diff --git a/doc/tutorial/climada_entity_Exposures_osm.ipynb b/doc/tutorial/climada_entity_Exposures_osm.ipynb index 65cb95ba5..915af1d73 100644 --- a/doc/tutorial/climada_entity_Exposures_osm.ipynb +++ b/doc/tutorial/climada_entity_Exposures_osm.ipynb @@ -252,7 +252,8 @@ "2. **Extract** the features of interest (e.g. a road network) as a geodataframe\n", "3. **Pre-process**; apply pre-processing steps as e.g. clipping, simplifying, or reprojecting the retrieved layer.\n", "4. **Cast** the geodataframe into a CLIMADA `Exposures` object.\n", - "Once those 4 steps are completed, one can proceed with the impact calculation. or more details on \n", + "5. **Disagreggate** complex shapes exposures into points for impact calculation.\n", + "Once those 5 steps are completed, one can proceed with the impact calculation. For more details on \n", "how to use lines and polygons as exposures within CLIMADA, please refer to the [documentation](https://climada-python.readthedocs.io/en/latest/tutorial/climada_entity_Exposures_polygons_lines.html).\n", "\n", "In the following, we illustrate how to obtain different exposures types such as healthcare facilities,\n", @@ -589,6 +590,50 @@ "source": [ "exp_mix.plot()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Disagreggate complex shapes into point data\n", + "The last step before proceeding to the usual impact calculation consists in transforming \n", + "all the exposure data that is in a format other than point (e.g. lines, polygons) into point data (disagreggation) and assigning them values. Those two tasks can be done simultaneously using the util function `exp_geom_to_pnt`. Disagreggating and assigning values to the disagreggated exposures requires the following:\n", + "1. Specify a resolution for the disaggregation (`res`).\n", + "2. Specify a value to be disaggregated (`disagg_val`).\n", + "3. Specify how to distribute the value to the disaggregated points (`disagg_met`).\n", + "\n", + "In the following, we illustrate how to disaggregate our mixed-types exposures to \n", + "a 10km-resolution, arbitrarily assigning a fixed value of 500k USD to each point. For more details on \n", + "how to use lines and polygons as exposures within CLIMADA, please refer to the [documentation](https://climada-python.readthedocs.io/en/latest/tutorial/climada_entity_Exposures_polygons_lines.html).\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "exp_mix_pnt = u_lp.exp_geom_to_pnt(\n", + " exp_mix,\n", + " res=10000,\n", + " to_meters=True,\n", + " disagg_met=u_lp.DisaggMethod.FIX,\n", + " disagg_val=5e5,\n", + ")\n", + "exp_mix_pnt.plot()" + ] } ], "metadata": {