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Bhavani Nagar

Raipur West Zone Zone 8 Ward 20

Ramkrishna Paramhans Ward Bhavani Nagar, Teachers Colony, Housing Board Colony

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54.39

Livability Report

Below city avg of 58.59/100

Score updated May 20, 2026

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Top Performing

Water Bodies 100

Air Quality 97.6

Amenities 89.4

Area Snapshot

Bhavani Nagar is a ward in Raipur (Zone 8) with 🟡 moderate livability livability (Score: 54.39/100, Grade: C).

Strengths: Water Bodies, Air Quality, Earthquake Safety · Needs improvement: Green Cover, Population Density

Best suited for: Investors Working Professionals

Monthly Cost of Living

23 surveys

₹10,700 – ₹20,095 /month

🍽️ Restaurants

₹1,118 – ₹2,399

🛒 Groceries

₹1,710 – ₹2,983

🚗 Transport

₹904 – ₹2,228

💡 Utilities

₹1,101 – ₹2,404

🏠 Housing

₹4,179 – ₹5,782

🏥 Health

₹181 – ₹651

🎭 Lifestyle

₹1,507 – ₹3,648

₹20,065 – ₹42,795 /month

🍽️ Restaurants

₹2,236 – ₹4,798

🛒 Groceries

₹3,420 – ₹5,966

🚗 Transport

₹1,356 – ₹3,342

💡 Utilities

₹1,431 – ₹3,125

🏠 Housing

₹8,999 – ₹18,790

🏥 Health

₹362 – ₹1,302

🎭 Lifestyle

₹2,261 – ₹5,472

₹28,933 – ₹65,135 /month

🍽️ Restaurants

₹3,354 – ₹7,197

🛒 Groceries

₹5,985 – ₹10,441

🚗 Transport

₹1,808 – ₹4,456

💡 Utilities

₹1,762 – ₹3,846

🏠 Housing

₹8,999 – ₹18,790

🎓 Education

₹3,468 – ₹11,156

🏥 Health

₹543 – ₹1,953

🎭 Lifestyle

₹3,014 – ₹7,296

₹9,178 – ₹16,096 /month

🍽️ Restaurants

₹671 – ₹1,439

🛒 Groceries

₹855 – ₹1,492

🚗 Transport

₹723 – ₹1,782

🏠 Housing

₹6,292 – ₹9,496

🏥 Health

₹181 – ₹651

🎭 Lifestyle

₹456 – ₹1,236

Full Breakdown

Click any metric card to explore detailed parameters

↓ Fav

Air Quality

Very Good

—

Water

—

Sanitation

80.53

Infrastructure

89.36

Amenities

—

Quiet Score

30.1

Green Cover

17.5

Flood Risk

Earthquake

56.2

Surface Temp

Open Area

47.2

Night Lights

100

Water Bodies

68.3

Population Density

63.1

Groundwater

46.7

Waterlogging

—

Risk Factors

SPECIAL ↑ Fav

69.6

Safety

Good

Score Trends

Historical parameter scores over the past 27 data points 11 Mar — 20 May 2026

Air Quality Details

Lower is Better

AQI: 56 — Very Good

Last Updated: 17 May 2026, 07:19 AM

Parameter	Value	NAAQS Limit	Direction
AQI Satisfactory	56	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	15.19 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	30.08 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	15.92 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	2.91 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	0.43 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	5.18 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	12.34 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: 17 May 2026, 07:19 AM

Air Quality Trends (Last 30 Days)

AQI Trend

PM2.5 & PM10 Trend

Residents Say

Real opinions from people living in Bhavani Nagar

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Livability Grade Card

Printable & shareable summary for Bhavani Nagar

54.4

Grade C

Bhavani Nagar

Raipur

Air Quality

97.6

Amenities

89.4

Green Cover

31.3

Infrastructure

80.5

Flood Safety

17.5

Seismic Safety

Surface Temp

56.2

Night Lights

47.2

Pros

Water Bodies
Air Quality
Earthquake Safety

Cons

Green Cover needs improvement
Population Density needs improvement

₹ ₹ ₹ ₹

Budget Living Cost

₹10,700 – ₹20,095/month (1 Person)

Pros & Cons

Pros

Clean air quality

Good access to hospitals, schools & markets

Developed roads & infrastructure

Well-lit area at night

Cons

Limited green cover

Flood-prone area

Similar Areas

Areas with comparable livability scores

Compare with these areas

Formula

The Amenity Score for Bhavani Nagar 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 Bhavani Nagar

