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Sector 31

Nava Raipur West Zone Sector S31 492101

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70.08

Livability Report

Below city avg of 72.4/100

Score updated Jul 22, 2026

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Top Performing

Water Bodies 100

Population Density 95.7

Open Area 90.8

Area Snapshot

Sector 31 is a sector in Nava Raipur (west) with 🟢 good livability livability (Score: 70.08/100, Grade: B). It scores -2.3 below the Nava Raipur average.

Strengths: Water Bodies, Open Area, Flood Safety · Needs improvement: Population Density, Nighttime Lights

Best suited for: All Residents

Monthly Cost of Living

21 surveys

₹11,283 – ₹20,979 /month

🍽️ Restaurants

₹1,165 – ₹2,503

🛒 Groceries

₹1,744 – ₹3,219

🚗 Transport

₹949 – ₹2,280

💡 Utilities

₹1,170 – ₹2,335

🏠 Housing

₹4,431 – ₹6,080

🏥 Health

₹211 – ₹702

🎭 Lifestyle

₹1,613 – ₹3,860

₹21,344 – ₹45,350 /month

🍽️ Restaurants

₹2,330 – ₹5,006

🛒 Groceries

₹3,488 – ₹6,438

🚗 Transport

₹1,424 – ₹3,420

💡 Utilities

₹1,521 – ₹3,036

🏠 Housing

₹9,739 – ₹20,256

🏥 Health

₹422 – ₹1,404

🎭 Lifestyle

₹2,420 – ₹5,790

₹30,829 – ₹69,062 /month

🍽️ Restaurants

₹3,495 – ₹7,509

🛒 Groceries

₹6,104 – ₹11,267

🚗 Transport

₹1,898 – ₹4,560

💡 Utilities

₹1,872 – ₹3,736

🏠 Housing

₹9,739 – ₹20,256

🎓 Education

₹3,862 – ₹11,908

🏥 Health

₹633 – ₹2,106

🎭 Lifestyle

₹3,226 – ₹7,720

₹9,299 – ₹16,663 /month

🍽️ Restaurants

₹699 – ₹1,502

🛒 Groceries

₹872 – ₹1,610

🚗 Transport

₹759 – ₹1,824

🏠 Housing

₹6,273 – ₹9,690

🏥 Health

₹211 – ₹702

🎭 Lifestyle

₹485 – ₹1,335

Full Breakdown

Click any metric card to explore detailed parameters

↓ Fav

Air Quality

Very Good

Water

76.5

Sanitation

36.71

Infrastructure

46.73

Amenities

68.9

Quiet Score

49.5

Green Cover

Flood Risk

Earthquake

Surface Temp

90.8

Open Area

Night Lights

100

Water Bodies

95.7

Population Density

66.9

Groundwater

—

Waterlogging

—

Risk Factors

SPECIAL ← Fav

24.3

Walkability

Extreme

SPECIAL ← Fav

63.8

Safety

Good

Score Trends

Historical parameter scores over the past 28 data points 02 May — 22 Jul 2026

What's Nearby

Click a category to toggle • Click markers for details

Showing 134 places across 17 categories from Google Maps

Air Quality Details

Lower is Better

AQI: 82 — Very Good

Last Updated: 14 Jul 2026, 07:36 AM

Parameter	Value	NAAQS Limit	Direction
AQI Satisfactory	82	0–50 Good	Lower is better
Air Quality Index — a single number (0–500) summarizing overall air pollution. Computed from the worst-performing pollutant. AQI ≤50 is "Good"; above 200 is "Very Poor" and dangerous for sensitive groups.
PM2.5	47.26 µg/m³	≤40 µg/m³ (24hr)	Lower is better
Fine particulate matter (diameter <2.5 µm). These ultra-fine particles penetrate deep into lungs and enter the bloodstream, causing respiratory disease, heart attacks, and reduced life expectancy. Main sources: vehicle exhaust, construction dust, industrial emissions, crop burning.
PM10	72.13 µg/m³	≤60 µg/m³ (24hr)	Lower is better
Coarse particulate matter (diameter <10 µm). Includes dust, pollen, and mold spores. Causes coughing, aggravates asthma, and irritates eyes/throat. Less dangerous than PM2.5 but still a major concern in dusty cities like Raipur.
NO₂	32.08 µg/m³	≤40 µg/m³ (annual)	Lower is better
Nitrogen Dioxide — reddish-brown gas from vehicle engines and power plants. Irritates airways, worsens asthma, and contributes to smog/acid rain formation. High NO₂ is a strong indicator of traffic-related pollution.
SO₂	51.83 µg/m³	≤50 µg/m³ (24hr)	Lower is better
Sulfur Dioxide — pungent gas from burning coal, oil, and industrial smelting (steel plants near Raipur). Causes burning sensation in nose/throat, breathing difficulty, and contributes to acid rain which damages buildings and vegetation.
CO	0.37 mg/m³	≤2 mg/m³ (8hr)	Lower is better
Carbon Monoxide — colorless, odorless gas from incomplete combustion (vehicles, generators, burning biomass). Reduces blood's ability to carry oxygen, causing headaches, dizziness. High levels indoors can be fatal. Especially dangerous in enclosed/poorly ventilated areas.
O₃	38.38 µg/m³	≤100 µg/m³ (8hr)	Lower is better
Ground-level Ozone — unlike protective ozone in upper atmosphere, ground-level O₃ is harmful. Formed when sunlight reacts with vehicle exhaust and industrial emissions. Peaks in afternoon heat. Triggers chest pain, coughing, and throat irritation. Especially harmful for children and elderly.
NH₃	- µg/m³	≤400 µg/m³ (24hr)	Lower is better
Ammonia — a pungent gas from agricultural fertilizers, animal waste, sewage treatment, and some industrial processes. High concentrations irritate eyes, nose, and throat. Contributes to secondary PM2.5 formation through atmospheric reactions.

