Tourism in Historic Urban Areas: Construction of Cultural Heritage Corridor Based on Minimum Cumulative Resistance and Gravity Model—A Case Study of Tianjin, China

Abstract

The effective protection and utilization of historical and cultural heritage in urban and rural areas are increasingly gaining public attention. Constructing a continuous and complete heritage spatial network is an important means for achieving holistic protection and utilization of heritage, and it is also a crucial approach to enhancing the overall connectivity of regional culture. How to construct a cultural heritage corridor is of great significance to the comprehensive protection of cultural heritage in historic urban areas. This study takes the cultural heritage of Tianjin’s historical urban areas as an example, uses the Minimum Cumulative Resistance (MCR) and Gravity Model (GM) to construct a cultural heritage corridor, and evaluates its suitability. This research enriches the theory of heritage conservation, aims to enhance the connectivity and integrity of cultural heritage, and provides a new perspective for the integrated development of regional culture and tourism in the process of activation and utilization.

1. Introduction

Historical urban areas are not just the foundation and soul of urban culture [1], they are also crucial carriers of Chinese civilization. They are vital in protecting and inheriting historical and cultural heritage. These areas document the city’s developmental history and evolution and nurture a rich living culture and valuable cultural heritage, reflecting the city’s unique spirit and character [2]. However, existing cultural heritage resources with significant historical and cultural value and potential for revitalization have not reached their full potential due to issues such as dispersed distribution, inconsistent conservation statuses, and underutilization. A thorough understanding of the historical and cultural value of cultural heritage in the revitalization and utilization requires in-depth studies of various cultural heritage elements and their interconnections within specific regions. This research not only facilitates the activation of secondary cultural heritage sites but also enhances nearby public infrastructure, catering to the public’s diverse and individualized needs at multiple levels.

The heritage corridor concept, derived from the 1980s American greenway concept, focuses on strategies and methods for protecting linear cultural heritage [3,4]. In 1984, the U.S. Congress enacted legislation establishing the world’s first national heritage corridor [5], significantly influencing the American environment in both material and ideological aspects [6]. Since then, the understanding and importance of heritage corridors have evolved, redirecting cultural heritage protection from isolated sites to comprehensive linear areas. Scholars, including Robert M. Seams, describe a heritage corridor as ‘a linear landscape with a unique assemblage of cultural resources’ [7].

In the 21st century, scholars such as Wang Zhifang in China introduced the heritage corridor concept, emphasizing an integrated planning approach focused on economic development and tourism enhancement [8]. Leveraging the Erie Canal National Heritage Corridor’s experience in the U.S., Chinese scholars have offered specific recommendations for the protection and utilization of China’s heritage corridors [9]. Heritage corridor research is increasingly focusing on localized theoretical studies that prioritize cultural heritage protection within the framework of urban master planning strategies [10]. Ebbe highlights that comprehensive protection of public areas, streets, and green spaces is crucial not only for cultural heritage preservation but also for boosting urban developmental vitality [11].

In cultural heritage corridor research, international scholars, especially from the United States, have a longstanding research history and lead in practical exploration. Over three decades, the United States has established a comprehensive cultural heritage corridor system. Recently, international researchers have concentrated on studying the future development of cultural heritage, focusing specifically on community involvement, sustainability, and cultural and tourism growth. Van Knippening and colleagues focused on community participation in heritage management, highlighting the need for adaptability, flexibility, and awareness of complexity in community engagement [12]. Katapidi and others contributed new insights into protecting and realizing the utilitarian value of cultural heritage, starting from a community-based understanding [13]. From a sustainable development standpoint, Foster, Gillian, and others examined ways to prolong the life of cultural heritage within the circular economy framework, contributing to the positive growth of buildings and surrounding areas [14]. Hristić and colleagues suggested safeguarding cultural heritage through sustainable spatial planning, which entails developing new content and activities that stimulate the economy and promote self-preservation [15]. Djabarouti and others have led to greater professional involvement in cultural heritage protection and development by transforming its concepts and methodologies [16]. In cultural and tourism development, Berred Khadija and others used the Rabat-Salé-Kénitra region as a case study to explore promoting tourism through heritage activities, contributing to the region’s overall growth [17]. Sanertra Szeliga and colleagues analyzed how cultural heritage leisure activities can enhance residents’ quality of life [18]. West Tamara and others investigated the diverse positive effects of cultural heritage development on urban economies [19].

In China, scholars have engaged in empirical research tailored to local contexts, drawing on international studies and focusing particularly on cultural routes like the Grand Canal [20], the Ancient Tea Horse Road [21], and the Silk Road [22]. Their research primarily concentrates on developing cultural heritage corridor systems, formulating utilization strategies, and assessing their feasibility [23].

