Developing a Multi-Criteria Decision Model to Unlock Sustainable Heritage Tourism Potential

Abstract

Heritage sites are vital resources for the tourism industry due to treasures such as world heritage sites. Caravanserais are newly inscribed world heritage sites that, beyond their historical roles, are now capable to be developed as tourist attractions. This study aims to propose a framework based on a multi-criteria decision-making system to evaluate Persian caravanserais’ potential for development as tourist attractions. This study focuses on Isfahan Province in Iran, with a specific emphasis on eight caravanserais within the province that are listed as UNESCO world heritage Sites. A total of 39 relevant criteria related to network connectivity and access, tourist attractions, facilities and services, climatic conditions, geomorphological features, and hazards were utilized to assess the heritage tourism potential. The BMW-WLC multi-criteria decision-making model was applied to determine tourism development suitability and rank the studied caravanserais. The results revealed that a significant portion of the area (34%) is classified as having very high suitability, while only 6% is identified as having very low suitability. The analysis reveals varying tourism potential among caravanserais. Gaz and Gaba Abad rank high overall but require climate adaptation strategies. The results demonstrate that the proposed framework effectively assesses the heritage tourism potential of caravanserais, providing a data-driven, multi-criteria approach to sustainable development.

1. Introduction

Heritage tourism, as one of the fastest-growing types of tourism [1,2], has increased global demand for visiting heritage places and influences the travel motivations of a substantial segment of international tourists [3]. Chhabra [4] underscores that 85% of international tourists consider heritage elements of other countries an integral part of their travel itinerary and experiences.

World heritage sites are sites of human shared heritage with the highest level of universal value [5] that, as public treasures [6], form the core of tourism in many destinations [7], independently stimulate tourists’ demand [8], enhance the reputation of destinations [9], and act as a magnet for tourists. This is why the development of these sites is increasingly used as a strategy for tourism development [10] in different countries [6,11]. Attracting more heritage tourists not only generates more income and creates more jobs [12,13] but also draws global attention to the significance and preservation of these sites. Moreover, it contributes to enhanced cultural awareness and knowledge among tourists [14,15,16].

The spatial distribution and geographical location of world heritage sites are significant components in their tourism development. There are factors that researchers have mentioned to be geo/spatial intricacies that affect world heritage sites’ potential development in the tourism industry. First, the geographical concentration of world heritage sites can bring great opportunities for their development. For instance, Lin and Hou [17] argue that high concentration in urban centers correlates with increased tourism potential due to accessible services and facilities. Similarly, Wang et al. [18] analyzed the spatial characteristics of world heritage sites in cities and provinces of China and found that sites that are located in downtown areas are more capable of accepting tourists. Moreover, Visser [19] found that the majority of world heritage sites located in Sub-Saharan Africa are far from main urban centers and that this spatial disparity hinders international tourists from visiting this region. Second, the cluster distribution around geographic features like coastal areas, rivers [20,21], and transportation lines [22] enhances their visibility and attractiveness for tourists. In other words, heritage areas with an attractive physical layout [23] and higher site density can drive more robust tourism development [24,25].

Caravanserais, due to their nature and structural characteristics, are among the world heritage sites that can accommodate different functions for tourism purposes in different geographies. This approach of adaptive reuse transforms caravanserais into tourism facilities such as accommodations, museums, or restaurants and can revitalize the urban fabric, create employment, and enrich the cultural and economic capital of local communities [26,27]. Preserving the cultural identity of these heritage sites [28], extending their lifespan [29], enhancing the brand identity and competitiveness of cultural destinations within the global tourism market [15,30,31], and fostering a sense of local pride that encourages greater community engagement in the tourism industry [16,32] are also benefits of the adaptive reuse of caravanserais.

Despite not all caravanserais being equally viable for transformation for tourism purposes, their geographical location and distribution can play a crucial role in determining their market appeal and investor interest. Therefore, the management of these heritage sites needs to consider this advantage/constraint and use spatial planning to analyze their suitability for sustainable development in the tourism industry [24]. In spite of the inherent spatial distribution of caravanserais, spatial analysis and planning have received relatively less attention in heritage tourism, and research focusing on the spatial suitability of these shared universal resources remains scarce [33].

This study is one of the first studies aimed to fill the gap of evaluating the tourism potential of networked caravanserais situated on the ancient Silk Road as world heritage sites based on a geographical approach. Also, by using a novel methodology in this context, we combine spatial analysis methods and tools to assess the tourism potential of caravanserais. So, within the context of Isfahan Province in Iran, which boasts an impressive number of eight caravanserais inscribed on the UNESCO World Heritage List in 2023, this study aims to systematically assess the tourism development potential of these sites. By meticulously analyzing the geographical factors that present both opportunities and constraints for each site’s transformation into a successful heritage tourism facility, this study seeks to identify the most promising candidates for development. The findings of this study will provide valuable insights for both academic research on sustainable heritage tourism and investors seeking to identify the most promising sites for development while simultaneously informing the development of comprehensive and strategic tourism plans for the studied caravanserais.

1.1. Heritage and Spatial Analysis

Spatial analysis constitutes a pivotal tool within the domain of heritage tourism, fulfilling a multifaceted role in understanding and optimizing the relationship between heritage resources and tourism development. This analytical framework enables researchers and policymakers to assess the spatial distribution and characteristics of heritage sites, evaluate their vulnerability to various tourism-related pressures, and ultimately inform sustainable development strategies [34,35,36,37,38]. Within this framework, the physical attributes of heritage sites and their surrounding infrastructure significantly influence site selection decisions [23,39]. Spatial analysis plays a crucial role in enhancing visitor experience at heritage sites by optimizing site selection, internal spatial layout, and accessibility [23,40,41]. This involves strategically arranging site elements, including visitor centers, pathways, and interpretive signage, to facilitate smooth and enjoyable visitor circulation [42]. Spatial analysis serves as a valuable tool for identifying significant cultural resources [43] and analyzing their spatial distribution [44] within the context of heritage tourism.

