explore_covid_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring COVID-19 data from Finland\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quick exploration of the COVID-19 cases in Finland based on a [data set shared by the Helsingin Sanomat datadesk](https://github.com/HS-Datadesk/koronavirus-avoindata) which I spotted on [Twitter](https://twitter.com/EsaMakinen/status/1237007961853173761).\n",
    "\n",
    "**Please note the data here is outdated**, but this notebook might give you tips and hints on how to get the data into Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing required python packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from pyproj import CRS\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read and preprocess input data\n",
    "\n",
    "\n",
    "- **Coronavirus cases in Finland**\n",
    "    - Data compiled by Helsingin Sanomat Datadesk\n",
    "    - Json file downloaded from https://github.com/HS-Datadesk/koronavirus-avoindata\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading the json as a dict\n",
    "# with a little help from here: https://stackoverflow.com/questions/28373282/how-to-read-a-json-dictionary-type-file-with-pandas\n",
    "with open('latestData.json') as json_data:\n",
    "    data_dict = json.load(json_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a placeholder also for deaths (no data on deaths at the time of writing this).."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['confirmed', 'deaths'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dict.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will continue with the confirmed cases:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>healthCareDistrict</th>\n",
       "      <th>infectionSourceCountry</th>\n",
       "      <th>infectionSource</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-29T13:00:00.000Z</td>\n",
       "      <td>Lappi</td>\n",
       "      <td>CHN</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2020-02-26T13:00:00.000Z</td>\n",
       "      <td>HUS</td>\n",
       "      <td>ITA</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2020-03-01T13:00:00.000Z</td>\n",
       "      <td>HUS</td>\n",
       "      <td>FIN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2020-02-28T13:00:00.000Z</td>\n",
       "      <td>HUS</td>\n",
       "      <td>ITA</td>\n",
       "      <td>unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2020-03-01T13:00:00.000Z</td>\n",
       "      <td>HUS</td>\n",
       "      <td>FIN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  id                      date healthCareDistrict infectionSourceCountry  \\\n",
       "0  1  2020-01-29T13:00:00.000Z              Lappi                    CHN   \n",
       "1  2  2020-02-26T13:00:00.000Z                HUS                    ITA   \n",
       "2  3  2020-03-01T13:00:00.000Z                HUS                    FIN   \n",
       "3  4  2020-02-28T13:00:00.000Z                HUS                    ITA   \n",
       "4  5  2020-03-01T13:00:00.000Z                HUS                    FIN   \n",
       "\n",
       "  infectionSource  \n",
       "0         unknown  \n",
       "1         unknown  \n",
       "2               2  \n",
       "3         unknown  \n",
       "4               4  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid = pd.DataFrame.from_dict(data_dict[\"confirmed\"])\n",
    "covid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of confirmed cases in this input data: 30\n"
     ]
    }
   ],
   "source": [
    "print(\"number of confirmed cases in this input data:\", len(covid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in health district polygons\n",
    "\n",
    "There is a separate notebook where I created the health district polygons. Here, we just read them in using Geopandas: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "districts = gpd.read_file('healthDistrictsEPSG3067.geojson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>healthCareDistrict</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ahvenanmaa</td>\n",
       "      <td>MULTIPOLYGON (((173277.623 6640282.925, 173136...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Etelä-Karjala</td>\n",
       "      <td>POLYGON ((595843.841 6772915.996, 592557.900 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Etelä-Pohjanmaa</td>\n",
       "      <td>POLYGON ((373036.311 6953044.961, 376149.768 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Etelä-Savo</td>\n",
       "      <td>POLYGON ((596327.952 6823806.064, 596718.363 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HUS</td>\n",
       "      <td>MULTIPOLYGON (((272609.681 6632304.439, 272418...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  healthCareDistrict                                           geometry\n",
       "0         Ahvenanmaa  MULTIPOLYGON (((173277.623 6640282.925, 173136...\n",
       "1      Etelä-Karjala  POLYGON ((595843.841 6772915.996, 592557.900 6...\n",
       "2    Etelä-Pohjanmaa  POLYGON ((373036.311 6953044.961, 376149.768 6...\n",
       "3         Etelä-Savo  POLYGON ((596327.952 6823806.064, 596718.363 6...\n",
       "4                HUS  MULTIPOLYGON (((272609.681 6632304.439, 272418..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "districts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13292bb5908>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "districts.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Join covid-19 cases to districts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "join confirmed cases to districts based on common key:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_and_regions = districts.merge(covid, on=\"healthCareDistrict\", how=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['healthCareDistrict', 'geometry', 'id', 'date',\n",
       "       'infectionSourceCountry', 'infectionSource'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_and_regions.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confirmed cases per region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "id_count = covid_and_regions.groupby(by='healthCareDistrict').id.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "healthCareDistrict\n",
