Computational analysis of graphic design visual elements and their practical utility in rural planning
Online veröffentlicht: 21. März 2025
Eingereicht: 06. Okt. 2024
Akzeptiert: 02. Feb. 2025
DOI: https://doi.org/10.2478/amns-2025-0639
Schlüsselwörter
© 2025 Yuqing Xia et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
In recent years, rural revitalization has become an important direction of China’s national development strategy. The application of graphic design in rural planning can not only help the countryside to attract more tourists and investors, but also publicize the rural landscape and enhance the self-confidence of rural culture [1-2].
First of all, graphic design can help villages attract more tourists and investors. By designing beautiful promotional posters, brochures and tourist guides, we can show the characteristics and advantages of the countryside [3-4]. These promotional materials can contain beautiful photos, detailed descriptions, and attractive activity arrangements to make people interested in the countryside [5-7]. In addition, rural tourism websites and APPs can be designed to provide convenient booking and navigation functions to attract more tourists to explore the beauty of the countryside. These rich and diversified design means can effectively promote the development of rural tourism and drive the prosperity of rural economy [8-11]. Secondly, graphic design can publicize the rural landscape and enhance the self-confidence of rural culture. The countryside has a unique history, tradition and culture, graphic design can help the countryside to show its characteristics to more people [12-14]. We can design posters and exhibition boards for rural cultural exhibitions to show the audience the special products, traditional handicrafts and local cultural activities of the countryside in a vivid and illustrated way [15-17]. At the same time, we can also design village brand logo and image spokesman to create a unique image of the village symbols, to establish the brand image of rural culture [18-19]. Through these publicity and promotion, the cultural value of the countryside has been better inherited and developed. In addition, the contribution of graphic design in rural revitalization is also manifested in stimulating the vitality of rural entrepreneurship and innovation [20-21].
The application of graphic design in planning and development has always been a hot topic, and this study starts from the perspective of visual elements to study the information transfer efficiency of visual elements in depth, and in this way to further improve the effect of design and planning. In the theoretical research part, this paper first constructs the semantic dimension of visual elements from the perspective of semiotics, discusses the model theory of the information transfer efficiency of visual elements, and calculates and analyzes its information dissemination efficiency through data envelopment analysis (DEA). On this basis, this paper combines experimental data to study the information acquisition, heart rate variability and supervisor cognitive preference of visual elements. Based on the results of experimental research, this paper proposes a realization path to emphasize the role of visual guidance by fully considering the practical utility of graphic design in rural planning.
Symbols are the basic building blocks of language and an important tool for human communication and exchange, through which people express their thoughts, convey information, and establish complex social links and cultural traditions. Symbols are also important carriers and expressions of culture, reflecting the values, beliefs, and customs of different societies and groups.
Visual elements belong to the category of semiotics, which refers to iconic elements that are representative and symbolic in a visual sense. Semiotics of visual elements is a theoretical design research method that starts from the study of semiotic elements. Based on semiotics, it is an in-depth study of the content, nature, and characteristics of symbols, and an exploration of the concept of visual elementalization in contemporary graphic design.
Design is an activity that takes conceptualization as a prerequisite for production, and then gradually produces the visual representation of the designed object, so that the production activity can be carried out continuously according to the purpose of human needs, which is the historical root of the design activity.
Graphic design as an independent sector, dependent on the production of craft production after the production of batch, mass production demand model, design and industrialization is closely related to the background of the continuous development of the gradual development of the independent, the range of visual elements and symbols of the existence of a close relationship. Graphic design is both practical and aesthetic, and its design products contain cognitive and aesthetic functions, the cognitive function is to prompt people to understand and grasp the use, content and meaning of the product, and the aesthetic function has the role of evoking consumers’ emotional experience and self-identification. This study takes the theory of design semiotics as its basis and provides a new way of observing and thinking about graphic design from a new perspective.
The logic of visual element dimensions based on semiotics is shown in Figure 1. By analyzing the four dimensions of semantics, semantics, semantics, semantics and context involved in the design research, the visual elements under the symbolic perspective in graphic design can be analyzed in a more comprehensive way, and it also provides a certain support for designers to build a clearer logical framework when designing.