Spatial analyses are indispensable for developing effective cultural heritage conservation strategies [45,46,47]. These analyses investigate the intricate interplay between heritage sites and their surrounding environments, facilitating the development of strategies that balance heritage conservation with the commercial exploitation of tourism resources [23,39,48,49]. Given the increased environmental impacts of tourism on heritage sites due to increasing visitor numbers [50], spatial analysis enables the assessment of critical issues such as carbon emissions and ecological footprints. This information empowers planners to implement sustainable tourism practices that are congruent with conservation objectives [51].

Spatial analysis plays a critical role in developing heritage tourism routes by identifying the spatial distribution and clustering patterns of heritage sites. This facilitates the creation of interconnected networks that enhance the overall tourist experience [44,45,52] and enrich the regional or destination heritage tourism product [53,54]. Furthermore, assessing the vulnerability of heritage sites to environmental hazards is a crucial component of spatial analysis within this domain. By leveraging spatial data for natural disaster management, researchers and managers can develop more effective strategies for mitigating hazards and safeguarding the physical integrity of heritage sites [55,56,57]. Spatial analysis can effectively examine the dynamic spatial changes occurring within the vicinity of heritage sites undergoing tourism development. These changes inevitably impact a diverse range of stakeholders associated with the site. By elucidating the roles and spatial relationships of these multiple actors, such analyses provide valuable insights for the effective planning and management of heritage sites [49].

Regarding spatial analysis, GISs serve as a pivotal tool for assessing the socio-economic impacts of heritage tourism. GISs enable the analysis of demographic data, economic indicators, and tourism patterns, empowering planners to make informed decisions regarding the promotion of specific heritage sites for tourism development. This data-driven approach facilitates the identification of geographic areas with the highest potential for generating economic benefits while simultaneously enabling the effective management of the socio-cultural impacts of tourism on local communities [58,59]. Furthermore, GISs facilitate the development of targeted strategies for attracting tourists and managing their distribution. By analyzing tourist visitation patterns, planners can effectively distribute tourist flows across different regions, mitigating overcrowding at popular sites [60]. Moreover, the utilization of interactive maps and image data significantly enhances the tourist experience. Interactive maps provide tourists with valuable information regarding heritage sites, amenities, and local attractions [61], enriching their understanding and engagement with the destination. GISs are particularly effective in mapping the spatial distribution of these resources and evaluating their spatial relationships with surrounding environments [40,62]. This spatial information enables the development of integrated tourism plans that ensure accessible and enriching visitor experiences at heritage sites [63].

1.2. Caravanserais and Adaptive Reuse

The word “Caravanserai” is rooted in two Persian words: “caravan”, which refers to a group of travelers, and “sara”, meaning a large house or building. It was a roadside inn or lodging that served as a stopping point for caravans traveling along the Silk Road. An array of caravanserais along the Road, typically within a day’s journey apart, ensured the safety of travelers. While developers’ main intention of constructing caravanserais were in line with the trading culture to serve the commercial caravans, the literature reveals multiple functions. Caravanserais not only provided temporary lodging, security, and essential services to merchants and their caravans but also became local markets, which turned them into cultural exchange hubs as a result of local community communication with caravans having different backgrounds and languages. Therefore, caravanserais were vital centers where ideas, religions, customs, and beliefs were exchanged. The direct sharing of stories, experiences, and knowledge could foster intercultural learning. Following this, caravanserais may have played a significant role in the spread of world religions [64].

Recently, renewing caravanserais as part of Iranian history through both restoration and revitalizing their space was discussed [65]. Like other historical and cultural heritage buildings, caravanserais can assume different functions from their original purposes over time, due to changes in political, economic, and socio-cultural circumstances. Therefore, attempts have been made to maintain their vitality by defining new functions for them in the contemporary era by adapting their use in a way that does not harm the physical structure. This is known as adaptive reuse, which precisely refers to a change in the use of a building to a purpose other than that for which it was originally constructed while preserving its historical value and significance. This approach tries to define new functions for historical structures by integrating modern capabilities and amenities within a historical context [65,66]. There are good examples of adaptive reuse in the case of caravanserais, such as commercial use as shops or shopping centers [67]; cultural or educational use as museums, libraries, and educational institutions [68]; use as community gathering centers [66]; tourism use as modern heritage hotels, event venues, and visitor centers [69,70,71], and use as concert halls [72]. The important thing required to be considered in this adaptation is preserving their architectural and cultural values for future generations [72].

1.3. Research Novelty

Caravanserais are among the heritage sites that due to their specific structural and geographical characteristics are well suited for adaptive reuse. This issue becomes particularly sensitive when it comes to caravanserais classified as UNESCO world heritage sites, as it necessitates a balanced approach that ensures both their preservation and their sustainable use for tourism. From this perspective, this research study is one of the first studies to examine the UNESCO World Heritage List caravanserais in terms of their potential for sustainability-oriented development as tourist attractions.

