diff --git a/scripts/V2024/create_full_info_uc.py b/scripts/V2024/create_full_info_uc.py index 7851f83..715154a 100644 --- a/scripts/V2024/create_full_info_uc.py +++ b/scripts/V2024/create_full_info_uc.py @@ -10,65 +10,65 @@ def create_full_info_uc(inputfile_uc, layer_uc, inputfile_grid, layer_grid): grid_sum = ( grid_df[ [ - "ID_UC_G0", - "GHS_POP", - "wc_built_up_sqkm", - "wc_tree_cover_sqkm", - "wc_sparse_vegetation_sqkm", + "urban_center_id", + "ghs_pop_2023", + "worldcover_2021_built_up_sqkm", + "worldcover_2021_tree_cover_sqkm", + "worldcover_2021_sparse_vegetation_sqkm", "selected_road_length_km", "reference_building_area_sqkm", - "prediction_improved_sqkm", - "osm_building_area_sqkm_2008-01", - "osm_building_area_sqkm_2009-01", - "osm_building_area_sqkm_2010-01", - "osm_building_area_sqkm_2011-01", - "osm_building_area_sqkm_2012-01", - "osm_building_area_sqkm_2013-01", - "osm_building_area_sqkm_2014-01", - "osm_building_area_sqkm_2015-01", - "osm_building_area_sqkm_2016-01", - "osm_building_area_sqkm_2017-01", - "osm_building_area_sqkm_2018-01", - "osm_building_area_sqkm_2019-01", - "osm_building_area_sqkm_2020-01", - "osm_building_area_sqkm_2021-01", - "osm_building_area_sqkm_2022-01", - "osm_building_area_sqkm_2023-01", - "osm_building_area_sqkm_2024-01", - "osm_building_area_sqkm_2024-05", + "prediction", + "osm_building_area_sqkm_2008_01", + "osm_building_area_sqkm_2009_01", + "osm_building_area_sqkm_2010_01", + "osm_building_area_sqkm_2011_01", + "osm_building_area_sqkm_2012_01", + "osm_building_area_sqkm_2013_01", + "osm_building_area_sqkm_2014_01", + "osm_building_area_sqkm_2015_01", + "osm_building_area_sqkm_2016_01", + "osm_building_area_sqkm_2017_01", + "osm_building_area_sqkm_2018_01", + "osm_building_area_sqkm_2019_01", + "osm_building_area_sqkm_2020_01", + "osm_building_area_sqkm_2021_01", + "osm_building_area_sqkm_2022_01", + "osm_building_area_sqkm_2023_01", + "osm_building_area_sqkm_2024_01", + "osm_building_area_sqkm_2024_05", ] ] - .groupby("ID_UC_G0") + .groupby("urban_center_id") .sum() ) grid_avg = ( grid_df[ [ - "ID_UC_G0", - "shdi", - "vnl_mean", - "osm_completeness_2008_01", - "osm_completeness_2009_01", - "osm_completeness_2010_01", - "osm_completeness_2011_01", - "osm_completeness_2012_01", - "osm_completeness_2013_01", - "osm_completeness_2014_01", - "osm_completeness_2015_01", - "osm_completeness_2016_01", - "osm_completeness_2017_01", - "osm_completeness_2018_01", - "osm_completeness_2019_01", - "osm_completeness_2020_01", - "osm_completeness_2021_01", - "osm_completeness_2022_01", - "osm_completeness_2023_01", - "osm_completeness_2024_01", - "osm_completeness_2024_05", + "urban_center_id", + "shdi_2021", + "vnl_2023", + "prediction_osm_completeness_2008_01", + "prediction_osm_completeness_2009_01", + "prediction_osm_completeness_2010_01", + "prediction_osm_completeness_2011_01", + "prediction_osm_completeness_2012_01", + "prediction_osm_completeness_2013_01", + "prediction_osm_completeness_2014_01", + "prediction_osm_completeness_2015_01", + "prediction_osm_completeness_2016_01", + "prediction_osm_completeness_2017_01", + "prediction_osm_completeness_2018_01", + "prediction_osm_completeness_2019_01", + "prediction_osm_completeness_2020_01", + "prediction_osm_completeness_2021_01", + "prediction_osm_completeness_2022_01", + "prediction_osm_completeness_2023_01", + "prediction_osm_completeness_2024_01", + "prediction_osm_completeness_2024_05", ] ] - .groupby("ID_UC_G0") + .groupby("urban_center_id") .mean() ) del grid_df @@ -76,7 +76,7 @@ def create_full_info_uc(inputfile_uc, layer_uc, inputfile_grid, layer_grid): grid_sum = pd.merge( grid_sum, grid_avg, - on="ID_UC_G0", + on="urban_center_id", how="left", ) del grid_avg @@ -85,12 +85,12 @@ def create_full_info_uc(inputfile_uc, layer_uc, inputfile_grid, layer_grid): uc_df = pd.merge( uc_df, grid_sum, - on="ID_UC_G0", + on="urban_center_id", how="left", ) del grid_sum - uc_df.to_file("../full_info_uc.gpkg", layer="full_info_uc", driver="GPKG") + uc_df.to_file("../abgabe.gpkg", layer="uc_full_info_V2024", driver="GPKG") if __name__ == "__main__": @@ -99,7 +99,7 @@ def create_full_info_uc(inputfile_uc, layer_uc, inputfile_grid, layer_grid): format="%(asctime)s - %(levelname)s - %(filename)s - %(funcName)s - %(message)s", ) - inputfile_uc = pathlib.Path("../jrc_uc_wgs84.gpkg") + inputfile_uc = pathlib.Path("../abgabe.gpkg") layer_uc = "uc_2025" inputfile_grid = pathlib.Path("../abgabe.gpkg") diff --git a/scripts/V2024/model_performance.py b/scripts/V2024/model_performance.py index c9f898d..cc1de78 100644 --- a/scripts/V2024/model_performance.py +++ b/scripts/V2024/model_performance.py @@ -15,7 +15,7 @@ def load_urban_centers_grid(input_file, layer_grid): df["region_wb_cat"] = pd.Categorical(df["region_wb"]) df["region_code"] = df.region_wb_cat.cat.codes - df["shdi"].fillna((df["shdi"].mean()), inplace=True) + df["shdi_2021"].fillna((df["shdi_2021"].mean()), inplace=True) df["selected_road_length_km"].fillna( (df["selected_road_length_km"].mean()), inplace=True ) @@ -25,7 +25,7 @@ def load_urban_centers_grid(input_file, layer_grid): "external_reference_building_area_sqkm", "microsoft_building_area_sqkm", "reference_building_area_sqkm", - "reference_osm_completeness", + "reference_completeness", "region_wb", "region_wb_cat", "region_code", @@ -42,11 +42,11 @@ def get_urban_center_centroids(inputfile, layer_uc, grid_df): """Get the centroids of the urban centers.""" # returns message, that centroids are likely incorrect because the data is in a geographic CRS. is reprojecting neccessary?? copy_df = grid_df[ - ["ID_UC_G0", "osm_building_area_sqkm_2024-05", "reference_building_area_sqkm"] + ["urban_center_id", "osm_building_area_sqkm_2024_05", "reference_building_area_sqkm"] ] - copy_df = copy_df.groupby("ID_UC_G0").sum() - copy_df["reference_osm_completeness"] = round( - copy_df["osm_building_area_sqkm_2024-05"] + copy_df = copy_df.groupby("urban_center_id").sum() + copy_df["reference_completeness"] = round( + copy_df["osm_building_area_sqkm_2024_05"] / copy_df["reference_building_area_sqkm"], 3, ) @@ -54,14 +54,14 @@ def get_urban_center_centroids(inputfile, layer_uc, grid_df): uc_grid = gpd.read_file(inputfile, layer=layer_uc) uc_grid = pd.merge( uc_grid, - copy_df[["reference_building_area_sqkm", "reference_osm_completeness"]], - on="ID_UC_G0", + copy_df[["reference_building_area_sqkm", "reference_completeness"]], + on="urban_center_id", how="left", ) # filter the columns out, where the (training) data might not be complete df = uc_grid[ - (uc_grid["reference_osm_completeness"] < 1.5) + (uc_grid["reference_completeness"] < 1.5) & (uc_grid["reference_building_area_sqkm"].notnull()) ] @@ -72,7 +72,7 @@ def get_urban_center_centroids(inputfile, layer_uc, grid_df): logging.info(f"got {len(df)} urban centers with centroid coordinates") - return df[["ID_UC_G0", "x", "y"]] + return df[["urban_center_id", "x", "y"]] def spatial_train_test_split_cluster(df, cluster_label, n=0): @@ -113,7 +113,7 @@ def estimate_model_performance(inputfile, layer_uc, layer_prediction, n_clusters cluster_df, n_clusters = kmeans_cluster_urban_centers( urban_centers_df, "x", "y", n_clusters ) - df = df.join(cluster_df.set_index("ID_UC_G0"), on="ID_UC_G0", how="inner") + df = df.join(cluster_df.set_index("urban_center_id"), on="urban_center_id", how="inner") region_groups = list(range(0, n_clusters)) # df for model @@ -165,7 +165,7 @@ def estimate_model_performance(inputfile, layer_uc, layer_prediction, n_clusters # save predictions to Geopackage df_export = df_test[ [ - "ID_UC_G0", + "urban_center_id", "identifier", "region_wb", "repeat", @@ -202,18 +202,18 @@ def estimate_model_performance(inputfile, layer_uc, layer_prediction, n_clusters format="%(asctime)s - %(levelname)s - %(filename)s - %(funcName)s - %(message)s", ) - inputfile = pathlib.Path("../jrc_uc_wgs84.gpkg") + inputfile = pathlib.Path("../abgabe.gpkg") layer_uc = "uc_2025" - layer_grid = "uc_grid" + layer_grid = "grid_full_info_v2024" layer_grid_prediction = "prediction_improved" COVARIATE_COLUMNS = [ - "wc_built_up_sqkm", - "wc_tree_cover_sqkm", - "wc_sparse_vegetation_sqkm", - "GHS_POP", - "vnl_mean", - "shdi", + "worldcover_2021_built_up_sqkm", + "worldcover_2021_tree_cover_sqkm", + "worldcover_2021_sparse_vegetation_sqkm", + "ghs_pop_2023", + "vnl_2023", + "shdi_2021", "selected_road_length_km", "region_code", ] diff --git a/scripts/V2024/run_prediction.py b/scripts/V2024/run_prediction.py index 6e0f1bf..c2434d5 100644 --- a/scripts/V2024/run_prediction.py +++ b/scripts/V2024/run_prediction.py @@ -1,5 +1,6 @@ import logging import pathlib +import sys import geopandas as gpd import pandas as pd @@ -13,7 +14,7 @@ def load_urban_centers_grid(input_file, layer_grid): df["region_wb_cat"] = pd.Categorical(df["region_wb"]) df["region_code"] = df.region_wb_cat.cat.codes - df["shdi"].fillna((df["shdi"].mean()), inplace=True) + df["shdi_2021"].fillna((df["shdi_2021"].mean()), inplace=True) df["selected_road_length_km"].fillna( (df["selected_road_length_km"].mean()), inplace=True ) @@ -23,7 +24,7 @@ def