AQI Scale Reference

⭐ 0–50 — Perfect (Good) ✅ 51–100 — Very Good (Satisfactory) ⚠️ 101–200 — Moderate 🟠 201–300 — Below Average (Poor) 🔴 301+ — Extreme (Severe)

Key Takeaways

All pollutants: Lower values are always better. Zero pollution is ideal but not realistic in urban areas.
PM2.5 is the most critical health parameter — it's the #1 cause of air pollution-related deaths in India.
NO₂ & CO are strong indicators of vehicular traffic — higher near busy roads and intersections.
SO₂ is elevated near industrial areas — Raipur's Siltara-Urla industrial belt is a significant source.
O₃ peaks during hot sunny afternoons — worst between 12 PM – 4 PM in summer months.
NH₃ comes from agricultural activities, sewage, and industrial processes — contributes to secondary PM2.5 formation.
NAAQS = National Ambient Air Quality Standards (CPCB, India). Limits shown are for short-term exposure.

Health Recommendations

All

Cardiovascular

Elderly

Family

Outdoor sports are fine. Prefer morning hours (6-8 AM) when pollution is typically lower.

Source: Google Air Quality API (per-coordinate model) Dominant: Pm10 Data recorded: 14 Jul 2026, 07:36 AM

Air Quality Trends (Last 30 Days)

AQI Trend

PM2.5 & PM10 Trend

Residents Say

Real opinions from people living in Sector 31

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Pros & Cons

Pros

Clean air quality

Safe drinking water

Well-maintained sanitation

Peaceful & quiet environment

Low flood risk

Cons

Limited nearby amenities

Limited green cover

Infrastructure needs development

Poor nighttime lighting

Similar Areas

Areas with comparable livability scores

Compare with these areas

Formula

The Amenity Score for Sector 31 is calculated by averaging 11 category sub-scores:

                        Amenity Score = ( Σ Category Scores ) ÷ 11
                    

Per-Category Scoring

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

Category	Search Radius	Max Count for 100	Points per Amenity	Formula
Hospitals (500m)	500m	3	33	`min(100, count × 33)`
Hospitals (2km)	2km	5	20	`min(100, count × 20)`
Schools (1km)	1km	5	20	`min(100, count × 20)`
Markets (1km)	1km	3	33	`min(100, count × 33)`
Parks (500m)	500m	3	33	`min(100, count × 33)`
BRTS/Bus Stop (1km)	1km	2	50	`min(100, count × 50)`
Pharmacy (2km)	2km	3	33	`min(100, count × 33)`
Banks (1km)	1km	3	33	`min(100, count × 33)`
Restaurants (1km)	1km	5	20	`min(100, count × 20)`
Worship (1km)	1km	3	33	`min(100, count × 33)`
Transport Services (1km)	1km	4	25	`min(100, count × 25)`

Calculation for Sector 31

Category	Found	Raw (count × pts)	Capped at 100
Hospitals (500m)	0	0 × 33 = 0	0
Hospitals (2km)	20	20 × 20 = 400	100 (capped)
Schools (1km)	3	3 × 20 = 60	60
Markets (1km)	1	1 × 33 = 33	33
Parks (500m)	0	0 × 33 = 0	0
BRTS/Bus Stop (1km)	1	1 × 50 = 50	50
Pharmacy (2km)	2	2 × 33 = 66	66
Banks (1km)	0	0 × 33 = 0	0
Restaurants (1km)	4	4 × 20 = 80	80
Worship (1km)	6	6 × 33 = 198	100 (capped)
Transport Services (1km)	1	1 × 25 = 25	25
Sum of all categories		912	514
Final Score = 514 ÷ 11			46.73/100

Data Source

Source: Google Places API (Nearby Search)
Method: Count amenities within specified radius of area center coordinates (lat: 21.1225142, lng: 81.7882712)
Categories queried: hospitals/clinics, schools/colleges, markets/supermarkets, parks, transit stations, pharmacies, banks/ATMs, restaurants/cafes, places of worship
Score range: 0 (no amenities) to 100 (all 10 categories at maximum)
Last updated: 27 Mar 2026, 12:00 AM

Overview

The Water Quality Score is a composite measure (0–100) derived from key drinking water parameters based on WHO/BIS standards and ground-truth data from the CWC Hot Spots Report 2022 (Central Water Commission).

Formula

Water Score = (TDS Score + pH Score + Supply Score + Fluoride Score) ÷ 4

Parameter Scoring Rules

Parameter	Condition	Score
TDS (mg/L)	≤ 300 ppm	100
	301 – 600 ppm	75
	601 – 900 ppm	50
	> 900 ppm	25
pH Level	6.5 – 8.5	100
	6.0 – 6.5 or 8.5 – 9.0	70
	< 6.0 or > 9.0	30
Water Supply	24 hrs/day	100
Water Supply	Less than 24 hrs	`(hours ÷ 24) × 100`
Fluoride	≤ 1.0 mg/L	100
	1.1 – 1.5 mg/L	60
	> 1.5 mg/L	20

Worked Example for Sector 31

Parameter	Value	Score
TDS	180.43 ppm	100
pH Level	7.97	100
Supply	9.6 hrs/day	40
Fluoride	0.596 mg/L	100

Water Score = (100 + 100 + 40 + 100) ÷ 4 = 85/100

CWC Monitoring Stations — Raipur District

Station	River	Parameter	Pre-Monsoon	Monsoon	Post-Monsoon	Status
Patharidih	Kharun	Nitrate (NO₃⁻-N)	20.71 mg/L	14.59 mg/L	16.40 mg/L	🔴 Hotspot all 3 seasons
Simga	Seonath	Nitrate (NO₃⁻-N)	12.96 mg/L	—	11.95 mg/L	🟠 Hotspot pre & post monsoon

Patharidih on the Kharun river (flows through Raipur city) is the most critical hotspot — it exceeded the BIS nitrate limit (45 mg/L as NO₃⁻ = 10.16 mg/L as N) in all three seasons of 2022. The Kharun is a direct tributary of the Seonath, which feeds into the Mahanadi, forming Raipur's primary water source basin.

