Yadunandan Nagar

Per-Category Scoring

Each category scores 0-100 based on how many amenities are found within a specific radius:

Calculation for Yadunandan Nagar

AQI Categories (NAQI / CPCB Standard)

Sub-Index Calculation

CPCB Breakpoints (Key Pollutants)

Current Values for Yadunandan Nagar

AQI → Livability Report Conversion

NDVI Interpretation

Green Cover Score Formula

Calculation for Yadunandan Nagar

Methodology

Scoring Formula

Parameter Breakdown

How Noise Levels are Estimated

Daytime and nighttime noise levels (dB) are estimated using the FHWA Traffic Noise Model (simplified). The model calculates noise attenuation based on distance:

Nighttime noise is estimated to be 5–10 dB lower than daytime (reduced traffic, no industrial operations). Multiple sources are combined using logarithmic addition.

CPCB Noise Standards (India)

The Central Pollution Control Board classifies areas into 4 noise zones with specific limits:

Score Interpretation

Worked Example

Data Sources

• OpenStreetMap (via Overpass API) — highway, industrial, and railway proximity data
• FHWA Traffic Noise Model — simplified noise attenuation estimation
• CPCB Noise Rules, 2000 — ambient noise standards for Indian cities
• WHO Environmental Noise Guidelines (2018) — health-based recommended limits

Scoring Formula

Parameter Breakdown

ODF Certification Levels (SBM Urban)

The Swachh Bharat Mission Urban uses a three-tier ODF certification framework:

Score Interpretation

Worked Example

Why Sanitation Matters for Health

• Waterborne diseases — Poor sanitation increases risk of cholera, typhoid, and hepatitis A
• Vector-borne diseases — Stagnant water from poor drainage breeds mosquitoes (dengue, malaria)
• Child mortality — WHO reports that improved sanitation reduces diarrheal deaths by 36%
• Groundwater contamination — Open drains and septic tank leakage pollute drinking water sources

Data Sources

• AMRUT SLIP — Service Level Improvement Plan for sanitation benchmarks
• SBM Urban Portal — Swachh Bharat Mission ODF certification data
• NGT Reports — National Green Tribunal compliance monitoring

The Flood Risk Score measures an area's vulnerability to river flooding and urban waterlogging. Lower score = safer area.

Parameters & Weights

Formula

Inverted Livability Score

What This Means for Home Buyers

• Score ≤ 20 — Safe zone. Minimal flood risk, standard construction sufficient.
• Score 21-40 — Moderate risk. Check monsoon waterlogging history before buying.
• Score 41-60 — At risk. Consider elevated construction, flood insurance, basement waterproofing.
• Score >60 — High risk. River flooding possible. Verify RERA flood disclosure and avoid ground floor investment.

Data Sources

• NDMA — National Disaster Management Authority, District Disaster Management Plan
• ThinkHazard — World Bank global hazard assessment for Bilaspur district
• CGWB — Central Ground Water Board, Bilaspur district hydrogeological report

The Livability Report is a single number (0–100) that represents the overall quality of life in a neighbourhood. It is the simple average of all available sub-scores.

Formula

Sub-Scores Used

Only parameters with available data are averaged — NULL values are skipped, not treated as zero. For example, Waterlogging is included only for areas that have waterlogging data, so the number of parameters can differ between areas.

Worked Example

Grade System

What Does the Score Mean for You?

• Home Buyers — Use the score to compare neighbourhoods objectively. A higher score means better air, water, safety, amenities, and environment.
• Renters — Check if the area's livability matches the rent being asked. A Grade C area should not command Grade A pricing.
• Investors — Areas with improving scores (check the trend chart) may see property appreciation. Grade B areas with rising scores are sweet spots.
• Families with Children — Pay special attention to Air Quality, Green Cover, and Amenities (schools, hospitals) sub-scores.
• Senior Citizens — Quiet Score, Air Quality, and nearby hospital access (Amenities) are most relevant.

Important Notes

• The composite score uses an equal-weight average — all parameters contribute equally.
• Risk parameters (Flood, Earthquake) are inverted before averaging so that lower risk = higher livability contribution.
• If a sub-score is unavailable (e.g., Infrastructure), it is excluded from the average, not treated as zero.
• Scores are recalculated whenever new data becomes available. Check the trend chart for historical movement.
• The score reflects current conditions — an area under rapid development may see score changes over time.

The Earthquake Risk Score measures an area's seismic vulnerability based on soil type and amplification potential. Lower score = safer area.

