diff --git a/.gitignore b/.gitignore index 589a0af..d60a5a4 100644 --- a/.gitignore +++ b/.gitignore @@ -25,3 +25,6 @@ dist-ssr *.njsproj *.sln *.sw? + +# Local PT accessibility merge input (generated locally, do not commit) +preprocessing/public-transport-accessibility/cities_with_pt_access.csv diff --git a/preprocess.ipynb b/preprocess.ipynb index bde2729..a79df02 100644 --- a/preprocess.ipynb +++ b/preprocess.ipynb @@ -370,6 +370,8 @@ "source": [ "cities = pd.read_json('public/cities.json', orient='records')\n", "\n", + "\n", + "\n", "cities_europe_candidates = [Path('public/cities_europe.json'), Path('cities_europe.json')]\n", "cities_europe_path = next((path for path in cities_europe_candidates if path.exists()), None)\n", "if cities_europe_path is not None:\n", @@ -458,6 +460,34 @@ " cities = pd.merge(cities, cities_europe[eu_merge_cols], on='id', how='left')\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "pt-access-merge", + "metadata": {}, + "outputs": [], + "source": [ + "pt_access = pd.read_csv(\n", + " Path('preprocessing/public-transport-accessibility/cities_with_pt_access.csv'),\n", + " usecols=['id', 'SDG1121pct'],\n", + ")\n", + "pt_access = pt_access.rename(columns={'SDG1121pct': 'public_transport_accessibility'})\n", + "pt_access['id'] = pd.to_numeric(pt_access['id'], errors='coerce')\n", + "pt_access['public_transport_accessibility'] = pd.to_numeric(\n", + " pt_access['public_transport_accessibility'], errors='coerce'\n", + ")\n", + "pt_access = pt_access.dropna(subset=['id'])\n", + "pt_access['id'] = pt_access['id'].astype(int)\n", + "pt_access = pt_access[['id', 'public_transport_accessibility']]\n", + "\n", + "cities['id'] = pd.to_numeric(cities['id'], errors='coerce')\n", + "cities = cities.dropna(subset=['id'])\n", + "cities['id'] = cities['id'].astype(int)\n", + "\n", + "cities = cities.drop(columns=['public_transport_accessibility'], errors='ignore')\n", + "cities = pd.merge(cities, pt_access, on='id', how='left')\n" + ] + }, { "cell_type": "code", "execution_count": 44, @@ -589,6 +619,7 @@ " 'hdi',\n", " 'female_gender_index',\n", " 'emissions',\n", + " 'public_transport_accessibility',\n", "]\n", "\n", "for col in metric_cols:\n", @@ -707,4 +738,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} +} \ No newline at end of file diff --git a/preprocessing/public-transport-accessibility/README.md b/preprocessing/public-transport-accessibility/README.md new file mode 100644 index 0000000..9bc3d02 --- /dev/null +++ b/preprocessing/public-transport-accessibility/README.md @@ -0,0 +1,16 @@ +# Public transport accessibility one-off preprocessing + +This folder archives the one-off preprocessing artifacts used to add SDG 11.2.1 +(urban access to public transport) into the explorer dataset. + +Included artifacts: +- `city-mapping-SDG-PT-2026-05-19.ipynb`: matching workflow notebook. + +The matched CSV (`cities_with_pt_access.csv`) is intentionally **not** stored in +the repository because its integrated values are already persisted in +`public/cities.json`. + +Primary source citation: +- Center For International Earth Science Information Network (CIESIN), Columbia University (2023). + *SDG Indicator 11.2.1: Urban Access to Public Transport, 2023 Release*. + https://doi.org/10.7927/1A5Z-3H71 diff --git a/preprocessing/public-transport-accessibility/city-mapping-SDG-PT-2026-05-19.ipynb b/preprocessing/public-transport-accessibility/city-mapping-SDG-PT-2026-05-19.ipynb new file mode 100644 index 0000000..301a96a --- /dev/null +++ b/preprocessing/public-transport-accessibility/city-mapping-SDG-PT-2026-05-19.ipynb @@ -0,0 +1,1054 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f96b1801", + "metadata": {}, + "source": [ + "## Matching with \"SDG Indicator 11.2.1: Urban Access to Public Transport, 2023 Release\"" + ] + }, + { + "cell_type": "markdown", + "id": "e4fe2a2b", + "metadata": {}, + "source": [ + "https://www.earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-sdgi-uapt-2023-2023.00" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86dec237", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import geopandas as gpd" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "id": "85be0845", + "metadata": {}, + "outputs": [], + "source": [ + "cities_ipcc = pd.read_json('../data/urban-wedge/cities.json')" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "id": "06fe5b1e", + "metadata": {}, + "outputs": [], + "source": [ + "ucdb = gpd.read_parquet('../data/GHS_UCDB_GLOBE_R2024A.parquet')\n", + "cities_ipcc = pd.merge(cities_ipcc, ucdb[['ID_UC_G0', 'geometry']], left_on='id', right_on='ID_UC_G0', how='left')\n", + "cities_ipcc = gpd.GeoDataFrame(cities_ipcc, geometry='geometry', crs=ucdb.crs)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "id": "ca7c1dbd", + "metadata": {}, + "outputs": [], + "source": [ + "gdf_pt_access = gpd.read_file('../data/urban-wedge/sdgi-11-2-1-urban-access-public-transport-2023-shp')" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "id": "c9ad7028", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "set()" + ] + }, + "execution_count": 200, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "country_rename = {\n", + " 'Cape Verde': 'Cabo Verde',\n", + " 'Czech Republic': 'Czechia',\n", + " 'Macedonia': 'North Macedonia',\n", + " 'Mexico': 'México',\n", + " 'Palestina': 'Palestine',\n", + " 'Republic of Congo': 'Republic of the Congo'\n", + "}\n", + "set(gdf_pt_access['CTR_MN_NM'].replace(country_rename)).difference(set(cities_ipcc['country']))" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "id": "1a918784", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Aruba',\n", + " 'French Guiana',\n", + " 'Martinique',\n", + " 'Mayotte',\n", + " 'Northern Cyprus',\n", + " 'Réunion',\n", + " 'Samoa',\n", + " 'Tonga',\n", + " 'Vanuatu'}" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set(cities_ipcc['country']).difference(set(gdf_pt_access['CTR_MN_NM'].replace(country_rename)))\n" + ] + }, + { + "cell_type": "markdown", + "id": "7de7580e", + "metadata": {}, + "source": [ + "### Matching based on city + country name (as sdgi also uses UCDB, but from 2015) (DEPRECATED)" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "id": "e5c9519e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Leftover 5110/13135 entries without matches.\n", + "Leftover 3452/11422 cities without PT matches.\n" + ] + } + ], + "source": [ + "cities_ipcc['city_country'] = cities_ipcc['name'] + ', ' + cities_ipcc['country']\n", + "gdf_pt_access['UC_NM_MN'] = gdf_pt_access['UC_NM_MN'].replace('N/A', np.nan)\n", + "gdf_pt_access['city_country'] = gdf_pt_access['UC_NM_MN'] + ', ' + gdf_pt_access['CTR_MN_NM'].replace(country_rename)\n", + "cities_w_pt = pd.merge(cities_ipcc, gdf_pt_access.dropna(subset='UC_NM_MN'), on='city_country', how='inner')\n", + "cities_w_pt['distance'] = cities_w_pt['geometry_x'].distance(cities_w_pt['geometry_y'].to_crs(cities_ipcc.crs))\n", + "gdf_pt_access_leftover = gdf_pt_access[~gdf_pt_access['city_country'].isin(cities_w_pt['city_country'])]\n", + "cities_leftover_pt = cities_ipcc[~cities_ipcc['city_country'].isin(cities_w_pt['city_country'])]\n", + "print(f\"Leftover {len(gdf_pt_access_leftover)}/{len(gdf_pt_access)} entries without matches.\")\n", + "print(f\"Leftover {len(cities_leftover_pt)}/{len(cities_ipcc)} cities without PT matches.