load_urban_centers_grid(input_file, layer_grid): "external_reference_building_area_sqkm", "microsoft_building_area_sqkm", "reference_building_area_sqkm", - "reference_osm_completeness", + "reference_completeness", "region_wb", "region_wb_cat", "region_code", @@ -37,14 +38,14 @@ def load_urban_centers_grid(input_file, layer_grid): def get_outliers(df, uc_file, layer_UC, threshold=0.005): - copy_df = df[["ID_UC_G0", "osm_building_area_sqkm_2024-05", "prediction_sqkm"]] - copy_df = copy_df.groupby("ID_UC_G0").sum() + copy_df = df[["urban_center_id", "osm_building_area_sqkm_2024_05", "prediction_sqkm"]] + copy_df = copy_df.groupby("urban_center_id").sum() uc_df = gpd.read_file(uc_file, layer=layer_UC) uc_df = pd.merge( uc_df, - copy_df[["osm_building_area_sqkm_2024-05", "prediction_sqkm"]], - on="ID_UC_G0", + copy_df[["osm_building_area_sqkm_2024_05", "prediction_sqkm"]], + on="urban_center_id", how="left", ) @@ -53,11 +54,11 @@ def get_outliers(df, uc_file, layer_UC, threshold=0.005): # select all rows where area is greater than threshold uc_df_subset = uc_df[ - (uc_df["osm_building_area_sqkm_2024-05"] - uc_df["prediction_sqkm"]) + (uc_df["osm_building_area_sqkm_2024_05"] - uc_df["prediction_sqkm"]) > uc_df["area"] * threshold ] - outliers = uc_df_subset["ID_UC_G0"].values + outliers = uc_df_subset["urban_center_id"].values logging.info( f"got {len(outliers)} urban center ids with prediction below threshold (th = {threshold})" ) @@ -74,18 +75,18 @@ def run_prediction(training_data, uc_file, layer_grid, layer_UC): urban_center_ids = get_outliers(df, uc_file, layer_UC, threshold=0.005) df[f"reference_building_area_sqkm_initial"] = df[f"reference_building_area_sqkm"] df.loc[ - (df["ID_UC_G0"].isin(urban_center_ids)), "reference_building_area_sqkm" - ] = df["osm_building_area_sqkm_2024-05"] + (df["urban_center_id"].isin(urban_center_ids)), "reference_building_area_sqkm" + ] = df["osm_building_area_sqkm_2024_05"] - df["reference_completeness_area_sqkm"] = round( - df["osm_building_area_sqkm_2024-05"] / df["reference_building_area_sqkm"], 3 + df["reference_completeness"] = round( + df["osm_building_area_sqkm_2024_05"] / df["reference_building_area_sqkm"], 3 ) df_train = df[ (df["reference_building_area_sqkm"] > 0) & # avoid urban centers for which training data might not be complete - (df["reference_osm_completeness"] < 1.5) + (df["reference_completeness"] < 1.5) ] logging.info(f"training samples: {len(df_train)}") @@ -126,9 +127,9 @@ def run_prediction(training_data, uc_file, layer_grid, layer_UC): gdf_temp.to_file(uc_file, layer="prediction", driver="GPKG") else: gdf_temp["prediction_improved_sqkm"] = y_pred - gdf_temp["osm_completeness"] = ( + gdf_temp["prediction_osm_completeness_2024_05"] = ( ( - gdf_temp["osm_building_area_sqkm_2024-05"] + gdf_temp["osm_building_area_sqkm_2024_05"] / gdf_temp["prediction_improved_sqkm"] ) * 100 @@ -143,18 +144,18 @@ def run_prediction(training_data, uc_file, layer_grid, layer_UC): format="%(asctime)s - %(levelname)s - %(filename)s - %(funcName)s - %(message)s", ) - uc_file = pathlib.Path("../jrc_uc_wgs84.gpkg") + uc_file = pathlib.Path("../abgabe.gpkg") layer_UC = "uc_2025" - layer_grid = "uc_grid" + layer_grid = "grid_full_info_v2024" layer_grid_prediction = "prediction" COVARIATE_COLUMNS = [ - "wc_built_up_sqkm", - "wc_tree_cover_sqkm", - "wc_sparse_vegetation_sqkm", - "GHS_POP", - "vnl_mean", - "shdi", + "worldcover_2021_built_up_sqkm", + "worldcover_2021_tree_cover_sqkm", + "worldcover_2021_sparse_vegetation_sqkm", + "ghs_pop_2023", + "vnl_2023", + "shdi_2021", "selected_road_length_km", "region_code", ] @@ -163,7 +164,8 @@ def run_prediction(training_data, uc_file, layer_grid, layer_UC): """python scripts/run_prediction.py reference_and_osm""" - # training_data = "reference" + # training_data = sys.argv[1] + #training_data = "reference" training_data = "reference_and_osm" if training_data == "reference": diff --git a/scripts/V2024/version_2_Table 4 - regional performance scores.ipynb b/scripts/V2024/version_2_Table 4 - regional performance scores.ipynb index 3973102..913cc04 100644 --- a/scripts/V2024/version_2_Table 4 - regional performance scores.ipynb +++ b/scripts/V2024/version_2_Table 4 - regional performance scores.ipynb @@ -40,15 +40,15 @@ " with agg_prediction as (\n", " select\n", " a.identifier\n", - " ,a.ID_UC_G0\n", + " ,a.urban_center_id\n", " ,a.split\n", " ,avg(a.prediction) as prediction\n", " from performance_20_clusters_reference_and_osm_v2024 as a \n", - " group by identifier, ID_UC_G0, split\n", + " group by identifier, urban_center_id, split\n", " )\n", " select\n", " a.identifier\n", - " ,a.ID_UC_G0\n", + " ,a.urban_center_id\n", " ,a.region_wb\n", " ,'rf_adjusted' as model_name\n", " ,b.split\n", @@ -56,13 +56,13 @@ " ,a.reference_completeness\n", " ,a.reference_building_area_sqkm\n", " ,a.osm_building_area_sqkm_2024_05 / b.prediction as prediction_osm_completeness\n", - " from grid_full_info_v2024 a\n", + " from rf_adjusted_prediction_reference_and_osm_v2024 a\n", " left join agg_prediction b\n", " on a.identifier = b.identifier\n", " where\n", " reference_building_area_sqkm is not null\n", " and\n", - " prediction is not null\n", + " b.prediction is not null\n", " \"\"\"\n", " df = pd.read_sql(query, con=con)\n", " print(f\"got dataframe with {len(df)} samples\")\n", @@ -74,9 +74,9 @@ " query = f\"\"\"\n", " select \n", " a.identifier as id\n", - " ,a.ID_UC_G0\n", + " ,a.urban_center_id\n", " ,b.region_wb \n", - " from grid_full_info_v2024 a\n", + " from rf_adjusted_prediction_reference_and_osm_v2024 a\n", " left join ne_10m_admin_0_countries b\n", " on a.iso_a3 = b.iso_a3\n", " \"\"\"\n", @@ -140,7 +140,7 @@ "output_type": "stream", "text": [ "got dataframe with 684718 samples from table: grid_full_info_v2024\n", - "got dataframe with 506170 samples\n" + "got dataframe with 506178 samples\n" ] }, { @@ -165,7 +165,7 @@ " \n", " \n", " identifier\n", - " ID_UC_G0\n", + " urban_center_id\n", " region_wb\n", " model_name\n", " split\n", @@ -182,7 +182,7 @@ ], "text/plain": [ "Empty DataFrame\n", - "Columns: [identifier, ID_UC_G0, region_wb, model_name, split, prediction, reference_completeness, reference_building_area_sqkm, prediction_osm_completeness]\n", + "Columns: [identifier, urban_center_id, region_wb, model_name, split, prediction, reference_completeness, reference_building_area_sqkm, prediction_osm_completeness]\n", "Index: []" ] }, @@ -233,10 +233,10 @@ " reference_and_osm\n", " rf_adjusted\n", " cluster_20\n", - " 0.762285\n", - " 0.764549\n", - " 0.002249\n", - " 0.03142\n", + " 0.760284\n", + " 0.761595\n", + " 0.002274\n", + " 0.031717\n", " \n", " \n", "\n", @@ -244,10 +244,10 @@ ], "text/plain": [ " training_data model_name split r2 explained_variance \\\n", - "0 reference_and_osm rf_adjusted cluster_20 0.762285 0.764549 \n", + "0 reference_and_osm rf_adjusted cluster_20 0.760284 0.761595 \n", "\n", - " MSE MAE \n", - "0 0.002249 0.03142 " + " MSE MAE \n", + "0 0.002274 0.031717 " ] }, "metadata": {}, @@ -373,11 +373,11 @@ " [East Asia & Pacific]\n", " cluster_20\n", " 209515\n", - " 90079\n", - " 0.818447\n", - " 0.820935\n", - " 0.001963\n", - " 0.030230\n", + " 90078\n", + " 0.811179\n", + " 0.811927\n", + " 0.002042\n", + " 0.030913\n", " \n", " \n", " 3\n", @@ -385,11 +385,11 @@ " [Europe & Central Asia]\n", " cluster_20\n", " 93133\n", - " 83352\n", - " 0.722304\n", - " 0.723721\n", - " 0.002064\n", - " 0.030147\n", + " 83299\n", + " 0.699532\n", + " 0.701281\n", + " 0.002234\n", + " 0.031619\n", " \n", " \n", " 0\n", @@ -397,11 +397,11 @@ " [Latin America & Caribbean]\n", " cluster_20\n", " 67200\n", - " 60561\n", - " 0.671691\n", - " 0.674178\n", - " 0.004992\n", - " 0.048461\n", + " 60562\n", + " 0.674183\n", + " 0.675388\n", + " 0.004954\n", + " 0.048299\n", " \n", " \n", " 5\n", @@ -409,11 +409,11 @@ " [Middle East & North Africa]\n", " cluster_20\n", " 44201\n", - " 38179\n", - " 0.758408\n", - " 0.760198\n", - " 0.002830\n", - " 0.036403\n", + " 38178\n", + " 0.786051\n", + " 0.787537\n", + " 0.002507\n", + " 0.034288\n", " \n", " \n", " 4\n", @@ -422,10 +422,10 @@ " cluster_20\n", " 70148\n", " 69614\n", - " 0.633874\n", - " 0.639590\n", - " 0.001536\n", - " 0.027998\n", + " 0.649635\n", + " 0.653006\n", + " 0.001470\n", + " 0.027338\n", " \n", " \n", " 2\n", @@ -434,10 +434,10 @@ " cluster_20\n", " 127747\n", " 101069\n", - " 0.857256\n", - " 0.857630\n", - " 0.001700\n", - " 0.024949\n", + " 0.849637\n", + " 0.851083\n", + " 0.001791\n", + " 0.025675\n", " \n", " \n", " 6\n", @@ -445,11 +445,11 @@ " [Sub-Saharan Africa]\n", " cluster_20\n", " 72351\n", - " 63316\n", - " 0.694441\n", - " 0.697217\n", - " 0.002068\n", - " 0.032376\n", + " 63378\n", + " 0.704850\n", + " 0.704976\n", + " 0.001997\n", + " 0.032115\n", " \n", " \n", "\n", @@ -466,13 +466,13 @@ "6 rf_adjusted [Sub-Saharan Africa] cluster_20 72351 \n", "\n", " reference_samples r2 explained_variance MSE MAE \n", - "1 90079 0.818447 0.820935 0.001963 0.030230 \n", - "3 83352 0.722304 0.723721 0.002064 0.030147 \n", - "0 60561 0.671691 0.674178 0.004992 0.048461 \n", - "5 38179 0.758408 0.760198 0.002830 0.036403 \n", - "4 69614 0.633874 0.639590 0.001536 0.027998 \n", - "2 101069 0.857256 0.857630 0.001700 0.024949 \n", - "6 63316 0.694441 0.697217 0.002068 0.032376 " + "1 90078 0.811179 0.811927 0.002042 0.030913 \n", + "3 83299 0.699532 0.701281 0.002234 0.031619 \n", + "0 60562 0.674183 0.675388 0.004954 0.048299 \n", + "5 38178 0.786051 0.787537 0.002507 0.034288 \n", + "4 69614 0.649635 0.653006 0.001470 0.027338 \n", + "2 101069 0.849637 0.851083 0.001791 0.025675 \n", + "6 63378 0.704850 0.704976 0.001997 0.032115 " ] }, "metadata": {}, @@ -482,7 +482,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "506170\n" + "506178\n" ] } ], @@ -522,6 +522,14 @@ " \n", " print(list_df[\"reference_samples\"].sum())" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75535912-5ffe-430f-8f85-04e974dc99b8", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/scripts/V2024/version_2_figure8_table4.ipynb b/scripts/V2024/version_2_figure8_table4.ipynb index 6d5aa56..f8d9f50 100644 --- a/scripts/V2024/version_2_figure8_table4.ipynb +++ b/scripts/V2024/version_2_figure8_table4.