Prepare georeferenced data for artificial disease outbreak in Europe

In this notebook geospatial data from a shapefile is loaded, relevant attributes extracte and

%matplotlib inline
import geopandas as gp
import pandas as pd

The geospatial vector dataset shapefile was downloaded from open access NaturalEarthData, which provides free vector and raster map data.

Download: http://www.naturalearthdata.com/http//www.naturalearthdata.com/download/110m/cultural/ne_110m_admin_0_countries.zip

shp = 'Shapefile/ne_110m_admin_0_countries.shp'
geo_df = gp.GeoDataFrame.from_file(shp)
geo_df.head(1)
featurecla scalerank LABELRANK SOVEREIGNT SOV_A3 ADM0_DIF LEVEL TYPE TLC ADMIN ... FCLASS_TR FCLASS_ID FCLASS_PL FCLASS_GR FCLASS_IT FCLASS_NL FCLASS_SE FCLASS_BD FCLASS_UA geometry
0 Admin-0 country 1 6 Fiji FJI 0 2 Sovereign country 1 Fiji ... None None None None None None None None None MULTIPOLYGON (((180.00000 -16.06713, 180.00000...

1 rows × 169 columns

Attributes in shapefile

geo_df.columns

Index(['featurecla', 'scalerank', 'LABELRANK', 'SOVEREIGNT', 'SOV_A3',
       'ADM0_DIF', 'LEVEL', 'TYPE', 'TLC', 'ADMIN',
       ...
       'FCLASS_TR', 'FCLASS_ID', 'FCLASS_PL', 'FCLASS_GR', 'FCLASS_IT',
       'FCLASS_NL', 'FCLASS_SE', 'FCLASS_BD', 'FCLASS_UA', 'geometry'],
      dtype='object', length=169)

Take only European countries and exclude Russia

geo_df_EU = geo_df[geo_df["REGION_UN"] == "Europe"]

geo_df_EU.set_index("ADMIN", inplace=True)
geo_df_EU.drop('Russia', axis=0, inplace=True)
geo_df_EU.reset_index(inplace=True)

/home/eneko/.miniconda3/envs/cook/lib/python3.7/site-packages/pandas/core/frame.py:4913: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  errors=errors,

Retrieve relevant data

geo_df_EU = geo_df_EU[['geometry', 'POP_EST', 'ADMIN']]
geo_df_EU.head()
geometry POP_EST ADMIN
0 MULTIPOLYGON (((15.14282 79.67431, 15.52255 80... 5347896.0 Norway
1 MULTIPOLYGON (((-51.65780 4.15623, -52.24934 3... 67059887.0 France
2 POLYGON ((11.02737 58.85615, 11.46827 59.43239... 10285453.0 Sweden
3 POLYGON ((28.17671 56.16913, 29.22951 55.91834... 9466856.0 Belarus
4 POLYGON ((31.78599 52.10168, 32.15944 52.06125... 44385155.0 Ukraine

Compute population density based on Population density the number of people per unit of area,

geo_df_EU['POP_DENSITY'] = geo_df_EU['POP_EST']/(geo_df_EU['geometry'].area)

/home/eneko/.miniconda3/envs/cook/lib/python3.7/site-packages/ipykernel_launcher.py:1: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  """Entry point for launching an IPython kernel.

Normalization of pop density into a contact rate between 10 and 100

from sklearn import preprocessing
min_max_scaler = preprocessing.MinMaxScaler(feature_range=(0, 1))
x_scaled = min_max_scaler.fit_transform(geo_df_EU['POP_DENSITY'].values.reshape(-1, 1))[:,0]
geo_df_EU['inf_rate'] = pd.Series(x_scaled)

geo_df_EU.head()
geometry POP_EST ADMIN POP_DENSITY inf_rate
0 MULTIPOLYGON (((15.14282 79.67431, 15.52255 80... 5347896.0 Norway 59095.218795 0.012681
1 MULTIPOLYGON (((-51.65780 4.15623, -52.24934 3... 67059887.0 France 923490.631806 0.276615
2 POLYGON ((11.02737 58.85615, 11.46827 59.43239... 10285453.0 Sweden 129464.357267 0.034167
3 POLYGON ((28.17671 56.16913, 29.22951 55.91834... 9466856.0 Belarus 334504.027829 0.096774
4 POLYGON ((31.78599 52.10168, 32.15944 52.06125... 44385155.0 Ukraine 628756.876391 0.186621 
geo_df_EU.plot('inf_rate');
../../_images/Prepare_Data_15_0.png

Give columns easy understandable names and remove unncessary columns

geo_df_EU.rename(columns={'ADMIN': 'country', 'POP_EST': 'population'},inplace=True )
geo_df_EU.drop(['POP_DENSITY'], axis=1, inplace=True)

