From 85383038e12aae6f1d1aa59b0e589eff17ac5311 Mon Sep 17 00:00:00 2001 From: Florian Nachtigall Date: Mon, 5 Oct 2026 19:55:15 +0200 Subject: [PATCH] Add informal settlements proportion indicator Integrate UN-Habitat SDG 11.1.1 into the preprocessing tail pipeline, using the latest non-null year per country and mapping values to city records. Include the metric in exported characteristics/percentiles and UI metric metadata, and document scope in the info panel. Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com> Signed-off-by: Florian Nachtigall --- .gitignore | 1 + preprocess.ipynb | 207 +++++++++++++++++++++++++++-------- public/cities.json | 2 +- src/components/InfoPanel.jsx | 3 + src/utils/metrics.js | 1 + 5 files changed, 170 insertions(+), 44 deletions(-) diff --git a/.gitignore b/.gitignore index 589a0af..d2e7c16 100644 --- a/.gitignore +++ b/.gitignore @@ -25,3 +25,4 @@ dist-ssr *.njsproj *.sln *.sw? +source-data/ diff --git a/preprocess.ipynb b/preprocess.ipynb index 7ba4f68..85a9717 100644 --- a/preprocess.ipynb +++ b/preprocess.ipynb @@ -460,33 +460,135 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "ghg_sector_cols = [\n", - " 'eu_ghg_emissions_energy_industry',\n", - " 'eu_ghg_emissions_residential',\n", - " 'eu_ghg_emissions_transport',\n", - " 'eu_ghg_emissions_waste',\n", - " 'eu_ghg_emissions_agriculture',\n", + "# --- SDG 11.1.1: Informal settlements proportion ---\n", + "import unicodedata\n", + "\n", + "slum_metric_key = 'informal_settlements_proportion'\n", + "slum_metric_column = 'Proportion of urban population living in slums or informal settlements (%) (a)'\n", + "slum_year_column = 'Data Reference Year'\n", + "\n", + "sdg_source_candidates = [\n", + " Path('source-data/SDG_11-1-1.xlsx'),\n", + " Path('SDG_11-1-1.xlsx'),\n", "]\n", - "available_ghg_cols = [col for col in ghg_sector_cols if col in cities.columns]\n", - "if available_ghg_cols:\n", - " ghg_total = cities[available_ghg_cols].sum(axis=1, min_count=1)\n", - " share_name_map = {\n", - " 'eu_ghg_emissions_energy_industry': 'eu_ghg_share_energy_industry',\n", - " 'eu_ghg_emissions_residential': 'eu_ghg_share_residential',\n", - " 'eu_ghg_emissions_transport': 'eu_ghg_share_transport',\n", - " 'eu_ghg_emissions_waste': 'eu_ghg_share_waste',\n", - " 'eu_ghg_emissions_agriculture': 'eu_ghg_share_agriculture',\n", + "sdg_source_path = next((path for path in sdg_source_candidates if path.exists()), None)\n", + "if sdg_source_path is None:\n", + " raise FileNotFoundError(\n", + " 'SDG_11-1-1.xlsx not found. Place the file in source-data/SDG_11-1-1.xlsx (preferred) or project root.'\n", + " )\n", + "\n", + "slum_raw = pd.read_excel(sdg_source_path, sheet_name='SDG 11-1-1 2024')\n", + "\n", + "slum_country_candidates = [\n", + " 'Country or Territory Name_1',\n", + " 'Country or Territory Name.1',\n", + " 'Country or Territory Name',\n", + "]\n", + "slum_country_column = next((col for col in slum_country_candidates if col in slum_raw.columns), None)\n", + "if slum_country_column is None:\n", + " raise KeyError(f'Missing expected country column; checked: {slum_country_candidates}')\n", + "\n", + "required_slum_columns = [slum_country_column, slum_metric_column, slum_year_column]\n", + "missing_slum_columns = [col for col in required_slum_columns if col not in slum_raw.columns]\n", + "if missing_slum_columns:\n", + " raise KeyError(f'Missing expected SDG columns: {missing_slum_columns}')\n", + "\n", + "\n", + "def normalize_country_name(value):\n", + " text = str(value or '').strip()\n", + " if not text:\n", + " return ''\n", + " text = unicodedata.normalize('NFD', text)\n", + " text = ''.join(ch for ch in text if unicodedata.category(ch) != 'Mn')\n", + " text = text.replace('’', '').replace(\"'\", '').replace('`', '')\n", + " text = ''.join(ch if ch.isalnum() else ' ' for ch in text)\n", + " return ' '.join(text.split()).lower()\n", + "\n", + "\n", + "country_aliases = {\n", + " 'united states': 'united states of america',\n", + " 'united kingdom': 'united kingdom of great britain and northern ireland',\n", + " 'russia': 'russian federation',\n", + " 'turkey': 'turkiye',\n", + " 'tanzania': 'united republic of tanzania',\n", + " 'palestine': 'state of palestine',\n", + " 'syria': 'syrian arab republic',\n", + " 'iran': 'iran islamic republic of',\n", + " 'laos': 'lao peoples democratic republic',\n", + " 'vietnam': 'viet nam',\n", + " 'brunei': 'brunei darussalam',\n", + " 'republic of the congo': 'congo',\n", + "}\n", + "\n", + "slum_clean = slum_raw[[slum_country_column, slum_metric_column, slum_year_column]].copy()\n", + "slum_clean = slum_clean.rename(\n", + " columns={\n", + " slum_country_column: 'country_raw',\n", + " slum_metric_column: 'value',\n", + " slum_year_column: 'year',\n", " }\n", - " for source_col, target_col in share_name_map.items():\n", - " if source_col in cities.columns:\n", - " cities[target_col] = np.where(\n", - " ghg_total > 0,\n", - " (cities[source_col] / ghg_total) * 100,\n", - " np.nan,\n", + ")\n", + "slum_clean['value'] = pd.to_numeric(slum_clean['value'], errors='coerce')\n", + "slum_clean['year'] = pd.to_numeric(slum_clean['year'], errors='coerce')\n", + "slum_clean['country_norm'] = slum_clean['country_raw'].map(normalize_country_name)\n", + "slum_clean = slum_clean.dropna(subset=['value', 'year'])\n", + "slum_clean = slum_clean[slum_clean['country_norm'] != '']\n", + "\n", + "# Keep latest non-null year per country\n", + "slum_latest = slum_clean.sort_values(['country_norm', 'year']).groupby('country_norm', as_index=False).tail(1)\n", + "slum_country_values = slum_latest.groupby('country_norm', as_index=False)['value'].mean()\n", + "slum_value_map = dict(zip(slum_country_values['country_norm'], slum_country_values['value']))\n", + "\n", + "city_country_norm = cities['country'].map(normalize_country_name)\n", + "city_country_norm = city_country_norm.map(lambda key: country_aliases.get(key, key))\n", + "cities[slum_metric_key] = city_country_norm.map(slum_value_map)\n", + "\n", + "print(\n", + " 'Informal settlements metric coverage:',\n", + " int(cities[slum_metric_key].notna().sum()),\n", + " '/',\n", + " len(cities),\n", + ")\n", + "print(\n", + " 'Source year range used (latest available per country):',\n", + " int(slum_latest['year'].min()),\n", + " '-',\n", + " int(slum_latest['year'].max()),\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "global_metric_keys = [\n", + " 'population',\n", + " 'population_growth',\n", + " 'population_density',\n", + " 'population_density_growth',\n", + " 'population_age',\n", + " 'gdp_ppp',\n", + " 'gdp_ppp_growth',\n", + " 'critical_infrastructure',\n", + " 'hdd',\n", + " 'cdd',\n", + " 'hdi',\n", + " 'female_gender_index',\n", + " 'informal_settlements_proportion',\n", + " 'emissions',\n", + "]\n", + "\n", + "if 'characteristics' in cities.columns:\n", + " for key in global_metric_keys:\n", + " if key not in cities.columns:\n", + " cities[key] = cities['characteristics'].map(\n", + " lambda d: d.get(key) if isinstance(d, dict) else np.nan\n", " )\n", "\n", "eu_metric_keys = [\n", @@ -516,6 +618,39 @@ " 'decentralization_index_fiscal',\n", "]\n", "\n", + "if 'eu_characteristics' in cities.columns:\n", + " for key in eu_metric_keys:\n", + " col = f'eu_{key}'\n", + " if col not in cities.columns:\n", + " cities[col] = cities['eu_characteristics'].map(\n", + " lambda d: d.get(key) if isinstance(d, dict) else np.nan\n", + " )\n", + "\n", + "ghg_sector_cols = [\n", + " 'eu_ghg_emissions_energy_industry',\n", + " 'eu_ghg_emissions_residential',\n", + " 