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "a32b8bd9", "metadata": {}, "outputs": [ @@ -56,8 +56,8 @@ " 'Sub-Saharan Africa_2014-01-01',\n", " 'Sub-Saharan Africa_2023-01-01',\n", " 'geowiki_grids_final',\n", - " 'uc_full_info_v2024',\n", - " 'grid_full_info_v2024',\n", + " 'rf_adjusted_prediction_reference_and_osm_urban_centers_v2024',\n", + " 'rf_adjusted_prediction_reference_and_osm_v2024',\n", " 'performance_20_clusters_reference_and_osm_v2024',\n", " 'model_performance_cluster_20_reference_and_osm',\n", " 'osm_user_contributions_per_urban_center_with_data_teams_csv',\n", @@ -65,7 +65,7 @@ " 'osm_user_contributions_per_urban_center_per_day_with_flag']" ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "79630fc0", "metadata": {}, "outputs": [ @@ -787,7 +787,7 @@ "[13189 rows x 36 columns]" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -800,7 +800,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "de86eb84", "metadata": {}, "outputs": [], @@ -818,7 +818,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "id": "69f2675a", "metadata": {}, "outputs": [ @@ -843,8 +843,8 @@ " 'Sub-Saharan Africa_2014-01-01',\n", " 'Sub-Saharan Africa_2023-01-01',\n", " 'geowiki_grids_final',\n", - " 'uc_full_info_v2024',\n", - " 'grid_full_info_v2024',\n", + " 'rf_adjusted_prediction_reference_and_osm_urban_centers_v2024',\n", + " 'rf_adjusted_prediction_reference_and_osm_v2024',\n", " 'performance_20_clusters_reference_and_osm_v2024',\n", " 'model_performance_cluster_20_reference_and_osm',\n", " 'osm_user_contributions_per_urban_center_with_data_teams_csv',\n", @@ -852,7 +852,7 @@ " 'osm_user_contributions_per_urban_center_per_day_with_flag']" ] }, - "execution_count": 7, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -864,7 +864,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "id": "0f7e218d", "metadata": {}, "outputs": [], @@ -875,18 +875,18 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "id": "a647e9f5", "metadata": {}, "outputs": [], "source": [ "# equivalent to bennis all_parameters_urban_centers layer\n", - "uc_l2 = gpd.read_file(uc_layers, layer=\"uc_full_info_v2024\")" + "uc_l2 = gpd.read_file(uc_layers, layer=\"rf_adjusted_prediction_reference_and_osm_urban_centers_v2024\")" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "id": "18f809dc", "metadata": {}, "outputs": [ @@ -911,53 +911,58 @@ " \n", " \n", " \n", - " ID_UC_G0\n", - " GHS_POP\n", - " wc_built_up_sqkm\n", - " wc_tree_cover_sqkm\n", - " wc_sparse_vegetation_sqkm\n", + " urban_center_id\n", + " ghs_pop_2023\n", + " worldcover_2021_built_up_sqkm\n", + " worldcover_2021_tree_cover_sqkm\n", + " worldcover_2021_sparse_vegetation_sqkm\n", " selected_road_length_km\n", " reference_building_area_sqkm\n", - " prediction_improved_sqkm\n", - " osm_building_area_sqkm_2008-01\n", - " osm_building_area_sqkm_2009-01\n", - " osm_building_area_sqkm_2010-01\n", - " osm_building_area_sqkm_2011-01\n", - " osm_building_area_sqkm_2012-01\n", - " osm_building_area_sqkm_2013-01\n", - " osm_building_area_sqkm_2014-01\n", - " osm_building_area_sqkm_2015-01\n", - " osm_building_area_sqkm_2016-01\n", - " osm_building_area_sqkm_2017-01\n", - " osm_building_area_sqkm_2018-01\n", - " osm_building_area_sqkm_2019-01\n", - " osm_building_area_sqkm_2020-01\n", - " osm_building_area_sqkm_2021-01\n", - " osm_building_area_sqkm_2022-01\n", - " osm_building_area_sqkm_2023-01\n", - " osm_building_area_sqkm_2024-01\n", - " osm_building_area_sqkm_2024-05\n", - " shdi\n", - " vnl_mean\n", - " osm_completeness_2008_01\n", - " osm_completeness_2009_01\n", - " osm_completeness_2010_01\n", - " osm_completeness_2011_01\n", - " osm_completeness_2012_01\n", - " osm_completeness_2013_01\n", - " osm_completeness_2014_01\n", - " osm_completeness_2015_01\n", - " osm_completeness_2016_01\n", - " osm_completeness_2017_01\n", - " osm_completeness_2018_01\n", - " osm_completeness_2019_01\n", - " osm_completeness_2020_01\n", - " osm_completeness_2021_01\n", - " osm_completeness_2022_01\n", - " osm_completeness_2023_01\n", - " osm_completeness_2024_01\n", - " osm_completeness_2024_05\n", + " prediction\n", + " osm_building_area_sqkm_2008_01\n", + " osm_building_area_sqkm_2009_01\n", + " osm_building_area_sqkm_2010_01\n", + " osm_building_area_sqkm_2011_01\n", + " osm_building_area_sqkm_2012_01\n", + " osm_building_area_sqkm_2013_01\n", + " osm_building_area_sqkm_2014_01\n", + " osm_building_area_sqkm_2015_01\n", + " osm_building_area_sqkm_2016_01\n", + " osm_building_area_sqkm_2017_01\n", + " osm_building_area_sqkm_2018_01\n", + " osm_building_area_sqkm_2019_01\n", + " osm_building_area_sqkm_2020_01\n", + " osm_building_area_sqkm_2021_01\n", + " osm_building_area_sqkm_2022_01\n", + " osm_building_area_sqkm_2023_01\n", + " osm_building_area_sqkm_2024_01\n", + " osm_building_area_sqkm_2024_05\n", + " shdi_2021\n", + " vnl_2023\n", + " prediction_osm_completeness_2008_01\n", + " prediction_osm_completeness_2009_01\n", + " prediction_osm_completeness_2010_01\n", + " prediction_osm_completeness_2011_01\n", + " prediction_osm_completeness_2012_01\n", + " prediction_osm_completeness_2013_01\n", + " prediction_osm_completeness_2014_01\n", + " prediction_osm_completeness_2015_01\n", + " prediction_osm_completeness_2016_01\n", + " prediction_osm_completeness_2017_01\n", + " prediction_osm_completeness_2018_01\n", + " prediction_osm_completeness_2019_01\n", + " prediction_osm_completeness_2020_01\n", + " prediction_osm_completeness_2021_01\n", + " prediction_osm_completeness_2022_01\n", + " prediction_osm_completeness_2023_01\n", + " prediction_osm_completeness_2024_01\n", + " prediction_osm_completeness_2024_05\n", " total_area_sqkm\n", + " reference_osm_completeness_2024_05\n", + " region_wb\n", + " iso_a3\n", + " ghs_pop_2023_class\n", + " shdi_2021_class\n", " geometry\n", " \n", " \n", @@ -971,7 +976,7 @@ " 0.027807\n", " 159.73268\n", " 0.000000\n", - " 2.028533\n", + " 2.324382\n", " 0.0\n", " 0.0\n", " 0.000000\n", @@ -990,27 +995,32 @@ " 1.520192\n", " 1.559349\n", " 1.568521\n", - " 0.702000\n", + " 0.707\n", " 4.351451\n", " 0.0\n", " 0.0\n", " 0.000000\n", " 0.000000\n", - " 5.442658\n", - " 17.116582\n", - " 17.547127\n", - " 18.073713\n", - " 20.074884\n", - " 21.559690\n", - " 21.692216\n", - " 25.674679\n", - " 28.566575\n", - " 33.087056\n", - " 35.619517\n", - " 78.965543\n", - " 81.644399\n", - " 81.975882\n", + " 0.063767\n", + " 0.163730\n", + " 0.166716\n", + " 0.171311\n", + " 0.188576\n", + " 0.202116\n", + " 0.203699\n", + " 0.237329\n", + " 0.264390\n", + " 0.313722\n", + " 0.336734\n", + " 0.720924\n", + " 0.744398\n", + " 0.747106\n", " 34.792\n", + " NaN\n", + " East Asia & Pacific\n", + " WSM\n", + " small urban areas\n", + " high\n", " MULTIPOLYGON (((-171.77356 -13.82480, -171.763...\n", " \n", " \n", @@ -1022,7 +1032,7 @@ " 0.065721\n", " 144.96438\n", " 0.000000\n", - " 1.700809\n", + " 1.843358\n", " 0.0\n", " 0.0\n", " 0.000000\n", @@ -1041,27 +1051,32 @@ " 1.633950\n", " 1.641720\n", " 1.685883\n", - " 0.652000\n", + " 0.745\n", " 4.939129\n", " 0.0\n", " 0.0\n", " 0.000000\n", " 0.000000\n", - " 0.095091\n", - " 0.101329\n", - " 2.087314\n", - " 17.391497\n", - " 84.595916\n", - " 84.660119\n", - " 84.605391\n", - " 97.066208\n", - " 97.040778\n", - " 97.069821\n", - " 97.148841\n", - " 97.382958\n", - " 97.760565\n", - " 100.641484\n", + " 0.000822\n", + " 0.000883\n", + " 0.018637\n", + " 0.152596\n", + " 0.783101\n", + " 0.783734\n", + " 0.783273\n", + " 0.901063\n", + " 0.900828\n", + " 0.901067\n", + " 0.902121\n", + " 0.904437\n", + " 0.907950\n", + " 0.935053\n", " 19.901\n", + " NaN\n", + " East Asia & Pacific\n", + " TON\n", + " small urban areas\n", + " high\n", " MULTIPOLYGON (((-175.19374 -21.13139, -175.172...