Introduction

Geographical indications (GIs) are markers for goods with distinct regional characteristics that often possess unique qualities and reputations (Tu and Zhu 2020). These characteristics reflect the natural and human factors of place of origin (Li 2020), thus granting the use of geographical names. These trademarked products represent the heritage of traditional knowledge and culture (Liu and Qin 2021), and play a crucial role in advancing local economies and rural development (Li 2021). The essence of GIs lies in the connections between products and their place of origin (Fei and Du 2020), indicating a distinct interplay between a region’s climate, geography, culture, and production technology. GIs are typically crafted within specific regions and become vital contributors to economic prosperity (Yu and Li, 2020). Additionally, the registration and protection of GI brands prevent illegal competition and infringement and uphold corporate reputation and market competitiveness (Lin and Sun 2020). The 19th National Congress of China highlighted agriculture, rural areas, and farmers as fundamental issues closely tied to the national economy and people’s livelihoods (Du 2021). In recent years, China’s Central No. 1 document has emphasized the importance of intellectual property protection in agriculture by developing green, organic, and regional agricultural products (Li et al. 2021). Furthermore, local governments increasingly prioritize declaring and registering GIs as new growth points for local economic development (Zhang et al. 2021). Therefore, as a crucial driver of rural revitalization in China, GIs are of significant practical importance as a contributor to accelerating agricultural modernization and promoting the agricultural economy.

Ji and Wang (2013) assert that GIs signifies the origin and quality attributes of agricultural products. Li (2021) emphasizes that GIs focus more on external forms to build regional brands. Cheng et al. (2017) categorize GIs as intellectual property with legal benefits. This study supplements and interprets the connotations of the compound geographical elements of GIs by establishing a robust theoretical foundation for future research. Existing studies on the spatial distribution patterns of regional geographical elements have primarily focused on traditional villages (Li et al. 2019), tourist resources (Ma et al. 2020), cultural heritage (Han 2017), agricultural products (Wang and Du 2021), and other socioeconomic factors. Furthermore, traditional industries face market imbalance and fierce competition due to rapid globalization and industrialization. Given GIs’ ties to internationally recognized products of specific regions, such as Chilean cherries, French champagne, and wine (Han 2020), numerous countries are increasingly focusing on GIs protection to enhance competitiveness in both domestic and international markets (Cassago et al. 2021; Hou 2021; Yang 2023). With China prioritizing GIs development and local enthusiasm for its applications, the growth of GIs in China is unprecedented. According to the 2021 Annual Report of the State Intellectual Property Office of China, there were 2490 registered GIs items and 6562 trademarks by the end of 2021. However, the report does not distinguish between foreign GIs and trademarks registered in China. The top five provinces in terms of GIs trademark registration were Shandong, Fujian, Sichuan, Hubei, and Jiangsu, accounting for 43.9% of the total registered volume. Some scholars have highlighted the challenge for China in achieving high-quality globally recognized GIs, as acceptance in international markets requires more exploration (Qian et al. 2023; Song 2018). French Bordeaux wine ranks first in the number of GIs trademarks owned by a single market entity (based on statistics from the State Intellectual Property Office), indicating its popularity in the Chinese market. The development of GIs in China started late, and their reputations were poor, necessitating reference and improvement in mature markets. Mutual recognition by GIs is poised to expedite this process. Despite China’s shortcomings in GIs management regulations, it is catching up with Europe’s pace of development.

Some studies on GIs focused on their economic significance, intellectual property rights, and impact on agricultural policies (Raustiala and Munzer 2007), where external environmental influences on GIs have been examined through a combined analysis of the macro-level economic environment and specific cases, and scholars proposing reference frames to offer new research directions for evaluation and analysis of GIs (Barjolle et al. 2009; Bramley et al. 2009). These studies intentionally explored the factors influencing GIs’ spatial patterns, serving as theoretical references and paving the way for research to investigate spatial distribution characteristics and influencing mechanisms. Though the multiple attributes of GIs are subdivided into various single attributes, such as commodity attributes (Deselnicu et al. 2013), economic attributes (Thiedig and Sylvander 2000), and political attributes (Cei et al. 2018), studies on the geographical properties have not received sufficient attention due to technical obstacles. Consequently, interpreting GIs’ connotations lacks crucial geographical attributes, potentially hindering their exploration based on spatial distribution characteristics. This oversight may lead to neglecting GIs’ demands for the geographical environment, thereby impacting exploration, quality assurance, and local economic development (Crescenzi et al. 2022).

Typically characterized by regional traits, GIs are easily transformed into tourism commodities (Mei 2014; Light and Young 2015). However, quantitative studies of their spatial patterns and influencing mechanisms are relatively scarce and lack multiscale, multidimensional, and dynamic analyses. To address these gaps, we used Shandong Province as an example and proposed a multiscale and multi-perspective quantification framework encompassing monomer- and regional-scale analyses and static and dynamic perspectives. This study aimed to provide a reference framework and case comparison for research on regional geographical elements similar to GIs.

Data sources and research methods

Data sources and preparation

The GIs data were sourced from the GIs information of 16 prefecture-level cities in Shandong Province, as published on the official website of the Ministry of Agriculture and Rural Affairs of China, from January 2008 to December 2021 (243 samples were utilized for this study). The geometric center of the area, that is, a township or village of the registration place was utilized to determine the location of the GIs. The administrative division data for Shandong Province were obtained from the Resources and Environmental Sciences and Data Center of the Chinese Academy of Sciences in 2015. Other geographical data were collected from the official websites of 16 prefecture-level cities and the Statistical Yearbook of Shandong Province for 2022. Additionally, the QGIS software was employed for geospatial data processing and visualization to display the spatial pattern characteristics of GIs, whereas the Geodetector was utilized to explore their influencing mechanisms (Du et al. 2001; Wang and Xu 2017).

Theoretical basis and research methods

Theoretical basis

GIs constitute a distinct category of geographical elements characterized by obvious regional traits and widespread spatial patterns, often displaying agglomeration, association, and heterogeneity features. Drawing inspiration from Tobler’s first law of geography (Tobler 1970; Miller 2004), this study primarily focuses on the association and heterogeneity of GIs in spatial distribution, employing a static perspective on spatial patterns. Globally distributed GI across various countries and regions has garnered international attention and attained a considerable scale. They exhibited a clustering trend in spatial patterns, enabling the exploration of connections between GIs and other geographical elements. Given that geographic entities adjacent to GIs bear greater relevance than non-adjacent entities, GIs possess distinct regional characteristics reflecting their origin’s natural and human geographical features. In a broader sense, they embody the characteristics of the human-land relationship within a region, extending beyond provinces and countries. Recognizing that external factors beyond the study area influence the existence and development of GIs, a comprehensive analysis was conducted, encompassing their geographical locations and surrounding external influencing factors at multiple scales, including monomer and regional scales. Considering multiple perspectives, such as static and dynamic, GIs can be viewed as regional tourism commodities with both explicit and implicit characteristics. Beyond traditional static factors, with the growing application of internet big data (Li et al. 2015), GIs exhibit a dynamic property in their occurrence space, evolving with changes in public perception. Internet hot words were utilized to represent public perceptions of GIs, capturing the correlation between the domestic and international fame of GIs. This approach was employed to explore the varying effects of various factors on public cognition.

Analysis framework

As illustrated in Fig. 1, to delve into GIs’ spatial patterns and influencing mechanisms, we formulated a comprehensive quantifying framework encompassing multiple scales and perspectives, namely, the spatial agglomeration, autocorrelation, and heterogeneity of GIs. This framework considers both monomer and regional scales and static and dynamic perspectives.

Fig. 1
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Research framework of this study.

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At the monomer scale, a geographical entity (i.e., a GI) was treated as a point, whereas the regional scale pertained to the entire region (i.e., all GIs within the region). Specifically, taking Shandong Province as an example, the monomer scale analysis delved into the spatial patterns and influencing mechanisms of GIs’ kernel density and GIs’ public perception based on Internet search engine webpage popularity in this study. It was convenient to show the spatial distribution characteristics of geographical elements (Qu et al. 2018). Simultaneously, the regional-scale analysis involved GIs’ spatial distribution patterns in prefecture-level cities of Shandong Province and their corresponding influencing mechanisms; for the analysis at this scale, it was convenient to provide a decision reference and case support for overall regional economic development (Li and Qu, 2021). Incorporating the regional-scale analysis of GIs could objectively reflect the overall spatial pattern distribution and the overall development level of GIs in a certain region and reflect the degree of support of the regional society and economy for their development, providing insights into the high-quality agricultural development of the region. As for the combination of static and dynamic perspectives (Fig. 1), the former delved into the spatial pattern of GIs’ kernel density and its influencing mechanism, whereas the latter involved the spatial pattern of GIs’ public perception and its influencing mechanism, considering the dynamic nature of Internet search engine page popularity over time.

Kernel density analysis

Kernel density estimation, a nonparametric method for estimating probability density functions, was employed in this study to assess the probability density function of the GI sample data. This technique computes the probability density function by positioning a kernel function at each data point, and subsequently weighting the average of these kernel functions. Unlike point density analysis, this approach obviates the need to specify the neighborhood and estimate density through the contribution value generated by the sample points to establish a feature relationship in the spatial distribution of GIs (Silverman 1986).

Equations (1) and (2) delineate the kernel density estimation process (Węglarczyk, 2018):

$$\hat{f}(x)=\frac{1}{{h}^{d}}\mathop{\sum }\limits_{i=1}^{N}{w}_{i}K\left(\frac{\parallel x-{x}_{i}{\parallel }_{p}}{h}\right),{where}\mathop{\sum }\limits_{i=1}^{N}{w}_{i}=1$$
(1)
$${\left\Vert x\right\Vert }_{p}={\left(\sum _{i=1}{\left|{x}_{i}\right|}^{p}\right)}^{\frac{1}{p}}$$
(2)

In Eqs. (1) and (2), \(\hat{f}(x)\) represents the value of the GIs’ kernel density, h denotes the search radius (h > 0), N is the number of GIs in Shandong Province. K(x) is the kernel function, and d signifies the dimensionality of spatial space, where d = 2 for two-dimensional spatial distributions. (x – xi) signifies the distance between sample points x and xi. Smaller distances between two sample points amplify the contribution of x to the density estimate (Zhou et al. 2020; Li et al. 2020).

Spatial autocorrelation analysis

Following Tobler’s first law of Geography, “everything is related to everything else, but near things are more related than distant things” (Miller 2004). Therefore, utilizing spatial autocorrelation as a statistical method provides a comprehensive assessment of the aggregation and dispersion of sample points, both globally and locally (Yan 2016).

Moran’s I index

The application of global Moran’s I objectively signifies whether the distribution of the sample points exhibits a numerical spatial correlation. Meanwhile, the local Moran’s I visually display correlations by showcasing the degree of aggregation in local areas. The formula is expressed as (Anselin 1995):

$$I=\frac{n}{{S}_{0}}\times \frac{{\sum }_{i=1}^{n}{\sum }_{j=1}^{n}{w}_{{ij}}\left({y}_{i}-\bar{y}\right)\left({y}_{j}-\bar{y}\right)}{{\sum }_{i=1}^{n}{\left({y}_{i}-\bar{y}\right)}^{2}}{,S}_{0}=\mathop{\sum }\limits_{i=1}^{n}\mathop{\sum }\limits_{j=1}^{n}{w}_{{ij}}$$
(3)

In Eq. (3), \(\overline{y}\) denotes the mean value of variable y, while yi and yj represent the variable values at spatial locations i and j (i ≠ j). Additionally, wij signifies the spatial weight function between (Anselin 1995; Soltani and Askari 2017; Vilinová 2020). Typically ranging from –1 to 1, Moran’s I values provide valuable insights. I > 0 indicates a positive spatial correlation, suggesting that larger (or smaller) attribute values tend to cluster together. Conversely, I < 0 indicates a negative spatial correlation, where larger (or smaller) attribute values tend to be dispersed. I = 0 signals spatial randomness. When spatially aggregated, the global Moran’s I index derives significance from its p-value (significance level) and z-score (critical value). p > 0.05 implies a significant correlation, while z > 1.96 signifies a significant positive correlation, and z ≤ 1.96 indicates a significant negative correlation.

In this study, a uniform weight was assigned to the GIs to ensure consistent differentiation within the region. Each element was selected to count the contained GIs samples, and the counting size of each “element” served as the weight for spatial autocorrelation analysis (Chen 2009; Chen et al. 2021; Chen 2021).

Geary’s C index

Geary’s C index, akin to Moran’s I index, is a valuable tool for measuring spatial autocorrelation, enabling the assessment of spatial clustering or dispersion within a geographic space. Geary’s C index is calculated as follows (Mathur 2015):

$$C=\frac{n-1}{2{\sum }_{i=1}^{n}{\sum }_{i=1}^{n}{w}_{{ij}}}\cdot \frac{{\sum }_{i=1}^{n}{\sum }_{j=1}^{n}{w}_{{ij}}{\left({x}_{i}-{x}_{j}\right)}^{2}}{{\sum }_{i=1}^{n}{\left({x}_{i}-\bar{x}\right)}^{2}}$$
(4)

In Eq. (4), where n denotes the sample size, xi represents the observed value of region i, \(\bar{x}\) represents the average value of all spatial unit observations, and \({w}_{{ij}}\) signifies the spatial weight between spatial units i and j by gauging their spatial relationship. The Geary’s C index ranges from 0 to 2. A value near 1 indicates random data distribution with no spatial autocorrelation, whereas a value near zero suggests spatial clustering. A value close to two signifies spatial dispersion with negative spatial autocorrelation (Mathur 2015).

