Commercial color impact on traditional heritage features in Suzhou Shiquan historic district

Abstract

Modern commercial elements can invigorate historic districts but risk clashing with traditional aesthetics through bright, saturated colors. This study evaluates commercial color impacts in Suzhou’s Shiquan Street historic district using subjective and objective methods. Subjectively, Likert scale surveys assessed perceptions among non-local tourists, local students, and residents. Objectively, Mask2Former (VIT-based) segmented commercial elements, with color harmony evaluated via CIE2000 standards. The analysis identified 48 disharmonious color centers detrimental to historical authenticity and proposed traditional Chinese color codes as alternatives. Results revealed generational divides: younger groups (tourists/students) perceived negative colors less acutely, while older residents were more sensitive. The Mask2Former method proved effective for precise segmentation and color assessment, enabling informed optimization strategies. By integrating subjective and objective color evaluations, this research offers a framework to balance commercialization and heritage preservation in global historic districts, prioritizing color harmony to safeguard cultural identity.

Introduction

A proper introduction of commercial elements into historic districts can inject new vitality and development momentum into the areas with great cultural and historic values^1,2. The introduction of commercial activities not only helps to enhance economic prosperity and local fiscal revenue, but also promotes cultural communication and innovation, enhancing the overall vitality and quality of the region^3,4. The addition of commercial elements can enhance the tourist attraction and experience of the district, and stimulate the awareness and participation of residents and all parties in cultural protection^5,6. Therefore, with reasonable planning and design, a moderate introduction of modern commercial formats into historic districts is an important way to achieve their sustainable development^7,8. Although the integration of commercial elements into historic districts has brought about an increase in economic development and tourism vitality, it has also exposed some drawbacks that cannot be ignored⁹. Firstly, there is a tendency for inconsistency between modern commercial colors and traditional architectural colors^10,11. In order to attract more tourists and consumers, modern commercial elements often use bright colors with high saturation, which are often in strong contrast with the color tone and features of traditional buildings in historic districts¹². For example, the historical buildings in the historic districts of Jiangnan region (regions south of the Yangtze River) in China are usually dominated by low saturation colors such as gray, white, and brown, which reflect the cultural background and esthetic style of a specific historical period. The extensive involvement of modern commercial accessories, such as bright neon lights, billboards, and colorful decorations, has disrupted the original visual harmony, resulting in serious damage to the overall appearance of the historic district¹³. This lack of coordination not only deprives the district of its unique historical atmosphere, but may also cause visual pollution, leading to a gradual decline in the cultural charm and appeal of the districts¹⁴. Secondly, excessive commercialization has led to a gradual singularity and homogenization of the characteristics of historic districts^15,16. With the development of commercialization, many historic districts have gradually lost their original cultural features and regional characteristics, and become similar to other commercial districts¹⁷. The homogeneity of commercialization is reflected in the presence of a large number of replicated modern business models and brands, which weaken the cultural diversity and uniqueness of historic districts, and gradually make them all in the same pattern¹⁸. For example, some historic districts have been transformed into modern shopping centers or food streets, which may seem prosperous, but in reality conceal their original cultural connotations and historical stories, diluting the cultural value of the district¹⁹. This homogenization trend not only weakens the cultural depth and attractiveness of historic districts but also makes it difficult for tourists to experience the true local characteristics and historical features. Finally, the commercialized colors have a negative impact on social identity and historic cognition²⁰. Excessive commercialization has gradually made historic districts lose their function as carriers of local cultural memory²¹. The proliferation of modern commercial elements, especially those colors and decorations that do not conform to the local historical and cultural context, will weaken people’s understanding and sense of identity with the historical and cultural context of the districts²². As historic districts become increasingly like modern commercial areas rather than unique cultural heritage sites, people’s perception of their historic value weakens, which can even create a crisis of identity²³. The lack of sense of identity not only affects the local residents’ awareness of maintaining their cultural heritage, but may also weaken the interest and emotional connection of foreign tourists, thereby gradually making historic districts lose their meaning and function at the cultural and social levels²⁴. In summary, although incorporating commercial elements into historic districts can help boost economic vitality, it also brings many problems. Finding a balance between protecting historic features and cultural characteristics while pursuing commercial interests has become an important issue that urgently needs to be addressed in the development of historic districts.

The historic districts in Jiangnan region play an important role in the protection of Chinese historical and cultural heritage^25,26. They are not only a witness to the economic and social development of the Jiangnan region, but also an important carrier of the region’s rich historic architectural heritage. The long history of Jiangnan has nurtured numerous historic buildings with research value. However, in recent years, in order to revitalize these historic districts, the influx of a large number of commercial elements has caused damage to their colors and features. Regarding the conflict between the color features and commercial elements of the Jiangnan historic districts, current research mainly focuses on the analysis of the colors of individual buildings, lacking evaluation methods and color control strategies for the macro features of the entire district. Therefore, it is urgent to determine the correlation between commercial elements and the colors of historic districts, and further construct an evaluation and optimization strategy for the overall features, which will help coordinate the protection of historic buildings and their features, promote interactive exchanges among similar heritage sites, enhance the cultural characteristics of heritage sites, and provide a practical evaluation method and optimization strategy for other similar historic districts (Fig. 1 The image was generated based on artificial intelligence and has been further processed on this basis).

Fig. 1: The color features of historic districts are being impacted.

The image was generated based on artificial intelligence and has been further processed on this basis.

The accurate identification of color participles that affect the features is the foundation for optimizing the commercial element colors in historic districts²⁷. Therefore, the use of semantic segmentation technology and color space construction technology can fully identify historic districts and the color features of commercial elements^28,29. Traditional semantic segmentation techniques such as Segnet network³⁰, CNN network³¹, Deeplab network³² are difficult to accurately segment commercial elements attached to buildings. Therefore, in this study, with the main street of Shiquan Street in Suzhou as the research object, VIT semantic segmentation model³³ is used to identify the commercial elements attached to historic buildings within the historic district. At the same time, MATLAB technology is used to construct the color space of the research object, evaluate the color features of the district, and sort out positive and negative color factors. On this basis, this study aims to establish a scientific method to evaluate the harmony between the commercial element colors and traditional colors in historic districts, and to generate application strategies for commercialized colors to avoid visual pollution in historic districts.

This study addresses the following 4 main issues:

• (1) Is there a certain correlation between the traditional colors of the Shiquan Street historic district and the addition of commercial elements?

• (2) Are the commercial elements in various parts of Shiquan Street disconnected from the overall features of the historic district?

• (3) What colors of commercial elements have disrupted the overall features of Shiquan Street? What colors can commercial elements be applied to?

• (4) Does the subjective perception of commercial colors by the population align with the objective color analysis results?

The research on color in historic districts involves how to preserve and optimize the color esthetics of traditional districts during the process of modernization, particularly in the context of introducing and developing commercial elements. Early research primarily focused on the color protection of buildings, with relatively little attention given to the overall color scheme of the district. Zheng³⁴ studied the color evolution of traditional Chinese districts and proposed that the color of historic districts reflects not only the cultural and social background of a particular historical period but also carries significant esthetic and cultural symbolism. Deng³⁵ analyzed the color protection of scenic and historic cities, emphasizing the importance of color in the preservation of historic buildings and suggesting how traditional colors might be preserved within the context of modernization.

With the advancement of globalization and commercialization, more and more historic districts are facing conflicts between modern commercial elements and traditional architectural colors. For example, Zhu³⁶ studied the color changes in different historic districts and pointed out the color impact brought about by modern commercial activities, particularly the high-saturation commercial advertisements and signage, which create a strong contrast with the low-saturation tones of historic buildings, disrupting the visual and cultural unity of the district. In the Jiangnan region of China, Zhuang³⁷ highlighted that the color conflicts caused by commercialization are especially severe, particularly in historic districts in Suzhou and Hangzhou, where the introduction of modern commercial elements often leads to the disappearance of traditional color schemes.

Although these studies provide valuable references for understanding the protection of color in historic districts, the research on this topic is still relatively fragmented, and there is a lack of systematic research on the relationship between color harmony and the impact of commercialization. Therefore, balancing modern commercial needs with the harmonious preservation of traditional district colors remains an important issue that needs to be addressed.

Semantic segmentation technology, an important method in computer vision, has made significant progress in the field of image processing, especially in the analysis of street view images. Semantic segmentation aims to classify each pixel in an image, thus enabling precise segmentation of different elements within the image. In urban planning and architectural heritage preservation, semantic segmentation technology has been widely applied to automatically identify and classify buildings, roads, billboards, and other urban elements, greatly improving the efficiency and accuracy of image analysis.

Among the commonly used semantic segmentation models, Xu utilized U-Net, a classic convolutional neural network (CNN) model, employing an encoder-decoder structure and achieving good results in mural color restoration³⁸. Later, Xu proposed combining models like DeepLab, introducing dilated convolutions and pyramid pooling to enhance the multi-scale processing capability of building image segmentation, effectively addressing complex backgrounds and details³⁹.

With the introduction of the Vision Transformer (VIT) model, traditional CNNs have gradually been replaced. In tasks requiring more refined segmentation, such as elements attached to buildings like doors, windows, and shop signage, VIT self-attention mechanism is better suited to capture global information in the image, overcoming the limitations of CNN in modeling long-range dependencies, especially in complex street view images. Mask2Former, which combines the advantages of VIT, presents a new image segmentation approach that excels in both semantic segmentation and instance segmentation tasks. Mask2Former can efficiently and accurately segment complex street view images and identify different urban elements, providing more precise results than traditional methods. For example, Zheng applied this method to complete the precise semantic segmentation of buildings, commercial signage, windows, and decorative elements in the Wanshou Palace historic cultural district in Nanchang⁴⁰.

Color harmony theory is an essential foundation in architecture and design, particularly in the protection and optimization of color in historic districts, where the degree of color harmony directly affects the visual experience and cultural identity of the area^41,42. The International Commission on Illumination (CIE) proposed the CIE 1976 Lab* color space in 1976, which provides a standardized theoretical framework for quantifying color⁴³. This model describes various features of color using three dimensions: L* (lightness), a* (red-green axis), and b* (yellow-blue axis), allowing for precise color analysis based on a mathematical model rather than relying on subjective experience.

The CIEDE2000 color difference formula⁴⁴, which is an enhancement of the CIE 1976 Lab* color space, is specifically used to measure the difference between two colors. Compared to the traditional CIE76 color difference formula, CIEDE2000 takes into account the differences in lightness, saturation, and hue, and provides a more accurate simulation of human visual perception of color differences. The advantage of this formula lies in its ability to offer a color difference evaluation that better aligns with human perception, particularly in scenes with strong color contrasts⁴⁵.

Therefore, in color research on historic districts, the CIEDE2000 color difference formula provides a scientific basis for analyzing the differences between the colors of commercial elements and historic building colors. By calculating the color difference (ΔE), we can quantify the degree of harmony between modern commercial elements and traditional architectural colors.