One of the main innovations of this research study lies in the design and development of a spatial multi-criterion decision-making (GIS-based MCDM) framework for assessing the sustainable tourism development potential of caravanserais inscribed on the UNESCO World Heritage List. While most previous studies on heritage tourism potential assessment have relied on traditional, qualitative, or limited indicator-based methods, the framework proposed in this study offers a novel opportunity for more comprehensive and quantitative analyses by integrating the WLC-BWM with spatial analysis technologies. This framework, by simultaneously considering 39 key criteria across geographical, climatic, accessibility, infrastructural, and environmental domains, makes it possible to evaluate the tourism potential of each caravanserai in a targeted, comparable, and scientific manner. The data-driven and hybrid approach adopted in this study not only reveals overlooked tourism capacities within these heritage sites but also provides a practical and precise tool for planners, policymakers, and stakeholders to support sustainable heritage tourism development.

2. Study Area

Isfahan, a province located in the heart of Iran, has established itself as a prominent heritage destination in the country. Renowned for its numerous heritage sites, including UNESCO world heritage sites, it attracts a significant number of both domestic and international visitors each year [73,74]. This culturally rich province experienced substantial economic, political, and cultural growth during the Safavid era, a period when the city of Isfahan was chosen as the capital of Iran [75]. Consequently, numerous structures were constructed during this period [76,77]. Caravanserais were among these structures, serving as inns for caravans traveling between the eastern and western as well as northern and southern parts of Iran. Additionally, they facilitated economic and cultural exchanges along the East–West Silk Road, connecting diverse civilizations such as China, Iran, and Europe [78,79], thereby highlighting Isfahan’s position as a crucial commercial route [80] and social-cultural center [81].

In 2023, eight caravanserais in Isfahan Province were inscribed on the UNESCO World Heritage List, joining the ranks of the world’s cultural heritage. Most of these caravanserais (Figure 1), dating back to the Safavid era (16th to 18th centuries), with the exception of the Nistanak caravanserai, have now been recognized as part of humanity’s shared heritage. Among the newly inscribed caravanserais are the Gaz caravanserai, located 17 km north of Isfahan; the Sheikh Ali Khan caravanserai, 40 km northwest of Isfahan; the Mehyar caravanserai and Aminabad caravanserai, both situated southwest of Shahriza; the Kuhpayeh caravanserai, located east of Isfahan; the Maranjab caravanserai, near Aran va Bidgol County; and the Gabaabad caravanserai, on the road from Kashan to Isfahan. The Nistanak caravanserai, which dates back to the Qajar era, is located in Nain County.

3. Materials and Methods

3.1. Data

In this study, spatial data related to sub-criteria such as network connectivity and access, tourist attractions, facilities and services, climatic conditions, geomorphological features, and hazards were utilized to assess the heritage tourism potential. These data were available in shapefile (point, polyline, and polygon) and paster formats. The shapefile data included key datasets such as airports, bus terminals, train stations, roads, rivers, land cover, forests, meadows, protected areas, deserts, industrial areas, fault lines, and flood zones. Data were obtained through requests to relevant organizations and freely accessed on publicly available websites such as Iran Civil Aviation Organization, Road Maintenance & Transportation Organization, Iranian Red Crescent Society, Ministry of Energy, and Iran Meteorological Organization. Additionally, OpenStreetMap and Google Maps were used for obtaining data on recreational sites, historical and cultural attractions, security centers, shopping centers, hotels, accommodations, restaurants, and roadside service centers. Climatic and environmental data, including air temperature, precipitation, air quality, and dust, were retrieved from sources such as the Iran Meteorological Organization, Copernicus Data Space, and MODIS. Moreover, elevation, slope, landform, and sunlight data were collected in raster format from the AW3D database. Although the coordinate systems of the datasets varied, all were reprojected to WGS 84 UTM Zone 39N to ensure consistency. The datasets were acquired for the year 2024.

To ensure the validity and quality of the data used in this study, a series of technical and content-related measures were undertaken. First, the cross-validation of the data was performed by comparing the spatial information from open sources such as OpenStreetMap with official national datasets. Subsequently, the metadata and technical documentation of credible sources were examined to assess the accuracy, comprehensiveness, currency, and data production methodologies. To establish spatial consistency, all layers were reprojected into the WGS 84 UTM Zone 39N coordinate system. Additionally, to prevent temporal bias, only datasets pertaining to the year 2024 were selected. Finally, to address data gaps and inconsistencies, spatial extrapolation techniques were employed, or layers with high uncertainty were excluded from the weighted analysis process.

3.2. Methods

Tourism development at caravanserai sites requires a rigorous and strategic approach. Thus, this study used an MCDM-GIS framework including five steps to rank these sites for tourism development based on a comprehensive set of criteria. The steps were as follows:

• Choosing the criteria: To identify relevant criteria, the process began with a literature review, followed by interviews to collect expert opinions. A total of 39 criteria were decided to be relevant for the aim of the study and were categorized into 5 main criteria considering their relevance to each other (

  Table 1. Criteria and BWM-derived weights.

  Criterion	Weight	Sub-Criterion	Weight
  Network connectivity and access	0.19	Distance from roads	0.4
  		Distance from airports	0.09
  		Distance from bus terminals	0.23
  		Distance from taxi stands	0.16
  		Distance from train stations	0.12
  Tourist attractions	0.32	Distance from recreational sites	0.16
  		Distance from historical and cultural attractions	0.21
  		Distance from forests	0.05
  		Distance from meadows	0.03
  		Distance from mountains	0.07
  		Distance from rivers	0.09
  		Distance from waterfalls	0.05
  		Distance from protected areas	0.04
  		Distance from springs	0.06
  		Distance from malls and shopping centers	0.13
  		Distance from deserts	0.11
  Climatic conditions	0.13	Air temperature	0.35
  		Precipitation	0.15
  		Sunlight	0.23
  		Air quality	0.27
  Geomorphological features and hazards	0.10	Flood	0.08
  		Distance from faults	0.1
  		Distance from landslide points	0.05
  		Dust	0.25
  		Elevation	0.18
  		Slope	0.15
  		Landform (plain–mountain)	0.07
  		Distance from industrial centers	0.12
  Facilities and services	0.26	Distance from restaurants	0.12
  		Distance from accommodation centers	0.11
  		Distance from roadside service centers	0.13
  		Distance from healthcare centers	0.08
  		Distance from emergency services	0.07
  		Distance from fire stations	0.06
  		Distance from security centers	0.1
  		Distance from water sources	0.15
  		Distance from villages	0.05
  		Distance from cities	0.09
  		Distance from telecommunications towers	0.04

