GeoPandas for Climate Scientists & Meteorologists¶
- A practical, hands‑on notebook introducing GeoPandas and the geospatial stack for climate & weather applications.
You’ll learn how to load and inspect vector data (stations, administrative regions), perform spatial operations (joins, buffers, overlays), manage CRS (coordinate reference systems), and integrate with xarray/rioxarray to extract/aggregate values from NetCDF (e.g., CHIRPS, ERA5).
What you’ll need (install instructions below): - Python 3.10+ - geopandas, shapely, pyproj, matplotlib - For climate raster work: xarray, rioxarray, rasterio, regionmask, salem
Outline:¶
- Installing GeoPandas
- Create GeoDataFrame and Inspect
- Plot GeoDataFrame
- Spatial Operations [Aggregation, Buffering, Dissolving, Overlay]
- Reprojection and CRS Management
- Extract Point data from NetCDF file
- (Optional) Save sample data to GeoJSON
- Masking NetCDF with Shapefile [salem]
- Reading and Writing Shapfile/GeoJSON file
Installing GeoPandas¶
# Using conda (recommended for geospatial stack)
# !conda install -c conda-forge geopandas xarray rioxarray rasterio shapely pyproj regionmask matplotlib -y
# Using pip (ensure system has GEOS/PROJ/GDAL preinstalled or use wheels on manylinux)
# !pip install geopandas shapely matplotlib xarray rioxarray rasterio regionmask salem
# Set working directory
import os
os.chdir("c:\\Users\\yonas\\Documents\\ICPAC\\python-climate")
processed_data_dir = os.path.join("data", "processed")
raw_data_dir = os.path.join("data", "raw")
Imports & Environment¶
import os
import json
import numpy as np
import pandas as pd
import geopandas as gpd
from shapely.geometry import Point, Polygon, box
import matplotlib.pyplot as plt
import xarray as xr
import salem
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
GeoPandas Fundamentals¶
-
GeoPandas extends pandas with a geometry column (typically
shapelygeometries) and CRS metadata. -
It supports typical table operations (filter, groupby) plus spatial operations (buffer, intersection).
Create sample data (stations & regions) for Ethiopia¶
- We’ll synthesize station points and region polygons roughly within Ethiopia’s bounds to avoid external downloads.
# Define Ethiopia bounding box
ethi_bbox = box(33, 3, 48, 15)
# Create GeoDataFrame for 3 regions: North, Center, South Ethiopia
regions = gpd.GeoDataFrame(
{
"region": ["North", "Center", "South"],
"geometry": [
box(36, 11, 44, 15), # North
box(36, 7, 44, 11), # Center
box(36, 3, 44, 7), # South
],
},
crs="EPSG:4326"
)
Inspect the dataframe¶
region geometry
0 North POLYGON ((44 11, 44 15, 36 15, 36 11, 44 11))
1 Center POLYGON ((44 7, 44 11, 36 11, 36 7, 44 7))
2 South POLYGON ((44 3, 44 7, 36 7, 36 3, 44 3))
<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich
# Basic plot of bounding box
regions.plot( figsize=(12, 6),
edgecolor='black',
facecolor='none',
column='region',
legend=True,
)
# Create GeoDataFrame for some weather stations in Ethiopia
station_records = [
{"station_id": "STA001", "lon": 37.5, "lat": 13.2, "elev_m": 2500},
{"station_id": "STA002", "lon": 38.3, "lat": 10.2, "elev_m": 2100},
{"station_id": "STA003", "lon": 39.5, "lat": 6.2, "elev_m": 1500},