BIS Seismic Zone

Why Soil Type is the Differentiator

Since all of Bilaspur is uniformly Zone II, the only meaningful differentiator is soil type. Soft, saturated, or filled soils amplify distant earthquake shaking more than hard alluvial or laterite soils. This is called site amplification effect per IS 1893.

Score Formula

Inverted Livability Score

What This Means for Home Buyers

• Score 10 (Low) — Safest. Hard soil, standard IS 1893 Zone II construction norms sufficient. No special foundation needed.
• Score 25 (Low-Medium) — Minor amplification possible due to soft/fill/riverine soil. Soil investigation recommended for buildings >3 storeys.
• Overall — Bilaspur is among the safest cities in India for earthquake risk.

Data Sources

• BIS IS-1893 — Bureau of Indian Standards, Indian Standard for Earthquake Resistant Design
• NDMA DDMP — National Disaster Management Authority, District Disaster Management Plan
• ThinkHazard — World Bank global seismic hazard assessment
• asc-india.org — PGA hazard contour data for Indian cities
• CGWB — Central Ground Water Board soil/geology data

The Land Surface Temperature (LST) Score measures the thermal comfort of an area from satellite data. Lower surface temperature = higher score (better livability).

Data Source

Score Formula

Why LST Matters for Livability

• Urban Heat Island Effect — Dense built-up areas with concrete/asphalt absorb and re-radiate heat, raising surface temperatures by 3-8°C compared to green areas.
• Energy Costs — Higher LST areas require more air-conditioning, increasing electricity bills and carbon footprint.
• Health Impact — Prolonged heat exposure increases risk of heat stroke, dehydration, and respiratory problems.
• Thermal Comfort — Areas near water bodies, parks, and tree cover have significantly lower LST, improving outdoor comfort.

Data Sources

• Landsat 8/9 C2 L2 — USGS/NASA satellite thermal infrared imagery
• Google Earth Engine — Cloud processing platform for satellite data analysis
• Buffer Analysis — 1000m + 3000m radius averaged for balanced local + neighbourhood assessment

The Open Area Score measures the percentage of non-built-up land — open spaces, parks, vegetation and room to breathe. Higher open space percentage = higher score.

Data Source

Score Formula

Why Open Area Matters

• Urban Heat — Open spaces with vegetation help cool the environment and reduce the Urban Heat Island effect.
• Rainwater Drainage — More open land allows natural percolation, reducing waterlogging during monsoons.
• Green Space Access — Areas with more open space typically have parks, gardens, and tree-lined roads.
• Air Quality — Open areas allow better wind circulation and more trees for natural air filtration.
• Quality of Life — Open spaces reduce stress, encourage physical activity, and improve mental well-being.

Data Sources

• ESA WorldCover 2021 — 10m resolution global land use/land cover (Class 50 = Built-up)
• Sentinel-1 + Sentinel-2 — ESA Copernicus satellite constellation
• Google Earth Engine — Cloud processing for area-wise statistics

The Nighttime Lights (NTL) Score measures urban illumination intensity as a proxy for infrastructure quality and economic activity. Higher radiance = higher score (better infrastructure).

Data Source

Score Formula

Why Nighttime Lights Matter

• Street Lighting & Safety — Well-lit areas correlate with lower crime rates and safer night-time movement for pedestrians.
• Economic Activity — NTL is a well-established proxy for GDP and commercial vitality in urban economics research.
• Power Infrastructure — Consistent brightness indicates reliable electricity supply and fewer outages.
• Development Level — Brighter areas typically have better roads, healthcare, education, and public services.
• Property Value Correlation — Research shows strong positive correlation between NTL intensity and real estate values.

Data Sources

• VIIRS DNB — NASA/NOAA Visible Infrared Imaging Radiometer Suite, Day/Night Band
• EOG/MINES — Earth Observation Group, Colorado School of Mines monthly composites
• Google Earth Engine — Cloud processing for area statistics

The Water Bodies Score measures the presence of surface water (lakes, ponds, rivers, reservoirs) in and around an area. More water bodies = higher score (better livability).

Data Source

Score Formula

Why Water Bodies Matter

• Urban Cooling — Water bodies reduce ambient temperature by 2-5°C through evaporative cooling ("lake breeze" effect).
• Biodiversity — Lakes and ponds support ecosystems — birds, fish, aquatic plants — enriching neighbourhood ecology.
• Mental Health — Proximity to water ("blue space") is linked to reduced stress, anxiety, and improved well-being in studies.
• Recreation — Lakefronts provide walking trails, boating, fishing, and scenic beauty that enhance quality of life.
• Groundwater Recharge — Natural water bodies help maintain water table levels and reduce dependence on borewells.
• Property Value — Waterfront and lake-view properties command 15-40% premium in Indian real estate markets.