To test the significance of the Geary’s C index using the Z statistic, the following formula was used:

$$Z=\frac{C-E(C)}{{Se}(C)}$$
(5)

In Eq. (5), where C represents the observed Geary’s C index, E(C) is the expected value of the Geary’s C index under the null hypothesis of no spatial autocorrelation (equal to one), and Se(C) is the variance of the Geary’s C index. The Z statistic calculates the p-value associated with the observed Geary’s C index, indicating the significance of the spatial autocorrelation. A significantly positive Z value suggests that the calculated Geary’s C-index value exceeds the expected value under a random spatial distribution, signifying strong spatial autocorrelation. Conversely, a significantly negative Z value indicates that the calculated Geary’s C index value is much lower than expected under a random spatial distribution, indicating strong spatial autocorrelation (Fortin et al. 1989; Getis 2009).

Internet hot word analysis

To a certain extent, the number of pages on the Internet containing a specific word reflects the word’s online prevalence and serves as a social-level indicator of attention (Xie et al. 2017). This study employed this dynamic indicator for a comprehensive exploration of relevant GIs’ public perception by conducting full-term searches on three widely used internet search engines: Baidu (www.baidu.com), Bing (www.bing.com), and Google (www.google.com). The search scope for Bing and Google was set from 2008 to 2023, which was consistent with the initial publication time of GIs from the official website of the Ministry of Agriculture and Rural Affairs of China. This formula is expressed as follows:

$$f(x)=\frac{{\sum }_{i=1}^{n}{\alpha }_{i}}{n}$$
(6)

In Eq. (6), f(x) represents a GI’s Internet hot word index, with n denoting the number of selected Internet search engines (i = 1, 2, 3; n = 3) and αi representing the count of web addresses retrieved by the Internet search engines. Equal weights were assigned to the three search engines. The domain names of the leading e-commerce websites (https://www.taobao.com/) were blocked, excluding them from this analysis to enhance research precision and reduce the impact of GIs’ products on commercial websites as much as possible.

Geodetector

Geodetector, introduced initially to analyze the influencing factors of newborn neural tube malformations in Heshun County of Shanxi Province, China and contributing to environmental health research (Wang et al. 2010), has evolved into a primary model for investigating spatial heterogeneity and its formation mechanisms. It finds widespread application in diverse fields such as land use, public health, regional economy, planning, tourism, ecology, environment, pollution, remote sensing, computer networks, and life sciences (Xu et al. 2018; Bai et al. 2019; Chen et al. 2019; He et al. 2019; Zhu et al. 2019; Fang et al. 2020; Liu et al. 2020; Tan et al. 2020; Zhu and Alimujiang 2020; Li et al. 2021; Wu et al. 2021).

In this study, factor detection, interaction detection, and ecological detection via Geodetector were employed to analyze the strength of the influence of the spatial distribution and spatial differentiation of GIs in Shandong Province, and to explore the interaction effect between these two factors. The model expression for the geodetector is as follows (Wang and Xu 2017):

$$q=1-\frac{{\sum }_{h=1}^{L}{N}_{h}{\sigma }_{h}^{2}}{{N}_{{\sigma }^{2}}}$$
(7)

In Eq. (7), q represents the magnitude of the influence of spatial differentiation factors on GIs in Shandong Province, and ranges from 0 to 1. A value of q = 1 indicates a complete alignment between the influencing factors and spatial distribution, signifying absolute control. Conversely, q = 0 denotes a total lack of association between the influencing factors and the spatial distribution. N represents the number of sample areas within the study region, that is, the 16 administrative divisions of Shandong Province. Nh refers to the number of GIs samples in each prefecture-level administrative area. σ2 is the distribution variance of GIs in the study area, indicating the degree of spatial difference of GIs in Shandong Province. \({\sigma }_{h}^{2}\) is the distribution variance of GIs in the unit study area, indicating the degree of spatial difference of GIs in each prefecture-level administrative area. L is the number of unit research areas, representing the number of clustering and grading partitions of influencing factors. The precondition for the validity of Eq. (7) is σ2 ≠ 0, ensuring the existence of spatial differentiation among GIs across Shandong Province. This formula gauges the impact of influencing factors on the spatial distribution of GIs and their number per unit area (Xu et al. 2023).

Case area

Shandong Province, located on the east coast of China in the lower reaches of the Yellow River, boasts a rich agricultural legacy and is recognized as one of the ancient civilization cradles and the largest agricultural province in China. Over the years, the industrial people of Shandong have contributed significantly to farming, inventing numerous agricultural tools and actively cultivating cash crops (Xia 2010; Peng 2011; Tian et al. 2021). While enhancing water resource efficiency for stable farmland irrigation, the province has actively integrated into the high-quality agricultural development of the Yellow River Basin. This involves resource-sharing and complementary advantages through collaboration with other provinces and cities in the basin (Ji 2015; Hua 2021; Zha et al. 2022). These distinctive natural and social conditions play pivotal roles in the formation and development of GIs in Shandong Province, contributing to its status as the province with the highest abundance of GIs (Zhang et al. 2021). A comprehensive analysis of the spatial distribution and factors influencing GIs in Shandong Province is important for the protection, development, and exploration of cultural connotations, thereby fostering the local economy (Fan et al. 2020).

Owing to variations in current statistical parameters, differences in statistical quantities may arise among different studies. Taking Shandong Province as an example, this study conducted a quantitative analysis of the spatial patterns of GIs, showcasing strong regional typicality in the underlying influencing mechanisms and offering generalizable research insights. Serving as a valuable reference for the research framework of high-quality agricultural development in Shandong Province and beyond, this study aids the promotion of high-quality agricultural products, enhancing their reputation, increasing farmers’ incomes, and facilitating sustainable regional economic development.

Data analysis and results

Spatial pattern characteristics of GIs in the case area

Quantity distribution of GIs in Shandong Province

As distinctive intellectual properties localized in specific regions, GIs hold brand and cultural values and play a vital role in fostering agricultural economic growth and rural revitalization (Yin et al. 2021). As depicted in Fig. 2 and detailed in Table 1, coastal cities host a significant share of GIs, constituting 59.26% of the total registered GIs in Shandong Province. This dominance suggests that the coastal geographical setting favors the production of high-quality seafood, endowing these areas with advantages in terms of market recognition and competition (Cao and Wang 2020). In summary, the distribution of GIs in Shandong Province was uneven across administrative divisions, with coastal regions enjoying a geographical edge over their inland counterparts.

Fig. 2
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GIs in Shandong Province.

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Table 1 Quantity distribution of GIs in Shandong Province.
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Spatial agglomeration and association analyses of GIs in the case area

Spatial agglomeration analysis of GIs in Shandong Province

To uncover the spatial agglomeration characteristics of GIs in Shandong Province, we employed kernel density analysis. Figure 3a illustrates the kernel density values of GIs’ spatial distribution, revealing that the GIs were primarily concentrated in Linyi, Qingdao, and Weihai. In Linyi City, the kernel density values of the GIs ranged from 24.0245 to 42.0423, indicating a relatively concentrated distribution. GIs from Linyi City, such as Mengyin peach and Cangshan pepper, thrive on hilly terrain, fertile soil, and significant temperature variations, fostering ideal conditions for agricultural product growth (Gu et al. 2021). Qingdao displayed a wider distribution range, with a kernel density ranging between 18.0184 and 42.0423, indicating clustered product distribution. Key GIs in Qingdao, including Tsingtao, Jiaodong, and Jimo pears, benefit from favorable natural conditions, such as significant temperature variations, ample rainfall, fertile soil, and abundant sunshine (Fu 2017). Similarly, in Weihai City, the GIs exhibited a concentrated distribution along the coastal areas, with kernel densities ranging from 18.0184 to 36.0367. Prominent GIs in Weihai City, such as Weihai hairy crabs and Weihai rice, thrive in habitats rich in marine resources and clear water near the Yellow Sea and Bohai Sea. The large temperature difference between the day and night in coastal areas further supports the growth of high-quality agricultural products.

Fig. 3: Spatial distribution characteristics of GIs in Shandong Province.
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a Kernel density analysis of GIs. b Local Moran’s I Index LISA plot of GIs.

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In summary, the kernel density analysis underscores the significant influence of natural factors on the distribution of GIs in Shandong Province. The heightened particle density values in Linyi, Qingdao, and Weihai can be attributed to favorable natural conditions in these regions.

Spatial association analysis of GIs in Shandong Province (Moran’s I index)

Table 2 presents the results of the spatial association analysis using the global Moran’s I index for the GIs in Shandong Province. These findings suggest that when considering Shandong Province as a whole area, the spatial distribution of GIs in a unit region tends to exhibit clustering rather than a random distribution. The global Moran’s I index in this analysis was greater than zero, indicating a positive correlation between the spatial data. Although Moran’s I value was relatively small in this part, the z-score surpassed 3.51, and the p-value was less than 0.01. This combination only suggests the high reliability of the spatial aggregation effect, but the degree of correlation warrants careful exploration (Chen 2023). When the distance threshold was adjusted (5, 10, and 20 km), Moran’s I decreased further when the threshold increased, particularly with a small value. This trend indicates that as the range of influence of the GIs quantity in unit space expands, the degree of correlation of spatial data in Shandong Province shows a decreasing trend.

Table 2 Moran’s I of different thresholds of GIs’ spatial distribution.
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From the LISA chart of the local Moran’s I index (Fig. 3b), GIs in Shandong Province exhibited no low or low-low aggregation as a whole. On the Jiaodong Peninsula, a pattern of high-low aggregation was observed, whereas in southwest Shandong Province, there was a pattern of large-area aggregation, specifically high-high aggregation. Moreover, Qingdao and Dongying, located between high and low and high-high aggregation areas, did not exhibit significant aggregation characteristics (Liu and Che 2019).

Spatial association analysis of GIs in Shandong Province (Geary’s C index)

Table 3 presents the calculation results of Geary’s C index for the spatial autocorrelation of GIs at the monomer scale, considering indicators Y1 (the kernel density value of spatial distribution of GIs) and Y2 (public perception of spatial distribution of GIs). The results show that Geary’s C-statistic for Y1 is 0.15968, significantly smaller than the expected value of 1 (p < 0.001), indicating a noteworthy spatial clustering autocorrelation for the kernel density indicator in the sample. For Y2, Geary’s C-statistic was 0.63721, which was smaller than the expected value of 1 (p < 0.036) at a significance level of 5%, signifying a significant spatial clustering autocorrelation for the dynamic social recognition of GIs.

Table 3 Geary’s C of GIs’ spatial distribution.
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Spatial heterogeneity and its influencing mechanisms of GIs in the case area

Possible influencing factors of GIs’ spatial heterogeneity in Shandong Province

Considering the geographic characteristics of the study area, data availability, and existing research (Xue et al. 2020; Yang et al. 2016), we categorized the factors influencing the spatial distribution of Y1, Y2, and Y3 into 15 influencing factors: natural (X1–X7 in Table 4) and socioeconomic (X8–X15 in Table 4). Analysis of the factors influencing the spatial distribution of GIs was aimed at exploring spatial heterogeneity using a geodetector. Notably, this analysis included towns and villages with rich historical and cultural heritage, potentially enriching the cultural connotations of GIs.

Table 4 Possible influencing factors of GIs’ spatial heterogeneity in Shandong Province.
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Spatial heterogeneity detection and its influencing mechanisms of GIs in Shandong Province

Factor interpretation power detection analysis

Factor detection in the Geodetector was employed to assess the explanatory power of the influencing factor X for a specific phenomenon value Y. By conducting the factor detection analysis, the q and p values of each influencing factor affecting the distribution of GIs in Shandong were obtained (Table 5). The q-value in the table indicates the influence of the differentiated factors on the distribution of GIs in Shandong, and the p-value signifies the degree of significance obtained using the statistical significance test method (Chen et al. 2022). Correlating with the information provided in Section 3.2.1, the meanings of X1–X15 correspond to those listed in Table 4. As shown in Table 5, Y1 represents the kernel density of GIs’ spatial distribution, Y2 refers to the public perception (i.e., the popularity of GIs terms through Internet search engines) of GIs’ spatial distribution, and Y3 denotes the quantity density of GIs in a region (i.e., a prefecture-level city).

Table 5 Exploratory power of the Y factor detection results.
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In Table 5, p1 values of X1 (temperature), X2 (precipitation), X5 (soil type), X9 (gross domestic product (GDP) per capita), X10 (number density of towns and villages with rich historical and cultural heritage in prefecture-level cities), X11 (proportion of primary industry), X12 (urbanization rate), X13 (proportion of tertiary industry), and X15 (output value of agriculture, forestry, fishery, and animal husbandry) were all less than 0.05, indicating that these factors significantly affected the spatial distribution of GIs. Notably, GDP per capita and agricultural output reached their two highest q-values, surpassing 0.4, demonstrating a statistically significant impact on GI distribution.

In analyzing the comprehensive popularity of GI terms using well-known Internet search engines, only the p-value of NDVI was less than 0.05, reflecting a significant correlation. According to the analysis, GIs in regions with a high vegetation cover index have a more significant effect on gaining popularity on the Internet, and their q-value is maximal but less than 0.1 among the 15 influence factors. Overall, all the factors that influence the perceived popularity of GIs have almost no impact, suggesting that the degree of perception of GIs in China is not high.