\n", " \n", " \n", @@ -1073,7 +1088,7 @@ " 0.245182\n", " 134.50359\n", " 1.670102\n", - " 1.816676\n", + " 1.831028\n", " 0.0\n", " 0.0\n", " 0.000000\n", @@ -1092,7 +1107,7 @@ " 0.139549\n", " 0.139863\n", " 0.141995\n", - " 0.899000\n", + " 0.940\n", " 14.999293\n", " 0.0\n", " 0.0\n", @@ -1105,14 +1120,19 @@ " 0.000000\n", " 0.000000\n", " 0.000000\n", - " 1.518298\n", - " 2.085576\n", - " 4.879578\n", - " 4.328472\n", - " 7.845958\n", - " 7.857594\n", - " 8.029926\n", + " 0.015071\n", + " 0.020622\n", + " 0.048817\n", + " 0.043124\n", + " 0.077236\n", + " 0.077356\n", + " 0.079047\n", " 14.926\n", + " 0.085\n", + " North America\n", + " USA\n", + " small urban areas\n", + " very high\n", " MULTIPOLYGON (((-158.01943 21.33908, -158.0090...\n", " \n", " \n", @@ -1124,7 +1144,7 @@ " 0.154894\n", " 512.06884\n", " 6.287605\n", - " 5.759209\n", + " 6.101699\n", " 0.0\n", " 0.0\n", " 0.004019\n", @@ -1143,27 +1163,32 @@ " 6.238917\n", " 6.248434\n", " 6.287605\n", - " 0.597166\n", + " NaN\n", " 6.794596\n", " 0.0\n", " 0.0\n", - " 0.035751\n", - " 0.035751\n", - " 0.724767\n", - " 1.395817\n", - " 16.395079\n", - " 18.912151\n", - " 24.325914\n", - " 24.818145\n", - " 29.492595\n", - " 30.606405\n", - " 116.695076\n", - " 116.707555\n", - " 116.529463\n", - " 116.802142\n", - " 117.615592\n", - " 117.778049\n", + " 0.000253\n", + " 0.000253\n", + " 0.006859\n", + " 0.012942\n", + " 0.132607\n", + " 0.156795\n", + " 0.207317\n", + " 0.210715\n", + " 0.250420\n", + " 0.260357\n", + " 1.020683\n", + " 1.020921\n", + " 1.018852\n", + " 1.020911\n", + " 1.025525\n", + " 1.029091\n", " 49.725\n", + " 1.000\n", + " East Asia & Pacific\n", + " PYF\n", + " small urban areas\n", + " very high\n", " MULTIPOLYGON (((-149.52669 -17.53410, -149.516...\n", " \n", " \n", @@ -1175,7 +1200,7 @@ " 2.574366\n", " 425.72589\n", " 3.203175\n", - " 3.363398\n", + " 3.425973\n", " 0.0\n", " 0.0\n", " 0.119848\n", @@ -1194,27 +1219,32 @@ " 0.565102\n", " 0.567626\n", " 0.583113\n", - " 0.697000\n", + " 0.788\n", " 23.702313\n", " 0.0\n", " 0.0\n", - " 3.293938\n", - " 3.423679\n", - " 3.423679\n", - " 4.608115\n", - " 4.600608\n", - " 1.948809\n", - " 2.504590\n", - " 2.521983\n", - " 2.338196\n", - " 13.090183\n", - " 13.087702\n", - " 14.564341\n", - " 16.630656\n", - " 16.571571\n", - " 16.613528\n", - " 17.015572\n", + " 0.032486\n", + " 0.033710\n", + " 0.033710\n", + " 0.044361\n", + " 0.044296\n", + " 0.018624\n", + " 0.024147\n", + " 0.024327\n", + " 0.023059\n", + " 0.132346\n", + " 0.132319\n", + " 0.145884\n", + " 0.168810\n", + " 0.168214\n", + " 0.168644\n", + " 0.172562\n", " 28.917\n", + " 0.182\n", + " Latin America & Caribbean\n", + " MEX\n", + " small urban areas\n", + " high\n", " MULTIPOLYGON (((-117.04765 32.41323, -117.0254...\n", " \n", " \n", @@ -1267,6 +1297,11 @@ " ...\n", " ...\n", " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", " 11681\n", @@ -1277,7 +1312,7 @@ " 2.040092\n", " 279.39488\n", " 0.000000\n", - " 7.515344\n", + " 7.137708\n", " 0.0\n", " 0.0\n", " 0.000000\n", @@ -1296,7 +1331,7 @@ " 0.373921\n", " 0.376469\n", " 0.377785\n", - " 0.516000\n", + " 0.801\n", " 11.507672\n", " 0.0\n", " 0.0\n", @@ -1307,16 +1342,21 @@ " 0.000000\n", " 0.000000\n", " 0.000000\n", - " 0.207872\n", - " 0.270684\n", - " 0.555425\n", - " 3.055792\n", - " 3.055790\n", - " 3.645045\n", - " 3.704992\n", - " 3.753177\n", - " 3.762510\n", + " 0.002320\n", + " 0.002986\n", + " 0.005998\n", + " 0.032858\n", + " 0.032858\n", + " 0.039957\n", + " 0.040609\n", + " 0.041699\n", + " 0.041780\n", " 53.798\n", + " NaN\n", + " East Asia & Pacific\n", + " CHN\n", + " medium-size urban areas\n", + " very high\n", " MULTIPOLYGON (((121.25828 28.16228, 121.26906 ...\n", " \n", " \n", @@ -1328,7 +1368,7 @@ " 0.290778\n", " 73.62697\n", " 0.000000\n", - " 1.983068\n", + " 1.916450\n", " 0.0\n", " 0.0\n", " 0.000000\n", @@ -1347,7 +1387,7 @@ " 0.021910\n", " 0.021910\n", " 0.021910\n", - " 0.516000\n", + " 0.801\n", " 12.250077\n", " 0.0\n", " 0.0\n", @@ -1362,12 +1402,17 @@ " 0.000000\n", " 0.000000\n", " 0.000000\n", - " 1.263001\n", - " 1.263001\n", - " 1.263001\n", - " 1.263001\n", - " 1.263001\n", + " 0.012559\n", + " 0.012559\n", + " 0.012559\n", + " 0.012559\n", + " 0.012559\n", " 10.958\n", + " NaN\n", + " East Asia & Pacific\n", + " CHN\n", + " small urban areas\n", + " very high\n", " MULTIPOLYGON (((121.15658 27.83154, 121.16734 ...\n", " \n", " \n", @@ -1379,7 +1424,7 @@ " 0.633112\n", " 105.84604\n", " 0.000000\n", - " 3.401703\n", + " 3.137552\n", " 0.0\n", " 0.0\n", " 0.000000\n", @@ -1398,7 +1443,7 @@ " 0.009270\n", " 0.009270\n", " 0.009270\n", - " 0.516000\n", + " 0.801\n", " 11.503886\n", " 0.0\n", " 0.0\n", @@ -1413,12 +1458,17 @@ " 0.000000\n", " 0.000000\n", " 0.000000\n", - " 0.187618\n", - " 0.187618\n", - " 0.187618\n", - " 0.187618\n", - " 0.187618\n", + " 0.002044\n", + " 0.002044\n", + " 0.002044\n", + " 0.002044\n", + " 0.002044\n", " 23.913\n", + " NaN\n", + " East Asia & Pacific\n", + " CHN\n", + " small urban areas\n", + " very high\n", " MULTIPOLYGON (((121.60975 28.32355, 121.59895 ...\n", " \n", " \n", @@ -1430,7 +1480,7 @@ " 0.815049\n", " 80.81759\n", " 0.000000\n", - " 2.688027\n", + " 2.516636\n", " 0.0\n", " 0.0\n", " 0.000000\n", @@ -1449,7 +1499,7 @@ " 0.026133\n", " 0.026133\n", " 0.026133\n", - " 0.516000\n", + " 0.801\n", " 6.423387\n", " 0.0\n", " 0.0\n", @@ -1464,12 +1514,17 @@ " 0.000000\n", " 0.000000\n", " 0.000000\n", - " 0.496961\n", - " 0.948052\n", - " 0.870041\n", - " 0.870041\n", - " 0.870041\n", + " 0.004004\n", + " 0.007571\n", + " 0.006967\n", + " 0.006967\n", + " 0.006967\n", " 31.881\n", + " NaN\n", + " East Asia & Pacific\n", + " CHN\n", + " small urban areas\n", + " very high\n", " MULTIPOLYGON (((121.60246 28.29808, 121.61325 ...\n", " \n", " \n", @@ -1481,7 +1536,7 @@ " 0.615766\n", " 109.68613\n", " 0.000000\n", - " 2.976785\n", + " 2.837875\n", " 0.0\n", " 0.0\n", " 0.000000\n", @@ -1500,75 +1555,93 @@ " 0.089338\n", " 0.089623\n", " 0.089623\n", - " 0.485000\n", + " 0.775\n", " 26.480700\n", " 0.0\n", " 0.0\n", " 0.000000\n", " 0.000000\n", " 0.000000\n", - " 0.027020\n", - " 0.027020\n", - " 0.027020\n", - " 0.027020\n", - " 0.027020\n", - " 0.645885\n", - " 0.784991\n", - " 0.784991\n", - " 0.784991\n", - " 2.344693\n", - " 2.344693\n", - " 2.350426\n", - " 2.350426\n", + " 0.000310\n", + " 0.000310\n", + " 0.000310\n", + " 0.000310\n", + " 0.000310\n", + " 0.006174\n", + " 0.007979\n", + " 0.007979\n", + " 0.007979\n", + " 0.024298\n", + " 0.024298\n", + " 0.024360\n", + " 0.024360\n", " 14.937\n", + " NaN\n", + " East Asia & Pacific\n", + " CHN\n", + " small urban areas\n", + " high\n", " MULTIPOLYGON (((119.80544 25.50990, 119.81608 ...\n", " \n", " \n", "\n", - "

11686 rows × 48 columns

\n", + "

11686 rows × 53 columns

\n", "" ], "text/plain": [ - " ID_UC_G0 GHS_POP wc_built_up_sqkm wc_tree_cover_sqkm \\\n", - "0 1 60043 5.988389 12.555578 \n", - "1 2 51992 5.915353 2.544346 \n", - "2 3 53721 7.765687 1.549071 \n", - "3 4 109748 18.007347 13.902134 \n", - "4 5 76707 16.166797 0.615121 \n", - "... ... ... ... ... \n", - "11681 11682 252067 21.815658 13.733894 \n", - "11682 11683 54242 5.823000 3.420040 \n", - "11683 11684 80162 10.861062 2.879976 \n", - "11684 11685 73418 8.691671 6.055332 \n", - "11685 11686 107086 8.636053 3.179274 \n", + " urban_center_id ghs_pop_2023 worldcover_2021_built_up_sqkm \\\n", + "0 1 60043 5.988389 \n", + "1 2 51992 5.915353 \n", + "2 3 53721 7.765687 \n", + "3 4 109748 18.007347 \n", + "4 5 76707 16.166797 \n", + "... ... ... ... \n", + "11681 11682 252067 21.815658 \n", + "11682 11683 54242 5.823000 \n", + "11683 11684 80162 10.861062 \n", + "11684 11685 73418 8.691671 \n", + "11685 11686 107086 8.636053 \n", + "\n", + " worldcover_2021_tree_cover_sqkm \\\n", + "0 12.555578 \n", + "1 2.544346 \n", + "2 1.549071 \n", + "3 13.902134 \n", + "4 0.615121 \n", + "... ... \n", + "11681 13.733894 \n", + "11682 3.420040 \n", + "11683 2.879976 \n", + "11684 6.055332 \n", + "11685 3.179274 \n", "\n", - " wc_sparse_vegetation_sqkm selected_road_length_km \\\n", - "0 0.027807 159.73268 \n", - "1 0.065721 144.96438 \n", - "2 0.245182 134.50359 \n", - "3 0.154894 512.06884 \n", - "4 2.574366 425.72589 \n", - "... ... ... \n", - "11681 2.040092 279.39488 \n", - "11682 0.290778 73.62697 \n", - "11683 0.633112 105.84604 \n", - "11684 0.815049 80.81759 \n", - "11685 0.615766 109.68613 \n", + " worldcover_2021_sparse_vegetation_sqkm selected_road_length_km \\\n", + "0 0.027807 159.73268 \n", + "1 0.065721 144.96438 \n", + "2 0.245182 134.50359 \n", + "3 0.154894 512.06884 \n", + "4 2.574366 425.72589 \n", + "... ... ... \n", + "11681 2.040092 279.39488 \n", + "11682 0.290778 73.62697 \n", + "11683 0.633112 105.84604 \n", + "11684 0.815049 80.81759 \n", + "11685 0.615766 109.68613 \n", "\n", - " reference_building_area_sqkm prediction_improved_sqkm \\\n", - "0 0.000000 2.028533 \n", - "1 0.000000 1.700809 \n", - "2 1.670102 1.816676 \n", - "3 6.287605 5.759209 \n", - "4 3.203175 3.363398 \n", - "... ... ... \n", - "11681 0.000000 7.515344 \n", - "11682 0.000000 1.983068 \n", - "11683 0.000000 3.401703 \n", - "11684 0.000000 2.688027 \n", - "11685 0.000000 2.976785 \n", + " reference_building_area_sqkm prediction \\\n", + "0 0.000000 2.324382 \n", + "1 0.000000 1.843358 \n", + "2 1.670102 1.831028 \n", + "3 6.287605 6.101699 \n", + "4 3.203175 3.425973 \n", + "... ... ... \n", + "11681 