  ). We also considered local conditions such as geographical features, availability of resources, and specific conditions of the area, which is famous as a cultural–historical destination.
• Pairwise comparison matrix for criterion weighting: A group of knowledgeable experts was involved in criterion weighting. They were given a pairwise comparison matrix, where they compared the relative importance of each criterion to that of the others. Based on their judgment, weights were assigned to each criterion. Among all techniques for weighting, we used the Best–Worst Method (BWM) because of its simplified data collection process and minimized number of pairwise comparisons regarding a large set of criteria, as we had 39.
• Criterion mapping: Spatial data related to each criterion were collected and mapped. They included raster and vector data for 39 criteria that were collected from national and international reliable sources.
• Standardization: The data for each criterion were standardized by using the min–max method to a common scale (e.g., 0–1) to allow for meaningful comparisons. This step ensured that all criteria were treated equally in the analysis.
• Potential map: In this step, ArcMap 10.8 software was used to create standardized criterion maps. Weights were then assigned to each criterion based on the results of the BWM. Weighted Linear Combination (WLC) was then used to combine the weighted criterion maps. This involved multiplying each standardized criterion map by its corresponding weight and summing the results to create a final suitability map. The final suitability map was classified into five classes (e.g., very high, high, moderate, low, and very low) based on the calculated suitability scores. Finally, the caravanserai sites were ranked based on their proximity to the highly suitable areas identified in the classified map.

3.2.1. The Criterion Choosing and Weighting Process

A questionnaire-based approach was used to gather expert opinions and assessments regarding the identification and weighting of effective criteria, which were initially determined through a comprehensive literature review and consultations with experts. In this regard, the involvement of experts was achieved through a dual approach of purposeful and snowball sampling techniques. In the first stage, experts were deliberately chosen due to their knowledge in the related field, including tourism planning, history and cultural heritage, archaeology, geography, and environmental science. To minimize potential biases, we ensured a comprehensive scope by including all relevant domains within the study. Following this, snowball sampling was used, so the initial participants were asked to recommend additional qualified experts. This helped us to enhance the diversity of representation within the targeted expert population. A total of 37 questionnaires were distributed. Finally, 31 questionnaires were completed, resulting in a final participation rate of 83.87%. Moreover, the participants were assured that the research study met ethical standards and that the rights and well-being of all participants involved in the study were protected.

A total of 5 criteria and 39 sub-criteria were identified to evaluate the suitability of “caravanserai sites” for tourism development. Successful tourism development at these sites depends on a set of criteria and sub-criteria which are essential to expanding tourism. The first set of criteria was categorized under “network connectivity and access”, including transportation infrastructure, telecommunications, and accessibility, which are important for all visitors nowadays. The second category included natural features and cultural heritage, known as “tourist attractions”, which are important reasons to visit tourism sites. The third group was “climatic conditions”, referring to temperature, precipitation, air quality, and so on, which affect tourist attractiveness. “Geomorphological features and hazards” were the fourth group, used to assess the geological stability and potential hazards associated with a site, which are considerable for the tourism business. Finally, “facilities and services”, such as accommodation, restaurants, recreational, and healthcare facilities, are essential to providing a satisfactory tourist experience. By a careful evaluation of these criteria and sub-criteria, stakeholders and investors can make informed decisions regarding the development and sustainability of tourism at caravanserai sites.

Following the identification and categorization of the criteria, to integrate expert opinions and determine the weights of each criterion, the Best–Worst Method (BWM) was used (Table 1). Developed by Rezaei [82], the BWM is a contemporary and robust multi-criterion decision-making technique for assigning weights to decision criteria. In this technique, the most and least important criteria are identified by experts. Subsequently, pairwise comparisons are conducted between these extremes and the other criteria [83]. The method then solves a constrained optimization problem (min–max problem) to derive the criterion weights while also incorporating a consistency ratio to assess the reliability of expert judgment. The BWM was selected due to its high consistency and data flexibility given the calculations with the least comparison matrix [84]. Furthermore, the BWM’s reduced data requirements improves efficiency by minimizing the number of pairwise comparisons; this is considerable for our study with 39 sub-criteria. This simplified data collection process, combined with its robust consistency, results in more reliable decision-making outcomes.

This method is a type of multi-criterion decision-making approach that operates based on pairwise comparisons between the “best” and “worst” criteria and all other criteria. The implementation process of this method consists of five steps:

• Step 1: Identification of Set of Criteria

A set of decision-making criteria is considered.

• Step 2: Selection of the Best and Worst Criteria

In this stage, the decision maker selects the most important (best) and the least important (worst) criteria from the set of identified criteria.

• Step 3: Determination of Preference Vector of the Best Criterion over Others

The degree of preference of the best criterion in comparison with each of the other criteria is specified.

• Step 4: Determination of Preference Vector of Other Criteria over the Worst One

In this step, the degree of preference of each criterion in comparison with the worst criterion is determined.

