Research & Methodology

🎯 Problem Statement

Finding the right neighborhood in Indian cities is challenging due to:

  • Fragmented information across multiple sources
  • Lack of systematic comparison tools
  • Subjective reviews without data backing
  • No personalized matching based on lifestyle
  • Difficulty in understanding commute patterns

📊 Data Sources

Primary Sources:

  • Census of India data
  • Municipal corporation records
  • Transport authority data
  • Crime statistics from police
  • Real estate market data
  • Local business directories

Challenges:

  • Data availability varies by city
  • Inconsistent data formats
  • Language barriers in local data
  • Rapid urban development changes

🧮 Algorithm Design

Score = (Safety × W1) + (Commute × W2) + (Cost × W3) + (Lifestyle × W4) + (Transport × W5)

Where W1-W5 are user-defined importance weights (1-10 scale)

Scoring Factors:

  • Safety: Crime rates, police presence, lighting
  • Commute: Distance to business hubs, traffic patterns
  • Transport: Metro/bus connectivity, frequency
  • Cost: Rent prices, daily expenses, utilities
  • Lifestyle: Restaurants, entertainment, culture
  • Family: Schools, hospitals, parks, community

🏙️ Indian Cities Coverage

Mumbai

Bandra, Andheri, Powai, Thane

Delhi NCR

Gurgaon, Noida, Lajpat Nagar

Bangalore

Koramangala, Whitefield, HSR

Pune

Koregaon Park, Hinjewadi, Viman Nagar

Chennai

Anna Nagar, T. Nagar, OMR

Hyderabad

Hitech City, Banjara Hills, Gachibowli

🔍 User Research Insights

Key Findings:

  • 80% prioritize safety above all factors
  • Commute time critical factor for 75% users
  • Budget constraints override preferences for 70%
  • Public transport access increasingly important
  • Family users prioritize schools and hospitals

Indian Context:

  • Joint family considerations important
  • Religious/cultural proximity matters
  • Vegetarian food availability crucial
  • Festival celebration spaces valued
  • Regional language comfort zones

⚠️ Limitations & Future Work

Current Limitations:

  • Limited to major metro cities
  • Data freshness varies by source
  • No real-time traffic integration
  • Cultural factors not fully quantified
  • Seasonal variations not considered

Planned Improvements:

  • Tier-2 city expansion
  • Real-time data integration
  • Machine learning refinement
  • Community feedback integration
  • Regional language support
Try the Algorithm