brisbane, Qld. : 1872 - 1947)QLDQLD4555Brisbane-27.467848153.028013
1164815The Brisbane Courier (Qld. : 1864 - 1933)QLDQLD4555Brisbane-27.467848153.028013
25081688Evening News (Sydney, NSW : 1869 - 1931)NSWNSW79218Sydney-33.873200151.209600
3101550The Mercury (Hobart, Tas. : 1860 - 1954)TASTAS00752Hobart-42.880001147.320007
4741513Launceston Examiner (Tas. : 1842 - 1899)TASTAS00338Launceston-41.439999147.139999
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" ], "text/plain": [ " title_id total title state \\\n", "0 840 4840 The Telegraph (Brisbane, Qld. : 1872 - 1947) QLD \n", "1 16 4815 The Brisbane Courier (Qld. : 1864 - 1933) QLD \n", "2 508 1688 Evening News (Sydney, NSW : 1869 - 1931) NSW \n", "3 10 1550 The Mercury (Hobart, Tas. : 1860 - 1954) TAS \n", "4 74 1513 Launceston Examiner (Tas. : 1842 - 1899) TAS \n", "\n", " place_id place lat lon \n", "0 QLD4555 Brisbane -27.467848 153.028013 \n", "1 QLD4555 Brisbane -27.467848 153.028013 \n", "2 NSW79218 Sydney -33.873200 151.209600 \n", "3 TAS00752 Hobart -42.880001 147.320007 \n", "4 TAS00338 Launceston -41.439999 147.139999 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get the geolocated data\n", "locations = pd.read_csv('data/trove-newspaper-titles-locations.csv', names=['title_id', 'title', 'state', 'place_id', 'place', 'lat', 'lon'])\n", "# Only keep the first instance of each title\n", "locations.drop_duplicates(subset=['title_id'], keep='first', inplace=True)\n", "# Merge the facets and the geolocated data\n", "df_located = pd.merge(df, locations, on='title_id', how='left')\n", "df_located.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Display top 20 newspapers\n", "\n", "Now we have titles for our newspaper facets, let's chart the top twenty." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}}, \"data\": {\"name\": \"data-26b0ffd267e7529aa1f672a3d13f84c5\"}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"type\": \"quantitative\", \"field\": \"total\", \"title\": \"Number of articles\"}, \"y\": {\"type\": \"nominal\", \"field\": \"title\", \"sort\": [\"The Telegraph (Brisbane, Qld. : 1872 - 1947)\", \"The Brisbane Courier (Qld. : 1864 - 1933)\", \"Evening News (Sydney, NSW : 1869 - 1931)\", \"The Mercury (Hobart, Tas. : 1860 - 1954)\", \"Launceston Examiner (Tas. : 1842 - 1899)\", \"The Maitland Daily Mercury (NSW : 1894 - 1939)\", \"Examiner (Launceston, Tas. : 1900 - 1954)\", \"The Age (Melbourne, Vic. : 1854 - 1954)\", \"The Daily Telegraph (Sydney, NSW : 1883 - 1923)\", \"Daily Telegraph (Launceston, Tas. : 1883 - 1928)\", \"The Australian Star (Sydney, NSW : 1887 - 1909)\", \"Queensland Times, Ipswich Herald and General Advertiser (Qld. : 1861 - 1908)\", \"Maryborough Chronicle, Wide Bay and Burnett Advertiser (Qld. : 1860 - 1947)\", \"The Sydney Morning Herald (NSW : 1842 - 1954)\", \"The Argus (Melbourne, Vic. : 1848 - 1957)\", \"Advocate (Burnie, Tas. : 1890 - 1954)\", \"Morning Bulletin (Rockhampton, Qld. : 1878 - 1954)\", \"South Australian Register (Adelaide, SA : 1839 - 1900)\", \"Evening Journal (Adelaide, SA : 1869 - 1912)\", \"Newcastle Morning Herald and Miners' Advocate (NSW : 1876 - 1954)\"], \"title\": \"\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.6.0.json\", \"datasets\": {\"data-26b0ffd267e7529aa1f672a3d13f84c5\": [{\"title_id\": 840, \"total\": 4840, \"title\": \"The Telegraph (Brisbane, Qld. : 1872 - 1947)\", \"state\": \"QLD\", \"place_id\": \"QLD4555\", \"place\": \"Brisbane\", \"lat\": -27.46784774, \"lon\": 153.02801330000003}, {\"title_id\": 16, \"total\": 4815, \"title\": \"The Brisbane Courier (Qld. : 1864 - 1933)\", \"state\": \"QLD\", \"place_id\": \"QLD4555\", \"place\": \"Brisbane\", \"lat\": -27.46784774, \"lon\": 