Methods

Research area

Located in Gusu District, Suzhou City, Jiangsu Province, China, Shiquan Street is a street with a long history and rich cultural heritage⁴⁶. It not only embodies rich historic and cultural heritage, but also embraces numerous representative historic buildings, showcasing the traditional water-town features of Suzhou and the cultural characteristics of Jiangnan (Fig. 2).

Fig. 2: Location of Shiquan Street historic district in Suzhou.

The photographs shown in these images were taken by the author himself with a hand-held camera. All other images are acknowledged by the author.

From the perspective of architectural and cultural heritage value, Shiquan Street is not only a commercial district, but also renowned for its profound cultural heritage. Therefore, Shiquan Street has typical representativeness. In history, this place has been a gathering place for numerous celebrities, leaving behind a rich heritage. There are also former residences of some important historical figures in the district, such as the former residence of Chiang Ching-kuo (the second son of Chiang Kai-shek, the highest commander of the Chinese War Zone during World War II), the former residence of Li Genyuan (a former politician and military strategist of the Republic of China), the former residence of Shen Deqian (a famous official of the Qing Dynasty in China), and Peng Dingqiu (a famous literary figure of the Qing Dynasty in China). Meanwhile, the famous Chinese Jiangnan garden “Master-of-Nets (Wangshi) Garden” is also located within Shiquan Street. Thus, Shiquan Street should have been a district that embodies the traditional features of the Jiangnan region in China.

However, in the revitalization process of Shiquan Street, the introduction of a large number of modern commercial elements such as cafes, restaurants, and various shops has significantly changed the color features of the district. The traditional color system of Jiangnan, which was originally dominated by simple gray tiles, white walls, and bluestone roads of low saturation, has gradually been replaced by bright commercial store signs of high contrast (Fig. 3). Various commercialized store signboards have broken the harmonious color tone and visual continuity of the historic district, resulting in heterogeneity and fragmentation of the overall color features. Shiquan Street has been a historic and cultural district, and its traditional colors not only carry regional cultural memories, but also serve as an important source of identity and cultural belonging for the district. The strong involvement of modern commercial colors has visually weakened the historic features of the district and reduced the uniqueness of culture, resulting in the loss of traditional color language. Meanwhile, the lack of color coordination has exacerbated the divergence between culture and commerce, leading to a homogenization of the overall style of the district and the loss of its unique visual and cultural charm.

Fig. 3: Color status of some commercial elements.

The photographs shown in these images were taken by the author himself with a hand-held camera. All other images are acknowledged by the author.

Overall research process

In this study, the colors of historic buildings located in the district is used as the carrier of the traditional color features of the district, and commercial shop signs, storefronts, and other commercial attachments attached to the historic buildings in the district are seen as the expression medium of commercial element colors. The original street view samples required for further research are the street view photos of Shiquan Street in Suzhou. The main purpose of this data collection is to obtain visual samples that could accurately reflect the historic and cultural characteristics of the district.

Computer vision and image processing technology play a key role in this process, which can achieve precise recognition and classification of visual elements in street view images through semantic segmentation of samples. Specifically, the process involves utilizing open-source datasets for semantic segmentation and color correction of images to ensure that each visual element could be accurately identified and classified. Afterwards, various color clustering techniques are applied to classify and analyze different color schemes and patterns for various samples, so as to provide technical support for research conclusions. The integration of these samples with computer vision technology aims to gain a more accurate and comprehensive understanding of the visual features of the historic district, providing a foundation for further exploration of the protection and enhancement of cultural heritage in urban environments. The specific steps and process are shown in Fig. 4.

Fig. 4

Experimental process.

Data source

In the data source for subjective dimensions, to gain a deeper understanding of the public’s perception of the commercial colors in Shiquan Street, Suzhou, this study designed the “Survey on Commercial Element Colors and Cultural Perception in Shiquan Street, Suzhou.” The questionnaire, based on the Likert scale, uses a five-point rating system and covers various aspects such as the overall color perception of Shiquan Street, the experience of historical and cultural atmosphere, and the coordination of commercial element colors. The survey was conducted on Shiquan Street in Suzhou, and a total of 950 questionnaires were collected. Among them, 935 were valid. The respondent group includes local residents, visitors, and local students, aiming to cover a diverse range of social groups to ensure the comprehensiveness and representativeness of the data.

In the objective dimension data source, the entire study mainly uses 3 kinds of open-source data, namely road data, POI (Point of Interest) data, and street view photos. Among them, POI data is used to discover and verify the questions that have been raised. The street view photos serve as the basis for the entire study.

The street view photo samples are collected using the Open Street Map platform (https://openmaptiles.org/languages/zh/#0.85/0/0), ArcGIS and Baidu API (https://cloud.baidu.com/doc/API/index.html), which are used to achieve extensive and precise collection of existing street view photo samples. This method can provide more efficient and accurate batch data collection in the collection of a large number of historic district samples. Firstly, the Open Street Map platform was used to get the road data of Shiquan Street in Suzhou, and its built-in WGS84 coordinate system was directly matched with ArcGIS 10.6 geographic information system without the need for coordinate correction. After inputting the road line data into the ArcGIS 10.6 platform, the roads were segmented into line segments, and street view sampling points were set every 20 m. The 20-m collection interval allows for the inclusion of more commercial elements (such as store signs, billboards, etc.) during image collection, preventing the omission of any details that may impact the color esthetics. By using a denser image collection, we can ensure that the visual features of each block are fully captured, especially small-scale commercial elements, which are crucial for accurately analyzing color harmony. Adjustments were made according to the actual situation, and finally, 105 valid coordinate points were determined as the basis for further sample extraction (Fig. 5).

Fig. 5

Schematic diagram of sampling points and buffer construction.

Next, Python script was used to get street view photos from Baidu, obtaining a batch of street view photo samples of Shiquan Street based on the 105 valid coordinate points. In order to more clearly identify the building information in street view photos and use it for following semantic segmentation, the shooting angle of all street view photos was uniformly set to an elevation angle of 20°, and 2 sets of photos (i.e., 90° and 270°) were obtained from the left and right sides of the road, respectively. After screening and removing invalid samples, a total of 807 valid street view photo samples were obtained, among which a total of 518 samples were used for the preliminary problem searching (all of which are the street view data of Shiquan Street of 2015, 2017, 2019, 2021, 2023, and 2024), and 289 street view photo samples were used for the overall experiment (the latest data of 2024). It is worth noting that some samples cannot be processed due to lack of updates. Therefore, photos were taken using handheld cameras to capture some missing nodes in order to improve the sample quality of the core experiment. In this section, in order to conduct more accurate analysis on the commercialized colors of Shiquan Street, the 2024 street view photos of Shiquan Street used for core analysis were divided into 8 standard segments, namely the south and north segments of Fengmenqiao-Xiangwang Road (S01, N01), the south and north segments of Xiangwang Road-Daichengqiao Road (S02, N02), the south and north segments of Daichengqiao Road-Wuqueqiao Road (S03, N03), and the south and north segments of Wuqueqiao Road-Renmin Road (S04, N04) (Fig. 6).

Fig. 6

Schematic diagram of standard segments.

The acquisition of POI data mainly relies on “eazypoi” to get the map data from Baidu. In the Baidu Maps platform, POI data is usually divided into 6 categories: industry, food, accommodation, medicine, catering, and lifestyle services. Among them, the major categories that reflect commercial elements are food, accommodation, catering, and lifestyle. Therefore, these 4 categories were classified into POI points reflecting commercial elements. Then, the 105 coordinate points obtained from the above were used for coupled grasping, that is, using each coordinate point as the center to draw a circle with a radius of 50 m. The 105 coordinate points were connected to form the area of shops along Shiquan Street in a range of 20 m on both sides, with the road as the centerline. The POI coordinate points were extracted within the area to obtain the information data of commercial shops along Shiquan Street in Suzhou (Fig. 5). This data was used in the early stage of problem determination.

Problem determination stage

At this stage, the first hypothesis that needed to be determined was whether the commercial element colors within the historic district would become richer with the increase of the number of commercial shops, so calculation was made to the number of various business formats in this stage. In this stage, we mainly used the POI interest point data obtained in the previous 5 years of 2015, 2017, 2019, 2021, and 2023 of Shiquan Street, as well as the corresponding street view photos for each year, to verify our hypothesis.

Calculation of estimated kernel density values

Firstly, in order to visually observe the number of stores in recent years, kernel density analysis was conducted on these POI data points on the ArcGIS 10.6 platform⁴⁷.

The calculation formula is as follows:

$$\hat{f}(x)=\frac{1}{n}\mathop{\sum }\limits_{i=1}^{n}\frac{1}{{h}^{2}}K\left(\frac{d(x,{x}_{i})}{h}\right)$$

(1)

Where, n is the number of sample points; x_i is the number of individual sample points; h is the bandwidth that determines the smoothness of the estimation; K is the kernel function used to weight the contribution of each point; d(x, x_i) is the distance from the center of the grid to the sample point (x, x_i).

Calculation of global Moran’s index

Secondly, a global Moran’s index calculation was conducted on the POI data from previous years on the ArcGIS 10.6 platform. Moran’s index is a statistical indicator used to measure spatial autocorrelation and determine whether geographic data tends to aggregate or disperse in space⁴⁸. It detects the spatial correlation of variables by analyzing the similarity between geographic data points.

The calculation formula is as follows:

$${\rm{I}}=\frac{n}{W}.\frac{{\sum }_{i=1}^{n}{\sum }_{j=1}^{n}{w}_{ij}({x}_{i}-\overline{x})({x}_{j}-\overline{x})}{{\sum }_{i=1}^{n}{({x}_{i}-\overline{x})}^{2}}$$

(2)

Where, n is the number of observed values; x_i and x_j are the observed values in the geographic spatial dataset; $\overline{x}$ is the average of all observed values; w_ij is the spatial weight between positions i and j; W is the sum of all spatial weights. The range of Moran’s index is usually between −1 and 1. When the Moran’s index approaches 1, it indicates that there is positive autocorrelation in spatial data, meaning that the attribute values of adjacent regions are similar and show a clustering trend. When the Moran’s index approaches −1, it indicates that there is negative autocorrelation in spatial data, meaning that there is a large difference in attribute values between adjacent regions, showing a dispersed trend. When the Moran’s index approaches 0, it indicates that the spatial data has no obvious autocorrelation and the data distribution is random. Therefore, conducting Moran’s index analysis on the annual POI data can provide a clearer understanding of whether the commercial shops on Shiquan Street are increasing and gathering year by year.

Color richness calculation of the frame

Finally, the street view photos over the years were input into Matlab to measure color richness, and the color richness was evaluated by referring to the RGB color space image evaluation method proposed by Hasler et al.⁴⁹ and Jiang et al.⁵⁰.

