## 4 Discussion and Conclusions
For cultural landscapes, as the distance of view decreases, the opportunity for involvement increases by increasing the sense of enclosure [67], leading to a close look at visual elements such as decorations and texture. As for natural landscapes, a far distance between the viewer and features tends to lower the complexity and increase the preference by making the viewer grasp notable patterns of an order [65]. Since the close-up view is the main factor for preference, a richness in different levels of view has not been able to demonstrate that cultural landscapes with balanced close-to-far depths of view are more popular. * The presented results enabled us to answer the two main research questions. In accordance with the trajectory data analysis, viewers tend to interact with the cultural landscapes along the main roads, particularly at the Old Dormitory. Density and diversity of the camera angles, depth of view, and the color of each scenic spot were selected as indicators for exploring the visual quality. In reference to the findings, observers have preferences for cultural landscapes with diverse viewing angles, high values of saturation, and the standard deviation of hue. The distance-to-height ratio of less than 1 indicates the close distance which can most arouse the viewer's interest in taking photographs. Based on these findings, government, urban planners, and landscape designers can manage cultural landscape and enhance the attractiveness of tourism destinations precisely. By increasing the complexity of buildings, integrating buildings into the environment, and maintaining a proper distance-to-height ratio, cultural landscapes such as historic designed landscapes and traditional villages might draw observers' attention and attract them to stay for longer and take photographs. The second contribution of this study refers to linking systematic theories to visual indicator analysis based on computer vision technology. The methodology demonstrated in this paper adds to a growing body of evidence for visual indicator application based on content analysis of social media photographs. The prior studies applying crowdsourced geodata on social media platforms in landscape perception analysis mainly focus on coding and scene recognition [46; 47; 48; 50; 78], which is highly efficient in evaluating public preferences and opinions by automatic computer programs. However, most of the prior studies have been unable to link mature landscape theories to emerging methodologies. Using [PERSON] et al. 's theory-based framework, this research assesses the feasibility of semi-automatic photo content analysis combined with the MATLAB algorithm and manual recognition. The third contribution of this paper is that the fixed-point photography experiment provides an approach to measuring the depth of view related to the classical way. The planimetric and panoramic simulations are used widely to quantify the dimensions of views in recent times [79], while photographs and field observations are traditional ways [57; 66; 68]. We developed the photographic method into a more precise measurement with the concept of distance-to-height ratio, which is more appropriate to determine the visual scale at a local scale compared with the simulations, especially in relation to specific buildings' visual elements. Though this essay has made contributions concerning big data, computational techniques and visual indicators, limitations exist in three aspects. First, as the present study had a quite experimental character, the findings might not be suitable for extrapolation to cultural landscapes at a large scale. Second, for different aims, certain social media platforms are used more than others in particular areas [80]. [PERSON] et al. [46] suggest a similar finding that Flickr hosts more photographs representing CES compared to Panoramio. In China, other social media platforms with considerable shared photographs have emerged recently, such as the Douyin app, the Ctrip website, and other platforms.