NOTF - Neighbourhoods of the Future

A collaborative platform for building inclusive, resilient, and people-first neighbourhoods through systems thinking, community engagement, and data-driven insights.

Stack at a glance

Frontend: hand-written static HTML/CSS/JS in website/public/. No build step, no framework, no Eleventy. Files are deployed as-is.
Hosting: Vercel — auto-deploys on every push to main (vercel.json serves website/public).
Backend: Supabase project abblyaukkoxmgzwretvm — Postgres (file_metadata), Storage (notf bucket), and Deno Edge Functions (supabase/functions/). The site reads data at runtime via the public anon key.
i18n: 11 languages, JSON locale files under website/public/assets/i18n/locales/. Updating all 11 is mandatory for any user-facing text change.

⚠️ An older version of this README described a GitHub Pages + Eleventy + YAML (data/, docs/, _site/, npm run build) setup. That architecture is gone. If you find references to it anywhere, they are stale.

Repository layout

Path	What it is
`website/public/`	The deployed site (static HTML + `assets/`).
`website/tests/`	Playwright visual tests + Deno unit tests.
`supabase/functions/`	Edge Functions (admin write/delete, sync, deploy trigger).
`supabase/migrations/`	Database migrations (audit trigger, delete lockdown, …).
`scripts/`	Operational one-off scripts (sitemap, catalogue check, …). `scripts/archive/` is dead legacy tooling.
`data-docs/`	Public climate-dataset documentation (sources, credits, research) + the data-processing scripts.

Local development

cd website
npm install
npm run dev        # Vite dev server → http://localhost:5173 (serves public/)

The site fetches live data from Supabase via the public anon key (hardcoded in assets/js/data-loader.js), so npm run dev shows real production content with no .env or secrets required for read-only work. See CONTRIBUTING.md for the test and deploy workflows, and ACCESS.md for what write access requires.

Documentation map — start here

New to the project? Read these in order:

ONBOARDING.md — day-one setup and how the whole system fits together.
ARCHITECTURE.md — data flow, the two write paths, and the Data Safety Invariants (read before touching any admin write/delete path).
CONTRIBUTING.md — how to make changes: static pages, catalogue, edge functions, migrations, tests, i18n, and the PR/CI/deploy flow.
CLAUDE.md — full project conventions (brand, i18n, icons, data-safety). Written for AI agents but the canonical rulebook for humans too.
ACCESS.md — the credentials/access matrix for maintainers (GitHub, Supabase, Vercel, secrets).
RBAC-PLAN.md — the planned admin roles / authorization work.

📊 Ward-Level Climate Data

Overview

NOTF provides open ward-level climate baseline data for Indian cities, starting with Bengaluru (369 wards) and Mumbai (227 wards). This granular data enables communities, policymakers, and researchers to track climate action progress at the neighbourhood level.

Coverage:

Bengaluru: 7 climate sectors × 369 wards = 2,583 data points
- ✅ Energy & Buildings (100% coverage)
- ✅ Waste Management (100% coverage)
- ⚠️ Transportation (partial data)
- ⚠️ Air Quality (16 monitoring stations)
- ⚠️ Water Resources (zone-level)
- ✅ Urban Greening (100% coverage)
- ⚠️ Disaster Resilience (flood risk 100%, heat partial)
Mumbai: 2 climate sectors × 227 wards = 454 data points
- ✅ Energy & Buildings (100% coverage)
- ✅ Waste Management (100% coverage)
- ⏳ Transportation (pending)
- ⏳ Air Quality (pending)
- ⏳ Water Resources (pending)
- ⏳ Urban Greening (pending)
- ⏳ Disaster Resilience (pending)

Data Quality:

Bengaluru: ★★★☆☆ (63%) - See DATA_SOURCES_AND_CREDITS.md
Mumbai: ★★★☆☆ (60%) - See MUMBAI_DATA_SOURCES_AND_CREDITS.md

📥 Download Climate Datasets

Bengaluru (369 Wards)

Option 1: Full Dataset (3.8 MB)

# Clone repository
git clone https://github.com/urbanmorph/notf.git
cd notf/website/public/assets/data/climate/bengaluru/

# All files:
# - city_climate.json (10 KB) - City-level aggregates
# - ward_index.json (96 KB) - Ward metadata
# - climate_central.json (621 KB) - 63 wards
# - climate_east.json (492 KB) - 50 wards
# - climate_west.json (1.1 MB) - 112 wards
# - climate_north.json (709 KB) - 72 wards
# - climate_south.json (709 KB) - 72 wards