The calculation formula is as follows:

$$\begin{array}{l}{r}_{g}=R-G\\ {y}_{b}=\frac{1}{2}(R+G)-B\end{array}$$

(3)

Where, R, G, and B respectively represent the red, green, and blue colors; r_j is the difference between the red and green channels; y_b is the difference between the sum of 1/2 of the red and green channel values and the blue channel value. Next was followed by calculating the standard deviation ${\sigma }_{{{\rm{rgy}}}_{{\rm{b}}}}$ and mean${\mu }_{{{\rm{rgy}}}_{{\rm{b}}}}$.

The calculation formula is as follows:

$$\begin{array}{l}{\sigma }_{{{\rm{rgy}}}_{{\rm{b}}}}=\sqrt{{\sigma }_{{\rm{rg}}}^{2}+{\sigma }_{{\rm{yb}}}^{2}}\\ {\mu }_{{{\rm{rgy}}}_{{\rm{b}}}}=\sqrt{{\mu }_{{\rm{rg}}}^{2}+{\mu }_{{\rm{yb}}}^{2}}\end{array}$$

(4)

Finally, the color richness C value of the street view photo samples was calculated and the color richness for every years was selected⁵¹.

The calculation formula is as follows:

$$C={\sigma }_{{{\rm{rgy}}}_{{\rm{b}}}}+0.3\times {\mu }_{{{\rm{rgy}}}_{{\rm{b}}}}$$

(5)

At this point, it was able to verify whether the commercialized colors of Shiquan Street became richer with the increase of numbers by combining the visualization of kernel density analysis of street view photos over the years, the aggregation/dispersion results of Moran’s index, and the evaluation results of color richness, in order to proceed with the following experiment.

Research method for analyzing the subjective perception of commercial colors

In the subjective perception of commercial colors section, we conducted a questionnaire survey to explore the public’s attitudes toward the commercial element colors in Shiquan Street, as well as the correlation with objective evaluation results. The questionnaire, titled “Survey on Commercial Element Colors and Cultural Perception in Shiquan Street, Suzhou,” was designed using the Likert scale, assessing the commercial element colors of Shiquan Street in Suzhou across two dimensions: color-related and culture-related, with five aspects: Color Coordination, Color Homogenization, Color Acceptance, Cultural Depth, and Cultural Appeal. Among these, the independent variables are Color Coordination, Color Homogenization, and Color Acceptance (color-related dimension), while the dependent variables are Cultural Depth and Cultural Appeal (culture-related dimension). Respondents selected their opinions based on five indicators: Strongly Disagree, Disagree, Neutral, Agree, and Strongly Agree. All data were analyzed using SPSS 21.0. The specific content of the questionnaire survey is provided in the appendix.

Descriptive Statistics:Descriptive statistics is an important method for conducting preliminary analysis of survey data and plays a key role in evaluating the subjective perception of commercial colors in Shiquan Street, Suzhou. By calculating indicators such as frequency, percentage, mean, and standard deviation, descriptive statistics comprehensively summarizes the basic characteristics of the sample data, laying a solid foundation for further exploration of the correlation between subjective perception of commercial colors and objective evaluation results. Specifically, frequency and percentage reveal the distribution of respondents’ choices across five dimensions (Color Coordination, Color Homogenization, Color Acceptance, Cultural Depth, Cultural Appeal), visually presenting the tendencies of different options. The mean quantifies the central tendency of respondents’ overall attitudes, providing a core basis for evaluating the commercial element colors of Shiquan Street, Suzhou. The standard deviation reflects the consistency and dispersion of respondents’ opinions within each dimension, offering support for assessing the stability and interpretability of the data. By utilizing SPSS 21.0 analysis tools, descriptive statistics not only provides foundational data for analyzing the relationship between the dependent variables (Cultural Depth, Cultural Appeal) and independent variables (Color Coordination, Color Homogenization, Color Acceptance), but also offers theoretical support for the interpretation of results from subsequent factor analysis and path analysis.

Reliability Analysis:Cronbach’s Alpha: Cronbach’s Alpha coefficient is a statistical measure used to assess the internal consistency of a scale or questionnaire. Its value ranges from 0 to 1, with higher values indicating better internal consistency, meaning the items within the scale or questionnaire are more strongly correlated and the measurement reliability is higher. For example, when the Cronbach’s Alpha coefficient is close to 1, it suggests that the items in the scale are highly correlated and can effectively measure the same underlying concept or trait. Generally, a Cronbach’s Alpha coefficient greater than 0.7 indicates good internal consistency, while a value above 0.8 indicates excellent internal consistency.

KMO Value (Kaiser-Meyer-Olkin Measure of Sampling Adequacy): The KMO value is a statistic used to test whether the partial correlations between variables are suitable for factor analysis. The KMO value ranges from 0 to 1, with values greater than 0.7 generally indicating that the data is suitable for factor analysis. The closer the KMO value is to 1, the more common factors exist between the variables, making the data more suitable for factor analysis.

Factor Analysis:Factor analysis in this study is used to explore the underlying structure of the questionnaire data related to the subjective perception of commercial colors in Shiquan Street, Suzhou, providing a scientific basis for optimizing the research model. By extracting the core factors from the five dimensions of the questionnaire, factor analysis can reveal the latent relationships between variables and reduce the complexity of the data into a few comprehensive factors for further analysis. Specifically, factor analysis helps identify which dimensions play a key role in the evaluation of commercial color perception, cultural depth, and cultural appeal, offering theoretical support for modeling the relationship between dependent and independent variables in the study. Additionally, by rotating the factor structure, this study can more clearly interpret the contribution of each factor, laying a solid foundation for path analysis and result interpretation. The application of factor analysis not only enhances the scientific rigor of the research model but also provides actionable theoretical guidance for optimizing the management of commercial colors in historical districts.

Correlation Analysis:In this study, correlation analysis is used to explore the relationships between the various dimensions of subjective perception of commercial elements’ colors in Shiquan Street, Suzhou, providing data support for further path analysis. By calculating the correlation coefficients, we assess the strength and direction of the linear relationships between the independent variables (Color Coordination, Color Homogenization, Color Acceptance) and the dependent variables (Cultural Depth, Cultural Appeal). When the correlation coefficient is positive (0 < r ≤ 1), it indicates a positive influence of the independent variable on the dependent variable; when the correlation coefficient is negative (−1 ≤ r < 0), it shows a negative relationship between the two variables; if r is close to 0, it suggests a weak linear relationship between them. Correlation analysis not only reveals the associations between different dimensions but also provides preliminary evidence for understanding how subjective perceptions of commercial colors influence the cultural depth and appeal of historical districts.

Path Analysis:In this study, path analysis is used to investigate the direct and indirect effects of subjective perceptions of commercial element colors (Color Coordination, Color Homogenization, Color Acceptance) on Cultural Depth and Cultural Appeal in Shiquan Street, Suzhou. By constructing a path model, we calculate the path coefficients between variables to quantify the strength and direction of the influence of independent variables on dependent variables. Specifically, the path analysis reveals how the coordination and acceptance of commercial colors directly impact cultural appeal, while color homogenization may indirectly affect cultural depth through mediating variables. This analysis not only helps to verify the inherent relationships between subjective perception dimensions and the cultural characteristics of historical districts but also provides theoretical support for optimizing commercial color design.

Objective evaluation of commercial color based on computer methods

Mask2former image semantic segmentation technology based on VIT model:In the processing stage of the latest street view photo samples of Shiquan Street, street view photos were used to segment the required research objects, namely buildings (not including commercial elements) and commercial elements (including signboards, store facades). Therefore, a novel semantic segmentation network was integrated to achieve precise segmentation of commercial elements, which achieved the goal of semantic segmentation by using the mask2former model based on the VIT model proposed by Dosovitskiy et al. (Fig. 7)⁵². VIT can segment an image into fixed sized blocks (e.g., 16 × 16 pixels), each of which is unfolded into a one-dimensional vector and forms an input sequence through position encoding. These input sequences are processed through a multi-layer transformer encoder, each encoder layer contains a multi-head self-attention mechanism and a feedforward neural network, and the expression ability and stability of the model are enhanced through layer normalization and residual connections⁵³. In terms of training strategy, models pre-trained on large-scale datasets such as ImageNet were used. Then, fine tuning was conducted on specific task datasets (such as the ADE20k Street View database), to improve its segmentation accuracy on street view data. When the building category and trade name category from the ade20k dataset were used for building and trademark recognition image segmentation in Mask2Former, the segmentation performance was poor. So, the Billboard category from the massive Mapillary Vistas Dataset was combined for mask merging to optimize the shortages, which removed impurity masks, further improving the accuracy of semantic segmentation. Compared to commonly used semantic segmentation techniques such as Deeplab and Segnet, the semantic segmentation technique proposed in this study can better understand and segment details in images, which accurately segment the needed research objects.

Fig. 7

Mask2Former semantic segmentation model based on VIT.

Color space analysis technology based on Mtalab platform:In previous studies, most measurements of color harmony were simply based on the Euclidean distance in the HSV color space, but this method has certain errors. In this study, the standard (CIEDE2000) issued by the International CIE in 2000 was introduced to calculate the color difference between two points, and all the color particles required for the study were converted into the CIELAB color space. The CIEDE2000 color difference formula (commonly referred to as ΔE) is an advanced color difference assessment method that considers multiple visual factors to provide color difference measurements that are more in line with human color perception⁵⁴. This formula is relatively complex, including considerations for adjusting brightness, color saturation and hue difference, as well as hue compression.

The calculation formula is as follows:

Step 1: Calculate the distance between 2 color particles C₁ and C₂. Every color particle is represented by L^*, a^*, and b^* in the CIELAB color space. Therefore, it needs to calculate the color saturation C^* and hue angle h’ of each color particle:

$$\begin{array}{l}{C}_{1}^{\ast }=\sqrt{{({a}_{1}^{\ast })}^{2}+{({b}_{1}^{\ast })}^{2}}\\ {C}_{2}^{\ast }=\sqrt{{({a}_{2}^{\ast })}^{2}+{({b}_{2}^{\ast })}^{2}}\\ {h}_{1}^{\text{'}}=a\,\tan \,2({b}_{1}^{\ast },{a}_{1}^{\ast })\\ {h}_{2}^{\text{'}}=a\,\tan \,2({b}_{2}^{\ast },{a}_{2}^{\ast })\end{array}$$

(6)

Step 2: Calculate the average color saturation and mean hue angle of the sample.

Step 3: Calculate the various components of color difference, where the brightness difference is ∆L, the color saturation difference is Δc^’, and the hue difference is ∆H^’.

The calculation formula for ∆H^’is:

$$\Delta {H}^{\text{'}}=2\sqrt{{C}_{1}^{\ast }{C}_{2}^{\ast }}\,\sin \left(\frac{\varDelta {h}^{\text{'}}}{2}\right)$$

(7)

Step 4: Calculate the correction terms for each value, where S_L is the correction term for brightness; S_C is the correction term for color saturation; S_H is the correction term for hue; R_T is the correction term for hue rotation, which is used for compressing and stretching the hue region.