       "HUS                  18\n",
       "Pirkanmaa             5\n",
       "Varsinais-Suomi       1\n",
       "Pohjois-Savo          1\n",
       "Pohjois-Pohjanmaa     1\n",
       "Lappi                 1\n",
       "Kanta-Häme            0\n",
       "Etelä-Karjala         0\n",
       "Etelä-Pohjanmaa       0\n",
       "Etelä-Savo            0\n",
       "Itä-Savo              0\n",
       "Kainuu                0\n",
       "Kymenlaakso           0\n",
       "Keski-Pohjanmaa       0\n",
       "Keski-Suomi           0\n",
       "Vaasa                 0\n",
       "Länsi-Pohja           0\n",
       "Pohjois-Karjala       0\n",
       "Päijät-Häme           0\n",
       "Satakunta             0\n",
       "Ahvenanmaa            0\n",
       "Name: confirmed_count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id_count.rename(\"confirmed_count\", inplace=True).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Join count of cases to district polygons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "districts = districts.merge(id_count, left_on='healthCareDistrict', right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set zero to no data for plotting\n",
    "districts.replace({0:np.nan}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE:** it is generally not a good idea to show absolute values on a map. This plot is just for exploratory purposes.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40857.339215, 765862.479085, 6575077.820610001, 7833703.7515899995)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot values on the map\n",
    "districts.plot(column='confirmed_count', \n",
    "               cmap='OrRd', \n",
    "               edgecolor=\"lightgray\", \n",
    "               linewidth=.2,\n",
    "               scheme=\"Natural_Breaks\", \n",
    "               k=3, \n",
    "               missing_kwds={'color': 'white'})\n",
    "\n",
    "# Remove frame\n",
    "plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cases per source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "source_count = covid_and_regions.groupby(by='infectionSourceCountry').id.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "infectionSourceCountry\n",
       "CHN     1\n",
       "FIN     8\n",
       "ITA    18\n",
       "Name: cases_per_country, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source_count.rename(\"cases_per_country\", inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Join sources to countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Geopandas comes with natural earth polygons\n",
    "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pop_est</th>\n",
       "      <th>continent</th>\n",
       "      <th>name</th>\n",
       "      <th>iso_a3</th>\n",
       "      <th>gdp_md_est</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>920938</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>FJI</td>\n",
       "      <td>8374.0</td>\n",
       "      <td>MULTIPOLYGON (((180.00000 -16.06713, 180.00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53950935</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>TZA</td>\n",
       "      <td>150600.0</td>\n",
       "      <td>POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>603253</td>\n",
       "      <td>Africa</td>\n",
       "      <td>W. Sahara</td>\n",
       "      <td>ESH</td>\n",
       "      <td>906.5</td>\n",
       "      <td>POLYGON ((-8.66559 27.65643, -8.66512 27.58948...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35623680</td>\n",
       "      <td>North America</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1674000.0</td>\n",
       "      <td>MULTIPOLYGON (((-122.84000 49.00000, -122.9742...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>326625791</td>\n",
       "      <td>North America</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>USA</td>\n",
       "      <td>18560000.0</td>\n",
       "      <td>MULTIPOLYGON (((-122.84000 49.00000, -120.0000...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     pop_est      continent                      name iso_a3  gdp_md_est  \\\n",
       "0     920938        Oceania                      Fiji    FJI      8374.0   \n",
       "1   53950935         Africa                  Tanzania    TZA    150600.0   \n",
       "2     603253         Africa                 W. Sahara    ESH       906.5   \n",
       "3   35623680  North America                    Canada    CAN   1674000.0   \n",
       "4  326625791  North America  United States of America    USA  18560000.0   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTIPOLYGON (((180.00000 -16.06713, 180.00000...  \n",
       "1  POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...  \n",
       "2  POLYGON ((-8.66559 27.65643, -8.66512 27.58948...  \n",
       "3  MULTIPOLYGON (((-122.84000 49.00000, -122.9742...  \n",
       "4  MULTIPOLYGON (((-122.84000 49.00000, -120.0000...  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "world = world.merge(source_count, left_on=\"iso_a3\", right_index=True, how=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>cases_per_country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>China</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Italy</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Finland</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name  cases_per_country\n",
       "139    China                1.0\n",
       "141    Italy               18.0\n",
       "151  Finland                8.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world[[\"name\",\"cases_per_country\"]].dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... I stop here, but feel free to use these code snippets for further purposes! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**REMINDER:** Data used in this repository is outdated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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