Mapping Where and What People See: Comparing Public and Expert-based Scenic Urban Views in London = 경관관리체계에 기반한 계획된 조망행태와 실제 조망행태에 관한 연구: 런던 경관관리체계를 중심으로

다국어 초록 (Multilingual Abstract)

Scenic urban view management policies shape how people visually engage with the built environment, yet expert-led frameworks often overlook everyday public viewing practices. In London, the View Management Framework (LVMF) protects historic skylines through designated viewpoints, viewing corridors, and height controls, privileging heritage preservation while assuming stable and shared scenic preferences. This study introduces an integrated analytical framework to evaluate the spatial and temporal alignment between expert-designated and user-generated scenic practices from 2000 to 2024. Expert viewpoints and viewing objects defined by the LVMF are compared with a 25-year corpus of geotagged photographs from Geograph using Ripley’s Cross-K function and grid-based local density cluster analysis.
Results reveal that expert–user alignment is episodic rather than persistent, showing a pronounced but short-lived peak during the LVMF’s formalization period (2011–2013). Alignment is strongest at long-distance viewing ranges (250–500 m) around major landmarks such as St Paul’s Cathedral and Tower Bridge, where expert priorities converge with public photographic behavior. By contrast, short-distance scenes—including street art, bridges, markets, and events—rarely overlap with expert designations, indicating systematic divergence in scenic preference. These findings demonstrate the temporal fragility and scale dependence of expert–user scenic alignment and underscore the need for adaptive, data-informed view management policies that integrate panoramic visibility with everyday, human-scale experiences.

목차 (Table of Contents)

• LIST OF FIGURES
• LIST OF TABLES
• ABSTRACT IN ENGLISH
• 1. INTRODUCTION
• 2. LITERATURE REVIEW
• 2.1. Scenic Urban View Management
• 2.2. Expert-based Approaches
• 2.2.1. Limitations of Expert-based Approaches
• 2.3. User-based Approaches
• 2.3.1. Unresolved Issues in User-based Scenic Analysis
• 2.4. Debate Between Expert-based and User-based Approaches
• 3. RESEARCH GAP & RESEARCH QUESTIONS
• 4. METHODS
• 4.1. Analytical Framework
• 4.2. Variables
• 4.3. Viewpoint and Viewing Object Alignment: A Cross-K Function Approach
• 4.4. Grid-based Local Density Cluster Analysis for Scenic Viewpoint–Viewing Object Alignment
• 4.5. Data
• 5. RESULTS
• 5.1. Expert-based Viewpoints and Viewing Objects
• 5.2. User-based Viewpoints and Viewing Objects
• 5.2.1. Short, Medium-Range Scenic Clustering Along Riverfronts and Intersections
• 5.2.2. Viewing Object Rankings: Persistent Landmarks and Emerging Viewing Objects
• 5.3. Annual Clustering of Viewpoints and Viewing Objects
• 5.3.1. Yearly Changes from Landmarks to Everyday Visual Encounters
• 5.3.2. Long-Range to Short-Range Transition in Scenic Practices
• 5.3.3. Viewpoint Activation Associated with Spatial Interventions
• 5.3.4. Cross-River Framing
• 5.3.5. Structural Duality of Bridges
• 5.3.6. Cultural Scene Anchoring
• 5.4. Cross-K Function Results: Spatiotemporal Patterns of Expert–User Alignment
• 5.4.1. Distance-Based Alignment Analysis
• 5.4.2. Alignment Patterns of Viewing Objects (VO)
• 5.4.3. Alignment Patterns of Viewpoints (VP)
• 5.4.4. Distance-Dependent Alignment Patterns
• 6. DISCUSSION
• 6.1. Spatiotemporal Volatility of Expert–User Alignment
• 6.2. Divergence Between Expert- and User-Based Scenic Preferences
• 6.3. Sustained Expert–User Alignment Over Time
• 6.4. Policy Implications for Adaptive View Management
• 7. CONCLUSION
• REFERENCES
• ABSTRACT IN KOREAN