AI-POWERED URBAN PLANNING ANALYSIS

Quantifying the Market Premium of Green Spaces

The economic value of Urban Green Spaces (UGS) is a significant driver of urban development. Our analysis in Vračar demonstrates a strong correlation between UGS and housing prices. Specifically, proximity to urban parks (x6) shows a strong negative correlation with housing prices – meaning shorter distances to parks increase property value. Conversely, the density of urban pockets (x8) and urban gardens (x9) exhibit a strong positive correlation, indicating that higher densities of these green amenities lead to increased housing prices.

Properties located in mixed-use green areas, particularly those close to urban parks and within walkable urban zones, command the highest economic value. This reflects a growing market demand for a high-quality urban lifestyle that prioritizes access to nature and amenities. It underscores that UGS are not just ecological assets but also significant economic contributors, with their value shaped by local regulatory histories and cultural narratives that define desirable urban assets.

Fostering Community & Well-being through UGS

The social value of Urban Green Spaces (UGS) extends beyond mere aesthetics, encompassing community cohesion, emotional connection to place, and public health benefits. Our model quantifies social value based on the density of parks (Dp), urban pockets (Dup), urban gardens (Dug), and local population (Pop) using a multiplicative equation: SO = Dp × Dup × Dug × Pop. This formulation highlights that the combined presence of multiple UGS types creates a synergistic social benefit.

In Vračar, the northeastern and central-western districts exhibit high social value due to a balance of green spaces and vibrant urban fabric. Conversely, southern and southeastern areas show lower social valuation, indicating a deficit in socio-environmental amenities. The analysis suggests that building new green spaces in areas with poor access to existing parks can significantly boost social value. This reinforces the idea that micro-UGS function as vital hubs within social networks, transcending mere spatial measurements to foster community formation and identity.

The UrbanSAT Platform: A Hybrid Analytical Pipeline

Our integrated methodology, deployed via the UrbanSAT platform, combines Geographic Information Systems (GIS), the Analytic Hierarchy Process (AHP), and an Artificial Intelligence-Based Genetic Algorithm (AI-GA) to optimize UGS planning.

• Data Ingestion & Processing: Geospatial data (UGS, POIs, population, housing prices) are collected, cleaned, normalized, and integrated into a spatial database.
• AHP Score Engine: Domain experts use AHP for pair-wise comparisons, assigning consistent weights to sustainability criteria (e.g., UGS density, accessibility, housing quality, amenities). These weights calculate a holistic sustainability score.
• Hybrid Prediction & Optimization Engine: A machine learning model (AI) predicts housing price impact for proposed UGS configurations. A Genetic Algorithm (GA) then optimizes UGS placement by maximizing a fitness function that balances both the AHP sustainability score and the predicted property value increase.
• Output & Evaluation: The platform generates multiple planning scenarios (e.g., Equity-Focused, Economic Growth, Connectivity) for expert panel and multi-stakeholder review, ensuring optimal plans balance algorithmic efficiency with practical knowledge and societal goals.

This systematic approach provides an evidence-based, transparent framework for urban planners to make informed decisions about UGS allocation.