0.000000 7.137708 \n", + "11682 0.000000 1.916450 \n", + "11683 0.000000 3.137552 \n", + "11684 0.000000 2.516636 \n", + "11685 0.000000 2.837875 \n", "\n", - " osm_building_area_sqkm_2008-01 osm_building_area_sqkm_2009-01 \\\n", + " osm_building_area_sqkm_2008_01 osm_building_area_sqkm_2009_01 \\\n", "0 0.0 0.0 \n", "1 0.0 0.0 \n", "2 0.0 0.0 \n", @@ -1581,7 +1654,7 @@ "11684 0.0 0.0 \n", "11685 0.0 0.0 \n", "\n", - " osm_building_area_sqkm_2010-01 osm_building_area_sqkm_2011-01 \\\n", + " osm_building_area_sqkm_2010_01 osm_building_area_sqkm_2011_01 \\\n", "0 0.000000 0.000000 \n", "1 0.000000 0.000000 \n", "2 0.000000 0.000000 \n", @@ -1594,7 +1667,7 @@ "11684 0.000000 0.000000 \n", "11685 0.000000 0.000000 \n", "\n", - " osm_building_area_sqkm_2012-01 osm_building_area_sqkm_2013-01 \\\n", + " osm_building_area_sqkm_2012_01 osm_building_area_sqkm_2013_01 \\\n", "0 0.062051 0.273118 \n", "1 0.003224 0.003370 \n", "2 0.000000 0.000000 \n", @@ -1607,7 +1680,7 @@ "11684 0.000000 0.000000 \n", "11685 0.000000 0.001669 \n", "\n", - " osm_building_area_sqkm_2014-01 osm_building_area_sqkm_2015-01 \\\n", + " osm_building_area_sqkm_2014_01 osm_building_area_sqkm_2015_01 \\\n", "0 0.291588 0.299160 \n", "1 0.027491 0.307089 \n", "2 0.000000 0.000000 \n", @@ -1620,7 +1693,7 @@ "11684 0.000000 0.000000 \n", "11685 0.001669 0.001669 \n", "\n", - " osm_building_area_sqkm_2016-01 osm_building_area_sqkm_2017-01 \\\n", + " osm_building_area_sqkm_2016_01 osm_building_area_sqkm_2017_01 \\\n", "0 0.344058 0.366325 \n", "1 1.425560 1.426726 \n", "2 0.000000 0.000000 \n", @@ -1633,7 +1706,7 @@ "11684 0.000000 0.000000 \n", "11685 0.001669 0.001669 \n", "\n", - " osm_building_area_sqkm_2018-01 osm_building_area_sqkm_2019-01 \\\n", + " osm_building_area_sqkm_2018_01 osm_building_area_sqkm_2019_01 \\\n", "0 0.370777 0.475503 \n", "1 1.424854 1.626145 \n", "2 0.000000 0.037462 \n", @@ -1646,7 +1719,7 @@ "11684 0.000000 0.000000 \n", "11685 0.016482 0.033762 \n", "\n", - " osm_building_area_sqkm_2020-01 osm_building_area_sqkm_2021-01 \\\n", + " osm_building_area_sqkm_2020_01 osm_building_area_sqkm_2021_01 \\\n", "0 0.561869 0.631707 \n", "1 1.625575 1.628737 \n", "2 0.045946 0.099080 \n", @@ -1659,7 +1732,7 @@ "11684 0.000000 0.015021 \n", "11685 0.033762 0.033762 \n", "\n", - " osm_building_area_sqkm_2022-01 osm_building_area_sqkm_2023-01 \\\n", + " osm_building_area_sqkm_2022_01 osm_building_area_sqkm_2023_01 \\\n", "0 0.665965 1.520192 \n", "1 1.629207 1.633950 \n", "2 0.084206 0.139549 \n", @@ -1672,7 +1745,7 @@ "11684 0.028400 0.026133 \n", "11685 0.089338 0.089338 \n", "\n", - " osm_building_area_sqkm_2024-01 osm_building_area_sqkm_2024-05 \\\n", + " osm_building_area_sqkm_2024_01 osm_building_area_sqkm_2024_05 \\\n", "0 1.559349 1.568521 \n", "1 1.641720 1.685883 \n", "2 0.139863 0.141995 \n", @@ -1685,135 +1758,265 @@ "11684 0.026133 0.026133 \n", "11685 0.089623 0.089623 \n", "\n", - " shdi vnl_mean osm_completeness_2008_01 \\\n", - "0 0.702000 4.351451 0.0 \n", - "1 0.652000 4.939129 0.0 \n", - "2 0.899000 14.999293 0.0 \n", - "3 0.597166 6.794596 0.0 \n", - "4 0.697000 23.702313 0.0 \n", - "... ... ... ... \n", - "11681 0.516000 11.507672 0.0 \n", - "11682 0.516000 12.250077 0.0 \n", - "11683 0.516000 11.503886 0.0 \n", - "11684 0.516000 6.423387 0.0 \n", - "11685 0.485000 26.480700 0.0 \n", + " shdi_2021 vnl_2023 prediction_osm_completeness_2008_01 \\\n", + "0 0.707 4.351451 0.0 \n", + "1 0.745 4.939129 0.0 \n", + "2 0.940 14.999293 0.0 \n", + "3 NaN 6.794596 0.0 \n", + "4 0.788 23.702313 0.0 \n", + "... ... ... ... \n", + "11681 0.801 11.507672 0.0 \n", + "11682 0.801 12.250077 0.0 \n", + "11683 0.801 11.503886 0.0 \n", + "11684 0.801 6.423387 0.0 \n", + "11685 0.775 26.480700 0.0 \n", + "\n", + " prediction_osm_completeness_2009_01 \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "... ... \n", + "11681 0.0 \n", + "11682 0.0 \n", + "11683 0.0 \n", + "11684 0.0 \n", + "11685 0.0 \n", + "\n", + " prediction_osm_completeness_2010_01 \\\n", + "0 0.000000 \n", + "1 0.000000 \n", + "2 0.000000 \n", + "3 0.000253 \n", + "4 0.032486 \n", + "... ... \n", + "11681 0.000000 \n", + "11682 0.000000 \n", + "11683 0.000000 \n", + "11684 0.000000 \n", + "11685 0.000000 \n", + "\n", + " prediction_osm_completeness_2011_01 \\\n", + "0 0.000000 \n", + "1 0.000000 \n", + "2 0.000000 \n", + "3 0.000253 \n", + "4 0.033710 \n", + "... ... \n", + "11681 0.000000 \n", + "11682 0.000000 \n", + "11683 0.000000 \n", + "11684 0.000000 \n", + "11685 0.000000 \n", + "\n", + " prediction_osm_completeness_2012_01 \\\n", + "0 0.063767 \n", + "1 0.000822 \n", + "2 0.000000 \n", + "3 0.006859 \n", + "4 0.033710 \n", + "... ... \n", + "11681 0.000000 \n", + "11682 0.000000 \n", + "11683 0.000000 \n", + "11684 0.000000 \n", + "11685 0.000000 \n", + "\n", + " prediction_osm_completeness_2013_01 \\\n", + "0 0.163730 \n", + "1 0.000883 \n", + "2 0.000000 \n", + "3 0.012942 \n", + "4 0.044361 \n", + "... ... \n", + "11681 0.000000 \n", + "11682 0.000000 \n", + "11683 0.000000 \n", + "11684 0.000000 \n", + "11685 0.000310 \n", + "\n", + " prediction_osm_completeness_2014_01 \\\n", + "0 0.166716 \n", + "1 0.018637 \n", + "2 0.000000 \n", + "3 0.132607 \n", + "4 0.044296 \n", + "... ... \n", + "11681 0.000000 \n", + "11682 0.000000 \n", + "11683 0.000000 \n", + "11684 0.000000 \n", + "11685 0.000310 \n", + "\n", + " prediction_osm_completeness_2015_01 \\\n", + "0 0.171311 \n", + "1 0.152596 \n", + "2 0.000000 \n", + "3 0.156795 \n", + "4 0.018624 \n", + "... ... \n", + "11681 0.000000 \n", + "11682 0.000000 \n", + "11683 0.000000 \n", + "11684 0.000000 \n", + "11685 0.000310 \n", + "\n", + " prediction_osm_completeness_2016_01 \\\n", + "0 0.188576 \n", + "1 0.783101 \n", + "2 0.000000 \n", + "3 0.207317 \n", + "4 0.024147 \n", + "... ... \n", + "11681 0.000000 \n", + "11682 0.000000 \n", + "11683 0.000000 \n", + "11684 0.000000 \n", + "11685 0.000310 \n", + "\n", + " prediction_osm_completeness_2017_01 \\\n", + "0 0.202116 \n", + "1 0.783734 \n", + "2 0.000000 \n", + "3 0.210715 \n", + "4 0.024327 \n", + "... ... \n", + "11681 0.002320 \n", + "11682 0.000000 \n", + "11683 0.000000 \n", + "11684 0.000000 \n", + "11685 0.000310 \n", "\n", - " osm_completeness_2009_01 osm_completeness_2010_01 \\\n", - "0 0.0 0.000000 \n", - "1 0.0 0.000000 \n", - "2 0.0 0.000000 \n", - "3 0.0 0.035751 \n", - "4 0.0 3.293938 \n", - "... ... ... \n", - "11681 0.0 0.000000 \n", - "11682 0.0 0.000000 \n", - "11683 0.0 0.000000 \n", - "11684 0.0 0.000000 \n", - "11685 0.0 0.000000 \n", + " prediction_osm_completeness_2018_01 \\\n", + "0 0.203699 \n", + "1 0.783273 \n", + "2 0.000000 \n", + "3 0.250420 \n", + "4 0.023059 \n", + "... ... \n", + "11681 0.002986 \n", + "11682 0.000000 \n", + "11683 0.000000 \n", + "11684 0.000000 \n", + "11685 0.006174 \n", "\n", - " osm_completeness_2011_01 osm_completeness_2012_01 \\\n", - "0 0.000000 5.442658 \n", - "1 0.000000 0.095091 \n", - "2 0.000000 0.000000 \n", - "3 0.035751 0.724767 \n", - "4 3.423679 3.423679 \n", - "... ... ... \n", - "11681 0.000000 0.000000 \n", - "11682 0.000000 0.000000 \n", - "11683 0.000000 0.000000 \n", - "11684 0.000000 0.000000 \n", - "11685 0.000000 0.000000 \n", + " prediction_osm_completeness_2019_01 \\\n", + "0 0.237329 \n", + "1 0.901063 \n", + "2 0.015071 \n", + "3 0.260357 \n", + "4 0.132346 \n", + "... ... \n", + "11681 0.005998 \n", + "11682 0.000000 \n", + "11683 0.000000 \n", + "11684 0.000000 \n", + "11685 0.007979 \n", "\n", - " osm_completeness_2013_01 osm_completeness_2014_01 \\\n", - "0 17.116582 17.547127 \n", - "1 0.101329 2.087314 \n", - "2 0.000000 0.000000 \n", - "3 1.395817 16.395079 \n", - "4 4.608115 4.600608 \n", - "... ... ... \n", - "11681 0.000000 0.000000 \n", - "11682 0.000000 0.000000 \n", - "11683 0.000000 0.000000 \n", - "11684 0.000000 0.000000 \n", - "11685 0.027020 0.027020 \n", + " prediction_osm_completeness_2020_01 \\\n", + "0 0.264390 \n", + "1 0.900828 \n", + "2 0.020622 \n", + "3 1.020683 \n", + "4 0.132319 \n", + "... ... \n", + "11681 0.032858 \n", + "11682 0.000000 \n", + "11683 0.000000 \n", + "11684 0.000000 \n", + "11685 0.007979 \n", "\n", - " osm_completeness_2015_01 osm_completeness_2016_01 \\\n", - "0 18.073713 20.074884 \n", - "1 17.391497 84.595916 \n", - "2 0.000000 0.000000 \n", - "3 18.912151 24.325914 \n", - "4 1.948809 2.504590 \n", - "... ... ... \n", - "11681 0.000000 0.000000 \n", - "11682 0.000000 0.000000 \n", - "11683 0.000000 0.000000 \n", - "11684 0.000000 0.000000 \n", - "11685 0.027020 0.027020 \n", + " prediction_osm_completeness_2021_01 \\\n", + "0 0.313722 \n", + "1 0.901067 \n", + "2 0.048817 \n", + "3 1.020921 \n", + "4 0.145884 \n", + "... ... \n", + "11681 0.032858 \n", + "11682 0.012559 \n", + "11683 0.002044 \n", + "11684 0.004004 \n", + "11685 0.007979 \n", "\n", - " osm_completeness_2017_01 osm_completeness_2018_01 \\\n", - "0 21.559690 21.692216 \n", - "1 84.660119 84.605391 \n", - "2 0.000000 0.000000 \n", - "3 24.818145 29.492595 \n", - "4 2.521983 2.338196 \n", - "... ... ... \n", - "11681 0.207872 0.270684 \n", - "11682 0.000000 0.000000 \n", - "11683 