Save results into new shapefle

geo_df_EU.to_file('geo_df_EU.shp', driver="ESRI Shapefile")

geo_df_EU
geometry population country inf_rate
0 MULTIPOLYGON (((15.14282 79.67431, 15.52255 80... 5347896.0 Norway 0.012681
1 MULTIPOLYGON (((-51.65780 4.15623, -52.24934 3... 67059887.0 France 0.276615
2 POLYGON ((11.02737 58.85615, 11.46827 59.43239... 10285453.0 Sweden 0.034167
3 POLYGON ((28.17671 56.16913, 29.22951 55.91834... 9466856.0 Belarus 0.096774
4 POLYGON ((31.78599 52.10168, 32.15944 52.06125... 44385155.0 Ukraine 0.186621
5 POLYGON ((23.48413 53.91250, 23.52754 53.47012... 37970874.0 Poland 0.279088
6 POLYGON ((16.97967 48.12350, 16.90375 47.71487... 8877067.0 Austria 0.260907
7 POLYGON ((22.08561 48.42226, 22.64082 48.15024... 9769949.0 Hungary 0.266325
8 POLYGON ((26.61934 48.22073, 26.85782 48.36821... 2657637.0 Moldova 0.206089
9 POLYGON ((28.23355 45.48828, 28.67978 45.30403... 19356544.0 Romania 0.208615
10 POLYGON ((26.49433 55.61511, 26.58828 55.16718... 2786844.0 Lithuania 0.088953
11 POLYGON ((27.28818 57.47453, 27.77002 57.24426... 1912789.0 Latvia 0.056778
12 POLYGON ((27.98113 59.47537, 27.98112 59.47537... 1326590.0 Estonia 0.053291
13 POLYGON ((14.11969 53.75703, 14.35332 53.24817... 83132799.0 Germany 0.547376
14 POLYGON ((22.65715 44.23492, 22.94483 43.82379... 6975761.0 Bulgaria 0.170384
15 MULTIPOLYGON (((26.29000 35.29999, 26.16500 35... 10716322.0 Greece 0.232713
16 POLYGON ((21.02004 40.84273, 20.99999 40.58000... 2854191.0 Albania 0.268249
17 POLYGON ((16.56481 46.50375, 16.88252 46.38063... 4067500.0 Croatia 0.183671
18 POLYGON ((9.59423 47.52506, 9.63293 47.34760, ... 8574832.0 Switzerland 0.475913
19 POLYGON ((6.04307 50.12805, 6.24275 49.90223, ... 619896.0 Luxembourg 0.622395
20 POLYGON ((6.15666 50.80372, 6.04307 50.12805, ... 11484055.0 Belgium 0.910184
21 POLYGON ((6.90514 53.48216, 7.09205 53.14404, ... 17332850.0 Netherlands 1.000000
22 POLYGON ((-9.03482 41.88057, -8.67195 42.13469... 10269417.0 Portugal 0.314522
23 POLYGON ((-7.45373 37.09779, -7.53711 37.42890... 47076781.0 Spain 0.264485
24 POLYGON ((-6.19788 53.86757, -6.03299 53.15316... 4941444.0 Ireland 0.186591
25 MULTIPOLYGON (((10.44270 46.89355, 11.04856 46... 60297396.0 Italy 0.525439
26 MULTIPOLYGON (((9.92191 54.98310, 9.28205 54.8... 5818553.0 Denmark 0.282656
27 MULTIPOLYGON (((-6.19788 53.86757, -6.95373 54... 66834405.0 United Kingdom 0.591287
28 POLYGON ((-14.50870 66.45589, -14.73964 65.808... 361313.0 Iceland 0.000000
29 POLYGON ((13.80648 46.50931, 14.63247 46.43182... 2087946.0 Slovenia 0.281129
30 POLYGON ((28.59193 69.06478, 28.44594 68.36461... 5520314.0 Finland 0.021063
31 POLYGON ((22.55814 49.08574, 22.28084 48.82539... 5454073.0 Slovakia 0.284090
32 POLYGON ((15.01700 51.10667, 15.49097 50.78473... 10669709.0 Czechia 0.316024
33 POLYGON ((18.56000 42.65000, 17.67492 43.02856... 3301000.0 Bosnia and Herzegovina 0.171569
34 POLYGON ((22.38053 42.32026, 22.88137 41.99930... 2083459.0 North Macedonia 0.229644
35 POLYGON ((18.82982 45.90887, 18.82984 45.90888... 6944975.0 Republic of Serbia 0.241080
36 POLYGON ((20.07070 42.58863, 19.80161 42.50009... 622137.0 Montenegro 0.123049
37 POLYGON ((20.59025 41.85541, 20.52295 42.21787... 1794248.0 Kosovo 0.439454