{"station_id": "STA004", "lon": 42.0, "lat": 8.8, "elev_m": 1800},
]
# Create DataFrame for station records
df = pd.DataFrame(station_records)
# Convert to GeoDataFrame with geometry column
gdf_stn = gpd.GeoDataFrame(
df,
geometry=gpd.points_from_xy(df["lon"], df["lat"]),
crs="EPSG:4326",
)
Data Loading & Inspection¶
<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich
station_id lon lat elev_m geometry
0 STA001 37.5 13.2 2500 POINT (37.5 13.2)
1 STA002 38.3 10.2 2100 POINT (38.3 10.2)
2 STA003 39.5 6.2 1500 POINT (39.5 6.2)
3 STA004 42.0 8.8 1800 POINT (42 8.8)
Plot stations over regions¶
# Plot stations over regions
ax = regions.boundary.plot(edgecolor="0.2")
gdf_stn.plot(ax=ax, color="tab:red", markersize=30)
ax.set_title("Stations and synthetic regions")
plt.show()
Basic plotting with .plot()¶
ax = regions.plot(column="region", legend=True, edgecolor="k")
gdf_stn.plot(ax=ax, color="black", markersize=25)
ax.set_title("Basic GeoPandas plotting")
plt.show()
Spatial Operations¶
Spatial Join: map stations to regions¶
# Spatial join: Find which stations fall within which regions
stn_in_regions = gpd.sjoin(gdf_stn,
regions,
how="left",
predicate="within") # or "intersects",
# Select relevant columns
stn_in_regions[["station_id","region","elev_m","geometry"]]
# Print the resulting GeoDataFrame
stn_in_regions
station_id lon lat elev_m geometry index_right region
0 STA001 37.5 13.2 2500 POINT (37.5 13.2) 0 North
1 STA002 38.3 10.2 2100 POINT (38.3 10.2) 1 Center
2 STA003 39.5 6.2 1500 POINT (39.5 6.2) 2 South
3 STA004 42.0 8.8 1800 POINT (42 8.8) 1 Center
Aggregation by region (example)¶
# Compute average elevation of stations per region
agg = stn_in_regions.groupby("region", dropna=False)["elev_m"].mean().reset_index(name="avg_elev_m")
agg
# Plot average elevation per region
agg.plot.bar(x="region", y="avg_elev_m", legend=False)
plt.ylabel("Average Elevation (m)")
plt.show()
Buffering (e.g., 50 km around stations): use a projected CRS in meters¶
# convert to projected CRS in meters (UTM zone 37N)
stn_utm = gdf_stn.to_crs("EPSG:32637")
# copy the GeoDataFrame
buf50 = stn_utm.copy()
# Create 50 km buffers around each station
buf50["geometry"] = stn_utm.buffer(50_000) # 50 km
# Convert buffers back to WGS84 for plotting
buf50_wgs84 = buf50.to_crs("EPSG:4326")
# Plot buffers around stations
ax = regions.boundary.plot(edgecolor="0.2")
buf50_wgs84.boundary.plot(ax=ax, color="orange")
gdf_stn.plot(ax=ax, color="red", markersize=25)
ax.set_title("50 km buffers around stations")
plt.show()
Dissolving polygons by attribute¶
# Dissolve regions by "region" column (no effect here since already unique)
regions_dissolved = regions.dissolve(by="region")
regions_dissolved
geometry
region
Center POLYGON ((44 7, 44 11, 36 11, 36 7, 44 7))
North POLYGON ((44 11, 44 15, 36 15, 36 11, 44 11))
South POLYGON ((44 3, 44 7, 36 7, 36 3, 44 3))
Overlay: intersection¶
# Select the north region
north = regions.query("region == 'North'")
# Convert to projected CRS in meters (UTM zone 37N)
north_utm = north.to_crs("EPSG:32637")
buf50_utm = buf50 # already EPSG:32637
# Perform intersection between buffers and north region
inter = gpd.overlay(buf50_utm, north_utm, how="intersection")
# Calculate area in square kilometers
inter["area_km2"] = inter.area / 1e6
# Show relevant columns
inter[["station_id","region","area_km2"]]
inter
station_id lon lat elev_m region \
0 STA001 37.5 13.2 2500 North
geometry area_km2
0 POLYGON ((387211.687 1454838.236, 386491.715 1... 7841.371226
Reprojection and CRS Management¶
- Check CRS:
.crs - Define CRS if missing:
.set_crs('EPSG:4326', inplace=True) - Transform:
.to_crs('EPSG:32637')
Extract Point data from NetCDF file¶
One timestep for all stations¶
# Choose the DataArray
da = ds["precip"]
# pick one time step (latest here)
da = da.isel(time=-1)
# Determine the CRS of the raster/grid and pass it to the grid_crs variable
grid_crs = getattr(da.rio, "crs", None) or getattr(ds.rio, "crs", None) or "EPSG:4326"
# Reproject stations to the grid CRS
stn = gdf_stn.to_crs(grid_crs)
# Use the correct spatial coord names from the grid or raster
x_name = "x"
y_name = "y"
# Extract the lon/lat or x/y values from station geometries
xs = xr.DataArray(stn.geometry.x.values, dims="points")
ys = xr.DataArray(stn.geometry.y.values, dims="points")
# Extract the values at station points from the dataset
vals = da.sel({x_name: xs, y_name: ys}, method="nearest")
# Attach values back to GeoDataFrame
out = stn.copy()
out["value"] = vals.values
out.head()
station_id lon lat elev_m geometry value
0 STA001 37.5 13.2 2500 POINT (37.5 13.2) 6.593847
1 STA002 38.3 10.2 2100 POINT (38.3 10.2) 5.579750
2 STA003 39.5 6.2 1500 POINT (39.5 6.2) 4.485852
3 STA004 42.0 8.8 1800 POINT (42 8.8) 2.406737
Extract the time series for one station¶
# Select a specific station (e.g., the first station)
station = gdf_stn.iloc[0]
# Extract the longitude and latitude of the station
lon = station.geometry.x
lat = station.geometry.y
# Extract the time series of precipitation data for the station
station_timeseries = ds["precip"].sel(x=lon, y=lat, method="nearest")
# Print the time series
station_timeseries
<xarray.DataArray 'precip' (time: 366)> Size: 3kB
array([ 8.90118385, 0.09824973, 9.84963881, 13.83217302, 4.82701112,
3.02796085, 8.96099998, 0.95233107, 0.75084645, 0.90436388,
5.74201964, 12.19744575, 20.36288197, 10.75139477, 3.28321376,
17.41700677, 0.69764574, 15.74290292, 5.32697186, 3.15239653,
9.84549182, 0.87593684, 8.01575844, 0.62819937, 5.59384104,
12.14868406, 13.45459792, 18.11698855, 3.35912622, 3.72355798,
5.03606898, 3.49338772, 12.95855409, 6.68114097, 7.4993605 ,
8.3016419 , 6.56083993, 2.40272902, 5.80579683, 9.99313429,
15.82951358, 5.63553899, 2.18377796, 6.39631524, 16.71267785,
11.76186451, 3.78251815, 1.67570394, 6.95784883, 15.35415267,
3.77168257, 8.81742417, 8.53823945, 11.86166171, 11.40575333,
1.64856121, 11.28622488, 5.83712258, 8.11210692, 1.27081348,
16.54482488, 5.02891885, 2.49685149, 6.83131134, 6.57492208,
2.25561692, 5.83733167, 12.02462412, 0.59350707, 27.85619701,
0.9008682 , 10.48115929, 1.02154927, 5.90818961, 6.17478874,
10.69234801, 10.61678972, 2.68351046, 17.6276772 , 0.90080039,
6.56512981, 5.40134233, 5.09189985, 8.36083916, 8.98695107,
5.38177287, 1.50759598, 4.10376271, 27.96360062, 0.76229859,
2.06080187, 4.10987403, 10.47795987, 4.9888542 , 3.28217836,
18.61593369, 8.18417935, 11.56894743, 6.08130478, 8.89417778,
...
0.4699834 , 3.15634966, 7.33356107, 7.12675824, 2.86563534,
1.5205371 , 7.42236143, 7.58697843, 22.52864212, 9.03096085,
2.66926489, 15.64574595, 10.92660135, 11.62142626, 1.00915691,
13.39591533, 10.3446771 , 3.46757717, 2.01734248, 7.40245163,
6.29116874, 2.3559016 , 7.39040017, 16.80829489, 1.15275807,
11.55917206, 4.85304436, 3.4175703 , 2.51960312, 3.45029352,
5.5147619 , 2.05643934, 10.74473374, 4.42487023, 4.43166259,
7.44804919, 15.9133309 , 14.3350987 , 6.68431506, 0.94000862,
1.37526922, 1.12974576, 8.12820603, 7.82927427, 16.11663327,
3.03036053, 8.35559638, 9.82348928, 4.57551529, 7.89555544,
17.4869349 , 2.21683835, 5.36261982, 6.6627613 , 19.50267987,
5.69839464, 25.87832717, 10.71372776, 1.10604522, 7.67118114,
4.93233254, 3.32011665, 11.89581228, 6.08836126, 3.68824626,
1.6512861 , 4.26023167, 2.41110457, 2.65326915, 16.21881174,
4.80258245, 8.27956227, 1.87075244, 12.61458757, 2.71844599,
3.95416159, 7.533296 , 5.13412516, 2.76724244, 7.7035626 ,
7.18841443, 22.64938164, 17.08308499, 4.94037975, 8.3034956 ,
7.84506904, 9.61600142, 16.80952201, 2.29957239, 10.01720154,
13.90717451, 12.14652 , 0.49778973, 3.61119683, 7.01184302,
6.59384701])
Coordinates:
* time (time) datetime64[ns] 3kB 2020-01-01 2020-01-02 ... 2020-12-31
y float64 8B 13.25
x float64 8B 37.5
spatial_ref int64 8B 0
Attributes:
units: mm/day
time
2020-01-01 8.901184
2020-01-02 0.098250
2020-01-03 9.849639
2020-01-04 13.832173
2020-01-05 4.827011
...
2020-12-27 12.146520
2020-12-28 0.497790
2020-12-29 3.611197
2020-12-30 7.011843
2020-12-31 6.593847
Freq: D, Name: precip, Length: 366, dtype: float64
# export to CSV dataframe
# station_timeseries_pd.to_csv(f"{processed_data_dir}/station_timeseries.csv", header=True)
Extract the time series for mutiple station¶
# Create an empty dictionary to store the time series for each station
station_timeseries = {}
# Iterate over each station in the GeoDataFrame
for index, station in gdf_stn.iterrows():
# Extract the longitude and latitude of the station
lon = station.geometry.x
lat = station.geometry.y
# Extract the time series of precipitation data for the station
try:
ts = ds["precip"].sel(x=lon, y=lat, method="nearest")
# Store as pandas Series
# Now station_timeseries is a dictionary where the keys are station IDs
station_timeseries[station["station_id"]] = ts.to_series()
except KeyError as e:
print(f"Error extracting data for station {station['station_id']}: {e}")
station_timeseries[station["station_id"]] = None
{'STA001': time
2020-01-01 8.901184
2020-01-02 0.098250
2020-01-03 9.849639
2020-01-04 13.832173
2020-01-05 4.827011
...
2020-12-27 12.146520
2020-12-28 0.497790
2020-12-29 3.611197
2020-12-30 7.011843
2020-12-31 6.593847
Freq: D, Name: precip, Length: 366, dtype: float64,
'STA002': time
2020-01-01 9.459547
2020-01-02 0.704595
2020-01-03 4.459861
2020-01-04 4.469626
2020-01-05 13.787346
...
2020-12-27 10.400662
2020-12-28 3.188982
2020-12-29 0.771026
2020-12-30 3.625833
2020-12-31 5.579750
Freq: D, Name: precip, Length: 366, dtype: float64,
'STA003': time
2020-01-01 5.355205
2020-01-02 0.855645
2020-01-03 1.000385
2020-01-04 1.277393
2020-01-05 25.535552
...
2020-12-27 9.396427
2020-12-28 5.579716
2020-12-29 3.140024
2020-12-30 4.106899
2020-12-31 4.485852
Freq: D, Name: precip, Length: 366, dtype: float64,
'STA004': time
2020-01-01 7.601374
2020-01-02 1.734879
2020-01-03 6.739741
2020-01-04 6.055603
2020-01-05 5.873361
...
2020-12-27 2.466144
2020-12-28 4.096557
2020-12-29 2.505724
2020-12-30 36.287754
2020-12-31 2.406737
Freq: D, Name: precip, Length: 366, dtype: float64}
# Export all time series to separate CSV files
for station_id, ts in station_timeseries.items():
if ts is not None:
ts.to_csv(
f"{processed_data_dir}/{station_id}_timeseries.csv", header=True)
print(f"Exported time series for station {station_id} to {station_id}_timeseries.csv")
else:
print(f"No time series data for station {station_id} to export.")
Exported time series for station STA001 to STA001_timeseries.csv
Exported time series for station STA002 to STA002_timeseries.csv
Exported time series for station STA003 to STA003_timeseries.csv
Exported time series for station STA004 to STA004_timeseries.csv
(Optional) Save sample data to GeoJSON¶
# Export stations and regions to GeoJSON files
stations_fp = f"{processed_data_dir}/stations_demo.geojson"
regions_fp = f"{processed_data_dir}/regions_demo.geojson"
# Export GeoDataFrames to GeoJSON files
gdf_stn.to_file(stations_fp, driver="GeoJSON")
regions.to_file(regions_fp, driver="GeoJSON")
print("Wrote:", stations_fp, "and", regions_fp)
Reading the Geojson file¶
# reading the Geojson file stations_demo.geojson file
gdf_stn_loaded = gpd.read_file(processed_data_dir + "/stations_demo.geojson")
gdf_stn_loaded.head()
station_id lon lat elev_m geometry
0 STA001 37.5 13.2 2500 POINT (37.5 13.2)
1 STA002 38.3 10.2 2100 POINT (38.3 10.2)
2 STA003 39.5 6.2 1500 POINT (39.5 6.2)
3 STA004 42.0 8.8 1800 POINT (42 8.8)
Exporing as Shapefile¶
# exporting as Shapefile
gdf_stn_loaded.to_file(f"{processed_data_dir}/stations_demo.shp", driver="ESRI Shapefile")