In the contemporary context of deep integration between culture and tourism, exploring suitable pathways for constructing cultural heritage corridors becomes particularly important. This study found that relying solely on the Minimum Cumulative Resistance (MCR) model to extract cultural heritage corridors could lead to redundancy, hence a rational selection is necessary. Considering the objectivity of the selection, the availability of data, and the ease of operation, this paper further deepens the discussion on methodology and empirical analysis based on these factors. Selecting the historical urban area of Tianjin as the research object, this study first conducted a spatial form analysis based on existing cultural heritage sites, defining these clusters as the starting points, or “sources”, for the research. Subsequently, based on the actual conditions of Tianjin and integrating the social and natural attributes of the heritage sources, the weights of various resistance factors and their accessibility and usability were determined. The Minimum Cumulative Resistance model was then applied at the regional level to construct a comprehensive resistance cost surface, revealing the potential network structure of the cultural heritage corridors. Additionally, by utilizing the Gravity Model to calculate the mutual attraction values between heritage sources, key cultural heritage corridors were selected, and a method for constructing a cultural heritage corridor system combining the MCR and Gravity Models was proposed, further categorizing the corridors into different levels. Finally, the regional scope of the cultural heritage corridors was determined from the perspectives of protection and tourism development. This study aims to achieve interconnectivity among key cultural heritage nodes within Tianjin’s historical urban area, promote the development of surrounding infrastructure, and meet the public’s multi-layered, diverse, and personalized needs, thereby enhancing the comprehensive utilization value of the heritage corridors.

This study aims to explore strategies for establishing cultural heritage corridors, specifically focusing on how to effectively connect various cultural heritage sites. Throughout the research process, we identified several key limitations, chiefly arising from the absence of a unified standard for determining the weights of multiple factors that influence the construction of heritage corridors. This lack of standardization could lead to subjective analysis results, potentially impacting the quality of final decisions. To mitigate this issue, we employed the Analytic Hierarchy Process (AHP), a decision-support methodology that uses pairwise comparisons to enhance objectivity in research. The AHP helped us introduce quantifiable standards in our evaluation process, allowing for a systematic analysis and comparison of factors to provide a more scientific basis for weighting in the assessment of heritage corridors. Despite the enhanced systematic nature of our analysis through the introduction of AHP, we must remain cautious about potential biases that could arise from reliance on expert judgments. In future research, we plan to incorporate a wider range of data sources and more objective evaluation standards to further minimize potential subjectivity, thereby ensuring the robustness and applicability of our research findings.

2. Research Aims

This study focuses on developing a cultural heritage corridor network, taking into account the surrounding traffic and public facility service levels. This study employs the Minimum Cumulative Resistance (MCR) and Gravity Model (GM) to achieve comprehensive protection of cultural heritage in the study area. It offers a theoretical foundation for protecting cultural heritage in complex settings and aids managers in resource allocation decisions for conservation. Considering the heightened cultural awareness and growth of the cultural tourism sector, this study takes an interdisciplinary approach, merging the MCR and GM to develop cultural heritage corridors. Research methods encompass (a) classifying heritage sites via kernel density analysis; (b) weighting indices using the Analytic Hierarchy Process (AHP); (c) creating a resistance cost surface with the MCR model; (d) forming corridor networks with ArcGIS cost path analysis; (e) choosing heritage corridors using the GM; and (f) confirming corridor rationality through network connectivity analysis.

3. Materials and Methods

This study focuses on Tianjin’s historical urban area to explore the development of a cultural heritage corridor system. Initially, this study conducts a spatial morphological analysis of cultural heritage sites in the historical district, treating these sites as this study’s starting “source”. Subsequently, considering the social, functional, accessible, and usable aspects of these heritage sites, the MCR is applied regionally to create a comprehensive resistance cost surface, uncovering the potential network of cultural heritage corridors. This study then uses the GM to calculate mutual attractions between heritage sites, enabling a hierarchical classification of the corridors. Finally, this study defines the regional scope of the cultural heritage corridors, focusing on protection and tourism development. Figure 1 illustrates this study’s methodological framework.

3.1. Study Area

As one of China’s earliest modern cities, Tianjin has undergone a century of development, characterized by concessions from nine countries, leading to a rich cultural landscape. Multiculturalism has flourished in Tianjin, especially along the Haihe River, resulting in a diverse architectural style that creates a historic district where Chinese and Western cultures converge (Figure 2). This cultural diversity enriches Tianjin’s urban landscape and serves as a vital historical context and cultural heritage resource in its urban development. Tianjin, situated in North China, lies in the northeastern North China Plain and the lower Haihe River Basin, between 116°43′ and 118°04′ east longitude and 38°34′ and 40°15′ north latitude. This study focuses on Tianjin’s urban area as established in 1949, currently recognized as the historical urban area (Figure 3). Covering about 50.3 square kilometers, this area includes the Heping, Nankai, Hongqiao, Hebei, Hexi, and Hedong districts in central Tianjin. It features 14 historical cultural blocks and 4 scenic areas.

