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<p>The three-dimensional visualization evaluation of cultural landscape has practical needs and theoretical significance in today’s world. Based on the perspective of cultural and tourism integration, the study adopts the method of stepwise regression analysis to explore the factors influencing the satisfaction of cultural landscape with a historical and cultural neighborhood landscape as the research sample. On this basis, it discusses the 3D visualization modeling process of cultural landscapes. It realizes the 3D visualization construction of a historical and cultural park, uses a questionnaire survey to collect relevant data, combines the principal component analysis method and IPA model to evaluate its visualization results, and divides different elemental functions. As a result, nine influencing factors with significant impact on the satisfaction of cultural landscape were obtained, with architectural style, traffic accessibility, and landscape scope being the most prominent, and the regression coefficients were 0.589, 0.435, and 0.367, respectively. The key performance elements of the three-dimensional visualization of the cultural landscape need to be improved, such as the reasonableness of the historical inference, the applicability of the technology, the sense of place, the sense of reality, the sense of aesthetics, the interactivity, and so on. The research presented in this paper can offer insights and lessons for the future design of cultural landscapes and their 3D visualization process.</p>
</abstract>

The three-dimensional visualization evaluation of cultural landscape has practical needs and theoretical significance in today’s world. Based on the perspective of cultural and tourism integration, the study adopts the method of stepwise regression analysis to explore the factors influencing the satisfaction of cultural landscape with a historical and cultural neighborhood landscape as the research sample. On this basis, it discusses the 3D visualization modeling process of cultural landscapes. It realizes the 3D visualization construction of a historical and cultural park, uses a questionnaire survey to collect relevant data, combines the principal component analysis method and IPA model to evaluate its visualization results, and divides different elemental functions. As a result, nine influencing factors with significant impact on the satisfaction of cultural landscape were obtained, with architectural style, traffic accessibility, and landscape scope being the most prominent, and the regression coefficients were 0.589, 0.435, and 0.367, respectively. The key performance elements of the three-dimensional visualization of the cultural landscape need to be improved, such as the reasonableness of the historical inference, the applicability of the technology, the sense of place, the sense of reality, the sense of aesthetics, the interactivity, and so on. The research presented in this paper can offer insights and lessons for the future design of cultural landscapes and their 3D visualization process.

Three-dimensional Visualisation of Cultural Landscape under the Perspective of Culture and Tourism Integration

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The three-dimensional visualization evaluation of cultural landscape has practical needs and theoretical significance in today’s world. Based on the...

Three-dimensional Visualisation of Cultural Landscape under the Perspective of Culture and Tourism Integration

Publié en ligne: 10 juin 2024

Pages: -

Reçu: 11 févr. 2024

Accepté: 23 avr. 2024

DOI: https://doi.org/10.2478/amns-2024-1466

Mots clés
Stepwise regression analysis, Influential factors, Principal component analysis, IPA model, 3D visualization, Cultural landscape

© 2024 Yanlin Lai, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Three-dimensional Visualisation of Cultural Landscape under the Perspective of Culture and Tourism Integration

Publié en ligne: 10 juin 2024

Pages: -

Reçu: 11 févr. 2024

Accepté: 23 avr. 2024

DOI: https://doi.org/10.2478/amns-2024-1466

Mots clésStepwise regression analysis, Influential factors, Principal component analysis, IPA model, 3D visualization, Cultural landscape

© 2024 Yanlin Lai, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Stepwise regression analysis, Influential factors, Principal component analysis, IPA model, 3D visualization, Cultural landscape