Category	Found	Raw (count × pts)	Capped at 100
Hospitals (500m)	1	1 × 33 = 33	33
Hospitals (2km)	19	19 × 20 = 380	100 (capped)
Schools (1km)	7	7 × 20 = 140	100 (capped)
Markets (1km)	6	6 × 33 = 198	100 (capped)
Parks (500m)	4	4 × 33 = 132	100 (capped)
BRTS/Bus Stop (1km)	1	1 × 50 = 50	50
Pharmacy (2km)	20	20 × 33 = 660	100 (capped)
Banks (1km)	7	7 × 33 = 231	100 (capped)
Restaurants (1km)	10	10 × 20 = 200	100 (capped)
Worship (1km)	5	5 × 33 = 165	100 (capped)
Transport Services (1km)	16	16 × 25 = 400	100 (capped)
Sum of all categories		2589	983
Final Score = 983 ÷ 11			89.36/100

Data Source

Source: Google Places API (Nearby Search)
Method: Count amenities within specified radius of area center coordinates (lat: 21.2530000, lng: 81.6050000)
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

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 Bhavani Nagar

Pollutant	Value	Unit	Category Impact
PM2.5	15.19	µg/m³	Good
PM10	30.08	µg/m³	Good
NO₂	15.92	µg/m³	Good
SO₂	2.91	µg/m³	Good
CO	0.43	mg/m³	Good
O₃	5.18	µg/m³	Good
NH₃	12.34	µg/m³	Good

Overall AQI = max of all sub-indices = 56 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 Bhavani Nagar: Air Score = max(0, 100 − 56) = 44/100

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: 17 May 2026, 07:19 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 Bhavani Nagar

Component	Raw Value	Sub-Score	Weight	Contribution
NDVI Mean	0.232	46.4/100	40%	18.56
Vegetation Coverage	24.9%	24.9/100	30%	7.47
Dense Green Cover	10.1%	20.2/100	20%	4.04
Parks within 500m	0	0/100	10%	0
Green Cover Score				30.1/100

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

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 Waterlogging Score estimates how prone an area is to water stagnation during heavy rainfall, using multi-source satellite terrain analysis. Lower score = less susceptible = better.

Data Sources

SRTM DEM — NASA Shuttle Radar Topography Mission (30m resolution) for elevation & slope
Sentinel-1 SAR — ESA C-band radar for historical wetness frequency (2023–2024 monsoon)
ESA WorldCover v200 — 10m land cover for impervious surface percentage
TerraClimate — University of Idaho for soil moisture (mm)
Grid: 500m × 500m cells, each cell scored independently
Cells linked to nearest area by centroid distance.

Score Formula

Parameter	Weight	Logic
Elevation (m)	25%	Lower elevation → higher score (water pools in low areas)
Slope (°)	20%	Flatter terrain → higher score (water can't drain)
TWI / Flow Accumulation	20%	Higher TWI → higher score (natural water collection points)
Impervious Surface (%)	15%	More concrete/asphalt → higher score (no infiltration)
SAR Wetness Frequency	10%	More frequent wet signals → higher score (historically floods)
Soil Moisture (mm)	10%	Higher moisture → higher score (saturated ground)

Score = Elevation(25%) + Slope(20%) + TWI(20%) + Impervious(15%) + SAR(10%) + Moisture(10%)

Why Waterlogging Matters

Property Damage — Waterlogged areas risk foundation damage, seepage, and mold in ground-floor properties.
Commute Disruption — Stagnant water blocks roads, making monsoon commutes unpredictable.
Health Hazards — Standing water breeds mosquitoes (dengue, malaria) and sewage overflow risks.
Infrastructure Stress — Repeated waterlogging accelerates road degradation and utility failures.
Investment Risk — Chronic waterlogging areas see lower appreciation and higher maintenance costs.

Raipur Context

Raipur receives ~1300–1800 mm rainfall annually, concentrated in the June–September monsoon. RMC data shows 97% incidence of waterlogging with a 52% drainage coverage gap. Low-lying areas near Kharun River, old city localities with poor storm-water drainage, and rapidly urbanised zones with high impervious cover are most vulnerable. Nava Raipur sectors benefit from planned drainage infrastructure but face risks in low-elevation zones.

Risk Scale

🟢 Low (0–35) — Minimal risk 🟡 Moderate (35–65) — Some risk 🔴 High (65–100) — Significant risk

Area Score vs Map Score

Why might the score here differ from the map?
The Waterlogging Map shows the score of each individual 500m grid cell. This area page shows the average score of all grid cells linked to this area. For example, if an area has 4 nearby cells scoring 59, 58, 54, and 51, the map shows each cell's own score while this page shows the average (~55.3). This gives a more holistic view of the area's overall waterlogging susceptibility.

Data Sources

SRTM DEM — NASA, 30m resolution elevation
Sentinel-1 GRD — ESA, C-band SAR for surface wetness
ESA WorldCover v200 — 10m land cover classification
TerraClimate — University of Idaho, ~4.6 km monthly climate
Google Earth Engine — Cloud processing for grid-wise analysis

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 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.

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