3.2. Data Sources and Data Processing

The research data in this paper primarily include cultural heritage site data, POI data for public service facilities, POI data for bus and subway stations, and traffic road data within Tianjin’s historical urban district. The cultural heritage site data mainly come from the latest public documents on the official websites of the Tianjin Municipal Bureau of Culture and Tourism https://whly.tj.gov.cn/ (accessed on 8 July 2024) and the Tianjin Municipal Planning and Natural Resources Bureau https://ghhzrzy.tj.gov.cn/ (accessed on 8 July 2024). Data for schools, hospitals, shopping malls, administrative institutions, recreational facilities, cultural facilities, and bus and subway station POIs were extracted using Python from the GaoDe map https://www.amap.com/ (accessed on 8 July 2024). Traffic road data were obtained from Open Street Map (OSM) https://www.openstreetmap.org/ (accessed on 8 July 2024) and verified through field surveys, comparison with Baidu Street View maps, and topological checks and corrections using ArcGIS 10.8.

3.3. Research Methodology

This study breaks down the construction of cultural heritage corridors into four key steps: (1) identifying heritage sources; (2) constructing a comprehensive resistance cost surface; (3) generating and hierarchically classifying potential corridors; and (4) conducting suitability tests and analyses. Subsequently, these components are integrated to create a complete cultural heritage corridor.

3.3.1. Heritage Source Identification

Heritage sources act as the starting points for heritage-related leisure activities. Kernel density analysis, a method for spatial analysis, estimates the density of point or line features per unit within a designated neighborhood. This method effectively demonstrates the extent and intensity of feature distribution and clustering in geographic space. It showcases the aggregation and dispersion of cultural heritage resources in Tianjin’s historical urban area, enabling the effective identification of heritage sites with similar cultural values and nearby locations. As a result, these sites can be grouped into smaller clusters of heritage points. The formula is as follows:

(1)$\mathrm{f}(\mathrm{x})=\frac{1}{\mathrm{n}{\mathrm{h}}^2}{\sum }_{\mathrm{i}=1}^{\mathrm{n}}\mathrm{K}(\frac{\mathrm{x}-{\mathrm{x}}_{\mathrm{i}}}{\mathrm{h}})$

where: f(x) denotes the kernel density at heritage point x; n is the sum of cultural heritage points; h represents the search radius; and K indicates the kernel function. The higher the value of f(x), the denser the distribution of the cultural heritage resources. Consequently, points with a higher f(x) are more suitable to be used as key nodes for the construction of the heritage corridor.

3.3.2. Comprehensive Resistance Cost Surface Construction

The purpose of cultural heritage corridors is to enhance connectivity between different cultural heritage sites and to mitigate the multifaceted resistance affecting users’ participation in heritage recreation activities. This combined resistance reflects the cumulative obstacles that must be overcome, showcasing the impact of regional heterogeneity on heritage activities. Access to heritage recreation within these corridors is influenced by two main factors: natural and social environments. Natural environmental factors include land use types, elevations, slopes, slope directions, and vegetation cover, all contributing to varying levels of resistance and interference. Human factors primarily relate to the distance from public transport stops, proximity to transport roads, and the service level of nearby public facilities.

Constructing a comprehensive resistance cost surface is a fundamental prerequisite for scientifically identifying cultural heritage corridors. This study focuses on the historic urban area of Tianjin, located in the North China Plain. In our research, factors such as land use types, natural elements, distance from public transportation stops, distance from traffic networks, and the level of surrounding public facilities were selected as resistance factors and assigned weights. We precisely selected seventeen resistance factors as the baseline for model construction. Based on these benchmarks, the resistance factors were quantitatively assigned values to assess their level of hindrance to cultural heritage leisure activities. The scores of the resistance factors ranged from 1 to 5, with higher scores indicating greater resistance and thus less suitability for heritage activities, and vice versa. Ultimately, a comprehensive resistance surface was constructed through a weighted summation method. The weights of each factor were determined using software for the Analytic Hierarchy Process (AHP) and validated through a consistency test.

To ensure the objectivity and accuracy of the resistance surface generated, we employed the Analytic Hierarchy Process (AHP) to assign weights to the resistance factors. Initially, the seventeen resistance factors were further organized and hierarchized to establish a hierarchical structural model. Subsequently, based on the consistency matrix method and considering practical situations, we used a 1-to-9 scale to analyze these factors, thereby constructing a judgment matrix for the comparison of the importance of each element, and calculated the weight values of each intermediate layer indicator relative to the target layer. Lastly, a consistency test was performed on the judgment matrix, resulting in a consistency ratio (CR) of 0.072, which is less than 0.1, indicating that the judgment matrix passed the consistency test and met the requirements (Table 1) These steps ensured the scientific validity and reliability of our research methodology (Table 2).

