Urban Systems Mapping Toolkit is a decision-first urban diagnostics framework that helps cities, NGOs, and researchers understand why traffic, waste, and pollution interventions fail—before investing in new infrastructure or policies.
Instead of treating cities as optimization or prediction problems, this project models cities as decision networks operating under timing conflicts, fragmented authority, and misaligned incentives.
Most urban failures do not occur because:
- data is missing
- infrastructure is insufficient
- technology is unavailable
They occur because:
- decisions are made too late
- authority is fragmented across departments
- independent actions collide in time
- incentives are misaligned
This toolkit exists to make those failures visible.
Cities fail at the decision layer, not the data layer.
This project maps:
- who decides
- when decisions collide
- how delays propagate
- where intervention is unsafe
It is a diagnostic system, not a control system.
- Directed graph of actors and decisions
- Explicit modeling of authority, dependency, and feedback
- Covers traffic, waste, and pollution as a single coupled system
Deterministic, auditable metrics including:
- Degree, betweenness, and eigenvector centrality
- Decision Load Index (DLI)
- Temporal Overlap Index (TOI)
- Pollution Decision Stress
- Network stress testing (node removal)
- Composite Urban Decision Risk Index (UDRI)
No machine learning.
No black boxes.
A complete decision-centric policy framework including:
- Infrastructure moratorium rules
- Decision timing reform rules
- Pollution enforcement prioritization
- Non-intervention safety rules
- Mandatory human oversight
Demonstrated on real Indian urban contexts, including:
- Delhi — Anand Vihar / Kaushambi / ISBT corridor
The framework is city-agnostic and works with low-data conditions.
Generated directly from analysis:
- Decision Bottleneck Map
- Top unsafe decisions
- “What to fix” and “What NOT to fix”
- 3–4 week pilot deployment plan
- Policy-ready language
- Automatic PDF reports
- No prediction of traffic or pollution
- No optimization or automation
- No smart-city dashboards
- No replacement of governance or political authority
- No AI-driven decision execution
All outputs require explicit human judgment.
. ├── Urban_Systems_Mapping_Toolkit.ipynb ├── README.md
The entire system is implemented in a single Jupyter notebook for transparency, auditability, and ease of deployment.
- Open the Jupyter notebook
- Read from top to bottom
- Replace sample data with one real city zone
- Run the analysis
- Review decision bottlenecks
- Apply policy rules
- Design a 3–4 week pilot
No setup, no dependencies beyond standard Python scientific libraries.
- Urban NGOs
- Municipal innovation cells
- Policy researchers
- Civic-tech practitioners
- Applied systems researchers
- Research-grade prototype
- Field-ready for pilots
- Policy-defensible
- NGO-deployable
- Safe by design
This is not a demo.
It is a complete diagnostic system.
Cities rarely fail because of lack of data or infrastructure.
They fail because decisions are made too late, by fragmented authorities, under misaligned incentives.
This toolkit exists to expose those failures—
before money, policy, or political capital is spent.
This project is intended for:
- research
- policy diagnostics
- pilot studies
Use responsibly.
Human judgment is mandatory.
If you are looking to predict cities, this is not the right tool.
If you want to understand why cities fail, this is exactly the tool you need.