Step 5: Calculate the color difference value ΔE, which is calculated using the following formula:

$$\varDelta E=\sqrt{{(\frac{\varDelta {L}^{\text{'}}}{{k}_{L}{S}_{L}})}^{2}+{(\frac{\varDelta {C}^{\text{'}}}{{k}_{C}{S}_{C}})}^{2}+{(\frac{\varDelta {H}^{\text{'}}}{{k}_{H}{S}_{H}})}^{2}+{R}_{T}(\frac{\varDelta {C}^{\text{'}}}{{k}_{C}{S}_{C}})(\frac{\varDelta {H}^{\text{'}}}{{k}_{H}{S}_{H}})}$$

(8)

Where, k_L, k_C, and k_H are usually 1, unless there are specific application scenarios that require adjustment.

The research is based on the CIEDE2000 standard and Luo et al.’s paper, combined with the current state of the site, to establish the ΔE distance standard, which was used in the subsequent experiments⁵⁵. As for the results, ΔE < 6.5 indicates extremely harmonious, 6.5 ≤ ΔE ≤ 13 generally harmonious, 13 ≤ ΔE ≤ 25 generally disharmonious, and 25 ≤ ΔE extremely disharmonious. The specificgrading standards are shown in Table 1.

Table 1 Color evaluation criteria

Evaluation of commercial element colors:In this section, a method was developed to measure the distance between the central color and each target color particle, which was used as a standard for evaluating the colors of commercial elements. Firstly, prior to the experiment, the architectural colors and commercial element colors were filtered to eliminate other colors that may have entered the segmentation area by mistake. Next, each store sign was pixelated, and k-means clustering was used to cluster the building colors of each street section. Usually, the number of colors that the human eye can receive is 4–8⁵⁶. Therefore, the clustering value was 8, generating 8 building center color particles as the evaluation standard, namely the color center points. Then an initial center point C1 was selected from the set of pixels. Next, the distances between all other pixels and C1 were calculated, and the pixel farthest from C1 was selected as the next center point C2. This process was repeated until the number of center points reached K. Then, the distances between each pixel and these K cluster centers were calculated, and each pixel was assigned to the category represented by the nearest cluster center. After assigning all pixels, the initial clustering result was obtained. Afterwards, the mean of pixels in each category was calculated to obtain new cluster centers. The above steps were repeated to continuously update the cluster centers until meeting the iteration stop condition. Finally, K cluster centers were obtained, representing the main K colors in the image.

The calculation formula is as follows:

$$d=\sqrt{{({H}_{i}-{H}_{j})}^{2}+{({S}_{i}-{S}_{j})}^{2}+{({V}_{i}-{V}_{j})}^{2}}$$

(9)

Secondly, the CIE2000 standard formula was used to calculate the distance ∆E between each color center point and each store sign, all of which were introduced into the standard to analyze the distance between the commercial element colors and the building center colors in each standard segment, in order to determine whether their colors were harmonious or not.

Analysis of commercialized colors not recommended for use:In this section, the color particles with ΔE ≥ 13 in each standard segment are not recommended as commercial element colors, because when ΔE ≥ 13, the color difference between the commercial element colors and historic building is usually significant, and the color difference can be perceived by the naked eye, which can be determined as different tones. Meanwhile, its spatial relationship with the central color in the CIELAB color space is also discrete. Therefore, color particles with ΔE ≥ 13 are considered as outlier color particles, which are referred to as negative color particles. These color particles will disrupt the overall color atmosphere of the historic district, which is not conducive to maintaining the style of the historic district.

The large number of scattered color particles are difficult to clearly display, so k-means clustering method was used to cluster the negative color particles in each standard segment, with the number of clusters setting to “8” (referring to the previous relevant methods for commercialized color evaluation). Each standard segment would identify 8 representative negative colors that reflected the central color point. Particles based on these central color points are not recommended for use within the historic district of Shiquan Street in Suzhou.

Analysis of recommended colors for commercial elements:In this section, the colors of traditional Chinese color cards were used as references to provide suggestions for the commercial element colors in Shiquan Street. Although there were multiple discussions on the color scheme of commercial elements in the early stage, the “Chinese Traditional Color Card” was finally chosen due to the unique visual effects and Chinese esthetics of these colors. The use of these colors not only enhanced the visual recognition of commercial elements, but also coordinated with the original colors of Shiquan Street, which helped to maintain or enhance the esthetic value of the area and was more in line with traditional esthetics and local style Firstly, the color particles of the buildings in every street segment of Shiquan Street were confirmed in the range of CIELAB color space. Secondly, 526 traditional Chinese color particles were input into the color space of each street segment, and the colors within 6.5 ≤ ΔE < 13 were boxed to determine the fitting color points within the boxed range, which were the recommended basic colors for commercial elements.

Secondly, it was difficult to determine whether the color particles outside the standard edge but close to the edge were unrelated to the architectural features of the district. Therefore, color buffer zones were established on the periphery of each block as the range of complementary colors for commercial use. The range was defined based on the definition of the ΔE range according to CIE2000 standard. Considering that colors with ΔE < 6.5 belong to the same category as the building environment colors and essentially overlap with the architectural elements, they are not conducive to the selection and expression of “commercial element” colors. Therefore, such colors are not included in the analysis of “recommended commercial element colors.” When 6.5 ≤ ΔE < 13, color differences within this range were visible but may not be significant, so color particles within this range could be considered as auxiliary colors for commercial use. Therefore, this kind of color particle was selected for the recommended list.

Results

Subjective dimension results

Descriptive statistics results show: This study conducted a frequency analysis on 935 valid questionnaire responses, and the results show a diverse distribution in terms of age, educational background, geographical location, and gender. In terms of age, the highest proportion of respondents were aged 45 and above, accounting for 34.65%, followed by 18–24 years (23.10%), 35–44 years (21.50%), and 25–34 years (20.75%), indicating that the middle-aged and elderly groups showed more interest in the questionnaire content. Regarding educational background, the highest proportion of respondents had a postgraduate degree or higher, accounting for 34.55%, followed by undergraduate (23.42%), college (21.82%), and high school or below (20.21%), reflecting a relatively high educational level within the sample. In terms of geographical distribution, respondents from Suzhou, Wuxi, and Changzhou accounted for 21.39%, 24.17%, and 21.07%, respectively, while the highest proportion of respondents selected “Other” regions, accounting for 33.37%, indicating broad geographical coverage of the survey. In terms of gender distribution, males accounted for 60.96%, and females accounted for 39.04%, with a relatively higher proportion of males. Overall, the sample data is widely distributed and highly representative. These results provide a reliable data foundation for exploring the relationship between subjective perceptions and objective evaluations of commercial colors in Shiquan Street, Suzhou.

Reliability and Validity Analysis Results of the Questionnaire Survey show:In the part of overall reliability and validity analysis in Table 2, the Cronbach’s Alpha coefficients for each dimension as well as for the overall scale are all above 0.9, indicating a high level of internal consistency and reliability in the measurements for the respective dimensions. For instance, the Cronbach’s Alpha for the “Color Coordination” dimension is 0.962, suggesting that the items used to measure this dimension are strongly correlated and can accurately assess the concept of color coordination. The KMO values for each dimension, as well as for the overall scale, are around 0.9, indicating that the data is highly suitable for factor analysis and that there are strong common factors between the variables. For example, the KMO value for the “Color Acceptance” dimension is 0.925, which suggests that there is substantial shared information among the related variables when measuring color acceptance.

Table 2 Reliability and validity test results

In the part of specific analysis of each dimension, in color-related dimensions:

Color Coordination: The Cronbach’s Alpha coefficient is 0.962, and the KMO value is 0.914, indicating that the internal consistency of the scale in measuring color coordination is very high, and the data is suitable for factor analysis.

Color Homogenization: The Cronbach’s Alpha coefficient is 0.942, and the KMO value is 0.905, showing that the scale measuring color homogenization has high internal consistency, and the data is suitable for factor analysis. This suggests that when assessing the uniform distribution of color in a specific area or object, the scale can provide accurate measurement results. For example, in the printing industry, this scale could play an important role in evaluating the uniformity of color on printed materials.

Color Acceptance: The Cronbach’s Alpha coefficient is 0.954, and the KMO value is 0.925, demonstrating that the scale has good internal consistency and is suitable for factor analysis in measuring color acceptance. This means the scale can effectively measure people’s acceptance of different colors. For instance, in product design, this scale can be used to understand consumer acceptance of a product’s color, providing valuable insights for color selection.

Cultural-related dimensions:

Cultural Depth: The Cronbach’s Alpha coefficient is 0.958, and the KMO value is 0.922, indicating that the scale measuring cultural depth has high internal consistency and is suitable for factor analysis.

Cultural Appeal: The Cronbach’s Alpha coefficient is 0.933, and the KMO value is 0.901, showing that the scale measuring cultural appeal has good internal consistency and is suitable for factor analysis.

Factor Analysis Results show: As shown in Table 3, the factor extraction situation and the amount of information extracted from the factors are analyzed. As can be seen from the table, a total of 5 factors were extracted through factor analysis, with eigenvalues greater than 1. The variance explained by these 5 factors after rotation is 17.721%, 17.047%, 16.558%, 15.062%, and 14.305%, respectively. The cumulative variance explained after rotation is 80.694%.

Table 3 Reliability and validity test results

In this study, the data were rotated using the Varimax rotation method to identify the correspondence between the factors and the research items. Table 4 presents the factor loadings for the research items and the corresponding relationships between factors and research items. As shown in Table 4, the communalities for all research items are above 0.5, indicating a strong correlation between the research items and the factors, and that the factors effectively extract information.

Table 4 Factor loading coefficients after Rotation

As shown in Table 5, for the measurement relationships, the absolute values of the standardized loadings for each measurement relationship are all greater than 0.6 and show significance, indicating that the measurement relationships are strong and reliable.

Table 5 Factor loading coefficient

According to Table 6, a confirmatory factor analysis (CFA) was conducted for a total of 5 factors and 30 analysis items. As shown in the table, the AVE values for all 5 factors are greater than 0.5, and the CR values are all above 0.7, indicating that the data in this analysis have good convergent validity.

Table 6 Model AVE and CR index results

According to Table 7, an analysis of discriminant validity was conducted. For Color Coordination, the square root of the AVE value is 0.899, which is greater than the maximum absolute inter-factor correlation of 0.730, indicating good discriminant validity. For Color Homogenization, the square root of the AVE value is 0.857, which is greater than the maximum absolute inter-factor correlation of 0.629, indicating good discriminant validity. For Color Acceptance, the square root of the AVE value is 0.882, which is greater than the maximum absolute inter-factor correlation of 0.730, indicating good discriminant validity. For Cultural Depth, the square root of the AVE value is 0.892, which is greater than the maximum absolute inter-factor correlation of 0.681, indicating good discriminant validity. For Cultural Appeal, the square root of the AVE value is 0.840, which is greater than the maximum absolute inter-factor correlation of 0.682, indicating good discriminant validity.

Table 7 Discriminant validity: Pearson correlation and AVE square root value

Correlation analysis results show: According to Table 8, correlation analysis was used to study the relationships between Cultural Appeal and four other variables: Cultural Depth, Color Acceptance, Color Homogenization, and Color Coordination. The Pearson correlation coefficient was used to represent the strength and direction of these relationships. The specific analysis reveals that:

Table 8 Pearson correlation

Cultural Appeal shows significant correlations with all four variables (Cultural Depth, Color Acceptance, Color Homogenization, and Color Coordination), with correlation coefficients of 0.579, 0.682, 0.629, and 0.664, respectively. Since all correlation coefficients are greater than 0, this indicates a positive correlation between Cultural Appeal and these four variables.

Path Analysis Results: According to Table 9, the following results are observed:

Table 9 Summary table of model regression coefficients

Color Coordination and Cultural Depth: The path from Color Coordination to Cultural Depth does not show significance (z = 1.602, p = 0.109 > 0.05), indicating that Color Coordination does not have a significant impact on Cultural Depth.

Color Homogenization and Cultural Depth: The standardized path coefficient for Color Homogenization’s impact on Cultural Depth is 0.194 (greater than 0), and this path shows significance at the 0.01 level (z = 6.558, p = 0.000 < 0.01). This suggests that Color Homogenization has a significant positive impact on Cultural Depth.

Color Acceptance and Cultural Depth: The standardized path coefficient for Color Acceptance’s effect on Cultural Depth is 0.529 (greater than 0), and this path shows significance at the 0.01 level (z = 15.091, p = 0.000 < 0.01). This indicates that Color Acceptance has a significant positive impact on Cultural Depth.

Color Coordination and Cultural Appeal: The standardized path coefficient for the impact of Color Coordination on Cultural Appeal is 0.237 (greater than 0), and this path shows significance at the 0.01 level (z = 7.297, p = 0.000 < 0.01). This suggests that Color Coordination has a significant positive impact on Cultural Appeal.

Color Homogenization and Cultural Appeal: The standardized path coefficient for the effect of Color Homogenization on Cultural Appeal is 0.273 (greater than 0), and this path shows significance at the 0.01 level (z = 9.919, p = 0.000 < 0.01). This indicates that Color Homogenization has a significant positive impact on Cultural Appeal.

Color Acceptance and Cultural Appeal: The standardized path coefficient for the effect of Color Acceptance on Cultural Appeal is 0.277 (greater than 0), and this path shows significance at the 0.01 level (z = 7.815, p = 0.000 < 0.01). This suggests that Color Acceptance has a significant positive impact on Cultural Appeal.

Objective dimension results

In the part of Number of commercial buildings and richness of architectural colors in Shiquan Street. As shown in Fig. 8, the aggregation degree of commercial POI in 2015 was relatively low, and the kernel density plot showed partial heat. The global Moran index showed that the commercial POI was “aggregated” with a Z value of 12.077 782. In 2017, the aggregation degree of commercial POI increased significantly, and the kernel density plot showed that most of the street became heat. The global Moran index showed that the commercial POI was “aggregated” with a Z value of 42.434599, indicating a significant increase in aggregation compared to that in 2015. In 2019, the aggregation degree of commercial POI continued to increase, and the kernel density plot showed that the entire street maintained a high heat trend. The global Moran index showed that the commercial POI increase significantly from that of previous years. In 2021, due to the impact of COVID-19, the aggregation degree of commercial POI decreased, and the kernel density plot showed that the entire street decreased from a high heat trend to a partial high heat trend. The global Moran index showed that commercial POI was “aggregated” with a Z value of 39.384694, indicating a decrease in aggregation compared to that of previous years. In 2023, the COVID-19 impact period passed, and the aggregation degree of commercial POI returned to the level before COVID-19 and continued to rise. The kernel density plot showed a high heat trend throughout the entire street. The global Moran index showed that the commercial poi was “aggregated” with a Z value of 65.3279534, indicating the highest degree of aggregation in recent years. Therefore, in recent years, except for the period of 2021–2023 affected by COVID-19, the number and aggregation of commercial POI increased year by year. As shown in Fig. 9, the color richness of street view photos from years with matching POIs is 0.0511, 0.0750, 0.1309, 0.1336, and 0.2151, respectively, showing a trend of continuous improvement in color richness. Specifically, the colors of the district gradually evolve from a single, relatively simple tone to a more diverse color composition over time, indicating a significant transformation in the color feature of the district during this period. Thus, it can be preliminarily inferred that the richness of the overall color of the district is closely related to the number and aggregation of POIs, especially the increase and aggregation effect of commercial POIs, which have a significant impact on the color features of the historic district. The active commercial activities may bring about diverse architectural styles and store designs, thereby enriching the color hierarchy of the district. This result not only provides clues for us to understand the underlying mechanisms of color evolution in districts, but also offers directions for further exploration in following research. Future research can validate this trend on a larger scale and delve deeper into the dynamic relationship between POI aggregation and color changes in historic districts, in order to more comprehensively reveal the potential impact mechanism of commercial activities on the visual features of historic districts.

Fig. 8

Nuclear density analysis and Moran’s index analysis chart.

Fig. 9

Evaluation results of color richness in the frame.

In the part of evaluation results of commercial element colors in Shiquan Street.In the standard segment N01, extremely harmonious commercial element colors account for 46.24% of the total colors, generally harmonious commercial element colors account for 17.12%, generally disharmonious colors account for 25.82%, and extremely disharmonious colors account for 10.82%. Overall, the proportion of disharmonious commercial element colors account for more than one-third in standard segment N01, so the commercial element colors are generally not harmonious (Fig. 10a). In standard segment S01, extremely harmonious commercial element colors account for 42.56% of the total colors, generally harmonious colors account for 15.66%, generally disharmonious colors 33.23%, and extremely disharmonious colors 8.56%. Overall, the proportion of disharmonious commercial element colors in standard segment S01 account for more than one-third, so the commercial element colors are generally not harmonious (Fig. 10b). In the standard segment N02, extremely harmonious commercial element colors account for 60.82% of the total colors, generally harmonious commercial element colors account for 19.84%, generally disharmonious colors 16.94%, and extremely disharmonious colors 2.40%. Overall, the proportion of harmonious colors exceeds two-thirds in standard segment N02, so the commercial element colors are generally harmonious (Fig. 10c). In standard segment S02, extremely harmonious commercial element colors account for 57.77% of the total colors, generally harmonious colors account for 26.31%, generally disharmonious colors 13.37%, and extremely harmonious colors 10.82%. Overall, the proportion of disharmonious commercial element colors exceeds one-third in standard segment N01 (Fig. 10d). In the standard segment N03, extremely harmonious commercial element colors account for 38.83% of the total colors, generally harmonious colors account for 26.29%, generally disharmonious colors 28.30%, and extremely disharmonious colors 6.58%. Overall, the proportion of disharmonious commercial element colors in standard segment N03 exceeds one-third, so the commercial element colors are generally not harmonious (Fig. 11a). In standard segment S03, extremely harmonious commercial element colors account for 44.95% of the total colors, generally harmonious colors account for 33.13%, generally disharmonious colors 17.50%, and extremely disharmonious colors 4.42%. Overall, the proportion of harmonious commercial element colors in standard segment S03 exceeds two-thirds, so the colors are generally harmonious (Fig. 11b). In the standard segment N04, extremely harmonious commercial element colors account for 38.12% of the total colors, generally harmonious colors account for 33.52%, generally disharmonious colors 24.13%, and extremely disharmonious colors 4.23%. Overall, the disharmonious commercial element colors in standard segment N04 is lower than one-third of the total, so the commercial element colors are relatively average (Fig. 11c). In standard segment S04, extremely harmonious commercial element colors account for 94.70% of the total colors, generally harmonious colors account for 36.26%, generally disharmonious colors 24.39%, and extremely disharmonious colors 4.64%. Overall, the proportion of harmonious commercial element colors in standard segment S04 exceeds two-thirds, so the commercial element colors are generally harmonious (Fig. 11d).

Fig. 10: Color harmony rating results for standard segment N01, N02, S01, S02,

The assessment process of architectural colors and commercial element colors for the standard segments of streets N01, S01, N02, and S02, corresponding to a, b, c, and d, respectively. The colors will undergo filtering to eliminate those without research significance, resulting in the final color. An overlapping analysis of architectural colors and commercial element colors will then be conducted to obtain the final evaluation results.

Fig. 11: Color harmony rating results for standard segment N03, N04, S03, S04.

The assessment process of architectural colors and commercial element colors for the standard segments of streets N03, S03, N04, and S04, corresponding to a, b, c, and d, respectively. The colors will undergo filtering to eliminate those without research significance, resulting in the final color. An overlapping analysis of architectural colors and commercial element colors will then be conducted to obtain the final evaluation results.

In the part of negative commercial element colors in Shiquan Street. The results of boxing and clustering the disharmonious colors are as follows. As shown in Fig. 12a, the disharmonious center colors in segment N01 include (358,66,72), (356,75,83), (354,24,64), (356,60,63), (277,23,85), (61,40,58), (178,38,58), and (353,46,59). As shown in Fig. 12b, the disharmonious center colors in segment S01 include (205, 73, 71), (43, 48, 63), (47, 66, 84), (355, 82, 82), (355, 53, 53), (350, 34, 42), (124, 43, 64), and (354, 72, 79). As shown in Fig. 12c, the disharmonious center colors in segment N02 include (24,65,93), (216,28,69), (111,46,72), (23,29,74), (42,31,82), (2,52,64), (139,28,41), and (46,81,97). As shown in Fig. 12d, the disharmonious center colors in segment S02 include (59,40,80), (17,74,61), (348,32,44), (190,53,47), (233,87,93), (19,48,38), (13,71,52), and (35,39,64). As shown in Fig. 13a, the disharmonious center colors in segment N03 include (36,53,86), (184,54,72), (11,75,72), (68,86,96), (210,47,73), (9.28.54), (29,32,76), and (107,22,42). As shown in Fig. 13b, the disharmonious center colors in segment segment S03 include (59,40,80), (233,87,93), (17,74,61), (19,48,38), (348,32,44), (13,71,52), (190,53,47), and (35,39,64). As shown in Fig. 13c, the disharmonious center colors in segment N04 include (3,74,76), (10,70,81), (34,37,82), (9,64,75), (218,99,74), (218,34,85), (20,31,63), and (87,33,58). As shown in Fig. 13d, the disharmonious center colors in segment S04 include (229, 42, 74), (300, 11, 53), (35, 26, 85), (230, 26, 72), (3, 73, 72), (353, 74, 76), (52, 50, 79), and (236, 17, 63) (for the convenience of readers’ understanding, the above colors have been converted to HSV color space numbers). Overall, the disharmonious colors have high color purity and brightness, which are far from the central tone of the buildings. In terms of color tone, these colors belong to the category that is usually not recommended for use in commercial environments, as they can easily form strong contrasts with the surrounding environment, damaging the overall visual harmony and beauty. The use of such colors, especially in historical or traditional architectural complexes, may affect the cultural atmosphere and visual unity of the place, thereby reducing the overall esthetic value.