By integrating natural and social factors, this study utilized the ArcGIS 10.8 map algebra tool to overlay and weight 17 resistance factors [24] (Figure 4), generating the resistance value patches. Subsequently, in conjunction with the Minimum Cumulative Resistance (MCR) model, we conducted a cost distance analysis to compute the minimal cumulative resistance values. This process allowed us to construct a comprehensive resistance cost surface [25]. This resistance cost surface provides a quantitative perspective to assess various challenges and obstacles in the protection and utilization activities of cultural heritage. This method ensures the accuracy and practicality of our research results, providing significant support for the scientific management and sustainable use of cultural heritage.

3.3.3. Potential Heritage Corridors Generation and Hierarchical Identification

This study applied the MCR model based on a comprehensive resistance cost surface. We used ArcGIS’s cost distance and path tools to calculate the shortest paths between heritage sources. The analysis was refined by comparing and eliminating duplicate corridors [26]. Lastly, we employed the GM to assess the interaction strength among heritage sources, differentiating primary from general corridors.

Proposed in 1992 by Knaapen and colleagues, the Minimum Cumulative Resistance model (MCR) explores the relationship between landscape pattern changes and cumulative resistance during species dispersion, creating a visual comprehensive resistance cost surface [27]. Later, scholars such as Yu Kongjian [28] and Chen Liding [29] introduced the model to China, focusing on studying the cumulative resistance faced by species as they move from a source through different impediments to their destinations.

(2)$\mathrm{MCR}={\mathrm{f}}_{\min }{\sum }_{\mathrm{j}=\mathrm{n}}^{\mathrm{i}=\mathrm{m}}{\mathrm{D}}_{\mathrm{i}\mathrm{j}}\times {\mathrm{R}}_{\mathrm{i}}$

where MCR represents the Minimum Cumulative Resistance model; f indicates a positive correlation; ‘min’ denotes the minimum value of the cumulative resistance for any cultural heritage point within unit i; D_ij represents the distance from a point i to the destination cultural heritage point j within the heritage corridor space; Ri is the resistance coefficient encountered in the process of traversing the space; and ∑ symbolizes the cumulative resistance that must be overcome to reach a particular cultural heritage point [30].

This study used the comprehensive resistance cost surface and spatial analysis tools, such as cost distance and path, to calculate the minimum cost paths between heritage settlements, identifying all potential heritage corridors. Employing the GM, an interaction matrix between heritage settlements was constructed [31], and quantitative methods were used to calculate the interaction forces among them [32]. This method enabled the assessment of the relative importance of each potential corridor and the identification of key corridors [33]. The formula used in this analysis is as follows:

(3)${\mathrm{G}}_{\mathrm{i}\mathrm{j}}=\frac{{\mathrm{N}}_{\mathrm{i}}{\mathrm{N}}_{\mathrm{j}}}{{\left({\mathrm{D}}_{\mathrm{i}\mathrm{j}}\right)}^2}=\frac{\frac{1}{{\mathrm{P}}_{\mathrm{i}}}\times \mathrm{l}\mathrm{n}{\mathrm{S}}_{\mathrm{j}}}{{\left(\frac{{\mathrm{L}}_{\mathrm{a}\mathrm{b}}}{{\mathrm{L}}_{\mathrm{m}\mathrm{a}\mathrm{x}}}\right)}^2}=\frac{{{\mathrm{L}}_{\mathrm{m}\mathrm{a}\mathrm{x}}}^2\mathrm{l}\mathrm{n}{\mathrm{S}}_{\mathrm{j}}\times \mathrm{l}\mathrm{n}{\mathrm{S}}_{\mathrm{j}}}{{{\mathrm{l}\mathrm{n}}_{\mathrm{i}\mathrm{j}}}^2\times {\mathrm{P}}_{\mathrm{i}}\times {\mathrm{P}}_{\mathrm{j}}},$

where G_ij represents the interaction force between heritage points i and j, reflecting the gravity value from i to j within the heritage corridor. The weight values N_i and N_j of heritage points i and j are derived from the resistance values P_i and the corresponding areas S_i at different heritage points. D_ij denotes the standardized cumulative resistance from point i to j along the potential heritage corridor. L_ij represents the cumulative resistance value of the potential heritage corridors from point i to j among the heritage points. L_max signifies the maximum cumulative resistance value within the heritage corridor network.

This study combines the MCR and GM to build a cultural heritage corridor network, pinpointing essential corridors. Initially developed for landscape and ecology, these models simulate wildlife migration paths and construct ecological safety patterns to protect and study biodiversity. Owing to their ability to manage multiple factors in ArcGIS, these models are extensively used in urban and rural planning, ecological planning, regional planning, and resource conservation. The integration with the GM has enabled the successful creation of a practical and implementable cultural heritage network.