The semantic dimension is mainly the level of “energy” and “reference” of symbols, and the study of the semantic dimension needs to convey the connotation level through the visual effect of the extension part. The pragmatic dimension pays more attention to the use and role of symbols, focusing on the role and effect of symbols in the actual use of the design. The main consideration is the relationship between the product and the consumer, whose different thinking and aesthetic preferences influence their choice, and whether the design can meet the user’s needs and expectations. The design semiotics of the construction dimension mainly studies the combination form and compositional relationship between the components within the symbol, i.e., focusing on how the design elements interact with each other during the design process to form a visual language with deeper meaning and purpose. The construction of contextual dimension is related to the principle of “symbolic arbitrariness”, which refers to the fact that the differences in the era, society and culture of consumers make the visual elements in the design symbols in a differentiated context, and the visual symbols need a more specific context to be able to judge the symbols. From the dimension of context, it is more concerned about the performance and effect of different modes and aesthetic forms presented by visual design in specific scenes and environments, which is crucial to ensure that the information conveyed by the design can be accepted and interpreted by the target audience, and the construction of context involves cultural, social, psychological and media aspects.
The dimensional logic of visual elements based on semiotics
The term efficiency originally referred to the ratio of inputs to outputs, i.e., the amount of work that can be accomplished in a given amount of time. In data visualization, information delivery efficiency refers to the ability to deliver data information to the user quickly and accurately. The proportional relationship between the efficiency of information transfer and the input elements of a visualization chart is reflected in this. In practical application, the efficiency of information transfer not only depends on the design and production of the graphic itself, but also relates to the user’s acceptance ability, use experience, and other factors. Therefore, it is necessary to consider not only the aesthetics and legibility of the charts, but also the user’s needs and use scenarios, in order to improve the information transfer efficiency of visual elements in graphic design.
The selection of visual elements based on information transfer efficiency is shown in Fig. 2, and the information transfer of visual elements in graphic design is divided into three categories, namely, information descriptive visual elements and attributes, information display visual elements and attributes, and information interactive visual elements and attributes. According to the above analysis for perspective elements based on semiotics, the study of the above three categories of visual elements is refined, and the potential elements in each category that have the greatest impact on the efficiency of information transfer are mainly studied, and they are used as inputs for the study of the efficiency model.
Visual element selection based on information delivery efficiency
In this paper, we use Data Envelopment Analysis (DEA), a nonparametric linear programming model that does not depend on any particular functional form and is able to take into account the interactions between multiple input and output factors. This makes it possible to evaluate and analyze data more accurately. In addition, the DEA model can calculate efficiency values separately for each assessment object, which makes it more relevant. In this paper, the input indicator is set as S, and the output indicator is set as Y. s1 ~ s12 stand for quantity, color, density, size, orientation, time, arrangement, structure, filtering, selection, calculation, and comparison, respectively. y1 ~ y4 denote efficiency, ease of use, extensibility, and interactivity. A score of 1 ~ 7 is assigned to each indicator, which is transformed into a quantitative variable based on the knowledge of probability theory and mathematical statistics and the specific data situation. Then the information transfer efficiency of visual elements in graphic design is calculated through DEA data envelopment, and the transfer efficiency of each index is shown in Table 1. It can be seen that in the comparison of all input indicators, the transfer efficiency of each indicator is between 0.980 and 1.000, and the information transfer efficiency of several input indicators is 0.986, which makes it impossible to make an accurate comparison. Therefore, all data points are split according to whether input redundancy values and output deficiency values occur, and the information transfer efficiency of input indicators is mainly studied when input redundancy values and output deficiency values occur. Among them, the transfer efficiency value of S7 indicator is 0.889, which is located in the first place. Meanwhile, comparing without splitting before, it is found that their rankings are basically identical, only for the visual elements in which the efficiency is close to indistinguishable, by increasing the differentiation, it is possible to understand their information transfer differences.
Transmission efficiency of each input index
| Input index | Transfer efficiency | Efficiency sort | ||
|---|---|---|---|---|
| Index number | Index name | Primary efficiency | Redundant value and insufficient time | |
| S1 | Quantity | 0.993 | 0.874 | 2 |
| S2 | Color | 0.992 | 0.855 | 3 |
| S3 | Density | 0.986 | 0.774 | 8 |
| S4 | Dimension | 0.990 | 0.828 | 4 |
| S5 | Bearing | 0.986 | 0.772 | 9 |
| S6 | Time | 0.988 | 0.799 | 6 |
| S7 | Layout | 0.994 | 0.889 | 1 |
| S8 | Structure | 0.989 | 0.809 | 5 |
| S9 | Filtration | 0.986 | 0.775 | 7 |
| S10 | Select | 0.986 | 0.766 | 10 |
| S11 | Calculate | 0.984 | 0.739 | 11 |
| S12 | Comparison | 0.981 | 0.704 | 12 |
By expanding the table of comparative information transfer efficiency of graphic design visual elements, the comparative transfer efficiency of visual elements is shown in Table 2. By expanding the information transfer efficiency of eight categories of visual elements in graphic visual elements, including geometric images, word diagrams, calendar diagrams, bar diagrams, gradient diagrams, line diagrams, relationship diagrams, and bubble diagrams, the information transfer efficiencies of the different visual elements, as well as their rankings, can be clearly seen. Through DEA analysis, the information transfer efficiency of different visual elements was calculated, in which geometric images ranked first with a combined efficiency of 0.993. The word map ranked the lowest with a combined efficiency of 0.947.