0.000000 0.000000 \n", - "11684 0.000000 0.000000 \n", - "11685 0.027020 0.645885 \n", + " prediction_osm_completeness_2022_01 \\\n", + "0 0.336734 \n", + "1 0.902121 \n", + "2 0.043124 \n", + "3 1.018852 \n", + "4 0.168810 \n", + "... ... \n", + "11681 0.039957 \n", + "11682 0.012559 \n", + "11683 0.002044 \n", + "11684 0.007571 \n", + "11685 0.024298 \n", "\n", - " osm_completeness_2019_01 osm_completeness_2020_01 \\\n", - "0 25.674679 28.566575 \n", - "1 97.066208 97.040778 \n", - "2 1.518298 2.085576 \n", - "3 30.606405 116.695076 \n", - "4 13.090183 13.087702 \n", - "... ... ... \n", - "11681 0.555425 3.055792 \n", - "11682 0.000000 0.000000 \n", - "11683 0.000000 0.000000 \n", - "11684 0.000000 0.000000 \n", - "11685 0.784991 0.784991 \n", + " prediction_osm_completeness_2023_01 \\\n", + "0 0.720924 \n", + "1 0.904437 \n", + "2 0.077236 \n", + "3 1.020911 \n", + "4 0.168214 \n", + "... ... \n", + "11681 0.040609 \n", + "11682 0.012559 \n", + "11683 0.002044 \n", + "11684 0.006967 \n", + "11685 0.024298 \n", "\n", - " osm_completeness_2021_01 osm_completeness_2022_01 \\\n", - "0 33.087056 35.619517 \n", - "1 97.069821 97.148841 \n", - "2 4.879578 4.328472 \n", - "3 116.707555 116.529463 \n", - "4 14.564341 16.630656 \n", - "... ... ... \n", - "11681 3.055790 3.645045 \n", - "11682 1.263001 1.263001 \n", - "11683 0.187618 0.187618 \n", - "11684 0.496961 0.948052 \n", - "11685 0.784991 2.344693 \n", + " prediction_osm_completeness_2024_01 \\\n", + "0 0.744398 \n", + "1 0.907950 \n", + "2 0.077356 \n", + "3 1.025525 \n", + "4 0.168644 \n", + "... ... \n", + "11681 0.041699 \n", + "11682 0.012559 \n", + "11683 0.002044 \n", + "11684 0.006967 \n", + "11685 0.024360 \n", "\n", - " osm_completeness_2023_01 osm_completeness_2024_01 \\\n", - "0 78.965543 81.644399 \n", - "1 97.382958 97.760565 \n", - "2 7.845958 7.857594 \n", - "3 116.802142 117.615592 \n", - "4 16.571571 16.613528 \n", - "... ... ... \n", - "11681 3.704992 3.753177 \n", - "11682 1.263001 1.263001 \n", - "11683 0.187618 0.187618 \n", - "11684 0.870041 0.870041 \n", - "11685 2.344693 2.350426 \n", + " prediction_osm_completeness_2024_05 total_area_sqkm \\\n", + "0 0.747106 34.792 \n", + "1 0.935053 19.901 \n", + "2 0.079047 14.926 \n", + "3 1.029091 49.725 \n", + "4 0.172562 28.917 \n", + "... ... ... \n", + "11681 0.041780 53.798 \n", + "11682 0.012559 10.958 \n", + "11683 0.002044 23.913 \n", + "11684 0.006967 31.881 \n", + "11685 0.024360 14.937 \n", "\n", - " osm_completeness_2024_05 total_area_sqkm \\\n", - "0 81.975882 34.792 \n", - "1 100.641484 19.901 \n", - "2 8.029926 14.926 \n", - "3 117.778049 49.725 \n", - "4 17.015572 28.917 \n", - "... ... ... \n", - "11681 3.762510 53.798 \n", - "11682 1.263001 10.958 \n", - "11683 0.187618 23.913 \n", - "11684 0.870041 31.881 \n", - "11685 2.350426 14.937 \n", + " reference_osm_completeness_2024_05 region_wb iso_a3 \\\n", + "0 NaN East Asia & Pacific WSM \n", + "1 NaN East Asia & Pacific TON \n", + "2 0.085 North America USA \n", + "3 1.000 East Asia & Pacific PYF \n", + "4 0.182 Latin America & Caribbean MEX \n", + "... ... ... ... \n", + "11681 NaN East Asia & Pacific CHN \n", + "11682 NaN East Asia & Pacific CHN \n", + "11683 NaN East Asia & Pacific CHN \n", + "11684 NaN East Asia & Pacific CHN \n", + "11685 NaN East Asia & Pacific CHN \n", + "\n", + " ghs_pop_2023_class shdi_2021_class \\\n", + "0 small urban areas high \n", + "1 small urban areas high \n", + "2 small urban areas very high \n", + "3 small urban areas very high \n", + "4 small urban areas high \n", + "... ... ... \n", + "11681 medium-size urban areas very high \n", + "11682 small urban areas very high \n", + "11683 small urban areas very high \n", + "11684 small urban areas very high \n", + "11685 small urban areas high \n", "\n", " geometry \n", "0 MULTIPOLYGON (((-171.77356 -13.82480, -171.763... \n", @@ -1828,10 +2031,10 @@ "11684 MULTIPOLYGON (((121.60246 28.29808, 121.61325 ... \n", "11685 MULTIPOLYGON (((119.80544 25.50990, 119.81608 ... \n", "\n", - "[11686 rows x 48 columns]" + "[11686 rows x 53 columns]" ] }, - "execution_count": 10, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -1851,7 +2054,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "7e43f297", "metadata": {}, "outputs": [], @@ -1861,7 +2064,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 10, "id": "b805b4bf", "metadata": {}, "outputs": [], @@ -1872,15 +2075,15 @@ " with agg_prediction as (\n", " select\n", " a.identifier\n", - " ,a.ID_UC_G0\n", + " ,a.urban_center_id\n", " ,'rf_adjusted' as model_name\n", " ,split\n", " ,avg(a.prediction) as prediction\n", " from performance_20_clusters_reference_and_osm_v2024 as a \n", - " group by ID_UC_G0, identifier, split, model_name\n", + " group by urban_center_id, identifier, split, model_name\n", " )\n", " select\n", - " a.ID_UC_G0\n", + " a.urban_center_id\n", " ,a.total_area_sqkm\n", " ,a.reference_osm_completeness_2024_05\n", " ,a.region_wb\n", @@ -1892,15 +2095,15 @@ " when a.osm_building_area_sqkm_2024_05 / SUM(b.prediction) > 1.5 then 1.5\n", " else a.osm_building_area_sqkm_2024_05 / SUM(b.prediction)\n", " end as prediction_osm_completeness\n", - " from uc_full_info_v2024 a\n", + " from rf_adjusted_prediction_reference_and_osm_urban_centers_v2024 a\n", " left join agg_prediction b\n", - " on a.ID_UC_G0 = b.ID_UC_G0\n", + " on a.urban_center_id = b.urban_center_id\n", " where\n", " reference_building_area_sqkm is not null\n", " and\n", " reference_osm_completeness_2024_05 < 1.5\n", " group by\n", - " a.ID_UC_G0\n", + " a.urban_center_id\n", " ,reference_osm_completeness_2024_05\n", " ,b.model_name\n", " ,a.osm_building_area_sqkm_2024_05\n", @@ -1919,12 +2122,12 @@ " query = f\"\"\"\n", " select \n", " a.identifier as id\n", - " ,a.ID_UC_G0\n", + " ,a.urban_center_id\n", " ,a.region_wb \n", - " from grid_full_info_V2024 a\n", + " from rf_adjusted_prediction_reference_and_osm_v2024 a\n", " \"\"\"\n", " df = pd.read_sql(query, con=con)\n", - " print(f\"got dataframe with {len(df)} samples from table: grid_full_info_V2024\")\n", + " print(f\"got dataframe with {len(df)} samples from table: rf_adjusted_prediction_reference_and_osm_v2024\")\n", " return df" ] }, @@ -1938,7 +2141,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 11, "id": "90f667e8", "metadata": {}, "outputs": [ @@ -1946,8 +2149,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "got dataframe with 7758 samples.\n", - "got dataframe with 675779 samples from table: all_parameters_urban_centers_grid\n" + "got dataframe with 7757 samples.\n", + "got dataframe with 677806 samples from table: rf_adjusted_prediction_reference_and_osm_v2024\n" ] } ], @@ -1971,7 +2174,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "id": "4e081dbf", "metadata": {}, "outputs": [ @@ -1979,7 +2182,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_39288/2200566900.py:30: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.\n", + "/tmp/ipykernel_17183/2200566900.py:30: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.\n", " cmap = matplotlib.cm.get_cmap('tab10')\n" ] }, @@ -1993,29 +2196,29 @@ "41 0.855\n", "45 1.000\n", " ... \n", - "6200 0.871\n", - "6208 0.902\n", - "6222 0.761\n", - "6240 1.000\n", - "6268 1.078\n", - "Name: reference_osm_completeness_2024_05, Length: 1377, dtype: float64 0.0 1.438\n", - "27 0.784535\n", - "32 0.971466\n", - "38 0.931734\n", - "41 1.083921\n", - "45 1.184852\n", + "6199 0.871\n", + "6207 0.902\n", + "6221 0.761\n", + "6239 1.000\n", + "6267 1.078\n", + "Name: reference_osm_completeness_2024_05, Length: 1376, dtype: float64 0.0 1.438\n", + "27 0.815030\n", + "32 0.922054\n", + "38 0.893098\n", + "41 1.136587\n", + "45 1.123000\n", " ... \n", - "6200 0.804035\n", - "6208 0.846972\n", - "6222 0.623468\n", - "6240 1.032679\n", - "6268 0.786795\n", - "Name: prediction_osm_completeness, Length: 1377, dtype: float64 0.0 1.5\n" + "6199 0.784621\n", + "6207 0.828293\n", + "6221 0.645118\n", + "6239 1.011687\n", + "6267 0.792918\n", + "Name: prediction_osm_completeness, Length: 1376, dtype: float64 0.0 1.5\n" ] }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -2165,7 +2368,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "id": "faba974f", "metadata": {}, "outputs": [], @@ -2201,7 +2404,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 14, "id": "f99ce446", "metadata": {}, "outputs": [ @@ -2209,12 +2412,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "got dataframe with 7758 samples.\n", + "got dataframe with 7757 samples.