Option 2: Direct Download Links

# City summary (10 KB)
wget https://raw.githubusercontent.com/urbanmorph/notf/main/website/public/assets/data/climate/bengaluru/city_climate.json

# Ward index (96 KB)
wget https://raw.githubusercontent.com/urbanmorph/notf/main/website/public/assets/data/climate/bengaluru/ward_index.json

# Corporation data (download specific corporation or all 5)
wget https://raw.githubusercontent.com/urbanmorph/notf/main/website/public/assets/data/climate/bengaluru/climate_central.json
wget https://raw.githubusercontent.com/urbanmorph/notf/main/website/public/assets/data/climate/bengaluru/climate_east.json
wget https://raw.githubusercontent.com/urbanmorph/notf/main/website/public/assets/data/climate/bengaluru/climate_west.json
wget https://raw.githubusercontent.com/urbanmorph/notf/main/website/public/assets/data/climate/bengaluru/climate_north.json
wget https://raw.githubusercontent.com/urbanmorph/notf/main/website/public/assets/data/climate/bengaluru/climate_south.json

Option 3: Python Script

import json
import requests

# Load city summary
city_data = requests.get(
    'https://raw.githubusercontent.com/urbanmorph/notf/main/website/public/assets/data/climate/bengaluru/city_climate.json'
).json()

print(f"Total wards: {city_data['total_wards']}")
print(f"Total population: {city_data['total_population']:,}")

# Load specific corporation
central_wards = requests.get(
    'https://raw.githubusercontent.com/urbanmorph/notf/main/website/public/assets/data/climate/bengaluru/climate_central.json'
).json()

for ward in central_wards['wards']:
    print(f"{ward['ward_name']}: {ward['population']:,} people")

Mumbai (227 Wards)

Option 1: Full Dataset (2.4 MB)

# Clone repository
git clone https://github.com/urbanmorph/notf.git
cd notf/website/public/assets/data/climate/mumbai/

# All files:
# - city_climate.json (10 KB) - City-level aggregates
# - ward_index.json (60 KB) - Ward metadata
# - climate_south.json (221 KB) - 21 wards
# - climate_central.json (369 KB) - 35 wards
# - climate_western.json (463 KB) - 44 wards
# - climate_eastern.json (515 KB) - 49 wards
# - climate_northern.json (820 KB) - 78 wards

Option 2: Direct Download Links

# City summary (10 KB)
wget https://raw.githubusercontent.com/urbanmorph/notf/main/website/public/assets/data/climate/mumbai/city_climate.json

# Ward index (60 KB)
wget https://raw.githubusercontent.com/urbanmorph/notf/main/website/public/assets/data/climate/mumbai/ward_index.json

# Zone data (download specific zone or all 5)
wget https://raw.githubusercontent.com/urbanmorph/notf/main/website/public/assets/data/climate/mumbai/climate_south.json
wget https://raw.githubusercontent.com/urbanmorph/notf/main/website/public/assets/data/climate/mumbai/climate_central.json
wget https://raw.githubusercontent.com/urbanmorph/notf/main/website/public/assets/data/climate/mumbai/climate_western.json
wget https://raw.githubusercontent.com/urbanmorph/notf/main/website/public/assets/data/climate/mumbai/climate_eastern.json
wget https://raw.githubusercontent.com/urbanmorph/notf/main/website/public/assets/data/climate/mumbai/climate_northern.json

Option 3: Python Script

import json
import requests

# Load city summary
city_data = requests.get(
    'https://raw.githubusercontent.com/urbanmorph/notf/main/website/public/assets/data/climate/mumbai/city_climate.json'
).json()

print(f"Total wards: {city_data['total_wards']}")
print(f"Total population: {city_data['total_population']:,}")

# Load specific zone
south_wards = requests.get(
    'https://raw.githubusercontent.com/urbanmorph/notf/main/website/public/assets/data/climate/mumbai/climate_south.json'
).json()

for ward in south_wards['wards']:
    print(f"{ward['ward_name']}: {ward['population']:,} people")

Documentation:

MUMBAI_DATA_SOURCES_AND_CREDITS.md - Comprehensive data source documentation
MUMBAI_227_WARDS_DATA_RESEARCH.md - Ward data research findings

📄 License: CC BY-NC-SA 4.0

You are free to:

✅ Share — copy and redistribute the material in any medium or format
✅ Adapt — remix, transform, and build upon the material

Under the following terms:

Attribution (BY) — You must give appropriate credit, provide a link to the license, and indicate if changes were made
NonCommercial (NC) — You may not use the material for commercial purposes
ShareAlike (SA) — If you remix, transform, or build upon the material, you must distribute your contributions under the same license

Full License: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode

📝 How to Cite

Academic Citation (APA)

Neighbourhoods of the Future. (2026). Ward-Level Climate Baseline Data for Bengaluru.
Retrieved from https://github.com/urbanmorph/notf/tree/main/website/public/assets/data/climate/bengaluru
Licensed under CC BY-NC-SA 4.0.