• Step 5: Mathematical Modeling for Weight Determination

To obtain the optimal weight for each criterion, the following optimization model is employed:

$$\begin{array}{c}\min \underset{\mathrm{j}}{\mathrm{m}\mathrm{a}\mathrm{x}}\left\{\left|{\displaystyle \frac{{\mathrm{W}}_{\mathrm{B}}}{{\mathrm{W}}_{\mathrm{j}}}}-{\mathrm{a}}_{\mathrm{B}\mathrm{j}}\right|, \left|{\displaystyle \frac{{\mathrm{W}}_{\mathrm{j}}}{{\mathrm{W}}_{\mathrm{W}}}}-{\mathrm{a}}_{\mathrm{j}\mathrm{W}}\right|\right\}\\ \mathrm{s}.\mathrm{t}.\\ {\displaystyle \sum _{\mathrm{j}}}{\mathrm{W}}_{\mathrm{j}}=1 {\mathrm{W}}_{\mathrm{j}}\ge 0, \mathrm{f}\mathrm{o}\mathrm{r} \mathrm{a}\mathrm{l}\mathrm{l} \mathrm{j}\end{array}$$

(1)

The above model can be reformulated as a linear programming model (Equation (2)), which can be solved by using software such as Lingo 14.0.

$$\begin{array}{c}\min \mathsf{\xi }\\ \mathrm{s}.\mathrm{t}.\\ \left|{\displaystyle \frac{{\mathrm{W}}_{\mathrm{B}}}{{\mathrm{W}}_{\mathrm{j}}}}-{\mathrm{a}}_{\mathrm{B}\mathrm{j}}\right|\le \mathsf{\xi }, \mathrm{f}\mathrm{o}\mathrm{r} \mathrm{a}\mathrm{l}\mathrm{l} \mathrm{j}\\ \left|{\displaystyle \frac{{\mathrm{W}}_{\mathrm{j}}}{{\mathrm{W}}_{\mathrm{W}}}}-{\mathrm{a}}_{\mathrm{j}\mathrm{W}}\right|\le \mathsf{\xi }, \mathrm{f}\mathrm{o}\mathrm{r} \mathrm{a}\mathrm{l}\mathrm{l} \mathrm{j}\\ {\displaystyle \sum _{\mathrm{j}}}{\mathrm{W}}_{\mathrm{j}}=1 {\mathrm{W}}_{\mathrm{j}}\ge 0, \mathrm{f}\mathrm{o}\mathrm{r} \mathrm{a}\mathrm{l}\mathrm{l} \mathrm{j}\end{array}$$

(2)

In this model, Wj represents the weight of criterion j, W_B is the weight of the best criterion, ${\mathrm{W}}_{\mathrm{W}}$ is the weight of the worst criterion, ${\mathrm{a}}_{\mathrm{B}\mathrm{j}}$ denotes the preference of the best criterion over criterion j, ${\mathrm{a}}_{\mathrm{j}\mathrm{W}}$ denotes the preference of criterion j over the worst one, and ξ represents the maximum deviation between actual and theoretical values, which should be minimized.

By solving this model, the final weights of the criteria are calculated in such a way that they exhibit the highest possible consistency with the decision maker’s preferences while minimizing the value of ξ.

3.2.2. Criterion Mapping

Various spatial tools were used to generate the criterion maps. The Euclidean Distance (Spatial Analyst) tool was employed to create distance-based criterion maps and was applied to point, polyline, and polygon shapefiles for different criteria. The criterion maps for air temperature and precipitation were generated by using the Topo to Raster (Spatial Analyst) tool, based on isotherm and isohyet lines. Additionally, the Polygon to Raster (Conversion) tool was used to create the criterion maps for landform and flood. The slope criterion map was produced by applying the Slope (Spatial Analyst) tool to the elevation map. These tools are available in ArcMap 10.8, and the spatial resolution of all criterion maps was set to 1000 m.

3.2.3. Criterion Standardization

To standardize the criteria for evaluating the suitability of heritage tourism development, we normalized them (made them dimensionless) by using their minimum and maximum values [83,85,86]. This standardization was based on the impact of each criterion. For criteria where higher values indicate better quality and desirability, Equation (3) was used. Conversely, Equation (4) was used for criteria where lower values indicate better quality and desirability.

$${\mathrm{S}\mathrm{C}}_{\mathrm{i}\mathrm{j}}={\displaystyle \frac{{\mathrm{C}}_{\mathrm{i}\mathrm{j}}-{\mathrm{C}}_{\mathrm{j}}^{\mathrm{m}\mathrm{i}\mathrm{n}}}{{\mathrm{C}}_{\mathrm{j}}^{\mathrm{m}\mathrm{a}\mathrm{x}}-{\mathrm{C}}_{\mathrm{j}}^{\mathrm{m}\mathrm{i}\mathrm{n}}}}$$

(3)

$${\mathrm{S}\mathrm{C}}_{\mathrm{i}\mathrm{j}}={\displaystyle \frac{{\mathrm{C}}_{\mathrm{j}}^{\mathrm{m}\mathrm{a}\mathrm{x}}-{\mathrm{C}}_{\mathrm{i}\mathrm{j}}}{{\mathrm{C}}_{\mathrm{j}}^{\mathrm{m}\mathrm{a}\mathrm{x}}-{\mathrm{C}}_{\mathrm{j}}^{\mathrm{m}\mathrm{i}\mathrm{n}}}}$$

(4)

where ${\mathrm{S}\mathrm{C}}_{\mathrm{i}\mathrm{j}}$ is the standardized value for the i-th location in the j-th criterion, ${\mathrm{C}}_{\mathrm{i}\mathrm{j}}$ is the value for the i-th location in the j-th criterion, and ${\mathrm{C}}_{\mathrm{j}}^{\mathrm{m}\mathrm{a}\mathrm{x}}$ and ${\mathrm{C}}_{\mathrm{j}}^{\mathrm{m}\mathrm{i}\mathrm{n}}$ are the maximum and minimum values for the j-th criterion, respectively. The standardized values ranged from 0 to 1, with values closer to 1 indicating excellent potential and those closer to 0 indicating poor potential. The Extract Multi Values to Points tool was used to extract these standardized values for each criterion at the studied caravanserai sites. Finally, the sites were evaluated and compared based on these standardized criterion values.