Simga on the Seonath river (boundary of Raipur & Durg districts) also showed persistent nitrate exceedance in pre- and post-monsoon seasons.

Data Source

Source: CWC Water Quality Hot Spots Report 2022 (Central Water Commission)
Monitoring Stations: Patharidih (Kharun river), Simga (Seonath river) — Raipur region
Key Findings: Patharidih identified as nitrate hotspot across all 3 seasons; Simga hotspot in pre & post monsoon
Supply source: Municipal
Turbidity: 2.59 NTU (not included in composite score)
Data recorded: 07 Mar 2026

What is AQI?

The Air Quality Index (AQI) is a standardised indicator (0–500) that communicates how polluted the air currently is. A lower AQI means cleaner air.

                        AQI = max( Sub-IndexPM2.5, Sub-IndexPM10, Sub-IndexNO₂, Sub-IndexSO₂, Sub-IndexCO, Sub-IndexO₃ )
                    

The overall AQI equals the highest sub-index among all measured pollutants. Each sub-index is calculated by linear interpolation between CPCB/EPA breakpoints for that pollutant's concentration.

AQI Categories (NAQI / CPCB Standard)

AQI Range	Category	Health Impact
0 – 50	Good	Minimal impact
51 – 100	Satisfactory	Minor breathing discomfort to sensitive people
101 – 200	Moderate	Breathing discomfort to people with lung/heart disease
201 – 300	Poor	Breathing discomfort on prolonged exposure
301 – 400	Very Poor	Respiratory illness on prolonged exposure
401 – 500	Severe	Affects healthy people; serious impact on those with existing diseases

Sub-Index Calculation

Each pollutant's sub-index is calculated using linear interpolation:

                        Ip = ( (IHi − ILo) / (BPHi − BPLo) ) × (Cp − BPLo) + ILo
                    

C_p = pollutant concentration (measured value)
BP_Hi / BP_Lo = concentration breakpoints ≥ and ≤ C_p
I_Hi / I_Lo = AQI breakpoints corresponding to BP_Hi and BP_Lo

CPCB Breakpoints (Key Pollutants)

AQI	PM2.5 (µg/m³) 24-hr avg	PM10 (µg/m³) 24-hr avg	NO₂ (µg/m³) 24-hr avg	SO₂ (µg/m³) 24-hr avg	CO (mg/m³) 8-hr avg	O₃ (µg/m³) 8-hr avg	NH₃ (µg/m³) 24-hr avg
Good (0–50)	0–30	0–50	0–40	0–40	0–1.0	0–50	0–200
Satisfactory (51–100)	31–60	51–100	41–80	41–80	1.1–2.0	51–100	201–400
Moderate (101–200)	61–90	101–250	81–180	81–380	2.1–10	101–168	401–800
Poor (201–300)	91–120	251–350	181–280	381–800	10.1–17	169–208	801–1200
Very Poor (301–400)	121–250	351–430	281–400	801–1600	17.1–34	209–748	1201–1800

Current Values for Sector 31

Pollutant	Value	Unit	Category Impact
PM2.5	47.26	µg/m³	Satisfactory
PM10	72.13	µg/m³	Satisfactory
NO₂	32.08	µg/m³	Good
SO₂	51.83	µg/m³	Satisfactory
CO	0.37	mg/m³	Good
O₃	38.38	µg/m³	Good
NH₃	-	µg/m³	-

Overall AQI = max of all sub-indices = 82 Satisfactory

AQI → Livability Report Conversion

AQI Range	Air Quality Score (for Livability)	Method
0 – 500	`max(0, 100 − AQI)`	Inverted: lower AQI = higher livability

For Sector 31: Air Score = max(0, 100 − 82) = 18/100

Data Source

Source: Google Air Quality API (per-coordinate interpolated model)
Method: Combines satellite imagery, ground station data, weather models, and traffic patterns to provide hyper-local air quality estimates for each area's exact coordinates
Standard: India NAQI (National Air Quality Index) as primary, plus Universal AQI (UAQI)
Pollutants tracked: PM2.5, PM10, NO₂, SO₂, CO, O₃, NH₃ (7 pollutants)
Coverage: Individual readings for all 125 areas — not extrapolated from a single station
Reading at: 14 Jul 2026, 07:36 AM

NDVI Formula

The Normalized Difference Vegetation Index (NDVI) measures vegetation health using satellite imagery:

                        NDVI = (NIR − Red) / (NIR + Red)
                    

NIR = Near-Infrared reflectance (Landsat 8/9 Band 5)
Red = Red light reflectance (Landsat 8/9 Band 4)
Range: −1 (water) to +1 (dense vegetation)

NDVI Interpretation

NDVI Range	Category	Description
< 0.0	Water	Water bodies, rivers, ponds
0.0 – 0.1	Barren	Bare soil, rock, sand, built-up areas
0.1 – 0.2	Sparse	Sparse vegetation, urban green, scattered shrubs
0.2 – 0.4	Moderate	Grassland, cropland, gardens, roadside trees
0.4 – 0.6	Dense	Dense parks, plantation, forest patches
> 0.6	Very Dense	Closed canopy forest, dense tropical vegetation

Green Cover Score Formula

                        Green Score = 

                            40% × NDVI Mean Score (0.0→0, 0.5→100)

                        + 30% × Vegetation Coverage % (pixels NDVI > 0.2)

                        + 20% × Dense Green % (pixels NDVI > 0.4, scaled 0-50%→0-100)

                        + 10% × Parks Score (0→0, 1→33, 2→66, 3+→100)

Calculation for Sector 31

Component	Raw Value	Sub-Score	Weight	Contribution
NDVI Mean	0.334	66.8/100	40%	26.72
Vegetation Coverage	41.8%	41.8/100	30%	12.54
Dense Green Cover	25.5%	50.9/100	20%	10.18
Parks within 500m	0	0/100	10%	0
Green Cover Score				49.5/100

Methodology

Satellite: Landsat 8/9 OLI (estimated) — 30m spatial resolution
Processing: Median composite of cloud-filtered scenes (<20% cloud cover), last 12 months
Buffer: Avg (1000m + 3000m)m radius from area center coordinates
Method: ee.Image.normalizedDifference(['B5', 'B4']) — per GEE Tutorial
Source: Average of 1000m and 3000m buffer analysis
Date: 12 Mar 2026

What is the Quiet Score?

