{"id":126,"date":"2020-09-02T21:46:16","date_gmt":"2020-09-02T18:16:16","guid":{"rendered":"http:\/\/mahshad.pro\/?p=126"},"modified":"2021-02-11T23:07:15","modified_gmt":"2021-02-11T19:37:15","slug":"how-to-make-animated-choropleth-maps-with-discrete-colors-using-python-and-plotly","status":"publish","type":"post","link":"https:\/\/mahshad.pro\/?p=126","title":{"rendered":"How to make animated choropleth maps with discrete colors using Python and Plotly"},"content":{"rendered":"\n

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This tutorial is also available on Medium.com<\/a>
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Since I was a little boy I have been obsessed with the maps! I remember that I used to buy paper sheet maps of different continents and have them pinned on my room\u2019s wall. Back in such old days, maps were used to be some static shapes that conveyed a limited amount of information. Fast forward to nowadays, we barely pass a day without benefiting from advancements of digital maps. <\/p>\n\n\n\n

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Image taken from the page 1130 of \u2018History of England and the British Empire. Photo by British Library<\/a> on Unsplash<\/a>
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Plotly<\/a> is a data visualization library that provides a wide variety of basic visualization charts, statistical charts, scientific charts, financial charts, maps, 3D charts, animated graphs, etc. for different types of visualization applications. Plotly Express<\/a> is an easy-to-use and high-level interface to Plotly, which operates with a variety of data and make it easy to create professional looking graphs. We can either use Mapbox or Plotly\u2019s built-in maps for visualization. We will use Plotly\u2019s maps in this tutorial.<\/p>\n\n\n\n

Animated maps are useful when we want to show a situation or value change in a variety of geographic regions over the course of time.<\/strong><\/p><\/blockquote>\n\n\n\n

Dataset<\/h4>\n\n\n\n

Since COVID-19 has been a hot topic during past few months and most users are familiar with how quick the virus can spread, we used the COVID-19 data set from Government of Canada\u2019s website<\/a> for this tutorial. The ultimate objective here is to create an animated choropleth map of Canadian provinces that shows the spread of COVID-19 through a weekly time frame.<\/p>\n\n\n\n

Geojson data<\/h4>\n\n\n\n

For choropleth maps we will need a .geojson file which in simple word, indicates the boundaries for a region with sets of vectors. Other information related to the boundary such as name, description, etc. is usually include in the .geojson file. For Canadian provinces geojson file, you can either download the boundary file from Canadian statistics<\/a> and convert it to a geojson file, or use the available geojson files. For this project I am using the geojson file taken from Carto<\/a>.<\/p>\n\n\n\n

Data cleaning<\/strong><\/h4>\n\n\n\n

We will need the date, number of cases, province id and name. Since we would like to have a weekly time frame, we will create timeframe <\/code>column which shows the month name and week number of that month as below. We can make such adjustments either in Excel or directly in Python. The cleaned dataset file is available on my GitHub<\/a>.<\/p>\n\n\n\n

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dataset after few adjustments in date format and the weekly time frame <\/figcaption><\/figure>\n\n\n\n

As you can see in the dataset, we have the total number of cases for each day for each province. By using \u2018cases\u2019 as the color value we will have a continuous color legend. But since we would like to have discrete color setting, we need to add a new column called \u2018category\u2019 and assign each category based on the number of cases for each row as below:<\/p>\n\n\n\n