3.2.4. Suitability Assessment for Tourism Potential Map

This study used the WLC method, a commonly used technique in tourism studies [83,87,88], to evaluate the suitability of the area for tourism development and ranking caravanserai sites with the highest potential. WLC is one of the most common techniques in multi-criterion evaluation analysis, also known as the “scoring method”. The method uses a system where each factor is given a score based on how important it is, where the analyst or decision maker decides which factors are the most crucial and assigns weights accordingly. Then, by multiplying the relative weight by the value of each option, final values are achieved for all options [89]. The option with the highest final value is considered the most appropriate [90,91]. In this method of decision, the value of each option is calculated by using Equation (5):

$$\mathrm{S}=\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{W}}_{\mathrm{i}}{\mathrm{X}}_{\mathrm{i}}$$

(5)

where S is the suitability value for each location within the study area, n is number of criteria, ${\mathrm{W}}_{\mathrm{i}}$ is weight of the i-th criterion, and ${\mathrm{X}}_{\mathrm{i}}$ is the standardized value of the i-th criterion for a location. The final suitability map shows the most suitable areas for tourism development regarding the criteria. The suitability map was classified into five suitability levels: very high (0.8–1.0), high (0.6–0.8), moderate (0.4–0.6), low (0.2–0.4), and very low (0.0–0.2). Each suitability level was assigned a numerical value (e.g., very high = 5, high = 4, etc.).

In summary, the process of evaluating and ranking the caravanserais based on the proposed MCDM-GIS framework in this study was carried out as outlined below.

First, spatial maps of the criteria relevant to tourism development were prepared, and their values were normalized by using standardization equations (Equations (3) and (4)). Then, the standardized values for each criterion were extracted at the locations of the eight caravanserais under study.

Afterwards, the weighting of criteria and sub-criteria was performed by using the Best–Worst Method (BWM), in accordance with Equations (1) and (2) and as presented in Table 1.

Finally, the standardized values and the corresponding weights of the criteria were combined through the Weighted Linear Combination (WLC) algorithm by using Equation (5) to calculate a final suitability index for each location. Based on the comparison of the computed WLC values for the locations of the eight caravanserais, a ranking was established. A higher WLC value at a given caravanserai location indicates a greater level of suitability and priority for tourism development compared with the others.

4. Results

4.1. Criterion Maps

In this study, a multi-criterion evaluation framework was used. It includes five primary criteria: (1) network connectivity and access, (2) tourist attractions, (3) climatic conditions, (4) geomorphological features and hazards, and (5) facilities and services. Each of these criteria was divided into several sub-criteria to develop criterion maps for a comprehensive spatial analysis. Figure 2 illustrates that the area has a good condition in terms of network connectivity and accessibility. Although the eastern part faces certain connectivity constraints, the central and western part shave a more cohesive and interconnected network. This advanced level of connectivity can be attributed to a combination of sub-criteria, including high density of roadways, access to public transportation options such as buses and taxis, and distance from transportation nodes such as train stations.

Figure 3 shows the spatial distribution of sub-criteria related to the tourist attractions within the study area. The maps present a detailed analysis of natural, cultural, and man-made attractions that enhance the suitability of an area. Distance from natural features—such as forests, mountains, rivers, waterfalls, meadows, springs, and protected areas—plays a crucial role, as areas located near these features generally demonstrate higher values. Furthermore, distance from recreational facilities and shopping centers appears to lift areas’ attractiveness. Cultural and historical sites also significantly contribute to suitability, as areas featuring such attractions tend to reflect higher values. Briefly, the criterion maps offer valuable insights into the spatial patterns of sub-criteria of tourist attractions, highlighting the necessity of an integrated analysis of various types of tourist destinations.

The spatial distribution of sub-criteria for climatic conditions across the study area is presented in Figure 4. The maps reveal distinct patterns in air temperature, precipitation, sunlight, and air quality. Air temperature shows an increasing trend of values from west to east, due to the geographical latitude and proximity to warmer regions. Precipitation indicates the concentration of higher values in specific areas, implying localized rainfall patterns and the influence of topographic features. Sunlight distribution appears to be steady across the area, with slight variations because of factors such as cloud cover and topography. Air quality shows a more complex pattern; as a result of pollution from industrial zones and sandstorms in arid areas and deserts, the areas close to these zones exhibit lower air quality.

The spatial distribution of sub-criteria for geomorphological features and hazards reveals different patterns in flood risk, proximity to faults and landslides, dust, elevation, slope, landform, and distance from industrial centers (Figure 5). Faults and landslide points are concentrated in specific areas, suggesting a great potential for geological hazards. Dust concentration in certain areas is due to arid or semi-arid conditions and wind patterns. Elevation and slope show significant spatial variability, impacting drainage patterns and the potential for erosion. Landform distribution reveals plains and mountains in the study area, each with specific characteristics and vulnerabilities. Finally, distance from industrial centers exhibits a clear spatial pattern concentrated mostly in the western and central parts of the area; areas closer to these centers have a higher potential for pollution and industrial hazard risks.