Data Attribution (Required)

When using this data in reports, visualizations, or derivative works, include:

Data Source: Neighbourhoods of the Future (NOTF)
GitHub: https://github.com/urbanmorph/notf
License: CC BY-NC-SA 4.0
Original Sources: Census of India 2011, OpenCity.in, BBMP, KSPCB

BibTeX

@dataset{notf_bengaluru_climate_2026,
  author = {{Neighbourhoods of the Future}},
  title = {Ward-Level Climate Baseline Data for Bengaluru},
  year = {2026},
  publisher = {GitHub},
  url = {https://github.com/urbanmorph/notf},
  license = {CC-BY-NC-SA-4.0}
}

📚 Data Sources & Credits

All data compiled from authoritative government and research sources. Full documentation available in DATA_SOURCES_AND_CREDITS.md.

Primary Data Providers

Government Agencies:

Census of India 2011 - Population, households, demographics (★★★★☆ 75%)
BBMP (Bruhat Bengaluru Mahanagara Palike) - Waste management, tree census, ward boundaries (★★★☆☆ 65%)
KSPCB (Karnataka State Pollution Control Board) - Air quality monitoring network (★★★★☆ 80%)
BESCOM - Electricity consumption estimates (★★☆☆☆ 50%)
KREDL (Karnataka Renewable Energy Development Limited) - Renewable energy data (★★★★☆ 85%)
BWSSB (Bangalore Water Supply and Sewerage Board) - Water supply data (★★★☆☆ 60%)
BMTC (Bangalore Metropolitan Transport Corporation) - Public transport coverage (★★★★☆ 75%)

Research & Data Portals:

OpenCity.in - Urban data portal aggregating official government datasets
IISc (Indian Institute of Science) - Urban research, water resources, urban heat studies
Praja Foundation - Civic data analysis, waste management monitoring
IGBC/GRIHA - Green building certifications

Data Licensing:

OpenCity.in: Open Database License (ODbL)
Government data: Government Open Data License - India (GODL)
Census data: Public domain

Confidence Scores:

★★★★★ (85-100%) - High quality, verified, recent data
★★★★☆ (70-84%) - Good quality, some limitations
★★★☆☆ (55-69%) - Medium quality, estimates or proxy data
★★☆☆☆ (40-54%) - Limited quality, significant gaps
★☆☆☆☆ (25-39%) - Low quality, placeholder estimates

🔧 Data Processing Scripts

Python scripts for generating ward-level climate data are available in data-docs/processing/:

split_corporation_data.py - Split 3.5 MB baseline JSON into 5 corporation files
generate_ward_index.py - Extract ward metadata for routing
generate_city_summary.py - Calculate city-level aggregates and rankings
generate_ward_pages.py - Generate 738 static HTML ward pages
add_source_metadata.py - Add OpenCity.in source attributions

# Run data pipeline
cd data-docs/processing
python3 split_corporation_data.py
python3 generate_ward_index.py
python3 generate_city_summary.py
python3 add_source_metadata.py
python3 generate_ward_pages.py

🗺️ Ward Boundaries (GeoJSON)

# Bengaluru ward boundaries
wget https://raw.githubusercontent.com/urbanmorph/notf/main/website/public/assets/data/boundaries/bengaluru-wards.geojson

# Mumbai ward boundaries (227 electoral wards)
wget https://raw.githubusercontent.com/urbanmorph/notf/main/website/public/assets/data/boundaries/mumbai-electoral-wards.geojson

# Mumbai administrative wards (24 wards - legacy)
wget https://raw.githubusercontent.com/urbanmorph/notf/main/website/public/assets/data/boundaries/mumbai-wards.geojson

Format: GeoJSON (RFC 7946) Projection: WGS84 (EPSG:4326) Sources:

Bengaluru: OpenCity.in + BBMP official boundaries
Mumbai: DataMeet Municipal Spatial Data (CC BY-SA 2.5 India) + MCGM

📊 Data Structure

City Summary (`city_climate.json`)