'eu_ghg_emissions_transport',\n", + " 'eu_ghg_emissions_waste',\n", + " 'eu_ghg_emissions_agriculture',\n", + "]\n", + "available_ghg_cols = [col for col in ghg_sector_cols if col in cities.columns]\n", + "if available_ghg_cols:\n", + " ghg_total = cities[available_ghg_cols].sum(axis=1, min_count=1)\n", + " share_name_map = {\n", + " 'eu_ghg_emissions_energy_industry': 'eu_ghg_share_energy_industry',\n", + " 'eu_ghg_emissions_residential': 'eu_ghg_share_residential',\n", + " 'eu_ghg_emissions_transport': 'eu_ghg_share_transport',\n", + " 'eu_ghg_emissions_waste': 'eu_ghg_share_waste',\n", + " 'eu_ghg_emissions_agriculture': 'eu_ghg_share_agriculture',\n", + " }\n", + " for source_col, target_col in share_name_map.items():\n", + " if source_col in cities.columns:\n", + " cities[target_col] = np.where(\n", + " ghg_total > 0,\n", + " (cities[source_col] / ghg_total) * 100,\n", + " np.nan,\n", + " )\n", + "\n", "for key in eu_metric_keys:\n", " col = f'eu_{key}'\n", " if col in cities.columns:\n", @@ -548,8 +683,8 @@ " },\n", " axis=1,\n", ")\n", - "cities.loc[cities['eu_characteristics'].map(lambda d: len(d) == 0), 'eu_characteristics'] = None\n", - "cities.loc[cities['eu_percentiles'].map(lambda d: len(d) == 0), 'eu_percentiles'] = None\n", + "cities.loc[cities['eu_characteristics'].map(lambda d: not isinstance(d, dict) or len(d) == 0), 'eu_characteristics'] = None\n", + "cities.loc[cities['eu_percentiles'].map(lambda d: not isinstance(d, dict) or len(d) == 0), 'eu_percentiles'] = None\n", "\n", "info_cols = [\n", " 'id',\n", @@ -575,21 +710,7 @@ " 'lon',\n", "]\n", "\n", - "metric_cols = [\n", - " 'population',\n", - " 'population_growth',\n", - " 'population_density',\n", - " 'population_density_growth',\n", - " 'population_age',\n", - " 'gdp_ppp',\n", - " 'gdp_ppp_growth',\n", - " 'critical_infrastructure',\n", - " 'hdd',\n", - " 'cdd',\n", - " 'hdi',\n", - " 'female_gender_index',\n", - " 'emissions',\n", - "]\n", + "metric_cols = global_metric_keys\n", "\n", "for col in metric_cols:\n", " cities[f\"{col}_pct\"] = (cities[col].rank(pct=True) * 100).round()\n", @@ -611,11 +732,11 @@ " },\n", " axis=1,\n", ")\n", - "cities.loc[cities['characteristics'].map(lambda d: len(d) == 0), 'characteristics'] = None\n", - "cities.loc[cities['percentiles'].map(lambda d: len(d) == 0), 'percentiles'] = None\n", + "cities.loc[cities['characteristics'].map(lambda d: not isinstance(d, dict) or len(d) == 0), 'characteristics'] = None\n", + "cities.loc[cities['percentiles'].map(lambda d: not isinstance(d, dict) or len(d) == 0), 'percentiles'] = None\n", "\n", "available_info_cols = [col for col in info_cols if col in cities.columns]\n", - "cities[available_info_cols].to_json('public/cities.json', orient='records')" + "cities[available_info_cols].to_json('public/cities.json', orient='records')\n" ] }, { @@ -753,4 +874,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} +} \ No newline at end of file diff --git a/public/cities.json b/public/cities.json index 2ecf148..d8cde88 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 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\ No newline at end of file diff --git a/src/components/InfoPanel.jsx b/src/components/InfoPanel.jsx index 95e883d..da46d8e 100644 --- a/src/components/InfoPanel.jsx +++ b/src/components/InfoPanel.jsx @@ -176,6 +176,9 @@ const InfoPanel = ({ selectedSample, samples, setSelectedSample, setSearchValue

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

+

+ The indicator “Informal settlements proportion” describes the proportion of urban population living in slums or informal settlements. It uses UN-Habitat SDG 11.1.1 at national level, mapped to cities by country, using the latest non-null country value (typically 2022, earlier if 2022 is unavailable). +