153.02801330000003}, {\"title_id\": 508, \"total\": 1688, \"title\": \"Evening News (Sydney, NSW : 1869 - 1931)\", \"state\": \"NSW\", \"place_id\": \"NSW79218\", \"place\": \"Sydney\", \"lat\": -33.8732, \"lon\": 151.2096}, {\"title_id\": 10, \"total\": 1550, \"title\": \"The Mercury (Hobart, Tas. : 1860 - 1954)\", \"state\": \"TAS\", \"place_id\": \"TAS00752\", \"place\": \"Hobart\", \"lat\": -42.88000107, \"lon\": 147.32000730000001}, {\"title_id\": 74, 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" }, "metadata": { "jupyter-vega": "#edfe7902-6cc1-4a37-ad05-32601adfbbb6" }, "output_type": "display_data" } ], "source": [ "alt.Chart(df_located[:20]).mark_bar().encode(\n", " y=alt.Y('title', sort=df_located['title'][:20].tolist(), title=''),\n", " x=alt.X('total', title='Number of articles')\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Map places of publication\n", "\n", "More than one newspaper can be associated with a place, so rather than map individual newspapers, we'll group them by place." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Group newspapers by place\n", "df_places = df_located.groupby(['place', 'lat', 'lon'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's the fun part. We'll create a map, then we'll loop through the places, getting the total number of articles from all the grouped newspapers." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m = folium.Map(\n", " location=[-30, 135],\n", " zoom_start=4\n", ")\n", "# We'll cluster the markers for better readability\n", "marker_cluster = MarkerCluster().add_to(m)\n", "\n", "for place, group in df_places:\n", " # Get the total articles from the grouped titles\n", " total = group['total'].sum()\n", " # Turn all the grouped title_ids into a string that we can use in a Trove search url\n", " titles = group['title_id'].astype('str').str.cat(sep='&l-title=')\n", " # Create the content of the marker popup -- includes a search link back to Trove!\n", " html = '{}
{} articles'.format(place[0], params['q'], titles, params.get('l-category', ''), total)\n", " # Add the marker to the map\n", " folium.Marker([place[1], place[2]], popup=html).add_to(marker_cluster)\n", "\n", "m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Play around with the map. Note the link on the total number of articles in the pop-ups — it should open Trove and find the matching articles!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Make a heatmap\n", "\n", "The map above is great from browsing, but doesn't give much of a sense of the **number** of results in each place. Let's try creating a heatmap instead.\n", "\n", "To populate a heatmap we just need a list of coordinates — one set of coordinates for each article." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Get the total number of articles for each place\n", "df_totals = df_places.sum()\n", "locations = []\n", "# Loop through the places\n", "for place in df_totals.index:\n", " # Get the total\n", " total = df_totals.loc[place]['total']\n", " # Add the coordinates of the place to the list of locations as many times as there are articles\n", " locations += ([[place[1], place[2]]] * total)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create another map\n", "m2 = folium.Map(\n", " location=[-30, 135],\n", " zoom_start=4\n", ")\n", "\n", "#Add the heatmap data!\n", "HeatMap(locations).add_to(m2)\n", "m2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's looking pretty interesting. Hmmmm, it would be nice if we could animate this through time, but we'd need more data. Perhaps a [future notebook](Map-newspaper-results-by-place-of-publication-over-time.ipynb) topic?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }