📊 wildfires: visualising average and standard deviations

dag/climate.yml

@@ -97,6 +97,8 @@ steps:
     - data://garden/climate/latest/weekly_wildfires
   data://grapher/climate/latest/wildfires_by_week:
     - data://garden/climate/latest/weekly_wildfires
+  data://grapher/climate/latest/wildfires_by_week_average:
+    - data://grapher/climate/latest/wildfires_by_week
   #
   # EPA - Climate change indicators (possibly not updateable).
   #

etl/steps/data/grapher/climate/latest/wildfires_by_week_average.meta.yml

@@ -0,0 +1,19 @@
+definitions:
+  common:
+    unit: hectares
+    short_unit: ha
+# Learn more about the available fields:
+# http://docs.owid.io/projects/etl/architecture/metadata/reference/tables/
+tables:
+  weekly_wildfires:
+    # Learn more about the available fields:
+    # http://docs.owid.io/projects/etl/architecture/metadata/reference/indicator/
+    variables:
+        upper_bound_cum_area:
+          title: Upper bound of the average cumulative area burned by wildfires
+
+        lower_bound_cum_area:
+         title: Lower bound of the average cumulative area burned by wildfires
+
+        avg_cumulative_area_burned_2012_2022:
+          title: Average cumulative area burned by wildfires 2012-2022

etl/steps/data/grapher/climate/latest/wildfires_by_week_average.py

@@ -0,0 +1,52 @@
+"""Load a garden dataset and create a grapher dataset."""
+
+import owid.catalog.processing as pr
+import pandas as pd
+
+from etl.helpers import PathFinder, create_dataset
+
+# Get paths and naming conventions for current step.
+paths = PathFinder(__file__)
+
+
+def run(dest_dir: str) -> None:
+    #
+    # Load inputs.
+    #
+    # Load garden dataset.
+    ds_grapher = paths.load_dataset("wildfires_by_week")
+    tb = ds_grapher["weekly_wildfires"].reset_index()
+
+    #
+    # Process data.
+    #
+    # Extract columns related to area and events
+    area_cum_columns = [col for col in tb.columns if "area_ha_cumulative" in col]
+
+    # Sort these columns by year (assuming the year is embedded in the column name after the first underscore)
+    area_columns_sorted = sorted(area_cum_columns, key=lambda x: int(x.split("_")[1]))
+
+    # Calculate the cumulative area burned (2012-2022)
+    tb["cumulative_area_burned_2012_2022"] = tb[area_columns_sorted].sum(axis=1)
+
+    # Average cumulative area burned (2012-2022)
+    tb["avg_cumulative_area_burned_2012_2022"] = tb["cumulative_area_burned_2012_2022"] / len(area_columns_sorted)
+
+    # 4. Calculate the standard deviation of area burned (2012-2022)
+    tb["std_dev_cum_area_burned_2012_2022"] = tb[area_columns_sorted].std(axis=1)
+
+    # 5. Calculate the upper and lower bounds of the standard deviation
+    tb["upper_bound_cum_area"] = tb["avg_cumulative_area_burned_2012_2022"] + tb["std_dev_cum_area_burned_2012_2022"]
+    tb["lower_bound_cum_area"] = tb["avg_cumulative_area_burned_2012_2022"] - tb["std_dev_cum_area_burned_2012_2022"]
+    tb = tb.drop(columns=["std_dev_cum_area_burned_2012_2022", "cumulative_area_burned_2012_2022"])
+
+    # Set 'country' and 'year' as the index of the DataFrame
+    tb = tb.set_index(["country", "year"])
+
+    #
+    # Save outputs.
+    #
+    # Create a new grapher dataset with the same metadata as the garden dataset.
+    ds_grapher = create_dataset(dest_dir, tables=[tb], default_metadata=ds_grapher.metadata)
+    ds_grapher.metadata.title = "Seasonal wildfire trends by week - Average and Standard Deviation"
+    ds_grapher.save()