Load the Shapefile¶
OBJECTID COUNTRY area Shape_Leng Shape_Area land_under \
0 1 Burundi 0.0 8.560371 2.193095 None
1 2 Djibouti 0.0 7.874779 1.781569 None
2 3 Eritrea 0.0 41.125347 10.077064 None
3 4 Ethiopia 0.0 49.028874 92.986294 None
4 5 Kenya 0.0 40.625985 47.319578 None
5 6 Rwanda 0.0 8.078222 2.054446 None
6 7 Somalia 0.0 53.331305 51.800944 None
7 8 Tanzania 0.0 57.988209 77.546629 None
8 9 South Sudan 0.0 46.515148 51.867644 None
9 10 Sudan 0.0 73.448957 158.194024 930459.06\r\n930459
10 11 Uganda 0.0 25.099705 19.616329 None
geometry
0 POLYGON ((30.36003 -2.35343, 30.36209 -2.3525,...
1 POLYGON ((42.66339 11.0715, 42.65628 11.07671,...
2 MULTIPOLYGON (((43.14681 12.71384, 43.14167 12...
3 POLYGON ((41.77824 11.54207, 41.77785 11.51077...
4 MULTIPOLYGON (((39.40283 -4.65471, 39.40523 -4...
5 POLYGON ((30.3675 -2.34399, 30.36209 -2.3525, ...
6 MULTIPOLYGON (((41.9267 -1.16192, 41.9226 -1.1...
7 MULTIPOLYGON (((40.42789 -10.38034, 40.42349 -...
8 POLYGON ((31.79577 3.82198, 31.79585 3.82126, ...
9 POLYGON ((24.32633 16.51445, 23.99918 16.50046...
10 POLYGON ((32.75026 -0.99732, 32.40119 -0.99728...
OBJECTID COUNTRY area Shape_Leng Shape_Area land_under \
3 4 Ethiopia 0.0 49.028874 92.986294 None
4 5 Kenya 0.0 40.625985 47.319578 None
geometry
3 POLYGON ((41.77824 11.54207, 41.77785 11.51077...
4 MULTIPOLYGON (((39.40283 -4.65471, 39.40523 -4...
<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich
array(['Burundi', 'Djibouti', 'Eritrea', 'Ethiopia', 'Kenya', 'Rwanda',
'Somalia', 'Tanzania', 'South Sudan', 'Sudan', 'Uganda'],
dtype=object)
0 POLYGON ((30.36003 -2.35343, 30.36209 -2.3525,...
1 POLYGON ((42.66339 11.0715, 42.65628 11.07671,...
2 MULTIPOLYGON (((43.14681 12.71384, 43.14167 12...
3 POLYGON ((41.77824 11.54207, 41.77785 11.51077...
4 MULTIPOLYGON (((39.40283 -4.65471, 39.40523 -4...
5 POLYGON ((30.3675 -2.34399, 30.36209 -2.3525, ...
6 MULTIPOLYGON (((41.9267 -1.16192, 41.9226 -1.1...
7 MULTIPOLYGON (((40.42789 -10.38034, 40.42349 -...
8 POLYGON ((31.79577 3.82198, 31.79585 3.82126, ...
9 POLYGON ((24.32633 16.51445, 23.99918 16.50046...
10 POLYGON ((32.75026 -0.99732, 32.40119 -0.99728...
Name: geometry, dtype: geometry
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30.19709000000006 -2.39638999999994, 30.202170000000024 -2.3899599999999737, 30.204170000000033 -2.387429999999938, 30.213900000000024 -2.380139999999926, 30.215370000000064 -2.3759599999999637, 30.216900000000066 -2.3562599999999634, 30.21855000000005 -2.351889999999969, 30.221550000000036 -2.349399999999946, 30.225690000000043 -2.3489799999999263, 30.23183000000006 -2.353019999999958, 30.24899000000005 -2.3642999999999574, 30.268620000000055 -2.3696199999999408, 30.28201000000007 -2.3710199999999304, 30.292850000000044 -2.3706299999999487, 30.30329000000006 -2.3702599999999734, 30.31886000000003 -2.363489999999956, 30.33976000000007 -2.362549999999942, 30.351000000000056 -2.3574899999999275, 30.36003000000005 -2.353429999999946))'
Access the geometry of the polygon¶
0 Polygon
1 Polygon
2 MultiPolygon
3 Polygon
4 MultiPolygon
5 Polygon
6 MultiPolygon
7 MultiPolygon
8 Polygon
9 Polygon
10 Polygon
dtype: object
def coord_lister(geom):
if geom.geom_type == 'Polygon':
coords = list(geom.exterior.coords)
elif geom.geom_type == 'MultiPolygon':
coords = []
for polygon in geom.geoms:
coords.extend(list(polygon.exterior.coords))
else:
