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Public AQI data is the strongest secondary layer for Neural City

Neural City already focuses on street-level urban observations such as roads, footpaths, cleanliness, mobility, and service quality. AQI adds an outcome-oriented environmental layer that helps compare how healthy each city feels for residents.

Recommended dataset

CPCB National Air Quality Index

Use CPCB / data.gov.in AQI records as the source of truth, then keep a local cleaned sample when live API access is unavailable.

Prototype scope

10 Indian cities

Pune, Mumbai, Delhi, Bengaluru, Chennai, Hyderabad, Ahmedabad, Kolkata, Jaipur, and Nagpur.

Average AQI

132

A single summary value supports quick benchmarking while the table and charts preserve city-level detail.

PDF requirement analysis

  • Explore the Neural City ranking context and improve city comparison with a public secondary dataset.
  • Select one relevant dataset for citizens, government users, or both.
  • Define a clear use case explaining better decisions and better city understanding.
  • Process the data into a usable format and build a small prototype.
  • Submit a prototype link, public dataset used, a 50-word note, and a cleaning explanation.

Cleaning and transformation

  • Kept one city-level record per city to match a ranking dashboard pattern.
  • Normalized city and state names for consistent search, display, and sorting.
  • Stored AQI, PM2.5, and PM10 as numbers so charts and averages can be computed safely.
  • Mapped AQI values to CPCB-style category labels for green, yellow, and red risk indicators.
  • Converted dates to ISO format so the UI can render localized update labels.

Local data structure

FieldTypePurpose
citystringPrimary comparison and search label
statestringGeographic context for each city
aqinumberRanking, charting, and KPI calculation
categorystringCPCB-style risk indicator
pm25numberFine particulate pollution signal
pm10numberCoarse particulate pollution signal
lastUpdatedISO dateFreshness and auditability