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In [1]:

    
import fiona
import geopandas as gpd
import glob
from rasterstats import zonal_stats
import pandas as pd
import pickle

from library.geoprocess import get_tif_ids, zonal_to_shp



In [2]:

    
shp = 'data/geo/countries/ne.shp'
gdf = gpd.GeoDataFrame.from_file(shp); gdf.head(3)









    Out[2]:






  
    
      
      ABBREV
      ABBREV_LEN
      ADM0_A3
      ADM0_A3_IS
      ADM0_A3_UN
      ADM0_A3_US
      ADM0_A3_WB
      ADM0_DIF
      ADMIN
      BRK_A3
      ...
      UN_A3
      WB_A2
      WB_A3
      WIKIPEDIA
      WOE_ID
      WOE_ID_EH
      WOE_NOTE
      featurecla
      geometry
      scalerank
    
  
  
    
      0
      Aruba
      5.0
      ABW
      ABW
      -99.0
      ABW
      -99.0
      1.0
      Aruba
      ABW
      ...
      533
      AW
      ABW
      -99.0
      23424736.0
      23424736.0
      Exact WOE match as country
      Admin-0 country
      POLYGON ((-69.99693762899992 12.57758209800004...
      3
    
    
      1
      Afg.
      4.0
      AFG
      AFG
      -99.0
      AFG
      -99.0
      0.0
      Afghanistan
      AFG
      ...
      004
      AF
      AFG
      -99.0
      23424739.0
      23424739.0
      Exact WOE match as country
      Admin-0 country
      POLYGON ((71.04980228700009 38.40866445000009,...
      0
    
    
      2
      Ang.
      4.0
      AGO
      AGO
      -99.0
      AGO
      -99.0
      0.0
      Angola
      AGO
      ...
      024
      AO
      AGO
      -99.0
      23424745.0
      23424745.0
      Exact WOE match as country
      Admin-0 country
      (POLYGON ((11.73751945100014 -16.6925779829998...
      0
    
  

3 rows × 64 columns



In [10]:

    
# calculate zonal stats for each country
gdf = zonal_to_shp('data/geo/images', 'data/geo/countries/countries_nf2.shp'); gdf.head(3)









    Out[10]:






  
    
      
      ADMIN
      CONTINENT
      ISO_A3
      REGION_UN
      REGION_WB
      SUBREGION
      WB_A3
      geometry
      F101992
      F101993
      ...
      F162004
      F162005
      F162006
      F162007
      F162008
      F162009
      F182010
      F182011
      F182012
      F182013
    
  
  
    
      0
      Aruba
      North America
      ABW
      Americas
      Latin America & Caribbean
      Caribbean
      ABW
      POLYGON ((-69.99693762899992 12.57758209800004...
      6177.0
      6232.0
      ...
      7335.0
      7242.0
      7184.0
      7553.0
      7282.0
      6744.0
      7138.0
      7336.0
      7562.0
      7371.0
    
    
      1
      Afghanistan
      Asia
      AFG
      Asia
      South Asia
      Southern Asia
      AFG
      POLYGON ((71.04980228700009 38.40866445000009,...
      25186.0
      18505.0
      ...
      75134.0
      86521.0
      98660.0
      112196.0
      89119.0
      97326.0
      177007.0
      159544.0
      191545.0
      175082.0
    
    
      2
      Angola
      Africa
      AGO
      Africa
      Sub-Saharan Africa
      Middle Africa
      AGO
      (POLYGON ((11.73751945100014 -16.6925779829998...
      42041.0
      42094.0
      ...
      73147.0
      62238.0
      85454.0
      102922.0
      128467.0
      149756.0
      246623.0
      226180.0
      268144.0
      271021.0
    
  

3 rows × 42 columns



In [14]:

    
# extract 1992 column
gdf_1992 = gdf[['ISO_A3', 'WB_A3', 'F101992']]; gdf_1992.head(3)



In [15]:

    
# pickle country-level zonal stats
with open('data/geo/pickles/zonal_stats_c.pickle', 'wb') as f:
    pickle.dump(gdf, f)



In [16]:

    
# pickle 1992 column
with open('data/geo/pickles/zonal_stats_c92.pickle', 'wb') as f:
    pickle.dump(gdf_1992, f)

	ABBREV	ABBREV_LEN	ADM0_A3	ADM0_A3_IS	ADM0_A3_UN	ADM0_A3_US	ADM0_A3_WB	ADM0_DIF	ADMIN	BRK_A3	...	UN_A3	WB_A2	WB_A3	WIKIPEDIA	WOE_ID	WOE_ID_EH	WOE_NOTE	featurecla	geometry	scalerank
0	Aruba	5.0	ABW	ABW	-99.0	ABW	-99.0	1.0	Aruba	ABW	...	533	AW	ABW	-99.0	23424736.0	23424736.0	Exact WOE match as country	Admin-0 country	POLYGON ((-69.99693762899992 12.57758209800004...	3
1	Afg.	4.0	AFG	AFG	-99.0	AFG	-99.0	0.0	Afghanistan	AFG	...	004	AF	AFG	-99.0	23424739.0	23424739.0	Exact WOE match as country	Admin-0 country	POLYGON ((71.04980228700009 38.40866445000009,...	0
2	Ang.	4.0	AGO	AGO	-99.0	AGO	-99.0	0.0	Angola	AGO	...	024	AO	AGO	-99.0	23424745.0	23424745.0	Exact WOE match as country	Admin-0 country	(POLYGON ((11.73751945100014 -16.6925779829998...	0

	ADMIN	CONTINENT	ISO_A3	REGION_UN	REGION_WB	SUBREGION	WB_A3	geometry	F101992	F101993	...	F162004	F162005	F162006	F162007	F162008	F162009	F182010	F182011	F182012	F182013
0	Aruba	North America	ABW	Americas	Latin America & Caribbean	Caribbean	ABW	POLYGON ((-69.99693762899992 12.57758209800004...	6177.0	6232.0	...	7335.0	7242.0	7184.0	7553.0	7282.0	6744.0	7138.0	7336.0	7562.0	7371.0
1	Afghanistan	Asia	AFG	Asia	South Asia	Southern Asia	AFG	POLYGON ((71.04980228700009 38.40866445000009,...	25186.0	18505.0	...	75134.0	86521.0	98660.0	112196.0	89119.0	97326.0	177007.0	159544.0	191545.0	175082.0
2	Angola	Africa	AGO	Africa	Sub-Saharan Africa	Middle Africa	AGO	(POLYGON ((11.73751945100014 -16.6925779829998...	42041.0	42094.0	...	73147.0	62238.0	85454.0	102922.0	128467.0	149756.0	246623.0	226180.0	268144.0	271021.0