\n", "rf_adjusted\n", - "[65078, 207399, 126965, 90600, 69835, 44028, 71874]\n", - "675779\n", - "[939, 1042, 1781, 1377, 397, 794, 1428]\n", - "7758\n" + "[65549, 207618, 127334, 90835, 70115, 44219, 72136]\n", + "677806\n", + "[939, 1042, 1781, 1376, 397, 794, 1428]\n", + "7757\n" ] }, { @@ -2253,10 +2456,10 @@ " reference_and_osm\n", " rf_adjusted\n", " cluster_20\n", - " 0.845043\n", - " 0.846926\n", - " 0.017915\n", - " 0.071607\n", + " 0.835506\n", + " 0.836939\n", + " 0.018725\n", + " 0.073298\n", " \n", " \n", "\n", @@ -2264,10 +2467,10 @@ ], "text/plain": [ " training_data model_name split r2 explained_variance \\\n", - "0 reference_and_osm rf_adjusted cluster_20 0.845043 0.846926 \n", + "0 reference_and_osm rf_adjusted cluster_20 0.835506 0.836939 \n", "\n", " MSE MAE \n", - "0 0.017915 0.071607 " + "0 0.018725 0.073298 " ] }, "metadata": {}, @@ -2367,7 +2570,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 15, "id": "e10e916b", "metadata": {}, "outputs": [ @@ -2409,84 +2612,84 @@ " rf_adjusted\n", " [East Asia & Pacific]\n", " cluster_20\n", - " 207399\n", + " 207618\n", " 1042\n", - " 0.884257\n", - " 0.884712\n", - " 0.016584\n", - " 0.074724\n", + " 0.888620\n", + " 0.888937\n", + " 0.015967\n", + " 0.073877\n", " \n", " \n", " 3\n", " rf_adjusted\n", " [Europe & Central Asia]\n", " cluster_20\n", - " 90600\n", - " 1377\n", - " 0.792050\n", - " 0.792068\n", - " 0.019663\n", - " 0.097606\n", + " 90835\n", + " 1376\n", + " 0.753282\n", + " 0.753679\n", + " 0.023302\n", + " 0.108071\n", " \n", " \n", " 0\n", " rf_adjusted\n", " [Latin America & Caribbean]\n", " cluster_20\n", - " 65078\n", + " 65549\n", " 939\n", - " 0.799641\n", - " 0.800985\n", - " 0.018199\n", - " 0.058141\n", + " 0.783814\n", + " 0.784504\n", + " 0.019640\n", + " 0.060427\n", " \n", " \n", " 5\n", " rf_adjusted\n", " [Middle East & North Africa]\n", " cluster_20\n", - " 44028\n", + " 44219\n", " 794\n", - " 0.873054\n", - " 0.881827\n", - " 0.013110\n", - " 0.050221\n", + " 0.923110\n", + " 0.924392\n", + " 0.007958\n", + " 0.037453\n", " \n", " \n", " 4\n", " rf_adjusted\n", " [North America]\n", " cluster_20\n", - " 69835\n", + " 70115\n", " 397\n", - " 0.888810\n", - " 0.893549\n", - " 0.010249\n", - " 0.073348\n", + " 0.887764\n", + " 0.891153\n", + " 0.010326\n", + " 0.072835\n", " \n", " \n", " 2\n", " rf_adjusted\n", " [South Asia]\n", " cluster_20\n", - " 126965\n", + " 127334\n", " 1781\n", - " 0.854878\n", - " 0.855343\n", - " 0.010301\n", - " 0.034753\n", + " 0.825174\n", + " 0.828024\n", + " 0.012381\n", + " 0.038803\n", " \n", " \n", " 6\n", " rf_adjusted\n", " [Sub-Saharan Africa]\n", " cluster_20\n", - " 71874\n", + " 72136\n", " 1428\n", - " 0.762735\n", - " 0.767093\n", - " 0.043132\n", - " 0.118540\n", + " 0.746885\n", + " 0.749210\n", + " 0.046025\n", + " 0.122853\n", " \n", " \n", "\n", @@ -2494,22 +2697,22 @@ ], "text/plain": [ " model_name region split samples \\\n", - "1 rf_adjusted [East Asia & Pacific] cluster_20 207399 \n", - "3 rf_adjusted [Europe & Central Asia] cluster_20 90600 \n", - "0 rf_adjusted [Latin America & Caribbean] cluster_20 65078 \n", - "5 rf_adjusted [Middle East & North Africa] cluster_20 44028 \n", - "4 rf_adjusted [North America] cluster_20 69835 \n", - "2 rf_adjusted [South Asia] cluster_20 126965 \n", - "6 rf_adjusted [Sub-Saharan Africa] cluster_20 71874 \n", + "1 rf_adjusted [East Asia & Pacific] cluster_20 207618 \n", + "3 rf_adjusted [Europe & Central Asia] cluster_20 90835 \n", + "0 rf_adjusted [Latin America & Caribbean] cluster_20 65549 \n", + "5 rf_adjusted [Middle East & North Africa] cluster_20 44219 \n", + "4 rf_adjusted [North America] cluster_20 70115 \n", + "2 rf_adjusted [South Asia] cluster_20 127334 \n", + "6 rf_adjusted [Sub-Saharan Africa] cluster_20 72136 \n", "\n", " reference_samples r2 explained_variance MSE MAE \n", - "1 1042 0.884257 0.884712 0.016584 0.074724 \n", - "3 1377 0.792050 0.792068 0.019663 0.097606 \n", - "0 939 0.799641 0.800985 0.018199 0.058141 \n", - "5 794 0.873054 0.881827 0.013110 0.050221 \n", - "4 397 0.888810 0.893549 0.010249 0.073348 \n", - "2 1781 0.854878 0.855343 0.010301 0.034753 \n", - "6 1428 0.762735 0.767093 0.043132 0.118540 " + "1 1042 0.888620 0.888937 0.015967 0.073877 \n", + "3 1376 0.753282 0.753679 0.023302 0.108071 \n", + "0 939 0.783814 0.784504 0.019640 0.060427 \n", + "5 794 0.923110 0.924392 0.007958 0.037453 \n", + "4 397 0.887764 0.891153 0.010326 0.072835 \n", + "2 1781 0.825174 0.828024 0.012381 0.038803 \n", + "6 1428 0.746885 0.749210 0.046025 0.122853 " ] }, "metadata": {}, diff --git a/scripts/V2024/version_2_figure_1.ipynb b/scripts/V2024/version_2_figure_1.ipynb new file mode 100644 index 0000000..55567f5 --- /dev/null +++ b/scripts/V2024/version_2_figure_1.ipynb @@ -0,0 +1,908 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "db7bf1fa", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "PROJ: proj_create_from_database: Cannot find proj.db\n" + ] + } + ], + "source": [ + "import sqlite3\n", + "import pandas as pd\n", + "import numpy as np\n", + "import geopandas as gpd\n", + "import fiona\n", + "\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.gridspec as gridspec\n", + "import seaborn as sns\n", + "\n", + "from sklearn import metrics" + ] + }, + { + "cell_type": "markdown", + "id": "76de4e94", + "metadata": {}, + "source": [ + "### Copy Figure 1\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "bb6679e8", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "PROJ: proj_create_from_database: Cannot find proj.db\n" + ] + } + ], + "source": [ + "import datetime\n", + "import string\n", + "\n", + "import pandas as pd\n", + "import sqlite3\n", + "\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.gridspec as gridspec\n", + "import seaborn as sns\n", + "import geopandas as gpd\n", + "import fiona" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d128aa73", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Europe & Central Asia_2022-01-01',\n", + " 'Sub-Saharan Africa_2022-01-01',\n", + " 'ne_10m_admin_0_countries',\n", + " 'graticule',\n", + " 'ne_50m_land',\n", + " 'all_parameters_urban_centers',\n", + " 'all_parameters_urban_centers_grid',\n", + " 'rf_adjusted_prediction_reference_and_osm',\n", + " 'rf_adjusted_prediction_reference_and_osm_urban_centers',\n", + " 'inequality_measures_urban_centers',\n", + " 'performance_20_clusters_reference_and_osm',\n", + " 'Europe & Central Asia_2010-01-01',\n", + " 'Europe & Central Asia_2014-01-01',\n", + " 'Europe & Central Asia_2023-01-01',\n", + " 'Sub-Saharan Africa_2010-01-01',\n", + " 'Sub-Saharan Africa_2014-01-01',\n", + " 'Sub-Saharan Africa_2023-01-01',\n", + " 'geowiki_grids_final',\n", + " 'performance_20_clusters_reference_and_osm_v2024',\n", + " 'rf_adjusted_prediction_reference_and_osm_urban_centers_v2024',\n", + " 'rf_adjusted_prediction_reference_and_osm_v2024',\n", + " 'model_performance_cluster_20_reference_and_osm',\n", + " 'osm_user_contributions_per_urban_center_with_data_teams_csv',\n", + " 'intra_urban_completeness_stats_clusters',\n", + " 'osm_user_contributions_per_urban_center_per_day_with_flag']" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "urban_completeness = (\"../data/global_urban_building_completeness.gpkg\")\n", + "fiona.listlayers(urban_completeness)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "57384a4d", + "metadata": {}, + "outputs": [], + "source": [ + "def load_dataframe(column):\n", + " con = sqlite3.connect(\"../data/global_urban_building_completeness.gpkg\")\n", + " query = f\"\"\"\n", + " select \n", + " {column} as group_name,\n", + " urban_center_id,\n", + " 100*prediction_osm_completeness_2008_01 as \"2008\",\n", + " 100*prediction_osm_completeness_2009_01 as \"2009\",\n", + " 100*prediction_osm_completeness_2010_01 as \"2010\",\n", + " 100*prediction_osm_completeness_2011_01 as \"2011\",\n", + " 100*prediction_osm_completeness_2012_01 as \"2012\",\n", + " 100*prediction_osm_completeness_2013_01 as \"2013\",\n", + " 100*prediction_osm_completeness_2014_01 as \"2014\",\n", + " 100*prediction_osm_completeness_2015_01 as \"2015\",\n", + " 100*prediction_osm_completeness_2016_01 as \"2016\",\n", + " 100*prediction_osm_completeness_2017_01 as \"2017\",\n", + " 100*prediction_osm_completeness_2018_01 as \"2018\",\n", + " 100*prediction_osm_completeness_2019_01 as \"2019\",\n", + " 100*prediction_osm_completeness_2020_01 as \"2020\",\n", + " 100*prediction_osm_completeness_2021_01 as \"2021\",\n", + " 100*prediction_osm_completeness_2022_01 as \"2022\",\n", + " 100*prediction_osm_completeness_2023_01 as \"2023\",\n", + " 100*prediction_osm_completeness_2024_05 as \"2024\"\n", + " \n", + "\n", + "\n", + " from rf_adjusted_prediction_reference_and_osm_urban_centers_v2024 as a\n", + " where {column} is not null\n", + " order by group_name \n", + " \"\"\"\n", + " df = pd.read_sql_query(query, con=con)\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "02086214", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 2, figsize=(12.5, 6))\n", + "gs1 = gridspec.GridSpec(1, 2)\n", + "gs1.update(wspace=0.2, hspace=0.3) # set the spacing between axes.