{
  "city": "Bengaluru",
  "total_wards": 369,
  "total_population": 8402887,
  "last_updated": "2026-01-23",
  "sectors": {
    "energy_buildings": {
      "clean_cooking": {
        "average": 16.2,
        "distribution": {"high": 0, "medium": 38, "low": 331},
        "top_5": [...],
        "bottom_5": [...],
        "data_source": {
          "provider": "Census 2011 + OpenCity.in",
          "url": "https://data.opencity.in/dataset/...",
          "confidence_score": 0.64,
          "confidence_stars": "★★★☆☆",
          "methodology": "...",
          "limitations": [...]
        }
      }
    }
  }
}

Ward Data (`climate_*.json`)

{
  "corporation": "South",
  "ward_count": 72,
  "wards": [
    {
      "ward_id": 42,
      "ward_name": "Jayanagar 4th Block",
      "ward_name_local": "ಜಯನಗರ 4ನೇ ಬ್ಲಾಕ್",
      "slug": "jayanagar-4th-block",
      "corporation": "South",
      "population": 18542,
      "energy_buildings": {
        "solid_fuel_households": {
          "value": 12.5,
          "percentage": 12.5,
          "confidence": 0.64,
          "source": "Census 2011",
          "data_source": {
            "provider": "Census 2011 + OpenCity.in",
            "url": "...",
            "confidence_score": 0.64,
            "methodology": "...",
            "limitations": [...]
          }
        }
      }
    }
  ]
}

🚀 Future Cities

Planned for 2026:

Delhi (272 wards)
Chennai (200 wards)
Hyderabad (150 wards)
Pune (144 wards)

Contribute: If you have ward-level climate data for other Indian cities, please open an issue or submit a pull request.

⚠️ Data Limitations & Validation

Known Limitations:

Data Age - Census 2011 is 15 years old; population estimates extrapolated
Ward Boundaries - Some wards reorganized since 2011
Incomplete Coverage - Only Energy & Waste sectors have 100% ward-level data
Estimates - Many metrics use city averages applied uniformly (electricity, renewable energy)
Informal Sector - Waste segregation by informal workers not captured

Validation Process:

Cross-referenced with official government portals
Spot checks on 10% random sample
Peer review by urban planning researchers
BBMP/KSPCB officials consulted

Report Data Issues:

GitHub Issues: https://github.com/urbanmorph/notf/issues
Email: data@neighbourhoodsofthefuture.org

📖 Additional Documentation

Full Methodology: DATA_SOURCES_AND_CREDITS.md
Mumbai Data Sources: MUMBAI_DATA_SOURCES_AND_CREDITS.md
Architecture: ARCHITECTURE.md

Contact

Nudge Unit: nudge-unit@notf.in

Navigation Menu

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Repository files navigation

NOTF - Neighbourhoods of the Future

Stack at a glance

Repository layout

Local development

Documentation map — start here

📊 Ward-Level Climate Data

Overview

📥 Download Climate Datasets

Bengaluru (369 Wards)

Mumbai (227 Wards)

📄 License: CC BY-NC-SA 4.0

📝 How to Cite

Academic Citation (APA)

Data Attribution (Required)

BibTeX

📚 Data Sources & Credits

Primary Data Providers

🔧 Data Processing Scripts

🗺️ Ward Boundaries (GeoJSON)

📊 Data Structure

City Summary (`city_climate.json`)

Ward Data (`climate_*.json`)

🚀 Future Cities

⚠️ Data Limitations & Validation

📖 Additional Documentation

Contact

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Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
NoTF - Iconography		NoTF - Iconography
data-docs		data-docs
scripts		scripts
supabase		supabase
website		website
.gitignore		.gitignore
ACCESS.md		ACCESS.md
ARCHITECTURE.md		ARCHITECTURE.md
CLAUDE.md		CLAUDE.md
CONTRIBUTING.md		CONTRIBUTING.md
ONBOARDING.md		ONBOARDING.md
RBAC-PLAN.md		RBAC-PLAN.md
README.md		README.md
package-lock.json		package-lock.json
package.json		package.json
vercel.json		vercel.json

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NOTF - Neighbourhoods of the Future

Stack at a glance

Repository layout

Local development

Documentation map — start here

📊 Ward-Level Climate Data

Overview

📥 Download Climate Datasets

Bengaluru (369 Wards)

Mumbai (227 Wards)

📄 License: CC BY-NC-SA 4.0

📝 How to Cite

Academic Citation (APA)

Data Attribution (Required)

BibTeX

📚 Data Sources & Credits

Primary Data Providers

🔧 Data Processing Scripts

🗺️ Ward Boundaries (GeoJSON)

📊 Data Structure

City Summary (city_climate.json)

Ward Data (climate_*.json)

🚀 Future Cities

⚠️ Data Limitations & Validation

📖 Additional Documentation

Contact

About

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City Summary (`city_climate.json`)

Ward Data (`climate_*.json`)