3.3.4. Verification of Connectivity of Cultural Heritage Corridors

Network connectivity quantifies the connection level between cultural heritage corridors and their contained heritage sources, evaluating the corridors’ overall connectivity and complexity [34]. The commonly used indices for assessing connectivity are the α index (network closure), β index (heritage source connection rate), and γ index (network connectivity). These indices, based on spatial topological relationships, reflect the connectivity between heritage sources and the corridors [35,36]. The formulas for these indices are as follows:

(4)$\mathsf{\alpha }=\frac{\mathrm{L}-\mathrm{V}+1}{2\mathrm{V}-5},\mathsf{\beta }=\frac{\mathrm{L}}{\mathrm{V}}, \mathsf{\gamma }=\frac{\mathrm{L}}{{\mathrm{V}}_{\mathrm{m}\mathrm{a}\mathrm{x}}}=\frac{\mathrm{L}}{3(\mathrm{V}-2)}$

where L represents the number of key heritage corridors, V is the total number of heritage sources (with V ≥ 3), and Vmax signifies the total number of potential heritage corridors. The α-index refers to the overall closure of the heritage corridor network, with a value range of [0, 1]. This index reflects the extent to which the heritage corridor network forms a loop, where a larger value indicates higher network connectivity. The β-index measures the point-to-line connectivity, within a range of [0, 3], and a larger value signifies a greater capacity of heritage sources to connect with the heritage corridor. The γ-index reflects the degree of network connectivity, with a value range of [0, 1]. A higher value in this index indicates a better facilitation of energy transfer within the space, thereby enabling smoother development of recreational activities.

4. Results

4.1. Heritage Source Determination

Tianjin’s official statistics indicate that the historic urban area contains 63 nationally protected heritage sites, 145 municipal heritage sites, and 798 historical buildings. These sites constitute 85.9% of Tianjin’s cultural heritage, with nationally protected sites making up 84%. This underscores the area’s role as a principal hub of Tianjin’s cultural heritage. As illustrated in Figure 5, Kernel density analysis shows a high concentration of heritage sites in the Wudadao tourist area. Centered around the Haihe River scenic area, the heritage sites are clustered along the riverbanks, forming a solid base for developing cultural heritage corridors. A significant concentration of heritage sites is also evident in the historic urban area’s northern part.

Following the “Tianjin Historical Architectural Conservation Regulations” and the “Tianjin Historical and Cultural City Protection Plan (2021–2035)”, this study merges the absolute and relative values of cultural heritage sites, considering their geographical locations, and divides Tianjin’s historic urban area heritage sites into 29 clusters. By employing ArcGIS’s central feature analysis tool, we identified the relative centers of these clusters, establishing 29 core cultural heritage clusters or heritage sources, as depicted in Figure 6 and Figure 7. This approach comprehensively assesses the value of cultural heritage sites and provides essential data for identifying optimal routes and key protection areas in cultural heritage corridors through geographic spatial analysis.

4.2. Comprehensive Resistance Cost Surface Construction

During the construction of the comprehensive resistance surface, this study used ArcGIS’s Euclidean distance tool to measure distances from heritage sources to linear features such as roads and rivers, followed by their categorization. Heritage sources are usually denser near rivers and primary transportation networks, including roads, due to shorter relative distances, leading to lower resistance values. Lower-grade roads, like third and fourth-level roads, are key pedestrian elements in heritage corridors; their proximity to heritage sources results in lower resistance values. Proximity to bus and subway stations reflects the area’s public transportation accessibility, where closer distances imply greater accessibility and lower resistance values. Public service facility densities, determined by kernel density analysis, indicate the area’s service level. Higher densities denote better services, resulting in lower resistance for heritage corridor construction and enhanced suitability for leisure activities.

This study then used the Analytic Hierarchy Process (AHP) to allocate weights to each resistance factor, considering their importance in cultural heritage conservation and utilization. Lastly, aggregating the resistance values of these 17 categories yielded a comprehensive resistance cost surface (Figure 8).

4.3. Preliminary Construction and Hierarchical Identification of Potential Corridor Networks for Cultural Heritage

The appropriateness analysis, conducted using the Minimum Resistance Model histogram for Tianjin’s historic urban area’s cultural heritage corridors, shows a ribbon-like spatial distribution in areas of high suitability. This distribution is primarily influenced by the layout of public transport stations, indicative of the area’s efficient transportation access. In contrast, the northern part of the historic urban area exhibits a more dispersed distribution of cultural heritage sites, attributed mainly to the lower service levels of nearby public facilities, resulting in reduced suitability in this area.

Building on the analysis, this study used the cost path method to calculate the minimum cost distances between settlements, simulating a potential cultural heritage corridor network. After vectorization, the results showed a complex network of interwoven corridors among the settlements. Upon removing overlapping or similar corridors, 406 unique corridors were identified, with a total length of 4392.78 km (Figure 9). The GM was then applied to evaluate the interaction forces between heritage sources, resulting in a hierarchical classification of the corridors.