Figure 6 illustrates the spatial distribution of selected sub-criteria related to facilities and services across the study area. Generally, areas closer to cities tend to exhibit better access to most facilities and services. This is clear in the maps showing distances from restaurants, accommodation centers, roadside service centers, healthcare centers, and emergency services. However, access to facilities such as fire stations and security centers is limited in the eastern part, which requires longer response times in emergencies. The maps also highlight the spatial distribution of water sources, which are crucial to the well-being of tourists and tourism business. Within the context of tourism studies, where climate change is a growing niche area of focus, accessible water resources play a crucial role in determining the resilience of a destination. This emphasizes the vulnerability of tourism-dependent regions to the impacts of climate change. Furthermore, distance from telecommunications towers reveals almost enhanced levels of connectivity in the area, which is essential to communication, information access, and economic development.

4.2. Unlocking Tourism Potential: A Suitability Assessment

To assess the suitability of the study area for tourism development, we benefited from a GIS-multi-criteria decision-making (GIS-MCDM) approach. This widely recognized approach is particularly effective for evaluating an area’s suitability considering diverse criteria with different levels of importance. It is also useful for a comprehensive assessment. Therefore, to this end, the standardized values for each criterion were assessed in the suitability analysis (

Table 2. Standardized values of effective criteria for caravanserai locations.

	Gaz	Amin Aabad	Maranjab	Neyestanak	Kuhpaye	Gaba Aabad	Mahyar	Sheikh Alikhan
Road	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00
Airport	0.96	0.73	0.87	0.76	0.86	0.96	0.90	0.95
Bus	1.00	0.86	0.71	0.81	0.99	0.91	0.83	0.89
Taxi	0.94	0.74	0.72	0.80	0.55	0.85	0.83	0.89
Train	0.90	0.99	0.81	0.80	0.96	0.90	0.97	0.90
Recreational	0.98	0.85	0.85	0.89	0.86	0.99	0.91	0.95
History and culture	1.00	0.82	1.00	0.99	1.00	1.00	0.84	0.87
Forest	0.66	0.78	1.00	0.88	0.87	0.88	0.77	0.61
Meadow	0.54	1.00	0.46	0.28	0.25	1.00	0.51	0.60
Mountain	0.74	0.94	0.96	0.85	0.75	0.97	0.94	0.94
River	0.97	0.96	0.82	0.66	0.70	0.92	0.55	0.97
Waterfall	0.90	0.71	0.61	0.43	0.59	0.79	0.78	0.89
Protected area	0.90	0.49	0.84	0.28	0.63	0.64	0.96	0.95
Spring	0.90	0.76	0.65	0.90	0.76	0.96	0.73	0.84
Mall	0.98	0.85	0.87	0.90	0.77	0.91	0.90	0.94
Desert	0.61	0.31	1.00	0.75	0.64	0.89	0.45	0.61
Temperature	0.36	0.53	0.06	0.44	0.33	0.50	0.41	0.42
Precipitation	0.05	0.07	0.01	0.03	0.03	0.10	0.06	0.06
Sunlight	0.68	0.67	0.65	0.68	0.71	0.67	0.65	0.67
Air quality	0.15	0.82	0.49	0.76	0.71	0.65	0.60	0.35
Flood	0.30	0.30	0.30	0.30	0.30	0.40	0.10	0.30
Fault	0.76	0.46	0.04	0.28	0.29	0.06	0.88	0.65
Elevation	0.74	0.60	0.95	0.66	0.69	0.73	0.72	0.70
Slope	0.99	0.97	0.97	0.98	0.93	0.85	0.86	0.97
Landform	0.60	0.60	0.60	0.60	0.60	0.60	0.60	0.60
Dust	0.53	0.38	0.46	0.35	0.43	0.26	0.53	0.40
Landslide	0.33	0.08	0.18	0.75	0.57	0.12	0.26	0.31
Industrial	0.14	0.07	0.32	0.02	0.03	0.07	0.05	0.10
Restaurant	0.96	0.69	0.64	0.98	0.99	0.96	0.94	0.86
Accommodation	0.94	0.72	0.75	0.75	1.00	0.95	0.81	0.85
Roadside	0.99	0.70	0.72	1.00	0.96	0.85	0.81	0.88
Healthcare	0.94	0.88	0.66	0.76	0.67	0.82	0.78	0.87
Emergency	0.95	0.85	0.83	0.90	0.99	0.81	0.91	0.93
Fire	0.98	0.70	0.71	0.80	0.98	0.94	0.85	0.89
Security	0.98	0.99	0.85	0.90	0.99	0.99	0.93	0.94
Water	0.89	0.74	0.31	0.67	0.53	0.62	0.66	0.89
Village	0.95	0.99	0.64	1.00	0.92	0.99	0.99	0.99
Cities	1.00	0.68	0.65	0.75	1.00	0.96	0.81	0.87
Tele	1.00	0.99	0.99	1.00	1.00	0.99	0.99	0.99

). These values range from 0 to 1, where values closer to 1 indicate higher suitability considering that specific criterion. The table provides a comparative overview of the standardized values for each caravanserai location based on sub-criteria. Key observations from the table reveal the caravanserais’ strengths and weaknesses across each criterion. High scores were consistently observed in several criteria, especially road and bus accessibility for all caravanserai, implying robust overall network connectivity. Proximity to historical and cultural attractions emerged as a significant factor for some locations, while others demonstrated higher scores in proximity to natural features such as forests and mountains. Climatic conditions demonstrate variability, with higher scores in terms of solar radiation and lower scores for precipitation in almost all locations. Accessibility to facilities and services was generally high for several caravanserais, suggesting a positive foundation for tourism development.