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "id": "35d47759", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "country\n", + "China 245\n", + "India 124\n", + "United States 21\n", + "Nigeria 8\n", + "Indonesia 7\n", + "Philippines 6\n", + "Ethiopia 5\n", + "Bangladesh 4\n", + "Vietnam 3\n", + "México 2\n", + "Brazil 2\n", + "Mozambique 1\n", + "Yemen 1\n", + "Pakistan 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cities_w_pt[cities_w_pt['distance'] > 0]['distance'].div(1000).hist(bins=30)\n", + "cities_w_pt[cities_w_pt['distance'] > 10000]['country'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "494b5df1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 147, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "ex = cities_w_pt[cities_w_pt['distance'] > 100000].sample(1)\n", + "m = ex[['geometry_x']].set_geometry('geometry_x').explore()\n", + "ex[['geometry_y']].set_geometry('geometry_y').explore(m=m, color='red')" + ] + }, + { + "cell_type": "markdown", + "id": "14fdfed0", + "metadata": {}, + "source": [ + "**Take-away**: Matching on city name and country not reliable as there are too many cities with the same name in one country." + ] + }, + { + "cell_type": "markdown", + "id": "955ff670", + "metadata": {}, + "source": [ + "### Matching based on spatial overlap of city area" + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "id": "f487662c", + "metadata": {}, + "outputs": [], + "source": [ + "import geopandas as gpd\n", + "import pandas as pd\n", + "\n", + "def merge_by_best_iou(gdf_source, gdf_target, value_col='SDG1121pct',\n", + " id_col='id', iou_threshold=0.25):\n", + " \"\"\"\n", + " For each polygon in gdf_source, find the polygon in gdf_target with the\n", + " largest IoU. If IoU >= threshold, copy gdf_target's value_col and id_col\n", + " into that row of gdf_source.\n", + "\n", + " gdf_source: receives value_col and id_col from its best IoU match.\n", + " gdf_target: holds SDG1121pct and the identifier column.\n", + " \"\"\"\n", + " if gdf_source.crs != gdf_target.crs:\n", + " gdf_target = gdf_target.to_crs(gdf_source.crs)\n", + "\n", + " source = gdf_source.reset_index(drop=True).copy()\n", + " target = gdf_target.reset_index(drop=True).copy()\n", + "\n", + " # Spatial join: only consider overlapping pairs\n", + " joined = gpd.sjoin(\n", + " source[['geometry']].reset_index().rename(columns={'index': 'source_idx'}),\n", + " target[['geometry', value_col, id_col]].reset_index().rename(columns={'index': 'target_idx'}),\n", + " how='inner',\n", + " predicate='intersects'\n", + " )\n", + "\n", + " # Compute IoU per candidate pair\n", + " def compute_iou(row):\n", + " g_src = source.geometry.iloc[row['source_idx']]\n", + " g_tgt = target.geometry.iloc[row['target_idx']]\n", + " inter = g_src.intersection(g_tgt).area\n", + " union = g_src.union(g_tgt).area\n", + " return inter / union if union > 0 else 0.0\n", + "\n", + " joined['iou'] = joined.apply(compute_iou, axis=1)\n", + "\n", + " # For each source polygon, keep only its single best target match\n", + " best = (joined.sort_values('iou', ascending=False)\n", + " .drop_duplicates('source_idx'))\n", + " best = best[best['iou'] >= iou_threshold]\n", + "\n", + " # Write target's value_col and id_col into the matched source rows\n", + " source[value_col] = pd.NA\n", + " source[f'matched_{id_col}'] = pd.NA\n", + " source['best_iou'] = pd.NA\n", + "\n", + " source.loc[best['source_idx'].values, value_col] = best[value_col].values\n", + " source.loc[best['source_idx'].values, f'matched_{id_col}'] = best[id_col].values\n", + " source.loc[best['source_idx'].values, 'best_iou'] = best['iou'].values\n", + "\n", + " return source\n", + "\n", + "\n", + "gdf_pt_access['UC_NM_MN'] = gdf_pt_access['UC_NM_MN'].replace('N/A', np.nan)\n", + "gdf_pt_access['city_country'] = gdf_pt_access['UC_NM_MN'] + ', ' + gdf_pt_access['CTR_MN_NM'].replace(country_rename)\n", + "cities_ipcc['city_country'] = cities_ipcc['name'] + ', ' + cities_ipcc['country']\n", + "gdf_pt_access['SDG1121pct'] = gdf_pt_access['SDG1121pct'].replace(-9999.0, pd.NA)\n", + "result = merge_by_best_iou(cities_ipcc, gdf_pt_access, value_col='SDG1121pct', id_col='city_country', iou_threshold=0.25)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1a49cbd8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Proportion of matches:\n", + "0.79\n", + "Proportion of matches with identical city + country name:\n", + "0.67\n", + "PT index coverage\n", + "0.44\n" + ] + } + ], + "source": [ + "result['name_matches'] = (result['name'] + ', ' + result['country']) == result['matched_city_country']\n", + "print(\"Proportion of matches:\")\n", + "print(result['matched_city_country'].notna().mean().round(2))\n", + "print(\"Proportion of matches with identical city + country name:\")\n", + "print(result['name_matches'].mean().round(2))\n", + "\n", + "print(\"PT index coverage\")\n", + "print((result['SDG1121pct'] > 0).mean().round(2))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4036dca", + "metadata": {}, + "outputs": [], + "source": [ + "# result[['id', 'name', 'country', 'region', 'type', 'SDG1121pct', 'matched_city_country', 'best_iou', 'name_matches']].rename(columns={'best_iou': 'iou'}).to_csv('../data/urban-wedge/cities_with_pt_access.csv', index=False)\n", + "# result.drop(columns=['ID_UC_G0', 'city_country']).to_parquet('../data/urban-wedge/cities_full_with_pt_access.parquet', index=False)" + ] + }, + { + "cell_type": "markdown", + "id": "a2efa522", + "metadata": {}, + "source": [ + "### NaN imputation" + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "id": "519de90a", + "metadata": {}, + "outputs": [], + "source": [ + "import geopandas as gpd\n", + "import numpy as np\n", + "import pandas as pd\n", + "from scipy.spatial import cKDTree\n", + "\n", + "\n", + "def normalize_popdens(gdf, popdens_col='pop_density', country_col='country',\n", + " region_col='region', out_col='pop_density_rank',\n", + " min_country_size=5):\n", + " \"\"\"\n", + " Add a percentile-rank column for population density in [0, 1].\n", + "\n", + " Normalization scope:\n", + " - If a country has > min_country_size cities, rank within the country.\n", + " - Otherwise, rank within the country's region (broader pool).\n", + "\n", + " Robust to outliers and to differences in distribution shape between frames\n", + " (e.g. whole-area vs built-up-only density).\n", + " \"\"\"\n", + " out = gdf.copy()\n", + " x = out[popdens_col].astype(float)\n", + "\n", + " # Decide grouping per row: country if country has enough cities, else region\n", + " country_size = out.groupby(country_col)[popdens_col].transform('size')\n", + " use_country = country_size > min_country_size\n", + "\n", + " group = np.where(use_country, out[country_col].astype(str),\n", + " 'region::' + out[region_col].astype(str))\n", + " group = pd.Series(group, index=out.index)\n", + "\n", + " out[out_col] = x.groupby(group).rank(pct=True)\n", + " out['_norm_scope'] = np.where(use_country, 'country', 'region')\n", + "\n", + " return out\n", + "\n", + "def gwr_infill_missing(\n", + " gdf_source,\n", + " gdf_target,\n", + " value_col='SDG1121pct',\n", + " country_col='country',\n", + " popdens_rank_col='pop_density_rank',\n", + " k=5,\n", + " power=2,\n", + " pct_window=0.15,\n", + "):\n", + " \"\"\"\n", + " Infill missing value_col in gdf_source using an inverse-distance-weighted\n", + " mean of the k nearest rows in gdf_target that:\n", + " - have a non-missing value_col,\n", + " - share the same country_col,\n", + " - have popdens_rank within +/- pct_window of the source row's popdens_rank.