Geographical Scene: The Natural Unit for Geographical Analysis and Its Recognition Based on Data with Spatial and Semantic Features

Chen Ding, Jianbo Tang, Zhongan Tang, Min Deng, Wenpei Wang and Huimin Liu

Annals of the American Association of Geographers, 2025, vol. 115, issue 8, 1926-1954

Abstract: Geographical analysis often faces challenges in selecting appropriate analysis units due to spatial heterogeneity, autocorrelation, and the modifiable areal unit problem (MAUP). Traditional spatial partitioning or aggregation methods using grids, administrative zones, and traffic analysis zones rely heavily on spatial correlations while neglecting semantic relationships between geographical elements. This limitation hinders their ability to capture complex real-world patterns. To address this, we introduce a new framework that treats geographical scenes as fundamental analytical units and propose an automatic partitioning method for geographical scene identification by integrating both spatial proximity and multilevel semantic features of geographical entities. The method consists of five steps, namely measuring spatial relationships between entities, identifying representative entities, iteratively grouping units based on semantic similarity, applying topic modeling to infer scene categories, and optimizing geographical scene boundaries. Compared to conventional techniques, our framework improves semantic accuracy and result interpretability while effectively representing spatial and semantic diversity. The contributions of this article include a novel spatial-semantic integration method for geographical scene identification, uncovering hidden connections between geographical entities, and advancing the understanding of human–environment interactions. This study establishes a foundation for geographical scene analysis and offers practical insights for creating natural neighborhood units and reducing MAUP impacts in spatial studies.

Date: 2025
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DOI: 10.1080/24694452.2025.2511945

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