This study employed an interaction force matrix to systematically identify key cultural heritage corridors. The strongest interaction force corridors, constituting the top 10% (Figure 10), were designated as primary corridors, comprising 44 corridors with a total length of 55.97 km (Figure 11). Practical considerations were accounted for during the selection process to ensure connectivity between heritage clusters. Research shows that these primary corridors excel in attractiveness, connectivity, and influence among heritage sources compared to others, indicating lower resistance to leisure activities along them. Moreover, the analysis indicates that heritage sites with weaker interaction forces, like Point 2 in the northeast and Point 16 in the southwest, encounter greater resistance and higher costs due to their distance, reducing their likelihood as choices for cultural tourism activities.

Research findings reveal that corridors are mainly concentrated in the central and southern regions, with the northern area displaying a sparser distribution. This distribution pattern is strongly linked to the service levels and development trends of adjacent public facilities. Specifically, the northern region has more scattered heritage clusters with reduced connectivity, reflecting its inferior public facility services. Consequently, this results in a predominance of non-circular, belt-like corridor structures in the northern area.

For the corridor network’s development strategy, prioritizing the central and southern areas as key development zones is advisable. Additionally, expanding the cultural heritage corridors into the northern region and enhancing connections with the heritage clusters there is essential. The development of a systematic cultural heritage corridor network should be a priority, with the goal of enhancing the heritage’s cultural and practical values. Providing adaptable, long-distance leisure routes tailored to user needs can significantly boost user interactivity and engagement.

4.4. Verification of Connectivity of Cultural Heritage Corridors

Network connectivity, an essential metric, mirrors the interconnectivity of the heritage corridor network in the study area and significantly impacts leisure activities within the corridors.

Quantitative analysis of the heritage corridor system in the area reveals an α value of 0.37, indicating a prevalence of circular routes, particularly in the smaller, denser networks of the central and southern regions. The strong connectivity between heritage sources provides various circular routes, facilitating the development of heritage leisure activities. A β value of 1.52 suggests a multitude of connecting corridors and a robust corridor structure, improving user mobility between heritage sources and access to activities. Nonetheless, the lower service levels of public facilities near certain heritage sources moderately affect the system’s overall suitability, resulting in the lack of closed loops between some sources. A γ value of 0.54 indicates high connectivity within the corridor network, linking multiple sources effectively, minimizing cultural heritage fragmentation, and improving the corridors’ overall utility.

5. Discussion

5.1. Regional Scoping of Cultural Heritage Corridors

The corridor network, based on the Minimum Cumulative Resistance model, represents pathways conceptually without actual width. To provide practical value to the network, a specific width needs to be assigned. This study establishes the corridors’ width using the straight-line distance from cultural heritage sites to the corridors and conducts statistical analysis on distances from various heritage sites and regional attractions. Analysis results (Table 3) indicate that as the corridor width expands from 100 to 500 m, the count of encompassed cultural heritage sites and attractions increases. Beyond 500 m, however, this upward trend starts to decelerate.

On this basis, this study further centered on 44 key corridors, conducting analyses of buffer zones at distances of 500 m and 800 m (illustrated in Figure 12 and Figure 13). This spatial characteristic analysis delineated the area within a 0–500 m radius around the corridors as the core zone, where the density of heritage sites reached 80.82%, encompassing the majority of cultural heritage sites and attractions within the research area. The area between 500 and 800 m was designated as the buffer zone, with a heritage site density of 95.03%, including some of the more dispersed attractions beyond the core area. Additionally, the range from 800 m to 1500 m was classified as the peripheral zone, covering a small number of relatively scattered heritage sites and attractions.

This study undertook a detailed examination of corridor areas to effectively assess the accessibility of cultural heritage sites. Based on existing research findings on pedestrian walking distances, it has been determined that 500 m is the optimal walking distance for pedestrians in daily life. This distance takes into account convenience and comfort and is generally considered the minimum distance that pedestrians are willing to walk. The advantageous walking distance is within 1200 m [37], while 1500 m is regarded as the maximum walking distance, which represents the farthest distance pedestrians are typically willing to accept. These results provide important references for urban planning and the design of pedestrian environments [38].

Building upon this foundation, this study utilized the Service Area tool within the ArcGIS Network Analysis toolkit to conduct a detailed analysis of the network topology between each heritage site and the surrounding attractions. By setting various interruption distance values (100 m, 200 m, 500 m, 1000 m, and 1500 m), different levels of service areas were distinguished. The results of the service area analysis (Figure 14) indicate a close connection between heritage sites along the corridor from the northeastern to the southern parts of the study area. Particularly in the south, the six heritage sites are significantly interconnected, making this region a core development area within the corridor space.

Ultimately, considering the alignment of the corridor system with the existing transportation network in practical usage, this study integrated the key corridors with the nearest roads to create a new route map (Figure 15). This strategy is designed to provide users with more travel options, allowing them to choose different routes based on their individual circumstances—whether selecting a specific path, a closed loop, or exploring the entire cluster area of cultural heritage sites. This integration not only offers diverse travel options but also effectively connects various types of cultural heritage sites. It energizes surrounding tourist resources and facilities, thereby fostering sustainable economic development.