\n", + "\n", + " pct_window is in percentile-rank units (0..1), e.g. 0.15 = +/-15 percentile points.\n", + " \"\"\"\n", + " if gdf_source.crs != gdf_target.crs:\n", + " gdf_target = gdf_target.to_crs(gdf_source.crs)\n", + "\n", + " out = gdf_source.copy()\n", + " tgt = gdf_target.copy()\n", + "\n", + " # Provenance\n", + " if 'infill_source' not in out.columns:\n", + " out['infill_source'] = np.where(out[value_col].notna(), 'iou', 'unfilled')\n", + " out['infill_n_neighbors'] = pd.NA\n", + "\n", + " # Centroids for distance\n", + " s_cent = out.geometry.centroid\n", + " t_cent = tgt.geometry.centroid\n", + " out['_x'], out['_y'] = s_cent.x.values, s_cent.y.values\n", + " tgt['_x'], tgt['_y'] = t_cent.x.values, t_cent.y.values\n", + "\n", + " # Only target rows with a value can be donors\n", + " tgt_donors_all = tgt[tgt[value_col].notna()].copy()\n", + "\n", + " for country, donors in tgt_donors_all.groupby(country_col):\n", + " receivers = out[(out[country_col] == country) & (out[value_col].isna())]\n", + " if receivers.empty or donors.empty:\n", + " continue\n", + "\n", + " donor_xy = donors[['_x', '_y']].to_numpy()\n", + " donor_vals = donors[value_col].to_numpy(dtype=float)\n", + " donor_rank = donors[popdens_rank_col].to_numpy(dtype=float)\n", + " tree = cKDTree(donor_xy)\n", + "\n", + " for r_idx, r in receivers.iterrows():\n", + " r_rank = r[popdens_rank_col]\n", + " if pd.isna(r_rank):\n", + " continue\n", + "\n", + " lo, hi = r_rank - pct_window, r_rank + pct_window\n", + " mask = (donor_rank >= lo) & (donor_rank <= hi)\n", + " if not mask.any():\n", + " continue\n", + "\n", + " # Query more than k, then filter by the percentile window\n", + " n_candidates = min(len(donor_xy), max(k * 4, k))\n", + " dists, inds = tree.query([r['_x'], r['_y']], k=n_candidates)\n", + " dists = np.atleast_1d(dists)\n", + " inds = np.atleast_1d(inds)\n", + "\n", + " keep = mask[inds]\n", + " dists, inds = dists[keep], inds[keep]\n", + " if len(inds) == 0:\n", + " continue\n", + "\n", + " dists, inds = dists[:k], inds[:k]\n", + " vals = donor_vals[inds]\n", + "\n", + " eps = 1e-9\n", + " w = 1.0 / (dists ** power + eps)\n", + " w /= w.sum()\n", + "\n", + " out.at[r_idx, value_col] = float(np.sum(w * vals))\n", + " out.at[r_idx, 'infill_source'] = 'gwr'\n", + " out.at[r_idx, 'infill_n_neighbors'] = len(inds)\n", + "\n", + " out = out.drop(columns=['_x', '_y'])\n", + " return out\n", + "\n", + "gdf_pt_access['region'] = gdf_pt_access['CTR_MN_NM'].replace(country_rename).map(cities_ipcc.drop_duplicates(subset='country').set_index('country')['region'])\n", + "gdf_pt_access['population_density'] = gdf_pt_access['Total_POP'] / gdf_pt_access.to_crs(cities_ipcc.crs).area.div(1e6)\n", + "gdf_pt_access_norm = normalize_popdens(gdf_pt_access.rename(columns={'CTR_MN_NM': 'country'}), popdens_col='population_density',\n", + " country_col='country',\n", + " region_col='region',\n", + "\n", + " out_col='pop_density_rank')\n", + "result_norm = normalize_popdens(result, popdens_col='population_density',\n", + " country_col='country',\n", + " region_col='region',\n", + " out_col='pop_density_rank')\n", + "\n", + "\n", + "result = gwr_infill_missing(result_norm, gdf_pt_access_norm,\n", + " value_col='SDG1121pct',\n", + " country_col='country',\n", + " popdens_rank_col='pop_density_rank',\n", + " k=5,\n", + " power=2,\n", + " pct_window=0.15)" + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "id": "4af0e396", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Proportion of matches:\n", + "0.79\n", + "Proportion of matches with identical city + country name:\n", + "0.67\n", + "PT index coverage\n", + "0.95\n", + "1.0\n", + "infill_source\n", + "gwr 0.52\n", + "iou 0.44\n", + "unfilled 0.05\n", + "Name: proportion, dtype: float64\n" + ] + } + ], + "source": [ + "result['name_matches'] = (result['name'] + ', ' + result['country']) == result['matched_city_country']\n", + "print(\"Proportion of matches:\")\n", + "print(result['matched_city_country'].notna().mean().round(2))\n", + "print(\"Proportion of matches with identical city + country name:\")\n", + "print(result['name_matches'].mean().round(2))\n", + "\n", + "print(\"PT index coverage\")\n", + "print((result['SDG1121pct'] > 0).mean().round(2))\n", + "print((result[result['infill_source'] == 'gwr']['SDG1121pct'] > 0).mean().round(2))\n", + "print((result['infill_source'].value_counts(normalize=True).round(2)))" + ] + }, + { + "cell_type": "code", + "execution_count": 226, + "id": "2c3e8d38", + "metadata": {}, + "outputs": [], + "source": [ + "result[['id', 'name', 'country', 'region', 'type', 'SDG1121pct', 'matched_city_country', 'best_iou', 'infill_source', 'name_matches']].rename(columns={'best_iou': 'iou'}).to_csv('../data/urban-wedge/cities_with_pt_access.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "id": "f32ab93b", + "metadata": {}, + "outputs": [], + "source": [ + "result.drop(columns=['pop_density_rank', 'infill_n_neighbors', '_norm_scope', 'ID_UC_G0', 'city_country']).to_parquet('../data/urban-wedge/cities_full_with_pt_access.parquet', index=False)" + ] + }, + { + "cell_type": "markdown", + "id": "4df45368", + "metadata": {}, + "source": [ + "### Visualization to sanity check NaN imputation" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "id": "b6d70a48", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/jk/rndbblbs2fs0_symsqry30400000gn/T/ipykernel_55908/405415418.py:65: DeprecationWarning: `basemap_style` is deprecated and will be removed in 0.14. Use `basemap` instead.\n", + " m = Map(\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib as mpl\n", + "from lonboard import Map, ScatterplotLayer\n", + "\n", + "# Ensure point geometries for ScatterplotLayer\n", + "points = result[['SDG1121pct', 'id', 'name', 'country', 'region', 'type', 'best_iou', 'infill_source', 'name_matches', '_norm_scope', 'pop_density_rank', 'geometry']].copy()\n", + "def fmt(s, decimals):\n", + " return s.map(lambda v: f\"{v:.{decimals}f}\" if pd.notna(v) else \"\")\n", + "\n", + "points['SDG1121pct'] = fmt(pd.to_numeric(points['SDG1121pct'], errors='coerce'), 1)\n", + "points['best_iou'] = fmt(pd.to_numeric(points['best_iou'], errors='coerce'), 2)\n", + "points['pop_density_rank'] = fmt(pd.to_numeric(points['pop_density_rank'], errors='coerce'), 2)\n", + "points['geometry'] = points.centroid.to_crs(4326)\n", + "\n", + "\n", + "iou_rows = points[points['infill_source'] == 'iou']\n", + "gwr_rows = points[points['infill_source'] == 'gwr']\n", + "unfilled_rows = points[points['infill_source'] == 'unfilled']\n", + "\n", + "cmap = mpl.colormaps['viridis']\n", + "norm = mpl.colors.Normalize(vmin=0, vmax=100)\n", + "\n", + "def values_to_rgb(values):\n", + " rgba = cmap(norm(values.astype(float)))\n", + " return (rgba[:, :3] * 255).astype(np.uint8)\n", + "\n", + "iou_colors = values_to_rgb(iou_rows['SDG1121pct'].values)\n", + "gwr_colors = values_to_rgb(gwr_rows['SDG1121pct'].values)\n", + "\n", + "# IoU: small, solid, no outline — \"observed\"\n", + "layer_iou = ScatterplotLayer.from_geopandas(\n", + " iou_rows,\n", + " get_fill_color=iou_colors,\n", + " get_radius=5000,\n", + " opacity=0.9,\n", + " stroked=False,\n", + ")\n", + "\n", + "# GWR: larger, lower opacity fill, thick magenta-ish outline — \"estimated\"\n", + "# Magenta sits opposite viridis on the color wheel, so the ring pops against\n", + "# every fill color in the ramp without competing with the data encoding.\n", + "layer_gwr = ScatterplotLayer.from_geopandas(\n", + " gwr_rows,\n", + " get_fill_color=gwr_colors,\n", + " get_line_color=[220, 30, 180], # magenta ring\n", + " get_line_width=800,\n", + " line_width_units='meters',\n", + " get_radius=2500,\n", + " opacity=0.45, # softer fill so the ring dominates\n", + " stroked=True,\n", + ")\n", + "\n", + "# Unfilled: hollow gray ring — \"missing\"\n", + "layer_unfilled = ScatterplotLayer.from_geopandas(\n", + " unfilled_rows,\n", + " get_fill_color=[0, 0, 0, 0],\n", + " get_line_color=[80, 80, 80],\n", + " get_line_width=500,\n", + " line_width_units='meters',\n", + " get_radius=2500,\n", + " opacity=0.9,\n", + " stroked=True,\n", + ")\n", + "\n", + "m = Map(\n", + " layers=[layer_unfilled, layer_gwr, layer_iou],\n", + " basemap_style=\"https://basemaps.cartocdn.com/gl/voyager-gl-style/style.json\",\n", + " show_tooltip=True,\n", + ")\n", + "html_path = \"cities_pt_access_infill_map.html\"\n", + "m.to_html(html_path)\n", + "\n", + "# Build a viridis gradient string for the colorbar\n", + "import matplotlib.colors as mcolors\n", + "stops = np.linspace(0, 1, 11)\n", + "gradient_stops = \", \".join(\n", + " f\"{mcolors.to_hex(cmap(s))} {int(s*100)}%\" for s in stops\n", + ")\n", + "\n", + "legend_html = f\"\"\"\n", + "
\n", + "
SDG 11.2.1 (%)
\n", + "
\n", + "
\n", + " 050100\n", + "
\n", + "\n", + "
Source
\n", + "
\n", + " \n", + " IoU match (observed)\n", + "
\n", + "
\n", + " \n", + " GWR estimate\n", + "
\n", + "
\n", + " \n", + " Unfilled (no match)\n", + "
\n", + "
\n", + "\"\"\"\n", + "\n", + "# Inject before \n", + "with open(html_path, 'r') as f:\n", + " html = f.read()\n", + "html = html.replace('', legend_html + '\\n')\n", + "with open(html_path, 'w') as f:\n", + " f.write(html)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ebc2828b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.11.11)", + "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": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/public/cities.json b/public/cities.json index b82bbe0..5ec0721 100644 --- a/public/cities.json +++ b/public/cities.json @@ -1 +1 @@ -[{"id":1,"name":"Apia","country":"Samoa","region":"Small Islands","type":"Type 1","n_studies":8,"n_solutions":0,"solution_domain_counts":{"Mobility":0,"Buildings":0,"Energy":0,"Thermal comfort and Heat stress management":0,"Food provisioning systems":0,"Water":0,"Waste management":0,"Disaster and risk management":0,"Carbon dioxide removal":0},"probability":0.6,"embedding":[3.3203856945,2.3606972694,1.5110242367,2.3884205818],"neighbors":[4646,3643,11098,3983,6445,8359,9932,7473,6389,2254,8098,541,7260,1691,10546,7271,10198,7920,7892,10486],"neighbor_distances":[0.134,0.2098,0.2414,0.244,0.2663,0.2676,0.2758,0.277,0.2929,0.2961,0.2966,0.3112,0.3113,0.3208,0.3217,0.3236,0.3267,0.3288,0.3292,0.3299],"type_probabilities":[0.6,0.26,0.02,0.12],"lat":-13.8333334666,"lon":-171.783105756,"characteristics":{"population":60041.65661,"population_growth":0.9290028886,"population_density":15384.7520942697,"population_density_growth":0.2786513659,"population_age":0.1710120549,"gdp_ppp":5051.005679022,"gdp_ppp_growth":2.7785631244,"critical_infrastructure":0.01,"hdd":58.0329284668,"cdd":801.9052734375,"hdi":0.54,"female_gender_index":0.53,"emissions":1.6588544458},"percentiles":{"population":15,"population_growth":52,"population_density":17,"population_density_growth":72,"population_age":31,"gdp_ppp":43,"gdp_ppp_growth":29,"critical_infrastructure":14,"hdd":28,"cdd":25,"hdi":14,"female_gender_index":22,"emissions":70},"eu_characteristics":null,"eu_percentiles":null},{"id":2,"name":"Nuku'alofa","country":"Tonga","region":"Small