return None # Or raise an exception, depending on your needs
return coords
coordinates = gha.geometry.apply(coord_lister)
Burundi_coord = coordinates[1]
Burundi_coord
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Ploting the Polygon¶
Plot Greater Horn of Africa regions¶
# Plot Greater Horn of Africa regions
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
gha.plot(column="COUNTRY",
legend=True,
edgecolor="k",
ax=ax,
)
ax.set_title("Greater Horn of Africa Regions")
plt.show()
fig, ax = plt.subplots(figsize=(18,6))
gha.plot(alpha=1.0, cmap ='tab20c', column='COUNTRY', edgecolor='black', ax=ax, legend=True )
ax.set_title('Greater Horn of Africa', fontsize=18)
ax.set_axisbelow(True)
ax.yaxis.grid(color='gray', linestyle='dashdot')
ax.xaxis.grid(color='gray', linestyle='dashdot')
ax.set_xlabel("Longitude (Degrees)", fontsize=12)
ax.set_ylabel("Latitude (Degrees)", fontsize=12)
leg = ax.get_legend()
leg.set_bbox_to_anchor((1.45,1.01))
plt.show()
Making Subplots¶
# Making Subplots
fig, ((ax1, ax2, ax3, ax4), (ax5, ax6, ax7, ax8), (ax9, ax10, ax11, ax12)) = plt.subplots(3, 4, figsize=(15,15))
fig.suptitle("Greater Horn of Africa", fontsize=18)
gha.loc[gha.COUNTRY == "Ethiopia"].plot(ax=ax1, facecolor='Blue', edgecolor='black')
ax1.set_title("Ethiopia")
gha.loc[gha.COUNTRY == "Kenya"].plot(ax=ax2, facecolor='Green', edgecolor='black')
ax2.set_title("Kenya")
gha.loc[gha.COUNTRY == "Uganda"].plot(ax=ax3, facecolor='Red', edgecolor='black')
ax3.set_title("Uganda")
gha.loc[gha.COUNTRY == "Tanzania"].plot(ax=ax4, facecolor='Orange', edgecolor='black')
ax4.set_title("Tanzania")
gha.loc[gha.COUNTRY == "Rwanda"].plot(ax=ax5, facecolor='Purple', edgecolor='black')
ax5.set_title("Rwanda")
gha.loc[gha.COUNTRY == "Burundi"].plot(ax=ax6, facecolor='Yellow', edgecolor='black')
ax6.set_title("Burundi")
gha.loc[gha.COUNTRY == "South Sudan"].plot(ax=ax7, facecolor='Cyan', edgecolor='black')
ax7.set_title("South Sudan")
gha.loc[gha.COUNTRY == "Somalia"].plot(ax=ax8, facecolor='Magenta', edgecolor='black')
ax8.set_title("Somalia")
gha.loc[gha.COUNTRY == "Djibouti"].plot(ax=ax9, facecolor='Brown', edgecolor='black')
ax9.set_title("Djibouti")
gha.loc[gha.COUNTRY == "Eritrea"].plot(ax=ax10, facecolor='Pink', edgecolor='black')
ax10.set_title("Eritrea")
gha.loc[gha.COUNTRY == "Sudan"].plot(ax=ax11, facecolor='Gray', edgecolor='black')
ax11.set_title("Sudan")
plt.show()
Merge the GeoDataframe¶
# Merge geometries of all countries into a single geometry
gha_merged = gha.geometry.union_all()
gha_merged
# Export the merged geometry to a new GeoDataFrame as shapefile
# Convert the multipolygon to a GeoDataFrame
gdf_merged = gpd.GeoDataFrame({'geometry': [gha_merged]}, crs=gha.crs)
# Export the GeoDataFrame to a shapefile
gdf_merged.to_file(f"{processed_data_dir}/gha_merged.shp", driver="ESRI Shapefile")
OBJECTID COUNTRY area Shape_Leng Shape_Area land_under \
3 4 Ethiopia 0.0 49.028874 92.986294 None
geometry
3 POLYGON ((41.77824 11.54207, 41.77785 11.51077...
<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich
Masking NetCDF with Shapefile [salem]¶
<xarray.Dataset> Size: 521kB
Dimensions: (time: 1, lat: 321, lon: 401)
Coordinates:
* time (time) datetime64[ns] 8B 2019-06-01
* lat (lat) float64 3kB 15.0 14.96 14.93 14.89 ... 3.113 3.075 3.037 3.0
* lon (lon) float64 3kB 33.0 33.04 33.08 33.11 ... 47.89 47.92 47.96 48.0
Data variables:
rfe (time, lat, lon) float32 515kB ...