5.2. Heritage Corridor Suitability Test

This research explores the link between public transportation accessibility and cultural heritage corridor value. This study found that public transportation convenience reflects not only equitable resource distribution in a region but also greatly affects the ease of heritage-related leisure activities. Using Arc GIS’s nearest facility analysis tool and treating time as an impedance in the urban traffic network, this study examined the topological relationship between cultural heritage sites and subway and bus stations. Results show that at an average walking speed of 80 m per minute, 99.8% of cultural heritage sites are within a 15 min reach from the nearest subway station, and all sites are accessible in the same time frame from the nearest bus station. Furthermore, in the region, 72.37% of subway stations and 74.73% of bus stations lie within a 15 min reach from the nearest cultural heritage site (Figure 16, Figure 17, Figure 18 and Figure 19).

Integrating the research findings, this study validates the effectiveness of cultural heritage corridors in improving regional accessibility and usability. This approach not only invigorates surrounding tourist attractions but also preserves the recreational appeal and overall integrity of the corridors. Furthermore, it shows that these constructed corridors, apart from ensuring efficiency, also exhibit scientific soundness and reliability.

5.3. Limitations

This study encounters specific limitations in developing heritage corridors. Firstly, a significant challenge in this study when constructing the comprehensive resistance cost surface is the lack of uniform standards for determining the weights of different factors, which could introduce subjectivity into the results. To mitigate this subjectivity, we employed the Analytic Hierarchy Process (AHP) to determine the weights of each factor, ensuring the reliability of our evaluations through pairwise comparisons and consistency tests. Despite this, the determination of weights remains subject to expert judgment, which is an inherent limitation of our research design. To further minimize the impact of expert judgment, we implemented several measures: (a) we expanded the expert team to increase the breadth and depth of the evaluation; (b) we invited interdisciplinary experts to assess the influence of each factor from multiple perspectives; and (c) whenever new data were introduced, we recalculated the consistency ratio and adjusted the weights. These measures aim to enhance the objectivity and scientific rigor of our study, providing methodological guidance for future related research.

Secondly, the research primarily concentrates on existing cultural heritage units, neglecting an adequate focus on intangible cultural heritage and other cultural landscapes. Since intangible cultural heritage, often linked to community, group, or individual practices, lacks a specific location, its incorporation in heritage corridor construction has been inadequate. To overcome these limitations, this paper proposes the integration of intangible cultural heritage into future cultural facility developments within the corridor network system. Despite the lack of a fixed location for intangible cultural heritage, it is feasible to establish specific nodes or spaces within the corridor network for display, education, and engagement with these heritages. For example, museums, performance centers, or educational facilities could be established to showcase local traditional crafts, performing arts, and folklore activities. This approach not only helps protect and transmit intangible cultural heritage but also increases the cultural value and appeal of the corridor network, offering the public opportunities to deeply understand and experience intangible cultural heritage.

Finally, although this study provides important insights into the impact of social factors on heritage leisure activities in the historic urban area of Tianjin, it also has some limitations. Specifically, this study primarily focuses on social factors, with less consideration given to natural factors. Since the area is located in the North China Plain where land use is relatively uniform, natural factors such as vegetation coverage and altitude vary little within the study area and seem to have a minor impact on heritage activities. Therefore, in constructing the comprehensive resistance cost surface, this study prioritized three main social factors: the distance from heritage sites to public transportation stops, the connectivity of the transportation network, and the service level of nearby public facilities.

6. Conclusions

This study employed both quantitative and qualitative analysis methods, considering a variety of social factors to successfully establish the cultural heritage corridors in the historic urban area of Tianjin. Utilizing kernel density analysis, the Minimum Cumulative Resistance (MCR) model, and the gravity model, this research not only scientifically integrated cultural heritage resources within the region but also optimized the structure of the heritage corridors. This approach clearly defined the regional scope of each corridor and developed a comprehensive cultural heritage corridor network system. Additionally, this study established a cultural heritage value assessment system and proposed advanced methods for the digital protection of cultural heritage. These strategies have enhanced the comprehensive protection and efficient utilization of cultural heritage resources and provided practical references for planning tourist routes and optimizing heritage corridor routes.

Moreover, through a comprehensive analysis of the value of cultural heritage sites and the level of surrounding public facility services, this study has provided concrete constructive support for the spatial layout of cultural facilities, thereby enhancing regional development vitality. Such methods have not only increased the overall value of the cultural heritage corridors in historical districts but have also had a positive impact on the protection and development of individual heritage sites, offering a theoretical basis for future tourism path development and planning and deepening the understanding of the historical and cultural societal values.