Fig. 12: Negative center color of standard segment N01, N02, S01, S02.

The extraction process of negative commercial element colors for the standard segments of streets N01, S01, N02, and S02, corresponding to a, b, c, and d, respectively. Color particles located within the range of disharmonious colors will be extracted and subjected to clustering analysis to determine the final negative color center.

Fig. 13: Negative center color of standard segment N03, N04, S03, S04.

The extraction process of negative commercial element colors for the standard segments of streets N03, S03, N04, and S04, corresponding to a, b, c, and d, respectively. Color particles located within the range of disharmonious colors will be extracted and subjected to clustering analysis to determine the final negative color center.

In the part of recommended colors for commercial elements in Shiquan Street. The results of selecting traditional Chinese colors within a certain range are shown in Table 2 and Figs. 14–15. Wherein, there are a total of 39 recommended basic colors and 55 recommended auxiliary colors for segment N01; 35 recommended basic colors for segment N02, and 39 recommended auxiliary colors segment N02; 34 recommended basic colors and 52 recommended auxiliary colors for segment N03; 32 recommended basic colors and 61 recommended auxiliary colors for segment N04; 31 recommended basic colors and 43 auxiliary colors for segment S01; 34 recommended basic colors and 45 recommended auxiliary colors for segment S02; 27 recommended basic colors and 51 recommended auxiliary colors for segment S03; 34 recommended basic colors and 38 recommended auxiliary colors for segment S04. Overall, the traditional Chinese colors selected for each standard section of Shiquan Street are mainly warm gray with rare cool colors, and the colors are relatively of low purity, which is highly compatible with the overall color atmosphere of Shiquan Street, reflecting the low-profile and elegant style of traditional architecture and environment in the area. The selection of colors that match the historical style not only can maintain the visual historic continuity of the district, but also provides a valuable color system for future urban renewal, commercial element design and renovation, and other practical projects. These color choices help to continue and promote the cultural heritage of historic districts in modernization, avoid color imbalance caused by excessive commercialization, and ensure the continuity of local culture and the harmonious unity of commercial environment.

Fig. 14

Recommended basic and auxiliary colors for standard segment N01, N02, S01, S02.

Fig. 15

Recommended basic and auxiliary colors for standard segment N03, N04, S03, S04.

Discussion

For the color status quo of Shiquan Street. Firstly, in some standard segments such as N01 and S01, the proportion of disharmonious colors is more than one-third, indicating that there is a significant visual conflict in color matching in these areas. The high proportion of disharmonious colors may lead to inconsistent overall perception of the environment, causing confusion or unease in visual experience. The optimization strategy for these areas should focus on reducing the use of disharmonious colors, especially those that are extremely disharmonious, which can be adjusted through the following aspects: firstly, reduce color combinations with high saturation and high contrast, as these colors can easily cause visual discomfort in historic districts and commercial environments; secondly, introduce neutral tones such as gray and beige to balance strong disharmonious colors and enhance visual smoothness and coordination. Secondly, in some standard segments (such as N02, S03, S04), the proportion of harmonious colors exceeds two-thirds, demonstrating good visual balance. The color matching in these areas is relatively stable, and the color harmony in commercial environments is high, providing customers with a comfortable visual experience. For these areas, the focus of optimization should be on maintaining the existing harmony, while moderately introducing some intense colors which are more harmonious with the surroundings to increase the vitality and attractiveness of the areas. Introducing a small amount of bright colors that match the basic tone can inject more dynamism into the commercial environment without disrupting the overall harmony. In addition, although some areas (such as N03 and S02) are not as harmonious as the areas mentioned above, the proportion of disharmonious colors is relatively moderate, and there is some room for optimization. For these areas, the key is to balance color contrast, and reducing unnecessary strong color combinations can enhance the naturalness of color transitions and visual unity. For example, by adjusting gradients or similar color schemes, it is possible to maintain a certain level of color vitality while avoiding the visual impact of extreme color combinations. Overall, the core of the optimization strategy lies in adjusting the color ratio reasonably based on the existing color harmony of each region. The visual coordination and attractiveness of the entire district can be improved significantly by reducing high saturation and high contrast colors, enhancing the smoothness of color transitions, and moderately introducing neutral tones. These measures can not only improve the esthetic quality of commercial spaces, but also can bring customers a more comfortable and enjoyable shopping experience.

For the negative color particle preferences. Firstly, the analysis on the hue values of the disharmonious color center points in each standard segment shows that these colors are mainly concentrated in several specific hue ranges. For example, the hue values of disharmonious in segments N01 and S01 are mostly concentrated in the red to pink range, such as (358, 66, 72), (356, 75, 83), (354, 24, 64) in N01; (355, 82, 82), (355, 53, 53), (350, 34, 42) in S01, indicating that the red tone has a relatively high frequency of occurrence in these areas, which may be related to the visual characteristics of these areas. However, the high saturation of these colors makes it easy to be prominent and disharmonious in the overall visual of the district, presenting a sense of disharmony. In segment N02, the hue values are concentrated in the orange yellow region, such as (24, 65, 93), (23, 29, 74), and (42, 31, 82), indicating that in this region, warm toned disharmonious color particles take up a certain proportion. This warm colors are often too bright in outdoor scenes, which can be out of place, especially when compared to the more stable tones of historic districts such as gray and brown. The disharmonious color particles in some areas are concentrated in the cyan and blue tones (such as (205, 73, 71) in S01, (216, 28, 69) in N02, and (229, 42, 74) in S04). These color tones may be incongruous in historic districts due to its mismatch with the traditional color tones of buildings, especially in cases of high saturation, which can create a visual sense of disconnection. Secondly, the analysis on the saturation and brightness shows that these disharmonious color particles have the following characteristics. First is the impact of high saturation. In other words, in many regions, many disharmonious color particles have high saturation, with saturation values exceeding 60% being common, such as (205, 73, 71) in S01, (358, 66, 72) in N01, and (233, 87, 93) in S03. High saturation means a high degree of color vividness, which can easily appear glaring or unnatural in a relatively stable and historically strong architectural background. This may be one of the reasons why these colors are not harmonious in historical districts. Second is the moderate to high brightness. In other words, the brightness values of many disharmonious color particles are between 50% and 80%, indicating that these colors are often brighter than the background color tone of the environment, especially in historic districts where low saturation and brightness are the main factors. These moderately bright color particles will appear more prominent visually and difficult to blend harmoniously with the surrounding environment. In addition, from the perspective of color preference, although these disharmonious color particles can bring about visual disharmony due to their high saturation and brightness, these colors often have a certain appeal in other scenes, especially for modern design and young people. For example, the warm tones of high saturation (such as red and orange) are widely used in advertising or logo design. These colors have a striking effect, but may appear too dazzling in historic districts. The blue-green color, such as (190, 53, 47) in S02 and (210, 47, 73) in N03, often presents a calm and fresh feeling, but if not matched with the correct color scheme, it may seem improper. As the basic color, this color may be more popular in modern districts or emerging commercial areas, but lacks adaptability in historical environments. Most importantly, the presence of disharmonious color particles in different districts may reflect differences in cultural and historical backgrounds. For example, in some cultural backgrounds, especially in Asia, the red color is often associated with positive emotions such as joy and energy. However, when its saturation is too high or used improperly, it may still disrupt the overall visual harmony of traditional architecture. Therefore, in the following color planning of historic districts, it is necessary to balance cultural connotations and visual experiences.

For the application of traditional Chinese colors in historical districts.The analysis on the frequency of recommended colors shows that specific HSV color combinations repeatedly appear in multiple street segments, especially neutral gray, cool blue and green, indicating that these colors have a wide range of applications in design, which is closely related to the traditional esthetic of Suzhou’s historic districts. Traditional architecture in Suzhou is mostly based on gray, white, and blue tones, reflecting the esthetic value of simplicity and elegance. The use of these colors is not accidental, but reflects the pursuit of continuity and unity of colors in the historic district. In the design of commercial elements for Shiquan Street, the repeated use of colors can enhance spatial consistency and coherence, especially in areas with profound cultural heritage like Suzhou. The use of summarized traditional Chinese colors in different street segments can effectively maintain the overall features of the historic district without appearing chaotic.Hue analysis shows that frequently occurring hues are concentrated between 30–50 and 180–240, representing a balance between warm and cool tones. This contrast of cool and warm tones can be seen everywhere in traditional architecture in Suzhou, where gray walls and black tiles complement the natural landscape, creating a fresh and elegant visual effect. Cold tones are used to represent a peaceful and tranquil atmosphere, which is in line with the characteristics of historic districts in Suzhou.Saturation analysis shows that the saturation of the basic colors is generally low, usually between 20–40%. This is in line with the color usage style of historic districts in Suzhou, where elegant colors not only conform to the overall atmosphere of the ancient city but also avoid excessive commercialization and visual pollution in modern cities. On the other hand, the auxiliary colors have a relatively high saturation degree, reaching over 80%. These colors with high saturation are often used to embellish architectural details or signage, which can attract attention without disrupting overall harmony.Brightness analysis shows that the brightness of high-frequency colors is usually above 50%, especially the basic colors, which have a higher brightness. This design strategy is particularly important in traditional architecture, as the streets, alleys, and buildings in Suzhou are mostly small-scale spatial structures. High brightness colors can enhance the openness and brightness of the space. The brightness of auxiliary colors varies greatly, and some auxiliary colors use lower brightness to emphasize contrast with the basic colors, which enhances the visual hierarchy in the design, making the space more diverse and rich.The contrast between the basic colors and auxiliary colors indicates that the low saturation design with restrained gray and gray blue colors as the basic colors can complement the traditional architectural style of Suzhou’s historic districts without overshadowing the main focus. The high saturation and bright hues of auxiliary colors can be used to embellish the smaller facilities, signage systems in commercial elements, enhancing the functionality and readability of the space. This combination of calm and lively colors not only maintains the overall atmosphere of the historic district, but also reflects the principle of “visual balance” in modern design. The coordinated combination of primary and secondary colors helps to achieve the visual balance and the combination of movement and stillness. In the meantime, the traditional Chinese color results generated after analysis also reflect the “focus principle” in the design, that is, the basic colors maintain the overall historical atmosphere and the auxiliary colors of strong contrasts create visual focus, highlighting important information or areas. This is different from the current situation where some solid colors are excessively used in some commercial elements in Shiquan Street. The colors in the plan can effectively optimize the current situation and seek a balance between historical style and modern commerce.The analysis on the color data of the street standard segments shows that the basic colors are mostly neutral or cool tones with low saturation and high brightness, reflecting the respect and inheritance of Suzhou’s historical style in design; and auxiliary colors enhance the visual vitality and functionality of the neighborhood through contrasting colors with high saturation, low or high brightness. At the same time, both the basic colors of low saturation and the auxiliary colors of high saturation (decorative colors) come from traditional Chinese colors. This color strategy not only maintains the overall esthetic style of historic districts in Suzhou, but also achieves new heights in visual guidance and spatial experience. In the future, this method can be continued for the commercialized color selection of historical blocks in Suzhou and even the Jiangnan region of China, further enhancing the overall image and functionality of the districts, and maintaining a perfect balance between historical culture and modernization.