Y3 focuses on the density of GIs in various cities and includes the ratio of registered GIs in each city to the land area of the administrative districts. The analysis employed region-based elements, including factors X1, X2, X10, X11, X12, X13, and X15 as analytical variables. The selected analytical factors did not significantly affect the density values. The q-values were highest for X15 and X12, at approximately 0.97 and 0.79, respectively. This underscores the importance of agricultural output and urbanization levels in relation to the density of GIs in the city. Conversely, the smallest values observed for X1 and X2 were below 0.1, suggesting no apparent correlation between temperature, precipitation, and GIs’ density in the city.

Combined with the multi-angle analysis of the three Y values, it can be inferred that the distribution quantity of GIs has a strong correlation with the level of urbanization, degree of economic development, and production levels of agriculture, forestry, fisheries, and animal husbandry in the city, whereas the correlation between natural factors and their distribution quantity does not show a significant correlation. It is inferred that the popularity of GIs may be a more comprehensive factor, and it is easier to produce better-known GIs in regions where the influencing factors are more comprehensive, and each factor has certain advantages.

Interaction detection and ecological detection

Interaction detection in a geodetector assesses the interaction value between two impact factors, indicating whether the Y value increases or decreases when the two factors work together. Referring to previous research (Xu et al. 2021), the 15 impact factors selected in Table 4 were used for interaction detection, and Python was employed for visualization (Figs. 4, 5). The different colors in Table 6 are used to distinguish between bivariate enhancement and nonlinear enhancement. Ecological detection in the geodetector was employed to determine whether a statistically significant difference existed between two factors, XA and XB. If factor XA was significantly greater than factor XB, it was denoted by “T”; conversely, if no significant difference was observed, it was denoted by “F.” In Table 6, the ecological detector detection (Wang et al. 2016) of 10 impact factors produced an evaluation table of the ecological detection of the impact factors.

Fig. 4: Relationship between factor detection and interaction detection (taking Y3 for example).
figure 4

a Factor interpretation power detection. b Interaction detection.

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Fig. 5: Interactive geographical detector results.
figure 5

a Result for kernel density value of GIs (Y1). b Result for public perception of GIs (Y2). c Result for GIs’ density in each city (Y3).

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Table 6 Interactive values of Y factor results.
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As shown in Fig. 5, the analysis of Y1 revealed that the highest values resulting from the interactions X2 ∩ X9, X5 ∩ X9, X7 ∩ X9, X2 ∩ X15, X5 ∩ X15, and X7 ∩ X15 exceeded 0.65. The interactions X7 ∩ X9 and X7 ∩ X15 exhibited a nonlinear enhancement effect, while the rest demonstrated bivariate enhancement. Shifting focus to the analysis of Y2, the interactions X7 ∩ X9, X7 ∩ X10, X7 ∩ X12, X7 ∩ X13, and X7 ∩ X15 all exceeded 0.45 and represented the maximum values. The effects of these interactions manifest as a nonlinear enhancement. For the analysis of Y3 displayed in Figs. 4 and 5, the interactions X2 ∩ X12, X2 ∩ X15, X10 ∩ X15, X11 ∩ X15, and X12 ∩ X15 all had approximate values close to 1. Only X2 ∩ X12 displayed a nonlinear enhancement, while the rest exhibited bivariate enhancement effects. In addition, the overall performance of the interaction was greater than that of the other groups, with 11 items having values greater than 0.95, indicating that the effect of the interaction of the impact factors was significant.

Discussion

Recently, there has been a growing focus on developing high-quality agricultural products. GIs have become a central aspect of agricultural products, driven by the need to upgrade the agricultural economy and increase the interconnectivity of world trade. This transformation has shifted these products from traditional development modes and concepts to modern international standards (Zhao et al. 2014). These changes have led people to embrace the concept of GIs, with some expressing a willingness to choose agricultural products that bear GIs’ marks (Török et al. 2020; Teuber 2011). Thus, this study intends to enrich the quantitative research on GIs’ spatial patterns and influencing mechanisms based on multiscale and multi-perspective analyses. The main contents are summarized as follows:

Spatial agglomeration, spatial association, and spatial heterogeneity of GIs in Shandong Province