\n", + "\n", + "\n", + "all_months = [\n", + " datetime.date(2008, 1, 1),\n", + " datetime.date(2009, 1, 1),\n", + " datetime.date(2010, 1, 1),\n", + " datetime.date(2011, 1, 1),\n", + " datetime.date(2012, 1, 1),\n", + " datetime.date(2013, 1, 1),\n", + " datetime.date(2014, 1, 1),\n", + " datetime.date(2015, 1, 1),\n", + " datetime.date(2016, 1, 1),\n", + " datetime.date(2017, 1, 1),\n", + " datetime.date(2018, 1, 1),\n", + " datetime.date(2019, 1, 1),\n", + " datetime.date(2020, 1, 1),\n", + " datetime.date(2021, 1, 1),\n", + " datetime.date(2022, 1, 1),\n", + " datetime.date(2023, 1, 1),\n", + " datetime.date(2024, 1, 1)\n", + " \n", + "]\n", + " \n", + "for k, column in enumerate([\n", + " \"region_wb\",\n", + " \"shdi_2021_class\", \n", + "]):\n", + " df = load_dataframe(column)\n", + " \n", + " if column == \"region_wb\":\n", + " title = \"World Bank Region\"\n", + " groups = wb_regions_groups = [\n", + " [\"Europe & Central Asia\"],\n", + " [\"North America\"],\n", + " [\"Sub-Saharan Africa\"],\n", + " [\"Latin America & Caribbean\"],\n", + " [\"East Asia & Pacific\"],\n", + " [\"Middle East & North Africa\"],\n", + " [\"South Asia\"],\n", + "\n", + " ]\n", + "\n", + " labels = [\n", + " \"Europe & Central Asia\",\n", + " \"North America\",\n", + " \"Sub-Saharan Africa\",\n", + " \"Latin America & Caribbean\",\n", + " \"East Asia & Pacific\",\n", + " \"Middle East & North Africa\",\n", + " \"South Asia\",\n", + " ]\n", + " \n", + " cmap = matplotlib.cm.get_cmap('tab10')\n", + " colors_dict = {\n", + " \"East Asia & Pacific\": cmap(0),\n", + " \"Europe & Central Asia\": cmap(0.125),\n", + " \"Latin America & Caribbean\": cmap(0.25),\n", + " \"Middle East & North Africa\": cmap(0.375),\n", + " \"North America\": cmap(0.5),\n", + " \"South Asia\": cmap(0.625),\n", + " \"Sub-Saharan Africa\": cmap(0.75),\n", + " \"all\": \"black\",\n", + " }\n", + "\n", + " linestyles_dict = {\n", + " \"East Asia & Pacific\": ('dotted', (0, (1, 1))),\n", + " \"Europe & Central Asia\": ('densely dashed', (0, (5, 1))),\n", + " \"Latin America & Caribbean\": ('densely dashed', (0, (5, 1))),\n", + " \"Middle East & North Africa\": ('solid', (0, ())),\n", + " \"North America\": ('dotted', (0, (1, 1))),\n", + " \"South Asia\": ('solid',(0, ())),\n", + " \"Sub-Saharan Africa\": ('densely dashed', (0, (5, 1))),\n", + " \"all\": ('solid', (0, ())),\n", + " }\n", + " \n", + " fontsize = 6\n", + " \n", + " elif column == \"shdi_2021_class\":\n", + " \n", + " \n", + " title = \"SHDI\"\n", + " groups = [\n", + " [\"low\"], [\"medium\"], [\"high\"], [\"very high\"]\n", + " ]\n", + "\n", + " labels = [\n", + " \"low\",\n", + " \"medium\",\n", + " \"high\",\n", + " \"very high\",\n", + " ]\n", + " \n", + " cmap = matplotlib.cm.get_cmap('Blues')\n", + " colors_dict = {\n", + " \"low\": cmap(0.25), # low\n", + " \"medium\": cmap(0.5), # medium\n", + " \"high\": cmap(0.75), # high\n", + " \"very high\": cmap(1.0), # very high\n", + " }\n", + " \n", + " linestyles_dict = {\n", + " \"low\": ('solid', (0, ())),\n", + " \"medium\": ('solid', (0, ())),\n", + " \"high\": ('solid', (0, ())),\n", + " \"very high\": ('solid', (0, ())),\n", + " }\n", + " \n", + " fontsize = 9\n", + " \n", + " \n", + " df = df.melt(\n", + " id_vars=[\"group_name\", \"urban_center_id\"], \n", + " var_name=\"year\", \n", + " value_name=\"prediction_osm_completeness\"\n", + " )\n", + " df[\"year\"] = df[\"year\"].apply(pd.to_datetime)\n", + " display(df)\n", + " \n", + " ax = plt.subplot(gs1[k])\n", + " max_y_values = []\n", + " \n", + " for i, group in enumerate(groups):\n", + " print(group)\n", + " \n", + " region_df = df.loc[df[\"group_name\"].isin(group)]\n", + " region_df.reset_index(inplace=True)\n", + " \n", + " sns.lineplot(\n", + " data=region_df,\n", + " x=\"year\",\n", + " y=\"prediction_osm_completeness\",\n", + " color=colors_dict[group[0]],\n", + " linestyle=linestyles_dict[group[0]][1],\n", + " errorbar=('ci', 95)\n", + " )\n", + "\n", + " max_y_value = region_df.loc[region_df[\"year\"] == '2024'][\"prediction_osm_completeness\"].mean()\n", + " max_y_values.append(max_y_value)\n", + " \n", + " '''if i == 0:\n", + " label_position = max_y_values[i]\n", + " elif (abs(max_y_values[i-1] - max_y_values[i]) < 2.5) and column == \"region_wb\":\n", + " print(max_y_values[i], max_y_values[i-1], label_position)\n", + " label_position = float(max_y_values[i]) - 2.5\n", + " elif (abs(max_y_values[i-1] - max_y_values[i]) < 2.5) and column == \"shdi_class\":\n", + " print(max_y_values[i], max_y_values[i-1], label_position)\n", + " label_position = float(max_y_values[i]) + 0.5\n", + " else:\n", + " label_position = max_y_values[i]'''\n", + " \n", + " def adjust_label_positions(max_y_values, min_distance=2.5):\n", + " adjusted_positions = []\n", + " \n", + " for i in range(len(max_y_values)):\n", + " # Start with the original position\n", + " label_position = max_y_values[i]\n", + " \n", + " # Compare with all previous labels\n", + " for prev_position in adjusted_positions:\n", + " if abs(label_position - prev_position) < min_distance:\n", + " # Adjust position by moving it further away (downward in this case)\n", + " label_position = prev_position - min_distance\n", + " \n", + " # Save the adjusted position\n", + " adjusted_positions.append(label_position)\n", + " \n", + " return adjusted_positions\n", + " \n", + " # Adjust the label positions based on overlap detection\n", + " adjusted_positions = adjust_label_positions(max_y_values)\n", + " \n", + " ax.annotate(\n", + " labels[i],\n", + " (datetime.date(2024, 4, 1), adjusted_positions[i]),\n", + " fontsize=fontsize,\n", + " color=colors_dict[group[0]]\n", + " )\n", + " \n", + "\n", + " ax.set_ylim([0, 70])\n", + " ax.set_xlim([datetime.date(2008, 1, 1), datetime.date(2029, 1, 1)])\n", + " ax.set_xticks([\n", + " datetime.date(2010, 1, 1),\n", + " datetime.date(2015, 1, 1),\n", + " datetime.date(2020, 1, 1),\n", + " datetime.date(2023, 1, 1),\n", + " datetime.date(2025, 1, 1),\n", + " ])\n", + " ax.set_xticklabels([\"2010\", \"2015\", \"2020\", \"'23\", \"2025\"])\n", + " ax.set_yticks([\n", + " 0, 20, 40, 60, 80\n", + " ])\n", + " ax.plot(\n", + " [datetime.date(2020, 1, 1), datetime.date(2020, 1, 1)],\n", + " [0, 100],\n", + " color=\"black\",\n", + " alpha=0.5\n", + " )\n", + " ax.set_ylabel(\"urban OSM building completeness [%]\")\n", + " ax.grid()\n", + " ax.set_title(f\"({string.ascii_lowercase[k]}) {title}\")\n", + " \n", + "fig.patch.set_facecolor('xkcd:white')\n", + "plt.savefig(\n", + " f\"../figures/completeness_per_month_by_region_2024.png\",\n", + " dpi=300,\n", + " bbox_inches = 'tight',\n", + " pad_inches = 0.25\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "1190d3d1", + "metadata": {}, + "source": [ + "### calculate difference between Jan 2023 and May 2024" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "f4f110cf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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region_wbdifference
0East Asia & Pacific0.015981
1Europe & Central Asia0.036755
2Latin America & Caribbean0.017562
3Middle East & North Africa0.060674
4North America0.061085
5South Asia0.011416
6Sub-Saharan Africa0.050764
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" + ], + "text/plain": [ + " region_wb difference\n", + "0 East Asia & Pacific 0.015981\n", + "1 Europe & Central Asia 0.036755\n", + "2 Latin America & Caribbean 0.017562\n", + "3 Middle East & North Africa 0.060674\n", + "4 North America 0.061085\n", + "5 South Asia 0.011416\n", + "6 Sub-Saharan Africa 0.050764" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#grouped by region\n", + "import geopandas as gpd\n", + "gdf = gpd.read_file(\"../data/global_urban_building_completeness.gpkg\", layer=\"rf_adjusted_prediction_reference_and_osm_urban_centers_v2024\")\n", + "gdf[\"prediction_osm_completeness_2024_05\"] = gdf[\"prediction_osm_completeness_2024_05\"].apply(lambda x: 1 if x > 1 else x)\n", + "gdf[\"prediction_osm_completeness_2023_01\"] = gdf[\"prediction_osm_completeness_2023_01\"].apply(lambda x: 1 if x > 1 else x)\n", + "gdf[\"difference\"] = gdf[\"prediction_osm_completeness_2024_05\"] - gdf[\"prediction_osm_completeness_2023_01\"]\n", + "grouped = gdf.groupby('region_wb')['difference'].mean().reset_index()\n", + "grouped" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "28b60433", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "11686\n" + ] + }, + { + "data": { + "text/html": [ + "
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shdi_2021_classdifference
0high0.028980
1low0.034935
2medium0.019966
3very high0.037329
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" + ], + "text/plain": [ + " shdi_2021_class difference\n", + "0 high 0.028980\n", + "1 low 0.034935\n", + "2 medium 0.019966\n", + "3 very high 0.037329" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#grouped by shdi class\n", + "gdf = gpd.read_file(\"../data/global_urban_building_completeness.gpkg\", layer=\"rf_adjusted_prediction_reference_and_osm_urban_centers_v2024\", where=\"total_area_sqkm>150\")\n", + "print(len(gdf))\n", + "gdf[\"prediction_osm_completeness_2024_05\"] = gdf[\"prediction_osm_completeness_2024_05\"].apply(lambda x: 1 if x > 1 else x)\n", + "gdf[\"prediction_osm_completeness_2023_01\"] = gdf[\"prediction_osm_completeness_2023_01\"].apply(lambda x: 1 if x > 1 else x)\n", + "gdf[\"difference\"] = gdf[\"prediction_osm_completeness_2024_05\"] - gdf[\"prediction_osm_completeness_2023_01\"]\n", + "grouped = gdf.groupby('shdi_2021_class')['difference'].mean().reset_index()\n", + "grouped" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4504323d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}