Comparison of transmission efficiency of visual elements
| Element | Integrated efficiency | Ranking | Technical efficiency | Ranking | Scale efficiency | Ranking |
|---|---|---|---|---|---|---|
| Calendar chart | 0.964 | 6 | 0.986 | 5 | 0.959 | 6 |
| Word map | 0.947 | 8 | 0.987 | 2 | 0.942 | 8 |
| Bar chart | 0.970 | 4 | 0.989 | 1 | 0.962 | 5 |
| Geometric figure | 0.993 | 1 | 0.987 | 2 | 0.989 | 1 |
| Gradient diagram | 0.968 | 5 | 0.987 | 2 | 0.963 | 4 |
| Line diagram | 0.988 | 3 | 0.986 | 5 | 0.985 | 3 |
| Bubble chart | 0.963 | 7 | 0.985 | 7 | 0.957 | 7 |
| Relational diagram | 0.989 | 2 | 0.985 | 7 | 0.986 | 2 |
Taken together, the information descriptive visual elements, information display visual elements, and information interactive visual elements are further divided into 12 input indicators, and according to the calculation, the average information transfer efficiency of the information descriptive visual elements is 0.81, the average information transfer efficiency of the information display visual elements is 0.80, and the average ranking is the average information transfer efficiency of the information interactive visual elements is 0.73 , so it can be seen that the information transfer efficiency is descriptive visual elements > display visual elements > interactive visual elements.
In order to verify the regular relationship between visual elements and users’ subconscious cognition and emotions, this paper, through user research, selects the two visual elements that users like most and dislike least in the research results, i.e., gradient effect element and shadow effect element, and conducts in-depth experimental investigation to analyze their influence on users’ subconscious cognition and emotions.
This experiment consists of two parts. The first part is an objective experiment. The main purpose is to study the relationship between gradient elements, shadow elements, and the human subconscious. There is a subjective preference experiment in the second part. The experiment was controlled by the product MacBook Air workstation, the heart rate variability data detection equipment was HIPEETE-5100Y-C non-invasive ambulatory electrocardiographic recorder, and the data analysis platform was HIPEE ambulatory electrocardiographic data analysis platform, which conformed to the experimental requirements for data accuracy. A total of 40 subjects were chosen for this study, ranging in age from 18 to 40 years old.
The results of the correlation test between the visual elements and the efficiency of information acquisition are shown in Table 3, and the data were subjected to Spearman’s correlation calculation by SPSS with a confidence level set at 95%. The significance between the gradient elements and the acquisition efficiency of graphical information is 0.001 (p<0.05) and the significance between the gradient elements and the acquisition of textual information is 0.895 (p>0.05). The significance between shaded elements and acquisition of graphical information is 0.023 (p<0.05) and between shaded elements and acquisition of textual information is 0.95 (p>0.05).
The correlation test results of visual elements and information acquisition efficiency
| Gradient element | Match sample T test | |
|---|---|---|
| Information category | Text | Icon |
| Correlation coefficient | 0.025 | -0.502** |
| Significance | 0.895 | 0.001 |
| Shadow element | Match sample T test | |
| Information category | Text | Icon |
| Correlation coefficient | 0.023 | 0.368* |
| Significance | 0.095 | 0.023 |
| Spinman Correlation test | ||
| Visual element | Gradation | Shadow |
| Correlation coefficient | -0.495** | 0.365* |
| Significance | 0.001 | 0.025 |
To ensure that the data is avoided to be affected by the normal distribution factor, the Spearman correlation was again calculated using SPSS on the data of the efficiency of access to visual elements and iconographic information, and the significance between the gradient elements and the efficiency of access to graphical information was 0.001 (p<0.05), with a correlation. The significance between the shadow elements and the acquisition of graphical information is 0.025 (p<0.05) with correlation. Because of the gradient of the original color of the P value is too small, again with the standard difference Effect Size value test indicator Cohen’s d value of the calibration of the validity of the calculation can be obtained d = 3.492, r = 0.868. Prove that the P-value is valid. Shadow elements and gradient elements have statistically significant effects on the efficiency of acquiring graphical information. Shadow elements and gradient elements have no effect on the efficiency of acquiring textual information, which is not statistically significant.