Therefore, this research provides a solid scientific theoretical foundation for the construction of cultural heritage corridors in historic districts and offers practical guidance for the protection and development of cultural heritage. Its outcomes provide replicable scientific strategies and practical solutions for the integration of culture and tourism development in Tianjin and other historic cities, demonstrating extensive potential for application and practical significance.

Author Contributions

Q.W. made corrections to improve the paper; C.Y. wrote the article; J.W. and L.T. reviewed the whole text and made comments and suggestions to improve it. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.

Acknowledgments

The authors would like to thank the reviewers for their useful comments and the editors for improving the manuscript.

Conflicts of Interest

The authors declare no competing interests.

Footnotes

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Figures and Tables

Figure 1. Research framework for the construction of cultural heritage corridors.

Figure 2. Current situation of the historic cultural districts in Tianjin. ((a) Tianjin Astor Hotel, (b) Former Site of Chartered Bank, (c) Former Site of Lane Crawford, (d) Former Site of Citibank, (e) Former Site of the Tianjin Branch of the Russo-Chinese Bank, (f) Former Site of Industrial and Commercial Bank of China-France, Tianjin Branch, (g) Former Site of the Yokohama Specie Bank Building in Tianjin, (h) Former Site of the Bank of Chosen, (i) Former Site of Indo-China Agency Bank/Calyon Bank, (j) Former Site of the Beiyang Industrial and Commercial Bank in Tianjin, (k) Former French Municipal Committee in Tianjin, (l) Former Site of Hongkong and Shanghai Banking Corporation in Tianjin).

Figure 3. Scope of Tianjin historic urban area. Source: Tianjin historical and cultural city protection plan (2020–2035).

Figure 4. 12 Categories of social factors ((a) distance to bus stops, (b) distance to metro station, (c) distance to river, (d) distance from secondary roads, (e) distance from tertiary roads, (f) distance to class IV roads, (g) Neighborhood Hospital Density, (h) Neighborhood Schools Density, (i) Neighborhood Shopping Mall Density, (j) density of neighboring administrations, (k) density of neighboring cultural facilities, (l) density of surrounding leisure and recreational facilities).

Figure 5. Kernel density analysis in the study area (the colors indicate density: red means high density, while blue means low density).

Figure 6. Region identification (the circles represent divided heritage colonies).

Figure 7. Colony center extraction (each point represents the source of the extracted heritage).

Figure 8. Combined resistance cost surface (the colors indicate combined resistance: green means high combined resistance, while brown means low combined resistance).

Figure 9. Schematic of all potential corridors generated based on the integrated resistance cost surface.

Figure 10. Calculation results of the inter-source interaction index.

Figure 11. Schematic diagram of the screened priority corridor network.

Figure 12. A 500 m buffer analysis map for priority channel networks.

Figure 13. An 800 m buffer analysis map for priority channel networks.

Figure 14. Availability analysis diagram for priority channel networks.

Figure 15. Availability analyses of the priority access network in conjunction with the actual road network.

Figure 16. Route and time analysis map of heritage sites from the nearest bus stops.

Figure 17. Route and time analysis map of heritage sites from the nearest metro stations.

Figure 18. Route and time analysis map of the nearest bus stops to heritage source.

Figure 19. Route and time analysis map of the nearest metro station to heritage source.

Table 1

Consistency test result.

Consistency Test Results
Maximum Eigenvalue	CI Value	RI Value	CR Value	Consistency Test Result
18.84	0.115	1.606	0.072	Pass

Table 2

List of resistance factors and weights of the integrated resistance cost surface.

Factor	Weights	Category of Elements	Weights	Drag Factor	Weights
Natural Factor	0.1	Land use type	0.017	Land use type	0.017
		Other natural factors	0.106	Slope direction	0.019
				Elevation	0.025
				Vegetation cover	0.025
				Altitude	0.037
Social Factor	0.9	Distance to public transport/m	0.303	Public transport station	0.131
				Metro station	0.171
		Distance to transport network/m	0.286	Stream	0.105
				Secondary road	0.050
				Tertiary road	0.083
				Class IV road	0.048
		Level of service of neighboring public facilities/density	0.288	Hospital	0.059
				School	0.053
				Shopping center	0.040
				Administrative organization	0.058
				Recreational facilities	0.036
				Cultural facility	0.041

Table 3

Analysis of buffer areas around the corridor.

Distance	National Key Cultural Relics Protection Unit	Tianjin Key Cultural Relics Protection Unit	Historic Buildings	Number of Attractions
100 m	41.27%	56.85%	40.18%	43.32%
200 m	61.90%	76.71%	59.20%	62.79%
500 m	92.06%	93.84%	81.10%	83.59%
800 m	88.89%	97.95%	89.74%	92.94%
1000 m	98.41%	97.95%	93.99%	96.37%
1200 m	100%	97.95%	98.00%	97.52%
1500 m	100%	100%	100%	100%

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