Data Sources

• JRC Global Surface Water v1.4 — EU Joint Research Centre, Pekel et al. (2016), Nature
• Landsat Archive — 38 years of satellite observations (1984-2021)
• Google Earth Engine — Cloud processing for area statistics

The Population Density Score measures how crowded a neighbourhood is based on gridded population estimates. Lower density = higher score (less congestion, more space).

Data Source

Score Formula

Why Population Density Matters

• Congestion — Higher density means more traffic, longer commutes, crowded markets, and strained public transport.
• Infrastructure Strain — Water supply, sewerage, electricity, and waste management struggle in very dense areas.
• Air & Noise Quality — Dense areas have more vehicles, construction, and commercial activity contributing to pollution.
• Green Space Per Capita — WHO recommends 9 m²/person of green space — dense areas rarely meet this benchmark.
• Disease Spread — COVID-19 and other infectious diseases spread faster in high-density neighbourhoods.
• Living Space — Lower density areas typically offer larger homes, better ventilation, and more privacy.

Data Sources

• WorldPop — University of Southampton, constrained individual countries 2020 (100m resolution)
• Census of India 2011 — Base population data disaggregated via random forest model
• Google Earth Engine — Cloud processing for area-wise aggregation

The Groundwater Score evaluates underground water balance and recharge conditions using high-resolution satellite-derived climate data. Higher soil moisture, positive recharge, and wetter PDSI = higher score.

Data Source

Score Formula

Why Groundwater Matters

• Borewell Reliability — Areas with higher soil moisture and positive recharge support more reliable borewells for drinking water.
• Water Security — Groundwater recharge ensures a critical backup during dry seasons when surface water is scarce.
• Drought Resilience — PDSI indicates whether an area is prone to drought conditions that lower water tables.
• Agricultural Impact — Declining soil moisture and negative recharge affect local food production and costs.
• Ecosystem Health — Groundwater feeds rivers, lakes, and wetlands — depletion affects local ecology.

Data Sources

• TerraClimate — University of Idaho, ~4.6 km monthly resolution (1958–present)
• CGWB Reports — Central Ground Water Board, Raipur District (reference)
• Google Earth Engine — Cloud processing for area-wise aggregation

The Risk Factor Score evaluates ground-level risks that affect livability — environmental hazards, drainage issues, pollution sources, infrastructure gaps, zoning problems, safety concerns, and more. Lower score = fewer risks = safer area.

Data Source

9 Risk Categories Assessed

Score Formula

Why Risk Factors Matter

• Property Value — Proximity to hazards (landfills, HT lines, flood zones) directly affects property resale and rental value.
• Health Impact — Environmental and pollution risks affect long-term health of residents.
• Infrastructure Reliability — Power outages, water supply gaps, and poor roads affect daily quality of life.
• Legal Risks — Unauthorised colonies, disputed land, and zoning changes can cause legal complications.
• Future Planning — Upcoming highway widening, industrial corridors, or land acquisition may impact your area.

Risk Scale

Data Sources

• GreaterRaipur Field Survey — On-ground risk identification and verification (2024)
• RMC / NRANVP — Municipal hazard records, ward-level drainage and infrastructure reports
• Google Maps + OpenStreetMap — Location verification and proximity calculation

The Infrastructure Score evaluates urban development quality using 9 satellite datasets — assessing built-up density, building heights, nighttime illumination, road surfaces, terrain, greenery, and urbanization level. Higher score = better infrastructure.

Data Source

Score Formula

Why Infrastructure Matters

• Daily Connectivity — Roads, power, and built-up density directly affect commute quality and access to services.
• Property Value — Well-developed infrastructure correlates with higher property appreciation and rental yields.
• Power Reliability — Nighttime light stability indicates consistent electricity supply, critical for residential comfort.
• Vertical Growth — Building height reflects planned vertical development vs low-rise sprawl.
• Green Balance — Good infrastructure includes tree cover and green spaces, not just concrete.

Data Sources

• ESA WorldCover 2021 — 10m land use/land cover classification
• GHSL (EC JRC) — Built Surface, Height, Volume, Settlement Model Grid
• VIIRS DNB — Monthly nighttime lights composite (NASA/NOAA)
• Dynamic World — Near real-time 10m land use (Google/WRI)
• ALOS World 3D DEM — 30m terrain elevation (JAXA)
• Google Earth Engine — Cloud processing for area-wise aggregation

Sub-Score Breakdown

Score Interpretation