Abstract:

[Purpose/Significance] As China's low-altitude economy transitions from pilot experimentation to large-scale deployment, city governments are increasingly confronted with intelligence challenges rather than mere information shortages. Development signals are scattered across heterogeneous sources-enterprise activities, patents and R&D outputs, infrastructure readiness, investment dynamics, and municipal policy documents - often with inconsistent definitions, update cycles, and measurement units. This fragmentation raises cognitive burden and decision uncertainty: policymakers may "know a lot" but still lack a structured understanding of urban development posture, making cross-city comparison, policy-tool matching, and pathway selection difficult. To address this gap, this study re-frames index construction from an information science perspective as a data-information-knowledge transformation process and develops an interpretable measurement tool to support urban situation assessment and policy reasoning in the early diffusion stage. [Method/Process] We propose a Low-altitude Economy City Development Index (LCDI) using the analytical boundary of three heterogeneous signal systems - industrial chain, technology chain and policy chain. The index operationalizes four interpretable dimensions: technological innovation vitality, market expansion potential, ecological coordination capability, and policy empowerment effectiveness. Multiple objective data sources are integrated and normalized to ensure cross-city comparability. Indicator weights are determined through expert judgment combined with the Analytic Hierarchy Process (AHP), translating perceived importance of signals into an explicit weighting structure. The empirical assessment covers 58 Chinese cities that have issued dedicated low-altitude economy policies and satisfy data availability and comparability requirements. Beyond computing composite scores and dimension profiles, Principal Component Analysis (PCA) is used as a structural representation test: it examines whether the four-dimensional signal system can be stably abstracted into a small set of dominant cognitive axes suitable for decision-oriented interpretation. Cities are further mapped into a two-axis space and categorized via a four-quadrant configuration to facilitate type recognition and mismatch diagnosis. Finally, a concise set of typical-city cases is employed for interpretive validation, checking whether index-implied structures can be meaningfully mapped to observable governance practices and implementation pathways. [Results/Conclusions] Results reveal a clear hierarchical gradient across cities. Leading cities tend to show coordinated advantages across multiple dimensions, indicating that urban low-altitude economy development depends on systemic coupling among technology, market, ecosystem coordination, and institutional supply rather than single-factor expansion. PCA suggests that urban development posture can be summarized along two dominant structural axes: an endogenous capability axis (driven by innovation, market expansion, and coordination) and an institutional empowerment axis (driven by policy and governance capacity). The four-quadrant typology highlights structural mismatches where capability accumulation and policy supply evolve asynchronously. While the study is constrained by data availability and the sector's early-stage diffusion, the LCDI provides a replicable, updatable, and interpretable intelligence tool for cross-city comparison, type-based diagnosis, and differentiated policy calibration, and it points to future work on dynamic monitoring and broader externality indicators.

Key words: low-altitude economy, new quality productive forces, indicator system, development trends, policy