CITYMIND — Documentation | Urban Human Systems Intelligence Framework

📖 Overview

"A city is not a single unified system — it is a collection of independent subsystems whose interaction must be governed through aggregation, not structural merging."

CITYMIND is a fully coupled, AI-augmented framework that treats urban health as a continuously governed composite indicator — not a static property frozen at the completion of a planning cycle.

An urban system operating under dynamic conditions is not in static equilibrium. It is a continuously evolving collection of subsystems: Transport Flow, Population Density, Energy Consumption, Mobility Behavior, and Infrastructure Load. CITYMIND quantifies each subsystem independently and aggregates them into the Urban Health Index (UHI).

🏗️ 5-Subsystem + AI Architecture

Subsystem 01 — TFS (Transport Flow Subsystem)

BPR congestion function with AI correction (ε_T ≤ 0.05). Real-time traffic volume measurement and capacity-based scoring.

Transport Flow Formula

T_score = 1 - (V_demand / C_capacity) + ε_T(t)
T(v) = T₀·[1 + α·(V/C)^β]

Subsystem 02 — PDS (Population Density Subsystem)

Spatial distribution analysis. Optimal density tracking. Live census data fusion.

Population Density Formula

P_score = 1 - |(Pop/Area) - Density_opt| / Density_max

Subsystem 03 — ECS (Energy Consumption Subsystem)

Supply-demand balance. Peak load monitoring. Smart meter data integration.

Energy Balance Formula

E_score = (Supply_grid - Demand_urban) / Supply_grid

Subsystem 04 — MBS (Mobility Behavior Subsystem)

Mode share analysis. Sustainable mobility tracking. Multinomial logit model.

Mobility Formula

M_score = (Trips_public + Trips_active) / Total_trips
P(choice) = exp(V)/Σexp(V)

Subsystem 05 — ILS (Infrastructure Load Subsystem)

Water, power, waste, transport, telecom, housing composite. K=6 systems.

Infrastructure Load Formula

I_score = 1 - (Load_infra / Load_critical)
L_geo = (Πᵢ load_i)^(1/6)

AISL — AI Enhancement Layer

Gradient boosting weight predictor. Mahalanobis anomaly detection. 48h UHI forecast. AI is ONLY for optimization and anomaly detection — not subsystem merging.

AI Constraints

Σwᵢ = 1.0, wᵢ ≥ 0
MAH_dist² = (x - μ)ᵀ·Σ⁻¹·(x - μ) < 3σ
UHI = w_T·T + w_P·P + w_E·E + w_M·M + w_I·I

UHI — Urban Health Index

Weighted composite of all five subsystems. Continuous real-time urban safety certification with 24-48h forecast.

UHI Formula

UHI = w_T·T_score + w_P·P_score + w_E·E_score + w_M·M_score + w_I·I_score
Optimized: UHI ≥ 0.85 | Stressed: 0.70 ≤ UHI < 0.85
Mitigation: 0.55 ≤ UHI < 0.70 | Critical: UHI < 0.55

📐 Core Equations

Eq. 1 — Transport Flow

T_score = 1 - (V/C) + ε_T

Congestion + AI correction

Eq. 2 — Population Density

P_score = 1 - |D - D_opt|/D_max

Spatial distribution

Eq. 3 — Energy Balance

E_score = (S - D)/S

Supply-demand margin

Eq. 4 — Mobility Behavior

M_score = (T_public + T_active)/T_total

Sustainable mode share

Eq. 5 — Infrastructure Load

I_score = 1 - L/L_crit

Geometric mean load

Eq. 6 — Urban Health Index

UHI = Σ w_i · Score_i

Σwᵢ = 1.0

⚙️ UHI Governance Protocol

Signal	Condition	Action	Governance Level
🟢 OPTIMIZED URBAN FLOW	UHI ≥ 0.85	Continue monitoring — normal operation	None
🟠 STRESSED SUBSYSTEM WARNING	0.70 ≤ UHI < 0.85	Activate anomaly detection — isolate affected subsystem	Level 1
🟠 SYSTEMIC MITIGATION PHASE	0.55 ≤ UHI < 0.70	Apply balancing measures — reroute paths — rationalize loads	Level 2
🔴 CRITICAL INFRASTRUCTURE BREACH	UHI < 0.55	Declare emergency — operational shutdown — evacuation	Stop

📦 Installation

bash — pip install

pip install citymind-engine

# From source
git clone https://github.com/gitdeeper13/CITYMIND.git
cd CITYMIND
pip install -e .

# Quick test
python -c "from citymind import CityMindAssessor; print('CITYMIND ready')"

🔧 API Reference

python — main interface

from citymind import CityMindAssessor

# Initialize assessor
assessor = CityMindAssessor()

# Run full CITYMIND pipeline
result = assessor.evaluate()

print(result.uhi_result.uhi)          # Urban Health Index ∈ [0,1]
print(result.uhi_result.signal.value)  # OPTIMIZED_FLOW | STRESSED_WARNING | SYSTEMIC_MITIGATION | CRITICAL_BREACH
print(result.subsystem_scores)        # {T,P,E,M,I} scores
print(result.ai_weights)              # AI-optimized weights Σ=1.0
print(result.anomaly_detected)        # Mahalanobis distance > 3σ

📊 Validation Summary

Scenario	UHI Accuracy	TFS Error	ECS Error	Anomaly Detection
V1 — Megacity (congestion)	±4.1%	±3.7%	±3.8%	92.3%
V2 — Industrial city (energy deficit)	±3.9%	±4.1%	±3.4%	90.8%
V3 — Smart city testbed	±4.6%	±3.4%	±3.6%	91.7%
MEAN	±4.2%	±3.73%	±3.6%	91.6%

👤 Author

🏙️

Samir Baladi

Principal Investigator — Urban Human Systems Intelligence

Samir Baladi is an interdisciplinary researcher at the intersection of urban systems intelligence, computational modeling, and AI-assisted optimization for urban infrastructure. Affiliated with the Ronin Institute and the Rite of Renaissance research program.

CITYMIND is the first project in the CITY-INTEL series (CITY-INTEL-01), applying independent subsystem governance principles to urban health assessment with AI strictly bounded to enhancement and optimization.

📧 gitdeeper@gmail.com 🔗 ORCID: 0009-0003-8903-0029 🐙 GitHub 🦊 GitLab

📝 Citation

@software{baladi2026citymind, author = {Samir Baladi}, title = {CITYMIND: Urban Human Systems Intelligence Framework — Independent Subsystem Modeling with AI-Enhanced Aggregation}, year = {2026}, version = {1.0.0}, publisher = {Zenodo}, doi = {10.5281/zenodo.20444647}, url = {https://doi.org/10.5281/zenodo.20444647}, note = {CITY-INTEL-01, Urban Intelligence} }

"A city is not a single unified system — it is a collection of independent subsystems whose interaction must be governed through aggregation, not structural merging. CITYMIND treats each urban subsystem as analytically separate, applying AI only as a bounded optimization layer for the final composite indicator." — CITYMIND v1.0.0

CITYMIND Documentation