The Quiet Score measures how quiet and peaceful an area is, on a scale of 0 to 100. A higher score means a quieter, more livable area — better for living, sleeping, and overall well-being. It is calculated using real geographic data from OpenStreetMap to determine proximity to noise sources like highways, industries, and railways.

Scoring Formula

                    Quiet Score = (Highway Proximity × 0.40) + (Industrial Proximity × 0.25)

                                 + (Railway Proximity × 0.15) + (Highway Density × 0.10)

                                 + (Industrial Density × 0.10)

Parameter Breakdown

Parameter	Weight	How It's Scored
Highway Proximity	40%	Distance to nearest motorway/trunk/primary road. 0m = 0 points, ≥2000m = 100 points. Linear interpolation.
Industrial Proximity	25%	Distance to nearest industrial zone. 0m = 0 points, ≥2000m = 100 points. Linear interpolation.
Railway Proximity	15%	Distance to nearest rail/light rail track. 0m = 0 points, ≥2000m = 100 points.
Highway Density	10%	Count of major roads within 2km. 0 roads = 100 points, ≥10 roads = 0 points.
Industrial Density	10%	Count of industrial zones within 2km. 0 zones = 100 points, ≥5 zones = 0 points.

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:

                    L(d) = L₀ - 20 × log₁₀(d / d₀)

                    Where L₀ is source noise at reference distance d₀

Source	Reference Level	At 100m	At 500m	At 1000m
Major Highway	78 dB @ 10m	58 dB	44 dB	38 dB
Industrial Zone	70 dB @ 50m	64 dB	50 dB	44 dB
Railway (Leq)	60 dB @ 25m	48 dB	34 dB	28 dB
Airport	75 dB @ 1km	—	81 dB	75 dB

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:

Zone	Day Limit	Night Limit	Typical Areas
Industrial	75 dB	70 dB	Factory areas, manufacturing hubs (Siltara, Urla)
Commercial	65 dB	55 dB	Markets, commercial zones (Pandri, Malviya Road)
Residential	55 dB	45 dB	Housing colonies, wards (Shankar Nagar, Tatibandh)
Silence	50 dB	40 dB	Near hospitals, schools, courts (within 100m)

Score Interpretation

Score Range	Rating	What It Means
80–100	Excellent	Very quiet, far from all noise sources — ideal for families and health-conscious residents
60–79	Good	Generally peaceful with minimal noise intrusion — suitable for comfortable living
40–59	Moderate	Some noise from traffic or industrial areas — consider soundproofing improvements
20–39	Below Average	Noticeable noise pollution — may affect sleep quality and daily comfort
0–19	Poor	High noise exposure — health risks from prolonged exposure (stress, hearing, cardiovascular)

Worked Example

Example: An area with highways 800m away, 3 highways in 2km, industrial zone 1200m away, 1 industrial zone, railway 1500m away

Highway Proximity: (800 / 2000) × 100 = 40.0 → × 0.40 = 16.0
Industrial Proximity: (1200 / 2000) × 100 = 60.0 → × 0.25 = 15.0
Railway Proximity: (1500 / 2000) × 100 = 75.0 → × 0.15 = 11.3
Highway Density: (1 - 3/10) × 100 = 70.0 → × 0.10 = 7.0
Industrial Density: (1 - 1/5) × 100 = 80.0 → × 0.10 = 8.0

Total Quiet Score = 16.0 + 15.0 + 11.3 + 7.0 + 8.0 = 57.3/100 (Moderate — some noise from traffic and industry)

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

What is the Sanitation Score?

The Sanitation Score measures the quality and coverage of sanitation infrastructure in an area, on a scale of 0 to 100. A higher score means better sewage treatment, drainage, waste collection, and open-defecation-free (ODF) compliance — all critical for public health and livability.

Scoring Formula

                    Sanitation Score = (ODF Status × 0.20) + (Sewage Treatment Coverage × 0.30)

                                   + (Drainage Coverage × 0.25) + (Waste Collection × 0.15)

                                   + (Sewage Disposal Method × 0.10)

Parameter Breakdown

Parameter	Weight	How It's Scored
ODF Status	20%	ODF++ = 100, ODF+ = 70, ODF = 40. Based on SBM Urban certification level. ODF++ means no open defecation plus solid & liquid waste management.
Sewage Treatment Coverage	30%	Direct percentage value (0–100%). Measures the proportion of area connected to sewage treatment plants (STPs). Higher coverage means less untreated sewage discharge.
Drainage Coverage	25%	Direct percentage value (0–100%). Measures the extent of paved/covered storm-water drains. Better drainage prevents waterlogging and disease vectors.
Waste Collection	15%	Daily (2x) = 100, Daily = 80, Alternate Day = 50. Reflects frequency and reliability of door-to-door waste collection by the municipal body.
Sewage Disposal Method	10%	Centralized STP = 100, STP Connected = 80, Septic Tank = 50, Open Drain = 10. Measures how responsibly sewage effluent is handled before discharge.