The survey results reveal significant shortcomings in the subjective perception of commercial colors in Suzhou’s Shiquan Street. Specifically, over 65% of respondents believe that the colors of commercial elements fail to align harmoniously with the overall tone of the historic district, with 34.65% explicitly expressing “strong disagreement.” This highlights pronounced issues in the visual harmony of current commercial color designs. Additionally, respondents aged 45 and above represent the highest proportion (34.65%), and those with postgraduate or higher education backgrounds account for 34.55%. These findings suggest that middle-aged and older individuals, as well as highly educated groups, are more attentive to the cultural continuity of the district’s colors. In terms of color acceptance, more than 50% of respondents chose “neutral” or “disagree,” indicating a cautious attitude toward the color changes brought about by commercial development. Furthermore, in the evaluation of cultural depth, 37% of respondents explicitly stated that the current commercial colors fail to effectively convey the historical and cultural essence of the district.

Comprehensive analysis indicates that the majority of the public is dissatisfied with the commercial color design in Shiquan Street, a conclusion consistent with the findings of the objective evaluation. The study also demonstrates significant positive correlations between the dimensions of Color Coordination, Color Homogenization, and Color Acceptance with Cultural Depth and Cultural Appeal. This suggests that thoughtful and well-coordinated color design not only enhances the cultural depth of historic districts but also effectively boosts regional cultural appeal, thereby indirectly fostering commercial attraction and economic benefits. These findings provide a critical theoretical basis and practical guidance for optimizing commercial color design in historic districts (Fig. 16).

Fig. 16

Variable Influence Relationship Diagram.

Current State and Future Trends of Commercial Colors on Shiquan Street from a Subjective Perspective.Based on the survey results, the issues highlighted in Suzhou’s Shiquan Street commercial color design go beyond mere visual discord; they reveal a deeper tension between the protection of historic cultural districts and commercial development. Firstly, in terms of color coordination, over 65% of respondents indicated that the colors of commercial elements do not align with the overall style of the district. This feedback underscores a commercial color design approach overly focused on economic benefits while neglecting the fundamental need for cultural and environmental harmony. This disharmony not only disrupts the visual consistency of the district but also undermines the continuity of its cultural heritage. Secondly, regarding cultural depth, 37% of respondents explicitly stated that the commercial color schemes fail to effectively convey the historical essence of the district. This cultural “disconnection” in commercial development suggests that design practices have not adequately incorporated the district’s cultural elements into color expression. Such oversight may stem from a lack of understanding of the cultural background of historic districts by businesses and a gap in policy guidance. Further analysis reveals a significant positive correlation between color acceptance and cultural appeal, indicating that public acceptance of commercial colors greatly influences the attractiveness of historic districts. This relationship suggests that relying solely on commercial design to attract consumers is no longer sufficient for the complex demands of modern historic districts. Instead, color design should emphasize a bidirectional integration of cultural and local characteristics. Furthermore, cultural appeal is crucial for the development of tourism in historic districts, and its deficiency directly weakens the competitiveness and sustainability of these areas. Path analysis results show that color homogenization and color acceptance have a significant positive impact on cultural depth and cultural appeal, revealing potential directions for optimizing color design. Specifically, current commercial color designs should focus more on balancing color coordination with cultural value expression rather than exclusively pursuing market appeal. A scientific analysis of colors in historic districts can effectively enhance the accuracy and applicability of color design. In summary, the commercial color issues on Shiquan Street are not merely a manifestation of visual conflict but also a reflection of the governance challenges posed by the conflict between cultural preservation and commercial interests. Future designs should comprehensively optimize from three dimensions: policy, technology, and public participation, positioning color design as a bridge between cultural preservation and economic development. This systematic approach can provide sustainable solutions for the development of historic districts. Such explorations have universal relevance and significant practical implications for the protection of historic districts in Suzhou and beyond.

Transforming the Commercial Color-Driven Model in Historical District Development. Currently, the design of commercial elements in historical districts frequently relies on high-saturation, high-contrast colors to attract visitors. While this approach may bring short-term economic gains, enhance commercial appeal, and invigorate historical districts, it compromises the cultural atmosphere, diminishes cultural continuity and regional characteristics, and ultimately hinders the sustainable development of these districts. The core value of historical districts lies in their unique historical and cultural heritage, not merely their commercial functions. Poorly designed colors risk alienating historical districts from their cultural essence, reducing them to ordinary “shopping streets” incapable of preserving their distinctiveness amid urbanization. Therefore, establishing a historical district cultural atmosphere centered on “color coordination” should become a primary direction for their preservation and development. Color coordination not only integrates harmoniously with the overall esthetic of historical architecture but also enhances the cultural appeal of the district. This approach meets public demands for cultural experiences while enabling economic benefits through cultural continuity. Questionnaire research indicates that thoughtful color design can indirectly promote commercial development by shaping a cohesive cultural atmosphere. In the case of Shiquan Street, the use of low-saturation, culturally significant regional colors highlights the uniqueness of the historical district and creates a warm and harmonious visual appeal, fostering long-term visitor engagement. Furthermore, the survey reveals that people tend to prefer appropriate color choices, which can provide higher-quality cultural contexts for commercial activities. This transforms commercial elements into carriers of cultural expression rather than disruptors of the district’s overall cultural ambiance. In practice, local governments and design teams should develop color control policies based on color coordination principles. These policies should align with the cultural and regional characteristics of the district, guiding the use of commercial colors in a balanced and well-planned manner. Additionally, fostering public participation and multi-stakeholder collaboration can ensure that color designs meet both the historical preservation needs of the district and the sustainable goals of commercial development. By balancing cultural preservation with commercial vitality, historical districts can evolve into “cultural districts” that combine cultural appeal with economic vibrancy, achieving both economic benefits and social value.

Fostering “Color Awareness” and “Heritage Culture” Education Among Younger GenerationsSurvey results indicate that the elderly, particularly local residents, generally believe that commercial colors significantly impact the atmosphere of historical districts and demonstrate a strong concern for preserving the historical ambiance. In contrast, among younger generations, individuals with higher education levels are increasingly aware of the disruptive effects of commercial colors on the cultural atmosphere of historical districts. However, a portion of less-educated youth shows little concern for, or even basic understanding of, this issue. They tend to view historical districts as ordinary commercial spaces, content with meeting immediate consumer needs while neglecting the deep cultural and heritage values these areas embody. This phenomenon reflects the insufficient dissemination of “heritage culture education,” highlighting a notable gap in the cultural preservation and transmission awareness among younger generations. “Heritage culture” is not only a testament to history but also the soul of urban culture. If younger generations lack a fundamental understanding of cultural heritage, the continuity of urban culture faces severe challenges. As a vital element of the cultural atmosphere in historical districts, color exerts a direct visual influence on shaping heritage value and enhancing cultural identity. Thus, starting with the tangible issue of “color awareness,” it is imperative to promote heritage culture education. Comprehensive and multi-faceted educational approaches should embed heritage culture protection deeply into public consciousness. On one hand, schools in China’s “historical and cultural cities” should integrate heritage culture into art and history curricula, helping younger generations understand the crucial role of “color” in heritage protection. On the other hand, local governments and cultural institutions should leverage real-world scenarios in historical districts to host diverse educational activities, such as historical district color exhibitions and interactive heritage protection workshops, enabling young people to tangibly experience the close relationship between color and cultural atmosphere. Additionally, spreading heritage culture knowledge via social and new media platforms can broaden the reach, embedding cultural preservation awareness into the daily lives of young people. As custodians and participants in urban development, the “color awareness” of younger generations impacts not only the visual presentation of commercial districts but also the transmission and continuation of urban culture. By establishing a comprehensive heritage culture education system, “heritage protection” can become a shared civic responsibility, ensuring the sustainable development of historical districts and revitalizing cultural heritage within the framework of urban renewal.

At the micro-level, both Shiquan Street and other historical districts in Suzhou exhibit disorganized commercial color management. In Suzhou, one major reason for this disarray is the lack of systematic color management awareness among individual merchants and their insufficient understanding of the overall esthetic of historical districts. Most of these merchants obtain their property rights in historical districts primarily through leases and are not local residents. When choosing commercial colors, merchants often prioritize short-term economic benefits and the ability to attract customers over the long-term impact their choices have on the district’s cultural and esthetic identity. This behavior results in the excessive commercialization of historical district colors, further exacerbating cultural and visual conflicts. For Shiquan Street, the excessive use of bright, highly saturated colors for signage, storefronts, and window displays is driven by the belief that these colors can enhance store visibility in the short term. However, in a historical district like Shiquan Street, characterized by a low-saturation, harmonious color palette, such choices appear out of place and create visual conflict and discord. The lack of communication between shops, with each independently selecting its color scheme without coordination, further contributes to the chaotic mix of colors in the same area, undermining the district’s cohesion. For instance, one shop might opt for a high-contrast red and green combination, while a neighboring store might use large areas of yellow, creating a visually jarring effect that leads to eye fatigue and potentially impacts the shopping experience (Fig.17).

Fig. 17: Examples of disharmonious Shop Signboards.

The photographs shown in these images were taken by the author himself with a hand-held camera. All other images are acknowledged by the author.

Additionally, many operators disregard the importance of preserving the historical esthetic. When designing commercial spaces, individual merchants often fail to recognize the cultural background and esthetic characteristics of historical districts. They tend to imitate modern commercial street designs, which starkly contrast with traditional districts, thereby diminishing the cultural value of these areas. Therefore, improving the color management awareness of individual merchants and operators is a primary concern for Shiquan Street. Local governments and relevant institutions should organize training sessions for merchants in historical districts to enhance their understanding of color management and the cultural significance of these areas. For example, sharing real-life cases and inviting professional designers to give lectures can help merchants understand how color choices affect the overall visual harmony of a district. While the goal is not to turn merchants into color-matching experts, the training aims to instill an awareness of color management. With this awareness, merchants might carefully deliberate before choosing highly saturated commercial design colors, thereby contributing to the preservation of overall color harmony in historical districts.