Heart rate variability indexes include PNN50, RMSSD, SDSD, SDSD, HRV triangular index, PNN50 reflects the tension of the vagus nerve, SDNN is the total change of heart rate variability, which reflects the stress function of the autonomic nervous system, RMSSD reflects the regulation of the vagus nerve, and SDSD reflects the combined regulation of sympathetic and parasympathetic activities, HRV The variance of the HRV triangular index decreases with increasing levels of sadness.
The results of the HRV data are shown in Table 4. From the table, it can be roughly seen that different visual elements stimulated the subjects’ HRV indices of heart rate variability. All indices (except SDNN) of the gradient elements were the highest, all indices (except SDNN) of the shadow elements were the lowest, and all indices (except SDNN) of the no-visual elements were in the middle.
Heart rate variability data results
| PNN50 | RMSSD | SDSD | SDNN | Delta index | |
|---|---|---|---|---|---|
| Ordinary | 3.752±11.252% | 21.676±19.335 | 12.831±9.177 | 43.202±56.438 | 9.123±5.357 |
| Gradation | 5.943%±9.067% | 25.066±15.943 | 15.033±11.979 | 38.733±70.396 | 9.319±6.505 |
| Shadow | 1.444%±4.563% | 18.725±10.723 | 10.924±7.084 | 40.153±39.635 | 8.561±5.814 |
The statistical significance of the data was analyzed using the paired samples t-test. Pearson correlation analysis was done to analyze the statistical results of the gradient group and the normal group, and the shadow group and the normal group, and the correlation test between the visual elements and HRV is shown in Table 5, the correlation between the presence or absence of gradient elemental background and the PNN50, RMSSD and SDSD data is 0.348, 0.360, -0.235, and p is less than 0.05, and the above three data present a strong correlation, which is statistically significant. The correlation with the HRV delta index data was -0.023 (p>0.05), which was not statistically significant. The presence or absence of shadow element background presented a weak correlation with all three data, PNN50, RMSSD and SDSD, with statistical significance. Meanwhile, the correlation with the HRV delta index data was -0.005 (p>0.05), which was not statistically significant. Therefore, the results of the statistical analysis of the experimental data can be concluded that the different visual element backgrounds had an effect on the HRV of the subjects’ heart rate variability.
The correlation test of visual elements and HRV
| Information content | Correlation dataCorrelation coefficient | HRV variable | ||||
|---|---|---|---|---|---|---|
| PNN50 | RMSSD | SDSD | SDNN | Delta index | ||
| Gradient element | Correlation coefficient | 0.348 | 0.360 | -0.235 | 0.241 | -0.023 |
| Significance | 0.009 | 0.015 | 0.020 | 0.020 | 0.078 | |
| Shadow element | Correlation coefficient | -0.020 | -0.019 | -0.075 | -0.075 | -0.005 |
| Significance | 0.001 | 0.015 | 0.015 | 0.013 | 0.320 | |
The SD data were analyzed using a paired-samples t-test, and the results of the correlation test between visual elements and directions are shown in Table 6. The correlation between the background of the gradient elements and the left and right directions of the gradient elements is 0.670 (p=0.500, p>0.05), and the correlation with the up and down directions is 2.541, (p<0.05), which is statistically significant. The correlation between the background of the shadow element and the upper left and lower right directions of the shadow element is -0.421 (p<0.05), which is statistically significant. The correlation between the background of the shadow element and the left-right direction of the shadow element is 0.001 (p>0.05), which is not statistically significant.
The correlation test results of visual elements and directions
| Pairing | Mean | Standard deviation | Standard error | Upper and lower limit | T | Freedom | Sig. | ||
|---|---|---|---|---|---|---|---|---|---|
| Upper | Lower | ||||||||
| Gradient element | Left and right | 0.117 | 1.304 | 0.169 | -0.220 | 0.454 | 0.670 | 60 | 0.500 |
| Up and down | 0.350 | 1.071 | 0.139 | 0.075 | 0.657 | 2.541 | 60 | 0.015 | |
| Shadow element | Upper left - lower right | -0.704 | 1.040 | 0.142 | -0.988 | -0.421 | -4.976 | 54 | 0.001 |
| Left and right | 0.000 | 1.441 | 0.201 | -0.394 | -0.394 | 0.001 | 54 | 0.095 | |
On the whole, when subjects watched the experimental materials with gradient elements as the background, the RMSSD index increased significantly, which proved that the gradient elements could trigger positive emotions, and the PNN50 index increased significantly, which proved that the gradient elements could inhibit the subjects’ depression, and the SDSD index was slightly lower than that of the reference group, which proved that the gradient elements could appropriately reduce the mental pressure that the subjects were subjected to. Meanwhile, from the trend of SDNN, it can be seen that the fatigue level of the subjects is the highest when they watch the gradient elements, which indicates that the gradient elements, although they can trigger the positive emotions of people, do not reduce the cognitive fatigue of people. In terms of subjective preference for gradient direction, users’ preference for left and right directions is not obvious. Instead, users prefer light on top, dark on bottom, or dark on top, light on bottom.