ODF Certification Levels (SBM Urban)

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

Level	Criteria	Score
ODF++	No open defecation + all sewage/septage safely treated + proper fecal sludge management	100
ODF+	No open defecation + no untreated sewage/septage discharged into water bodies/open drains	70
ODF	No open defecation — all households have access to toilets and use them	40

Score Interpretation

Score Range	Rating	What It Means
80–100	Excellent	Comprehensive sanitation coverage — ODF++ certified, high sewage treatment, reliable waste collection
60–79	Good	Good infrastructure with room for improvement — most areas connected to STPs, daily waste collection
40–59	Moderate	Basic sanitation in place but gaps in coverage — some open drains, partial sewage treatment
20–39	Below Average	Significant gaps in sanitation — poor drainage, infrequent waste collection, septic-tank-dependent
0–19	Poor	Severe sanitation deficits — open defecation areas, untreated sewage discharge, disease risk

Worked Example

Example: An area with ODF+ status, 72% sewage treatment, 85% drainage, daily waste collection, STP Connected

ODF Status: ODF+ = 70 → × 0.20 = 14.0
Sewage Treatment: 72% → × 0.30 = 21.6
Drainage Coverage: 85% → × 0.25 = 21.25
Waste Collection: Daily = 80 → × 0.15 = 12.0
Sewage Disposal: STP Connected = 80 → × 0.10 = 8.0

Total Sanitation Score = 14.0 + 21.6 + 21.25 + 12.0 + 8.0 = 76.85/100 (Good — well-developed sanitation infrastructure)

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

Nagar Nigam Raipur — City Sanitation Plan (CSP) ward-level data
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
NRANVP Development Plan — Nava Raipur sector-level infrastructure data

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

Parameters & Weights

Parameter	Weight	Description	Risk → Score
River Flood Risk	50%	Proximity to rivers (Kharun, Pairy, Seonath) and flood history	Low = 10, Low-Medium = 25, Medium = 40, High = 70, Very High = 85
Urban Waterlogging	50%	Storm-water drainage adequacy and monsoon waterlogging history	Low = 10, Low-Medium = 25, Medium = 40, High = 70, Very High = 85

Formula

Flood Risk Score = (River Flood × 50%) + (Waterlogging × 50%)

Worked Example

Example: Zone 9 (Mowa/Kachna) — Directly on Kharun River bank

River Flood: High = 70 → × 0.50 = 35.0
Waterlogging: High = 70 → × 0.50 = 35.0

Total = 35.0 + 35.0 = 70.0/100 (High Risk — directly on river bank, highest flood vulnerability in Raipur)

Zone-wise Risk Overview (Raipur)

Zone	Area	River Flood	Waterlogging	Overall	Score
Zone 4	Civil Lines/Sadarbazar	Low	Low	🟢 Lowest	10
Zone 10	Kamhal Vihar/Devpuri	Low	Low	🟢 Lowest	10
Zone 8	Tatibandh/Mahadevahat	Low-Medium	Low	🟡 Low-Medium	17.5
Zone 1	North-West	Medium	Low	🟡 Low-Medium	25
Zone 5	Dangania/South	Medium	Medium	🟡 Medium	40
Zone 6	Bhathagaon/Tikrapara	Medium	Medium	🟡 Medium	40
Zone 2	Railway/Pandri	Medium	High	🔴 High	55
Zone 3	Shankar Nagar/Telibandha	Medium	High	🔴 High	55
Zone 7	Tatya Para/Geeta Nagar	Medium	High	🔴 High	55
Zone 9	Saddu/Mowa/Kachna	High	High	🔴 Highest	70

Key Risk Factors

Kharun River — Primary flood source. Wards along the river corridor (Zone 9) face highest risk during monsoon.
Telibandha Lake — Overflow during heavy rains causes waterlogging in Shankar Nagar area (Zone 3).
Old City Drainage — 52% drainage gap in RMC limits. Old areas (Pandri, Railway) suffer most waterlogging.
Mahanadi Basin — Entire Raipur district drains into Mahanadi via Kharun, Pairy, and Seonath rivers.

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 Raipur district
CGWB — Central Ground Water Board, Raipur district hydrogeological report
RMC SLIP — Raipur Municipal Corporation storm-water drainage plan

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

Livability Report = Average of all available sub-scores

Each sub-score is converted to a 0–100 scale where higher = better for livability:
• Standard parameters (Water, Sanitation, Amenities, Green Cover, Quiet Score) → used directly
• Air Quality → 100 − AQI (lower AQI = better air = higher score)
• Flood Risk → 100 − flood_risk_score (lower risk = safer = higher score)
• Earthquake Risk → 100 − earthquake_score (lower risk = safer = higher score)

Sub-Scores Used

#	Parameter	Source	Conversion for Composite
1	Air Quality	Google AQI / AQICN	`100 − AQI` (AQI 41 → 59)
2	Water Quality	BIS IS-10500 parameters	Direct (0–100)
3	Sanitation	RMC/NRANVP sanitation data	Direct (0–100)
4	Amenities	Google Places API	Direct (0–100)
5	Quiet Score	Noise level analysis	Direct (0–100)
6	Green Cover	NDVI satellite analysis	Direct (0–100)
7	Flood Risk	NDMA/ThinkHazard	`100 − score` (risk 10 → 90)
8	Earthquake Risk	BIS IS-1893/NDMA	`100 − score` (risk 10 → 90)

Infrastructure score is planned but not yet included. Only parameters with available data are averaged — NULL values are skipped, not treated as zero.