Based on Shandong Province, the spatial pattern characteristics of GIs were investigated by exploring spatial agglomeration, association, and heterogeneity at regional and monomer scales. As the emergence and development of GIs depend on the combined effects of natural and socioeconomic conditions, the effects of spatial agglomeration and the association of GIs at regional (i.e., prefecture-level cities) and monomer scales were used by utilizing kernel density estimation and spatial autocorrelation analysis. At the regional scale, areas with higher kernel density values were concentrated in the southeastern Shandong Province, encompassing cities such as Linyi, Qingdao, and Weihai. While no significant correlation was identified, it was evident that GIs’ spatial distribution was not random. There was a noteworthy spatial clustering autocorrelation on the monomer scale for the dynamic social recognition of GIs. Moreover, the spatial pattern of GIs was correlated with the spatial distributions of natural, economic, social, cultural, and other factors. From a static perspective, the correlation of the spatial distribution patterns between socioeconomic factors and GIs’ spatial density outweighs that of natural factors. However, no significant correlation was found for public perceptions of GIs from a dynamic perspective. Considering the indicator of GIs terms’ popularity through Internet search engines, it can be inferred that the spatial autocorrelation between public perceptions of GIs and natural/socioeconomic conditions may be less crucial under the influence of Internet connectivity.

Furthermore, kernel density analysis often represents the spatial distribution’s dispersion degree for point geographical objects and reveals the spatial distribution law by counting the number of geographical objects in a specific neighborhood (Li et al. 2023). The GIs of Shandong Province had prominent spatial agglomeration distribution characteristics, as seen in the kernel density analysis. Considering that the Moran’s I index in GIs’ spatial association analysis was more significant than zero and relatively small, it did not have significant spatial autocorrelation; the analyses of the Moran’s I index and Geary’s C index were reliable, yet the spatial autocorrelation analysis involved a paradox, which suggested a high reliability of the spatial aggregation effect, while the degree of correlation was worth exploring (Chen 2023).

Influencing mechanisms of GIs’ spatial heterogeneity in Shandong Province revealed through single factor detection and interaction detection

Analysis of GIs’ spatial distribution patterns and 15 influencing factors using a geodetector revealed a double-factor enhancement and nonlinear enhancement effect. Joint interactions had a more significant impact than any single influencing factor, emphasizing the complexity of the spatial heterogeneity of GIs. This heterogeneity exhibited a stronger correlation with socioeconomic factors than natural factors, evident in both single-factor and interaction analyses. The results indicated that factors influencing GIs’ spatial heterogeneity mutually promoted each other, highlighting the phenomenon affecting GIs’ spatial differentiation not driven by a single factor but rather by a combination of multiple factors.

Evaluation framework and development suggestions for GIs presented through case reference

GIs have significantly contributed to national economic growth (Mesić et al. 2017). This study proposes a multiscale and multi-perspective quantification framework for GIs’ spatial patterns and their influencing mechanisms. This offers a scientific reference for understanding spatial distribution laws, formation mechanisms, and sustainable utilization. Notably, the spatial heterogeneity analysis of the public perception of GIs from a dynamic perspective provides valuable data and methodological insights for domestic sales and reputations of agricultural products and the export of domestic GIs products, using Shandong Province as an example.

Furthermore, enterprises producing and selling GIs should consider increasing capital investment to enhance output quality and embrace production modernization. Governments play a crucial role in implementing measures to protect the natural environment, improve the economic environment, and formulate policies conducive to GI development.

Conclusions

This study makes two significant contributions to existing literature. First, it introduces a multiscale and multi-perspective quantifying framework for understanding GIs’ spatial patterns and their influencing mechanisms. It can even provide a relevant reference for studying other geographical elements with spatial differentiation. Multiscale analysis encompasses monomer and regional scales, whereas multi-perspective analysis includes static and dynamic perspectives. This framework offers valuable insights into decision-making for agricultural brand construction and high-quality development of agriculture, particularly in regions represented by GI products, and analyzing other geographical elements. European studies on GIs are extensive, but there is a need for further exploration in other regions; plans include a comparative study involving different countries to validate and enhance the proposed method.

Second, this study achieved a relatively comprehensive quantification of the mechanisms influencing the spatial heterogeneity of GIs and revealed intricate connections between the spatial heterogeneity of GIs and influencing factors by utilizing 15 influencing factors in the analysis and employing both single-factor detection and interaction detection.

However, certain aspects require further investigation. The analysis did not consider other conditions for GI development, such as local government incentive policies, owing to quantification ambiguity. While a composite analysis framework for geographical elements was proposed, the degree of exploration was deemed insufficient, and no significant factors for spatial differentiation were identified in public perception. Future research should also explore the implications of high-quality development and sustainable use of GIs in different countries. As spatial autocorrelation analysis involves a paradox (Chen 2023), the degree of correlation deserves further exploration through a comparative analysis of the study area. Given the commodity attributes of GIs’ products and the development background of the “Internet +“ model in China, GIs’ public perception based on “hot word analysis” may be used as a potential dynamic indicator, considering the dynamic nature of Internet search engine page popularity over time. Thus, more suitable dynamic analysis indexes need to be discussed further.