Spatiotemporal influence analysis model for cultural landscapes based on a virtual geographic environment
Abstract A cultural landscape can have a lasting impact on the surrounding region, significantly influencing its culture, customs, and development patterns. It is therefore important to understand the spatiotemporal influence range of a particular cultural landscape. The scope of influence is affect...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-97171-4 |
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| Summary: | Abstract A cultural landscape can have a lasting impact on the surrounding region, significantly influencing its culture, customs, and development patterns. It is therefore important to understand the spatiotemporal influence range of a particular cultural landscape. The scope of influence is affected by subjective factors regarding the views, hobbies, communication, and cognitive abilities of all individuals involved in landscape interaction. These factors are difficult to account for using the learning process of traditional methods. To address this problem, we propose a spatiotemporal influence analysis model for cultural landscapes based on a virtual geographic environment (SC-VGE). A virtual geographic environment is constructed to interactively recognize the relationships between individuals and cultural landscapes, forming key parameters of the cultural landscape influence propagation process under various conditions. On this basis, we construct influence propagation agents representing different groups of people. Based on the movement process of the agents in the virtual space, we can determine the spatiotemporal influence range of a specific landscape or landscape object. In experiments, we analyzed two typical cultural landscape examples in the city of Changchun. We conducted a comparative analysis of our approach against several traditional methods, including the Spatial Straight-Line Distance, Spatial Accessible Distance, Cellular Automata, and Multiagent. The SC-VGE method surpassed these traditional methods in overall prediction accuracy, achieving 89.7 ± 1.3% and 87.8 ± 1.5%. This is more than 9% higher than the highest accuracy among the traditional methods. Higher accuracy enables SC-VGE to more precisely reflect the influence ranges of cultural landscapes, thereby providing better support for the management and analysis of cultural landscape value. |
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| ISSN: | 2045-2322 |