This paper explores the changes in users’ subconscious cognition and emotions under the influence of visual elements, as well as their subjective cognitive preferences for visual elements. Through the experimental conclusions, it lays the foundation for the subsequent realization and application of graphic design visual elements in rural planning.
In practice, social construction activities are becoming more and more systematic, organized, professional and industrialized, and the design activities are also constantly developing to intensive and large-scale, and the combination with urban problems, environmental problems and social problems is getting closer and closer, and architecture is gradually becoming a cross-discipline. In the development of rural planning, in order to adapt to the development of society, strengthen the legal and social responsibility of rural construction, and improve the quality of rural planning and design is an important issue in the development of rural planning. This paper comprehensively explores the application path and value of its visual elements in rural planning from the perspective of graphic design.
In graphic design, “attention” is enhanced by separating individual forms from the group, and “attention” is diminished by hiding individual forms from the group. Essentially, the intensification or diminution of visual attention is the planning of hierarchical and morphological contrasts between individuals and groups. Although the whole picture consists of several larger groups, it is common to intensify the attention of only one group while weakening the others to enhance the wholeness and attractiveness of the work. These hierarchical relationships create a hierarchical order that gives the work both visual simplicity and rich content.
Visual motion refers to the trajectory of visual attention at the point of attention in a flat space. Since the viewing process consists of a number of visual attention points in a sequential order, works with disorganized visual dynamics are often confusing to the viewer. Designs that confuse the viewer and cause them to become annoyed are bound to be discarded due to the viewer’s boredom caused by the increased burden of reading fatigue. Therefore, it is particularly important to plan the visual flow.
This paper is based on the rural planning perspective of graphic design visual elements application path shown in Figure 3, graphic design visual elements in rural planning should emphasize the role of its visual guidance, combined with the previous analysis of the impact of visual on cognitive subconsciousness can be seen, different visual elements can affect people’s cognitive subconscious. Therefore, in the development of rural planning, the visual elements of graphic design should be combined with the needs of planning and development to provide positive guidance and motivation for the development of rural planning. In addition, based on the calculation of the information dissemination efficiency of visual elements, it is necessary to comprehensively consider the dissemination efficiency of visual elements in graphic design to provide practical planning and design solutions.
The plane design the visual element application path
This paper explores the use of graphic design visual elements in rural planning, based on semiotics, cognitive psychology, subconscious cognition, emotional regulation, and other aspects. Combined with the existing theoretical and experimental research results, it presents a realization path that emphasizes the role of visual guidance.
In this paper, the information transfer of visual elements in graphic design is divided into three categories: information descriptive visual elements and attributes, information display visual elements and attributes, and information interactive visual elements and attributes, and the research on the three categories of visual elements is refined as input research for the efficiency model. The information transfer efficiency of different visual elements was calculated by expanding the table of comparison of information transfer efficiency of graphic design visual elements, in which geometric images ranked first with a comprehensive efficiency of 0.993. The word map ranked the lowest, with a comprehensive efficiency of 0.947. The 12 index inputs of information descriptive visual elements, information display visual elements, and information interactive visual elements were divided into 12 index inputs, and the order of information transfer efficiency according to the calculation was descriptive visual elements>display visual elements>interactive visual elements.
In order to study the influence of visual elements on cognitive subconsciousness, this study designs experimental research from information acquisition efficiency, psychological variability and intuitive cognitive preference. According to the analysis of the experimental data results, it can be seen that shadow elements and gradient elements have an effect on the acquisition efficiency of graphic information, and shadow elements and gradient elements have no effect on the acquisition efficiency of text information. Meanwhile, different visual element backgrounds have an effect on the subjects’ HRV of heart rate variability. And the users’ preference for the left and right directions of the gradient visual elements is not obvious, and the users prefer light on top and dark on bottom, and dark on top and light on bottom.
1) 2022 Anhui Provincial Research Preparation Program Project--Key projects (Project No. 2022AH050304).
2) Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes (Anhui University of Technology) (Project ID: CS2022-02).
3) Guangdong Province 2023 Education Science Planning Project (Higher Education Project) (Project ID: 2023GXJK508).