import pandas as pd\n\ndf = pd.read_csv(\"ca_pr_day_n.csv\")\ndf['category'] = ''\n\n#categorizing the number of cases and assign each category to each row\ndef set_cat(row):\n    if row['cases'] == 0:\n        return '0'\n    if row['cases'] > 0 and row['cases'] < 1001:\n        return '1 - 1,000'\n    if row['cases'] > 1001 and row['cases'] < 5001:\n        return '1,001 - 5,000'\n    if row['cases'] > 5001 and row['cases'] < 10001:\n        return '5,001 - 10,000'\n    if row['cases'] > 10001 and row['cases'] < 30001:\n        return '10,001 - 30,000'\n    if row['cases'] > 30001 and row['cases'] < 50001:\n        return '30,001 - 50,000'\n    if row['cases'] > 50001:\n        return '50,001 and higher'\n\ndf = df.assign(category=df.apply(set_cat, axis=1))<\/code><\/pre>\n\n\n\n
Map creation<\/h4>\n\n\n\n
Having the data cleaned and grouped in the data frame, now we can proceed to create the choropleth map:<\/p>\n\n\n\n
import pandas as pd\nimport plotly_express as px\nimport json\n\ndf = pd.read_csv(\"ca_pr_day_n.csv\")\ndf['category'] = ''\n\n#categorizing the number of cases and assign each category to each row\ndef set_cat(row):\n    if row['cases'] == 0:\n        return '0'\n    if row['cases'] > 0 and row['cases'] < 1001:\n        return '1 - 1,000'\n    if row['cases'] > 1001 and row['cases'] < 5001:\n        return '1,001 - 5,000'\n    if row['cases'] > 5001 and row['cases'] < 10001:\n        return '5,001 - 10,000'\n    if row['cases'] > 10001 and row['cases'] < 30001:\n        return '10,001 - 30,000'\n    if row['cases'] > 30001 and row['cases'] < 50001:\n        return '30,001 - 50,000'\n    if row['cases'] > 50001:\n        return '50,001 and higher'\n\ndf = df.assign(category=df.apply(set_cat, axis=1))\n\n# assign mp to the geojson data\nwith open(\"canada_provinces.geojson\", \"r\") as geo:\n    mp = json.load(geo)\n\n# Create choropleth map\nfig = px.choropleth(df,\n                    locations=\"cartodb_id\",\n                    geojson=mp,\n                    featureidkey=\"properties.cartodb_id\",\n                    color=\"category\",\n                    color_discrete_map={\n                        '0': '#fffcfc',\n                        '1 - 1,000' : '#ffdbdb',\n                        '1,001 - 5,000' : '#ffbaba',\n                        '5,001 - 10,000' : '#ff9e9e',\n                        '10,001 - 30,000' : '#ff7373',\n                        '30,001 - 50,000' : '#ff4d4d',\n                        '50,001 and higher' : '#ff0d0d'},\n                    category_orders={\n                      'category' : [\n                          '0',\n                          '1 - 1,000',\n                          '1,001 - 5,000',\n                          '5,001 - 10,000',\n                          '10,001 - 30,000',\n                          '30,001 - 50,000',\n                          '50,001 and higher'\n                      ]\n                    },\n                    animation_frame=\"timeframe\",\n                    scope='north america',\n                    title='<b>COVID-19 cases in Canadian provinces<\/b>',\n                    labels={'cases' : 'Number of Cases',\n                            'category' : 'Category'},\n                    hover_name='province',\n                    hover_data={\n                        'cases' : True,\n                        'cartodb_id' : False\n                    },\n                    # height=900,\n                    locationmode='geojson-id',\n                    )\n\n# Adjust map layout stylings\nfig.update_layout(\n    showlegend=True,\n    legend_title_text='<b>Total Number of Cases<\/b>',\n    font={\"size\": 16, \"color\": \"#808080\", \"family\" : \"calibri\"},\n    margin={\"r\":0,\"t\":40,\"l\":0,\"b\":0},\n    legend=dict(orientation='v'),\n    geo=dict(bgcolor='rgba(0,0,0,0)', lakecolor='#e0fffe')\n)\n\n# Adjust map geo options\nfig.update_geos(showcountries=False, showcoastlines=False,\n                showland=False, fitbounds=\"locations\",\n                subunitcolor='white')\nfig.show()\n<\/code><\/pre>\n\n\n\n
Few points worth to be mentioned here:<\/p>\n\n\n\n