Attributes: (12/13)
CDI: Climate Data Interface version 2.0.5 (https://mpimet.mpg.de...
Conventions: CF-1.5
institution: TAMSAT Research Group, Meteorology Department, University o...
title: TAMSAT Rain Fall Estimate (RFE)
contact: tamsat@reading.ac.uk
history: Sun Oct 02 19:59:08 2022: cdo enssum rfe2019_06-dk1.v3.nc r...
... ...
latmax: 15.0
lonmin: 33.0
lonmax: 48.0
latres: 0.0375
lonres: 0.0375
CDO: Climate Data Operators version 2.0.5 (https://mpimet.mpg.de...
# remove other countries
shp_ethio = shp_world.loc[shp_world['CNTRY_NAME'] == 'Ethiopia']
shp_ethio.plot()
shp_ethio = shp_world.loc[shp_world['CNTRY_NAME'] == 'Ethiopia']
rfe_et = tamsat_2019_june['rfe'].salem.roi(shape=shp_ethio)
fig, ax = plt.subplots(figsize=(10, 8)) # Create figure and axes
rfe_et.isel(time=0).plot(ax=ax, cmap='viridis', cbar_kwargs={'label': 'Precipitation'})
shp_ethio.plot(ax=ax, facecolor="none", edgecolor="black", linewidth=3)
ax.set_title('TAMSAT Precipitation over Ethiopia')
ax.set_xlabel('Longitude')
ax.set_ylabel('Latitude')
plt.show()
shp_ethio = shp_world.loc[shp_world['CNTRY_NAME'] == 'Ethiopia']
rfe_et = tamsat_2019_june['rfe'].salem.roi(shape=shp_ethio)
fig, ax = plt.subplots(figsize=(10, 8), subplot_kw={'projection': ccrs.PlateCarree()})
# Add the precipitation data
im = rfe_et.isel(time=0).plot(ax=ax, cmap='viridis', add_colorbar=False)
# Add the country boundary
ax.add_geometries(shp_ethio['geometry'], crs=ccrs.PlateCarree(), facecolor='none', edgecolor='black', linewidth=1)
# Add coastlines and borders for context
ax.coastlines(resolution='110m')
ax.add_feature(cfeature.BORDERS, linewidth=0.5)
ax.add_feature(cfeature.LAND, facecolor='lightgray')
ax.add_feature(cfeature.OCEAN, facecolor='lightblue')
# Set the title and labels
ax.set_title('TAMSAT Precipitation over Ethiopia')
ax.set_xlabel('Longitude')
ax.set_ylabel('Latitude')
# Add colorbar
cbar = plt.colorbar(im, ax=ax, orientation='vertical', pad=0.02, aspect=16, shrink=0.6)
cbar.set_label('Precipitation (mm)')
# Set the extent of the plot (optional, but recommended)
ax.set_extent([shp_ethio.bounds.minx.min(), shp_ethio.bounds.maxx.max(), shp_ethio.bounds.miny.min(), shp_ethio.bounds.maxy.max()], crs=ccrs.PlateCarree())
plt.show()
📝 Test Your Knowledge¶
Ready to test your understanding of GeoPandas? Take the interactive quiz to assess your knowledge of geospatial data handling, spatial operations, and climate data integration.
📚 Summary¶
In this tutorial, you've learned:
✅ GeoPandas Fundamentals - GeoDataFrames, geometry columns, and CRS
✅ Data Creation - Creating spatial data from coordinates
✅ Visualization - Plotting maps and spatial data
✅ Spatial Operations - Buffers, dissolve, overlay, spatial joins
✅ CRS Management - Coordinate system transformations and reprojection
✅ Climate Data Integration - Extracting point data from NetCDF files
✅ Masking - Using shapefiles to mask climate data with salem
✅ File I/O - Reading and writing shapefiles and GeoJSON
✅ Advanced Visualization - Integration with Cartopy for professional maps