Worked Example

Example: An area with AQI 41, Water 75, Sanitation 62, Amenity 38, Quiet 37, Green 20, Flood Risk 55, Earthquake 10

Air: 100 − 41 = 59
Water: 75
Sanitation: 62
Amenity: 38
Quiet: 37
Green: 20
Flood: 100 − 55 = 45
Earthquake: 100 − 10 = 90

Composite = (59 + 75 + 62 + 38 + 37 + 20 + 45 + 90) ÷ 8 = 53.25/100 → Grade C

Grade System

Grade	Score Range	Meaning	Recommendation
A	80–100	Excellent livability	Top-tier neighbourhood. Premium pricing justified. Ideal for families.
B	60–79	Good livability	Well-served area. Good balance of infrastructure and environment. Recommended for most buyers.
C	40–59	Average livability	Some gaps in amenities or environment. Check specific weak parameters before deciding.
D	20–39	Below average	Significant gaps. Suitable only if specific advantages (price, location) outweigh livability concerns.
F	0–19	Poor livability	Major livability issues. Not recommended for residential investment without significant improvement plans.

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

Raipur District = BIS Seismic Zone II (Lowest hazard zone in India, Zone Factor = 0.10g)
India has 4 seismic zones (II–V). Zone II covers the Deccan Plateau and central India — historically the least seismically active region. Raipur has no recorded destructive earthquake in modern history.

Why Soil Type is the Differentiator

Since all of Raipur 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

Score = Soil-based raw risk (1–5) mapped to 0–100 scale

Raw Score	Earthquake Score	Soil Type	Risk Level
1/5	10	Hard alluvial / laterite / rock	🟢 Low
2/5	25	Soft alluvial / fill / saturated riverine	🟡 Low-Medium
3/5	45	Mixed weak soil	🟠 Medium
4/5	65	Very soft / liquefiable	🔴 High
5/5	85	Extreme soft / reclaimed	🔴 Very High

Zone-wise Earthquake Risk (Raipur)

Zone	Area	Soil Type	Liquefaction	Risk	Score
Zone 1	North-West Raipur	Hard alluvial	None	🟢 Low	10
Zone 2	Railway/Pandri	Old fill / made land	Low	🟡 Low-Medium	25
Zone 3	Shankar Nagar/Telibandha	Mixed alluvial, lake-fringe	None	🟢 Low	10
Zone 4	Civil Lines/Sadarbazar	Hard alluvial	None	🟢 Low	10
Zone 5	Dangania/South	Mixed alluvial	None	🟢 Low	10
Zone 6	Bhathagaon/Tikrapara	Mixed alluvial	None	🟢 Low	10
Zone 7	Tatya Para/Geeta Nagar	Soft alluvial (Kharun corridor)	Low	🟡 Low-Medium	25
Zone 8	Tatibandh/Mahadevahat	Hard alluvial	None	🟢 Low	10
Zone 9	Saddu/Mowa/Kachna	Soft saturated riverine	Medium	🟡 Low-Medium	25
Zone 10	Kamhal Vihar/Devpuri	Hard alluvial, plateau approach	None	🟢 Low	10
Nava Raipur (all sectors): Hard laterite / rock — 🟢 Low (score: 10) — Best seismic profile

PGA Data (Peak Ground Acceleration)

Raipur City (21.2514°N, 81.6296°E)
PGA (475-yr return period): 0.0155g — 10% probability of exceedance in 50 years
PGA (2475-yr return period): 0.0325g — 2% probability of exceedance in 50 years

Source: asc-india.org seismic hazard data. For context, PGA > 0.1g is typically when structural damage begins. Raipur's values are well below this threshold.

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 — Raipur is among the safest cities in India for earthquake risk. Even the "riskiest" zones (2, 7, 9) have only minor amplification from soft soil.

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
nano-ntp.com — Soil classification study for Raipur region

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

Landsat 8/9 Collection 2 Level 2 — Thermal Infrared Sensor (TIRS), Band 10
Resolution: 30 metres per pixel
Processing: Surface temperature derived from thermal radiance after atmospheric correction by USGS.
Two buffer radii analysed: 1000m and 3000m around each area centroid, then averaged.

Score Formula

LST Score = max(0, min(100, ((45 - mean_temp) / 25) × 100))

Mean LST (°C)	Score	Category
≤ 20°C	100	❄️ Cool — Perfect
25°C	80	🌤️ Comfortable
30°C	60	🌤️ Moderate
35°C	40	☀️ Warm
40°C	20	🔥 Hot
≥ 45°C	0	🔥 Extreme Heat

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.

Raipur Context

Raipur has a hot sub-humid climate with summer temperatures regularly exceeding 45°C. Areas with dense tree canopy (Telibandha Lake surrounds, Nava Raipur green corridors) show 3-5°C lower LST than heavily concreted commercial zones (GE Road, Pandri Market).

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

ESA WorldCover 2021 — European Space Agency global land cover map
Resolution: 10 metres per pixel (highest-resolution freely available global land cover)
Classification: Uses Sentinel-1 (radar) + Sentinel-2 (optical) to classify every pixel as built-up or non-built-up.
Two buffer radii: 1000m and 3000m around each area centroid, then averaged.

Score Formula

Open Area Score = 100 - builtup_percentage

Open Space %	Built-up %	Score	Category
90-100%	0-10%	90-100	🌳 Spacious
70-90%	10-30%	70-90	🏡 Very Open
50-70%	30-50%	50-70	🏘️ Moderate
30-50%	50-70%	30-50	🏙️ Limited
10-30%	70-90%	10-30	🏗️ Very Limited
0-10%	90-100%	0-10	🏢 Extremely Dense

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.

Raipur Context

Old Raipur wards (Purani Basti, Sadar Bazar, Jawahar Market) typically show only 10-20% open space — these are historical commercial/residential areas with minimal breathing room. Nava Raipur sectors, by contrast, show 85-97% open space with planned green corridors and wide setbacks.

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

VIIRS Day/Night Band (DNB) Monthly Composites — NOAA/NASA Suomi NPP satellite
Resolution: ~500 metres per pixel
Measurement: Average radiance in nanoWatts/cm²/sr (steradian) — stray light and aurora removed.
Two buffer radii: 1000m and 3000m around each area centroid, then averaged.

