A research-driven Urban Intelligence Platform transforming fragmented city data into trusted, reproducible, and actionable intelligence.
"Nagpur's future deserves decisions backed by evidence—not assumptions."
NagpurLens is an open Urban Intelligence Platform that integrates fragmented urban datasets into a unified, validated, and reproducible analytical system.
Rather than building dashboards first, NagpurLens follows a research-first and data-first engineering philosophy, where every dataset, SQL query, analytical model, and insight is supported by documented evidence, transparent methodologies, and reproducible workflows.
The platform combines locality information, infrastructure, demographics, connectivity, accessibility, public services, and urban indicators to generate meaningful insights for citizens, planners, researchers, developers, and policymakers.
This repository documents the complete engineering lifecycle—from defining the problem to delivering urban intelligence.
Urban information is often:
- Fragmented across multiple agencies
- Difficult to validate
- Poorly documented
- Missing relationships
- Challenging to analyze collectively
- Difficult to reproduce
Most investment, planning, and development decisions are therefore made using assumptions instead of evidence.
NagpurLens exists to solve this problem by building a transparent, reproducible Urban Intelligence Platform.
Every component inside this repository ultimately contributes toward answering three questions.
Identify underserved localities using measurable infrastructure indicators.
Analyze infrastructure expansion, connectivity improvements, demographic changes, accessibility, and development patterns over time.
Generate transparent analytical indicators using documented methodologies and reproducible scoring models.
NagpurLens provides decision-support analytics, not financial advice.
NagpurLens aims to become a trusted Urban Intelligence Platform for Nagpur by creating verified datasets, transparent methodologies, and reproducible analytical workflows.
The long-term vision extends beyond a single city.
The engineering architecture is intentionally designed so that additional cities can be integrated through standardized research methodologies, reusable database structures, and scalable analytical pipelines.
Every published insight follows the same engineering lifecycle.
Problem Definition
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Research & Evidence Collection
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Raw Data Acquisition
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Validation & Quality Assurance
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Database Engineering
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Analytical Models
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Urban Intelligence
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Reports & Dashboards
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Decision Support
Dashboards are outputs.
Validated data is the product.
NagpurLens/
├── README.md
├── LICENSE
│
├── 01_problem/
│ ├── vision.md
│ ├── problem_statement.md
│ ├── three_questions.md
│ ├── success_metrics.md
│ └── user_personas.md
│
├── 02_research/
│ ├── sources.md
│ ├── research_log.md
│ ├── assumptions.md
│ ├── evidence/
│ │ ├── besa/
│ │ ├── hingna/
│ │ ├── dharampeth/
│ │ └── ...
│ └── source_registry.csv
│
├── 03_data/
│ ├── raw/
│ ├── staging/
│ ├── validated/
│ ├── final/
│ └── data_dictionary.md
│
├── 04_quality/
│ ├── coordinate_validation.md
│ ├── join_validation.md
│ ├── duplicate_report.md
│ ├── null_report.md
│ └── quality_score.md
│
├── 05_database/
│ ├── schema.sql
│ ├── constraints.sql
│ ├── seed_data.sql
│ └── queries/
│ ├── infra_gap.sql
│ ├── growth_trend.sql
│ └── investment_score.sql
│
├── 06_intelligence/
│ ├── methodologies/
│ ├── notebooks/
│ ├── scoring_models/
│ └── findings/
│
├── 07_dashboard/
│ └── streamlit/
│
├── 08_reports/
│ ├── executive_summary.md
│ ├── findings_report.md
│ └── presentations/
│
└── PROJECT_STATUS.md
Every folder represents one stage of the engineering workflow.
| Stage | Purpose |
|---|---|
| 01 Problem | Define objectives, users, and measurable success |
| 02 Research | Collect evidence and document sources |
| 03 Data | Store raw, processed, validated, and production datasets |
| 04 Quality | Validate integrity, completeness, and reliability |
| 05 Database | Build the analytical PostgreSQL database |
| 06 Intelligence | Develop methodologies, models, notebooks, and findings |
| 07 Dashboard | Present insights through interactive applications |
| 08 Reports | Publish executive reports and analytical documents |
The initial platform focuses on building high-quality foundational datasets.
- Locality Master Dataset
- Infrastructure Dataset
- Demographic Dataset
- Geographic Reference Dataset
- Connectivity Dataset
- Accessibility Dataset
- Urban Indicators Dataset
Every production dataset includes:
- Source attribution
- Collection methodology
- Version history
- Validation status
- Geographic verification
- Data dictionary
- Quality score
Data quality is treated as a first-class engineering objective.
Each production dataset is evaluated for:
- Source traceability
- Coordinate validation
- Referential integrity
- Duplicate detection
- Missing value assessment
- Schema validation
- Join validation
- Documentation completeness
- Reproducibility
Datasets are promoted to production only after passing documented quality standards.
NagpurLens uses PostgreSQL as its analytical foundation.
The database layer focuses on:
- Normalized schema design
- Referential integrity
- Constraints
- Seed datasets
- Analytical SQL
- Business queries
- Urban indicators
The database is designed for reproducibility rather than one-time analysis.
Urban Intelligence transforms validated datasets into actionable insights.
Current analytical focus includes:
- Infrastructure Gap Analysis
- Urban Growth Indicators
- Connectivity Analysis
- Accessibility Assessment
- Locality Benchmarking
- Investment Potential Scoring
Every methodology is documented and reproducible.
- PostgreSQL
- SQL
- Python
- Pandas
- Streamlit
- Tableau
- Power BI (Planned)
- Git
- GitHub
| Area | Status |
|---|---|
| Problem Definition | ✅ Complete |
| Research Framework | 🚧 In Progress |
| Evidence Collection | 🚧 In Progress |
| Data Pipeline | 🚧 In Progress |
| Data Validation | 🚧 In Progress |
| PostgreSQL Database | 🚧 In Progress |
| Analytical SQL | 🚧 In Progress |
| Intelligence Models | 🚧 In Progress |
| Dashboard | 🚧 Planned |
| Reports | 🚧 Planned |
- Define urban problems
- Research framework
- Source registry
- Documentation
- Master datasets
- Data collection
- Validation pipeline
- PostgreSQL database
- Quality framework
- Documentation
- Infrastructure analytics
- Growth indicators
- Scoring methodologies
- Research notebooks
- Findings
- Interactive dashboards
- Public API
- Executive reports
- Multi-city architecture
- AI-assisted Urban Intelligence
Contributions are welcome.
Before submitting a pull request, please ensure that your contribution:
- Follows documented methodologies
- Preserves reproducibility
- Includes appropriate documentation
- Maintains data quality standards
- References verifiable sources where applicable
NagpurLens is built on five engineering principles.
- Research before implementation
- Evidence before conclusions
- Quality before quantity
- Reproducibility before convenience
- Transparency before complexity
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