Islands","type":"Mixed","n_studies":3,"n_solutions":2,"solution_domain_counts":{"Mobility":0,"Buildings":0,"Energy":1,"Thermal comfort and Heat stress management":0,"Food provisioning systems":0,"Water":0,"Waste management":0,"Disaster and risk management":1,"Carbon dioxide removal":0},"probability":0.5,"embedding":[3.2627022266,2.2404105663,1.1260199547,2.2330617905],"neighbors":[7518,11098,3517,1948,7348,8758,1066,4682,10388,2811,7553,6088,4534,7654,8603,5176,4497,4608,4060,5656],"neighbor_distances":[0.0735,0.0738,0.132,0.1949,0.2393,0.2693,0.2718,0.2735,0.2758,0.2818,0.285,0.2858,0.286,0.2895,0.2898,0.3075,0.309,0.3178,0.3181,0.3181],"type_probabilities":[0.5,0.3,0.06,0.14],"lat":-21.1442613482,"lon":-175.2031602189,"characteristics":{"population":51990.7662,"population_growth":0.8016806933,"population_density":18664.6336074051,"population_density_growth":0.6504621605,"population_age":0.1713703811,"gdp_ppp":4705.9907995739,"gdp_ppp_growth":1.9507857495,"critical_infrastructure":0.12,"hdd":58.0329284668,"cdd":801.9052734375,"hdi":0.51,"female_gender_index":0.49,"emissions":1.0893257522},"percentiles":{"population":3,"population_growth":48,"population_density":25,"population_density_growth":81,"population_age":31,"gdp_ppp":41,"gdp_ppp_growth":20,"critical_infrastructure":85,"hdd":28,"cdd":25,"hdi":12,"female_gender_index":14,"emissions":60},"eu_characteristics":null,"eu_percentiles":null},{"id":3,"name":"Ewa Beach","country":"United States","region":"Small Islands","type":"Type 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"hdd": 757.3325805664, "cdd": 1607.3258056641, "hdi": 0.7, "female_gender_index": 0.69, "emissions": 1.0441134392, "public_transport_accessibility": 14.30383557318279}, "percentiles": {"population": 51, "population_growth": 32, "population_density": 53, "population_density_growth": 39, "population_age": 69, "gdp_ppp": 88, "gdp_ppp_growth": 81, "critical_infrastructure": 3, "hdd": 57, "cdd": 47, "hdi": 49, "female_gender_index": 54, "emissions": 59, "public_transport_accessibility": 38}, "eu_characteristics": null, "eu_percentiles": null}] \ No newline at end of file diff --git a/src/components/InfoPanel.jsx b/src/components/InfoPanel.jsx index 853b317..317b8eb 100644 --- a/src/components/InfoPanel.jsx +++ b/src/components/InfoPanel.jsx @@ -173,6 +173,9 @@ const InfoPanel = ({ selectedSample, samples, setSelectedSample, setSearchValue

Metrics are sourced from the Global Human Settlement Layer (GHSL) and related datasets.

+

+ Public transport accessibility is based on SDG indicator 11.2.1 from CIESIN (2023). +

Percentile ranks indicate the share of cities worldwide that have lower values, showing how each city compares to its global peers for each characteristic.

diff --git a/src/utils/metrics.js b/src/utils/metrics.js index 7ebbfd8..4983bac 100644 --- a/src/utils/metrics.js +++ b/src/utils/metrics.js @@ -9,6 +9,7 @@ export const metricList = [ { key: "hdd", label: "Heating Degree Days", unit: "°C", decimals: -1 }, { key: "cdd", label: "Cooling Degree Days", unit: "°C", decimals: -1 }, { key: "critical_infrastructure", label: "Critical infrastructure index", decimals: 2 }, + { key: "public_transport_accessibility", label: "Public transport accessibility (SDG 11.2.1)", unit: "%", decimals: 1 }, { key: "female_gender_index", label: "Female gender index", decimals: 2 }, { key: "hdi", label: "Human development index", decimals: 2 }, { key: "emissions", label: "GHG emissions", unit: "tCO₂e/cap", decimals: 2 },