From data to decisions: A Need-Weighted optimization framework for evidence-based urban planning

Urban planners face significant challenges in aligning citizen priorities with impactful, budget-constrained policy decisions. A knowledge gap persists in how to mathematically integrate these subjective resident perceptions with objective resource constraints, particularly when the empirical impact of policy interventions is characterized by statistical uncertainty. This paper addresses this gap by introducing a novel Need-Weighted Cost-Benefit optimization framework that integrates empirical survey data with a 0–1 knapsack model to inform public investment. Our approach defines policy value by synthesizing its empirically-estimated impact on urban aspiration, implementation cost, and residents’ expressed needs. Applying this framework to Jakarta and Phnom Penh, we reveal distinct, context-specific development strategies: Jakarta’s optimal portfolio is dominated by Personal-Cohesion policies, while Phnom Penh’s model identifies a dual-track approach balancing economic empowerment with infrastructure. Beyond these specific case studies, the methodology has broader relevance for urban science. It provides a robust and replicable tool that supports proactive, evidence-based strategic planning by municipal governments, reducing reliance on reactive, department-specific budgeting and helping to strengthen collective urban aspiration.