Score Formula

NTL Score = min(100, (avg_radiance / 50) × 100)

Avg Radiance (nW/cm²/sr)	Score	Category
0-5	0-10	🌑 Dark — Minimal infrastructure
5-15	10-30	🌙 Dim — Rural/developing
15-25	30-50	🔆 Moderate — Residential
25-35	50-70	💡 Bright — Active commercial
35-50	70-100	💡 Very Bright — Major hub
≥ 50	100	💡 Maximum — City centre

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.

Raipur Context

Raipur's commercial core (Jawahar Market, Shastri Chowk, GE Road) shows radiance of 35-45 nW/cm²/sr — among the brightest in Chhattisgarh. Nava Raipur sectors range 5-25 nW/cm²/sr depending on development status, with Sector 17-21 (ATAL Nagar CBD) being brightest.

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

JRC Global Surface Water v1.4 — European Commission Joint Research Centre
Resolution: 30 metres per pixel
Coverage: Maps every water body on Earth from 1984-2021 using 4.7 million Landsat scenes.
Classifies water as permanent (water >75% of time) or seasonal (wet periodically).
Two buffer radii: 1000m and 3000m around each area centroid, then averaged.

Score Formula

Permanent Score = min(100, (permanent_water_pct / 10) × 100)
Seasonal Score = min(100, (seasonal_water_pct / 15) × 100)
Water Score = (Permanent × 0.7) + (Seasonal × 0.3)

Permanent Water %	Approx Score	Category
0%	0	🏜️ No water bodies
0.1-1%	7-10	💦 Minimal
1-3%	10-30	💧 Some water
3-5%	30-50	💧 Moderate
5-10%	50-100	🌊 Significant water
≥ 10%	100	🌊 Maximum — Lakefront

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.

Raipur Context

Raipur has several significant water bodies: Telibandha Talab (largest urban lake), Budha Talab, Kankali Talab, Katora Talab, and segments of the Kharun River. Areas near these water bodies (Telibandha, Budha Talab ward) score significantly higher. Nava Raipur has planned artificial lakes and stormwater management ponds.

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

WorldPop Global Project Population Data 2020
Resolution: 100 metres per pixel (~1 hectare)
Method: Census data disaggregated to grid cells using machine learning and satellite imagery (building footprints, land use, nighttime lights).
Measurement: People per pixel → aggregated to people/km² within buffer.
Two buffer radii: 1000m and 3000m around each area centroid, then averaged.

Score Formula

Pop Density Score = max(0, ((20000 - density_per_km²) / 20000) × 100)

Density (people/km²)	Score	Category
0-2,000	90-100	🏡 Low Density — Spacious
2,000-5,000	75-90	🏡 Moderate-Low
5,000-8,000	60-75	🏘️ Moderate
8,000-12,000	40-60	🏙️ Dense
12,000-18,000	10-40	🏢 Very Dense
≥ 20,000	0	🏢 Extremely Dense

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.

Raipur Context

Old Raipur core (Purani Basti, Sadar Bazar) has population density of 8,000-12,000/km² — typical of historical Indian city centres. Nava Raipur sectors average only 500-3,000/km² due to planned low-density development with green buffers. Peripheral Raipur wards (Kachna, Daldal Seoni) also show lower density as they're still developing.

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

TerraClimate — University of Idaho (IDAHO_EPSCOR/TERRACLIMATE)
Resolution: 1/24° × 1/24° (~4.6 km) — area-level differentiation
Variables: soil (Soil Moisture, mm), pr (Precipitation, mm), aet (Actual ET, mm), ro (Runoff, mm), pdsi (Palmer Drought Severity Index)
Period: 2024 for current state, 2021 vs 2024 for trend analysis.
Two buffer radii assessed: 1000m and 3000m, then averaged.
Derived: GW Recharge = Precipitation − Actual ET − Runoff

Score Formula

Parameter	Weight	Scoring Range
Soil Moisture (mm)	30%	0 = 0, 200+ = 100
GW Recharge (mm/yr)	25%	−200 = 0, +400 = 100
PDSI (drought index)	20%	−4 = 0, +4 = 100
SM Trend (mm change)	15%	−50 = 0, +50 = 100
Precipitation (mm/yr)	10%	800 = 0, 1800 = 100

Score = (SM×0.30) + (Recharge×0.25) + (PDSI×0.20) + (Trend×0.15) + (Precip×0.10)

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.

Raipur Context

Greater Raipur relies on a mix of municipal supply (Kharun/Mahanadi river) and borewells. Nava Raipur sectors have deeper water tables (30-50m) but better recharge due to planned green belts and lower built-up density. Old Raipur wards may face seasonal groundwater stress due to high built-up density reducing natural recharge. Raipur receives ~1300-1800 mm rainfall annually (monsoon-dominant), making recharge highly seasonal. CGWB classifies most of Raipur district as "Safe" to "Semi-Critical" for groundwater extraction.

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 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

Google Earth Engine — Multi-dataset satellite analysis
Datasets: ESA WorldCover (10m land use), GHSL Built Surface/Height/Volume/SMOD (building metrics), VIIRS Nighttime Lights (electrification), Dynamic World (impervious surface), ALOS DEM (terrain slope), Sentinel-2 (vegetation indices)
Two buffer radii assessed: 1000m and 3000m around area centroid, then averaged.

Score Formula

Parameter	Weight	Scoring Logic
Urbanization Index (SMOD)	20%	Direct 0–100 scale from GHSL SMOD
Built-up Density (ESA)	15%	Optimal 30–60%; 0%=0, 60%=100, >60% declines
Nighttime Light Stability	15%	Stability index × 100 (proxy for reliable power)
Avg Building Height (GHSL)	10%	0m=0, 10m=100 (taller = better vertical infra)
Impervious Surface %	10%	Optimal 10–40%; >40% declines (roads/concrete)
Nighttime Radiance (VIIRS)	10%	0=0, 50 nW/cm²/sr=100 (electrification proxy)
Terrain Slope (ALOS DEM)	10%	Flat=100, steep declines (road-building ease)
Green Cover (Tree+Grass)	10%	0%=0, 30%=100 (livability greenery balance)

Score = Weighted average of available parameters (NULL values excluded)

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.