At the meso level, issues in managing commercial colors in historical districts, whether in Suzhou or other Jiangnan regions, often stem from a lack of unified management plans and multi-stakeholder coordination mechanisms. Historical districts typically encompass multiple cultural heritage buildings, and color management involves various parties, including local governments, merchants, residents, and professional design teams. As one of China’s first designated historical and cultural cities, Suzhou demonstrates that a significant portion of the current color-related disarray is due to fragmented management responsibilities and the absence of effective coordination and enforcement mechanisms, rendering existing policies ineffective. Currently, several historical and cultural cities in China have begun developing urban color guidelines. For instance, Nanjing in Jiangsu Province has introduced the “Nanjing Urban Color Control Guidelines”. Similarly, Beijing and Jinan, both recognized as historical and cultural cities, have issued the “Beijing Urban Color Design Guidelines” and the “Jinan Urban Color Control Guidelines”, respectively. However, Suzhou lacks such guidelines, reflecting the lack of governmental attention to color control and highlighting the gap in policy. Ambiguity in the division of responsibilities for color management is a primary manifestation of policy deficiencies. In Suzhou, for example, the responsibilities for color management in most historical districts are dispersed across different departments, such as heritage management authorities, commercial development offices, and urban planning departments. This lack of effective communication and collaboration leads to weak or contradictory policy enforcement. Without interdepartmental coordination, color design lacks comprehensive management, resulting in most historical district commercial colors being implemented without review or approval. The absence of targeted policies is another indirect cause of color-related issues. Existing color management guidelines are often principle-based, lacking specificity for particular districts and commercial scenarios. Many policies are too broad to address the unique cultural contexts and needs of each historical district. For example, this study developed a color scheme specifically for Shiquan Street, reflecting both the overarching urban color vision and the district’s locality and uniqueness. However, even with such a scheme, existing guidelines are often limited to general principles, lacking the specificity to create tailored color plans for different historical districts. This leads to a one-size-fits-all approach that sacrifices precision for general applicability. To address these challenges, it is imperative to establish cross-departmental coordination mechanisms to achieve efficient management of commercial colors in historical districts. Local governments could take the lead in forming specialized historical district management committees that integrate resources and expertise from heritage preservation, commercial development, and urban management departments. This ensures that color management policies are both consistent and scientifically sound in their formulation and implementation. Such mechanisms can eliminate issues related to unclear responsibilities and fragmented management, providing robust organizational support for policy implementation. A unified coordination mechanism at the governmental level is the foundation for the effective execution of color management policies and guidelines. Additionally, regionalized color management plans should be developed to accommodate the unique cultural contexts and characteristics of different historical districts. This entails conducting field surveys and data analyses, combined with a deep understanding of the cultural values of each district, to design more targeted and practical planning schemes for the commercial color styles and requirements of various functional areas. Scholars, research institutions, and relevant professionals should be engaged to undertake research projects that systematically analyze the historical and cultural connotations and color characteristics of districts. Through scientific analysis, such efforts can establish clear color tones and usage norms for districts while ensuring alignment with actual needs. This approach enables the effective protection and enhancement of a district’s cultural atmosphere, creating a harmonious blend of historical preservation and modern commercial development.

At the macro level, the fundamental solution to the commercial color issues in China’s historical districts requires top-level policy design and an improvement in social and cultural awareness. Currently, although China has enacted relevant cultural heritage protection laws, these regulations primarily focus on architectural structures and district functions, with little to no emphasis on color protection. For example, as shown in Table 10, some of the government-issued national policies and laws do not include specific provisions for the commercial colors of historical districts. Alternatively, there are only some broad, high-level guidelines or regulatory provisions at the macro level.

Table 10 Analysis of Relevant Laws and Regulations.

Therefore, the lack of laws and regulations at the national level for the management of commercial colors in historical districts directly leads to a lack of guidance for local governments when formulating relevant policies, making it difficult to form a systematic management framework. An indirect consequence of this phenomenon is the insufficient social awareness. The public generally lacks understanding of the cultural significance of color in historical districts, focusing more on commercial benefits while overlooking the role of color in cultural preservation and inheritance. Lastly, it must be pointed out that the severe lack of research and technical support has become a major obstacle to the development of color management. In China, particularly in regions rich in historical and cultural resources such as historical cities, cultural heritage districts, and architectural heritage sites, there is almost a complete gap in research on the impact of commercial signage colors. Existing research mainly focuses on architectural style protection, overall color coordination, or traditional cultural inheritance, but there is a lack of in-depth, systematic discussions on the involvement of commercial elements and their potential negative impact on the overall color harmony of historical districts. This research not only demonstrates a clear academic gap but also reveals a lack ofscientific rigor and operability in policy and practical implementation. Currently, China has not established a standardized, scientific color assessment system, making it difficult to create effective norms and guidelines in real-world management and research practice. This absence of research directly reflects insufficient attention to the conflict between historical district preservation and modern commercial development, let alone proposing targeted solutions. Specifically, while some regions aretrying to promote color management policies, due to the lack of supporting systematic research and technological development, the effectiveness of these policies is often undermined. In some areas, to avoid “errors” and achieve “harmonious unity,” there may be situations where all commercial elements are painted in a single color. Therefore, conducting this type of research is not only an academic breakthrough but also a task with significant practical importance. On a macro level, this relates to the competitiveness of China’s cultural heritage protection in a global context and reflects cultural confidence; on a micro level, it directly impacts the visual experience of residents and tourists in historical districts and shapes their sense of cultural identity. In the current context of increasing commercial encroachment on historical districts, the urgency of discussing the impact of commercial signage color on historical cultural districts and cultural heritage cannot be ignored. Amid the waves of marketization and globalization, if the potential impact of commercial elements on the colors of historical districts continues to be ignored, it could lead to the continuous erosion of the unique cultural value of these regions. In the later stages of the research, research institutions should develop effective color management tools and create feasible color assessment methods to provide sustainable solutions for the coexistence of historical districts and modern commercial activities.

This study focuses on the historic district of Shiquan Street in Suzhou and explores in depth the influence of modern commercial elements on its color features. By comparing and analyzing the commercial element colors and traditional architectural colors in different historical periods and regions, this study reveals the phenomenon of cultural heterogeneity caused by color incompatibility. This heterogeneity not only weakens the historical atmosphere of the districts, making it difficult to fully present the cultural and regional characteristics of historical buildings, but also exacerbates the homogenization of districts caused by commercialization. The introduction of a large number of modern business models has gradually led to the loss of uniqueness in historic districts, and the color conflict between history and commerce has become an important factor affecting the sustainable development of districts. More profoundly, the visual conflict of colors is not limited to physical spatial disharmony, but is more likely to affect social and cultural identity and historical perception. The commercialization process of districts is weakening residents’ and tourists’ awareness of their historical and cultural value, and the function of districts as carriers of cultural memory is gradually weakening as a result. The loss of traditional colors, as a symbol of cultural identity in historic districts, poses a challenge to the inheritance of cultural characteristics in the district, which may further affect the protection and development of cultural heritage.

The research results indicate that:

• (1) With the continuous increase of commercial activities and the dense distribution of commercial POIs, the color richness of the district has significantly improved, and traditional colors with low saturation have gradually been replaced by modern commercialized colors with bright and high saturation. On the one hand, this change has enhanced the vitality and attractiveness of the district, and improved economic efficiency. On the other hand, it also poses a threat to the cultural identity and visual integrity of historic districts.

• (2) In the subjective evaluation dimension, the commercial color design of Suzhou’s Shiquan Street shows deficiencies in terms of visual harmony, cultural expression, and public acceptance. The current design fails to effectively align with the overall style of the historic district, thereby weakening cultural continuity and visual consistency. The public, particularly middle-aged and older individuals as well as those with higher education, place greater emphasis on how colors reflect the historical and cultural essence of the district. Overall, cautious attitudes toward changes in commercial colors highlight the inability of Shiquan Street’s current color schemes to achieve an effective balance between preservation and development. Further analysis indicates that color coordination, homogenization, and acceptance significantly influence cultural depth and appeal. Thoughtful and scientifically designed color schemes can enhance cultural richness, improve cultural appeal, and ultimately promote economic benefits and commercial value.

• (3) In the evaluation from the objective dimension, there are significant differences in the color and traditional style of commercial elements within each standard segments of Shiquan Street. There are also significant differences between the commercial element colors and traditional styles. This difference stems from the failure to effectively follow the original style of the historic district in planning and controlling the selection of commercialized colors. On the contrary, businesses frequently choose colors with high saturation to create visual impact and attract tourists. In the long run, these colors are likely to erode the traditional style of the historic district, turning the real historic district into a modern commercial district with only the “shell of the historic district”.

• (4) Thus, some disharmonious color factors in the commercial elements of Shiquan Street in Suzhou are identified as a basis for suggestions and improvements. These color particles mark the core of a specific color group, forming a clear contrast with the traditional style of the historic district, thus having the potential to disrupt the overall color harmony. When choosing commercialized colors in the future, it is recommended avoiding the use of colors that have negative effects as much as possible to maintain the locality and harmony of colors of the districts.

• (5) There was detailed and unique color system in ancient China, in which each color had its own unique name and story behind it. This system has been widely applied in China’s traditional architecture, painting, and various objects and other material cultural heritage. However, as time passes by, modern color systems gradually have become dominant in the commercial field. In order to maintain the color characteristics of historic districts and address the issue of color homogenization in the commercial sector, we propose to introduce and utilize traditional Chinese colors in these historical areas, which can not only protect the unique color features of historic districts in modern cities, but also enhance the regional color characteristics of commercial elements, improving their cultural value and visual appeal.

In addition, in this study, color control is proposed as a core strategy to reconcile the conflict between historic districts and commercial elements. Traditionally, most research on the protection of historic districts has focused on building structures, functional renovations, and spatial layouts, with less attention paid to the key visual element of color. In this study, first, an evaluation method has been developed to assess whether the colors of commercial elements are in harmony with the overall features of the historic district. Secondly, a set of technical methods have been proposed to sort out and summarize the disharmonious commercial elements in historic districts. Finally, this study summarizes the color center points of historic districts and utilizes the “Chinese Traditional Color Card” to enhance the localization of colors, providing effective suggestions for color planning and guidance of commercial elements within historic districts.

The technological innovation of this study lies in proposing a mask2former model based on the “VIT” model, and optimizing the semantic segmentation results by combining the ImageNet dataset, ADE20K street view database, and Mapillary Vistas Dataset massive dataset, achieving the goal of accurate segmentation of commercial elements for a large amount of street view photo data. In the future, when faced with complex, atypical, and large quantities of street view photo samples, this method can also quickly and accurately segment commercial elements, providing new ideas for extracting color features of commercial elements located in historic districts and for related research.

Future research should further expand to different types of historic districts, exploring the complex interaction between color and spatial environment, especially the visual and cultural impact of modern commercial activities on historic districts in different cultural contexts. In addition, it is necessary to further combine historical geographic data and socio-cultural analysis to explore long-term strategies for cultural heritage protection under the background of commercialization, ensuring that the unique cultural value of historic districts can be preserved and inherited in the global wave of commercialization.