Raipur Context

Greater Raipur shows significant infrastructure variation — central old-city wards have dense built-up areas but narrow roads, while Nava Raipur sectors feature planned wide roads, better drainage, and modern utility networks. Satellite data captures these differences through built-up patterns, building heights, and nighttime illumination intensity.

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

What is the Walkability Score?

The Walkability Score measures how pedestrian-friendly an area is based on the presence and quality of walking infrastructure, road safety features, street lighting, and road surface quality. Scored on a 0–100 scale (higher = more walkable).

                        Walkability = Ped. Infra × 0.30 + Road Safety × 0.25 + Lighting × 0.20 + Road Quality × 0.25
                    

Each sub-score is individually normalised to 0–100 using capped counts, then the weighted sum produces the final score. This is a special experimental parameter not included in the composite livability score.

Sub-Score Breakdown

Sub-Score	Weight	Components	Normalisation
Pedestrian Infrastructure	30%	Sidewalks (25%) — cap: 20 Pedestrian Zones (20%) — cap: 3 Footpaths (20%) — cap: 15 Crossings (20%) — cap: 10 Cycle Paths (15%) — cap: 5	min(count / cap × 100, 100)
Road Safety	25%	Traffic Signals (40%) — cap: 8 Speed Bumps (20%) — cap: 5 Lit Roads (40%) — cap: 15	min(count / cap × 100, 100)
Street Lighting	20%	Street Lamps — cap: 30 Lit Roads (fallback at 70%)	max(lamp_score, lit_road_score × 0.7)
Road Quality	25%	Paved vs. Unpaved roads ratio	paved / (paved + unpaved) × 100

Score Interpretation

Score Range	Rating	Meaning
80 – 100	Excellent	Highly walkable with complete pedestrian infrastructure
60 – 79	Good	Good walking conditions with minor gaps
40 – 59	Moderate	Basic walkability — some infrastructure missing
0 – 39	Poor	Limited walkability — significant infrastructure gaps

Data Source

OpenStreetMap (Overpass API)

All walkability infrastructure data is sourced from OpenStreetMap via the Overpass API. Features like sidewalks, pedestrian zones, crosswalks, traffic signals, street lamps, and road surface tags are queried within each area's geographic boundary.

What is the Safety Score?

The Safety Score evaluates the availability and proximity of emergency services — police, fire, hospitals, and pharmacies — within an area. Scored on a 0–100 scale (higher = safer / better covered).

                        Safety = Police × 0.20 + Fire × 0.15 + Hospital × 0.35 + Pharmacy × 0.15 + Coverage × 0.15
                    

Each sub-score is normalised to 0–100 using count and proximity scoring with distance-based decay. This is a special experimental parameter not included in the composite livability score.

Sub-Score Breakdown

Sub-Score	Weight	How It's Scored	Details
Police Presence	20%	Minimum service check At least 1 station = 100	Binary presence check: scores 100 if any police station exists nearby (OSM or Google data), 0 otherwise.
Fire Station Response	15%	Minimum service check At least 1 station = 100	Binary presence check: scores 100 if any fire station exists nearby (OSM or Google Maps data), 0 otherwise.
Hospital Access	35%	Hospitals within 500m (50%) — cap: 3 Hospitals within 2 km (50%) — cap: 8	Includes hospitals and clinics from Google Maps data. Highest weighted sub-score because healthcare proximity is critical.
Pharmacy & First Aid	15%	1 pharmacy = 50 2+ pharmacies = 100	Minimum service check capped at 2. Pharmacies provide first-line medical access.
Emergency Coverage	15%	25 pts for each type present (Police, Fire, Hospital, Pharmacy)	Binary check: does the area have at least one of each emergency service type? Max 100 if all four present.

Score Interpretation

Score Range	Rating	Meaning
80 – 100	Excellent	Comprehensive emergency coverage with nearby services
60 – 79	Good	Good emergency access with minor gaps
40 – 59	Moderate	Partial coverage — some services distant or absent
0 – 39	Poor	Limited emergency infrastructure — significant gaps

Data Sources

Google Maps Places API + OpenStreetMap

Emergency services data is sourced from Google Maps Places API (police stations, fire stations, hospitals, clinics, pharmacies) enriched with OpenStreetMap amenity data. Distance calculations use the Haversine formula from each area's centroid to the nearest service locations.

Nearby RERA-Registered Projects

CG-RERA registered projects near Sector 31

View all in Project IQ

Filter: Within 2 km 2-4 km

#	Project	RERA No.	Status	Completion	Distance
1	AVINASH NEW COUNTY Arang	PCGRERA080618000115	Ongoing	100%	0.82 km	View
2	naya raipur mukhyamantri awas yojna pmay sector-30 Arang	PCGRERA070718000493	Ongoing	80%	0.92 km	View
3	GT LIFE SPACES Arang	PCGRERA270418000009	Ongoing	100%	1.05 km	View
4	SECTOR 30- RESIDENTIAL Arang	PCGRERA010220001086	New	100%	1.07 km	View
5	SECTOR-29 TOWNSHIP, NAYA RAIPUR Arang	PCGRERA040718000436	Ongoing	95%	1.4 km	View
6	lakevilas Abhanpur	PCGRERA130123001586	New	52%	1.99 km	View
7	evaraa by deevee Mandir Hasaud	PCGRERA080725001945	New	2%	2.78 km	View
8	naya raipur mukhyamantri awas yojna pmay sector-34 Abhanpur	PCGRERA050718000464	Ongoing	18%	3.18 km	View
9	QUINCE GOLF Raipur	PCGRERA210818000709	Ongoing	100%	3.3 km	View

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