By analyzing each criterion category, we developed suitability maps showing areas with varying potential for tourism development (Figure 7). Area with high accessibility, a rich concentration of attractions like historical sites and natural landscapes, favorable climate with moderate temperatures and suitable rainfall, low risk of natural hazards and favorable geomorphological features, and well-developed infrastructure and services are likely more suitable for tourism development. Precisely, areas with a predominance of high and very high ratings in these criteria, represented by the blue and dark blue shades on the maps, are considered the most suitable areas for tourism development. In contrast, areas classified as very low and low may face challenges in attracting tourists and tourism business because of the limitations in accessibility, unfavorable climatic conditions, significant natural hazards, or a lack of adequate infrastructure. Overall (Figure 8), the analysis suggests that while the area has a significant potential for tourism development, with strengths in network connectivity (60% of the area was classified as very high and high), attractions (61%), and facilities and services (56%), challenges exist in terms of climate (61% in very low and low suitability classes).

The overall assessment indicates a diverse spatial distribution of tourism development potential. The western area, especially that surrounding the majority of the caravanserais, reflects a high to very high level of suitability as a result of a combination of the criteria. It should be noted that a significant portion of the area (34%) is classified as having very high suitability, while only 6% is identified as having very low suitability (Figure 9).

4.3. Ranking Caravanserais

While all caravanserais’ locations are classified in high and very high classes on the suitability map (Figure 9), a comparative analysis based on the selected criteria was conducted to rank them (

Table 3. Ranking of caravanserais (higher scores implies greater potential for development).

Name	Access	Attractions	Climate	Geohazards	Services	All
Gaz	8	7	2	8	8	8
Amin Aabad	3	1	8	2	2	2
Maranjab	1	6	1	7	1	1
Neyestanak	2	4	7	3	6	4
Kuhpaye	4	3	5	4	7	5
Gaba Aabad	6	8	6	1	4	7
Mahyar	5	2	4	6	3	3
Sheikh Alikhan	7	5	3	5	5	6

). Six distinct criteria categories were considered for ranking, including access, attractions, climate, geohazards, services, and an integration of all categories. In terms of accessibility and relevance infrastructure, Gaz and Sheikh Alikhan exhibit strong accessibility, likely due to well-maintained roads, proximity to major transportation hubs, or the presence of airports or train stations nearby. This ease of access is a significant advantage for attracting tourists, as it minimizes travel time and effort. Additionally, Gaz and Kuhpaye boast well-rated services, suggesting the presence of essential amenities like hotels, restaurants, transportation options within the destination, and potentially even tour operators and guides. This existing infrastructure provides a solid foundation for supporting tourism growth. Moving on to attractions, Gaba Aabad, Gaz, and Maranjab stand out due to their compelling attractions. These might include natural wonders such as stunning landscapes, historical sites, cultural experiences, and unique activities. These attractions serve as major draws for visitors, enticing them to explore these destinations. Considering environmental factors, Amin Aabad enjoys a climate that is conducive to tourism, such as pleasant temperatures and air quality. This favorable climate enhances the overall visitor experience and encourages outdoor activities. However, Maranjab and Gaz face climatic challenges that could impact tourism, such as extreme temperatures or lower air quality. Careful planning and adaptation strategies may be necessary to mitigate these challenges. Furthermore, Gaba Aabad and Amin Aabad are situated in areas prone to natural hazards such as earthquakes and landslides. These geohazards pose potential risks to both tourists and infrastructure, so thorough risk assessments and mitigation measures are crucial during the development and planning phases of tourism projects at these locations.

According to the findings, Gaz and Gaba Aabad exhibited high ranking across the majority of criteria, indicating a good capacity for development. On the other hand, while Amin Aabad has an acceptable score in climate criteria and Maranjab in attractions, their rankings are hindered by limited access and inadequate service. Gaz stands out as the leading caravanserai with the highest overall score due to its advantageous combination of access and attractions while requiring more consideration in regards to climate conditions. The analysis also highlights the varying tourism development potential of each caravanserai. While all sites exhibit potential, Gaz and Gaba Aabad consistently ranked high across most criteria, indicating strong prospects for development. However, their relatively low scores in climate conditions require careful planning and mitigation strategies. Conversely, Amin Aabad and Maranjab, despite possessing certain strengths, face challenges related to limited access and inadequate services.

Considering the traditional use of caravanserais and the adaptive reuse approach, after the overall ranking as well as the rankings of each criterion, here are some suggestions for each caravanserai: Given its favorable accessibility and suitable infrastructure, Gaz has the potential to become a traditional tourist accommodation. Additionally, its scores in tourist attractions and geohazards support its development as a recreational center with traditional elements, such as a bazaar. However, its unfavorable climatic conditions necessitate the addition of canopies, green spaces, and ventilation and cooling systems to create a more comfortable environment for tourists. With a high score in tourist attractions and a relatively favorable climate, Gaba Aabad is ideal for ecotourism, camping, and nature-based tourism. Developing facilities such as eco-friendly accommodations and hiking trails could further enhance its appeal. Moderately rated caravanserais such as Sheikh Alikhan require interventions to develop sustainable tourism. With a good score in accessibility and attractions, these caravanserai have high potential for tourism and camping tours. They are also suitable for use as residences due to their good accessibility. However, due to their low score in climatic conditions, they require similar measures to Gaz. Kuhpaye’s relatively favorable score in climate and services and relatively low score in accessibility and attractions suggest that it could be developed as a stopover for travelers and sports tourism such as cycling. Improving advertising and access could increase its attractiveness. With good climatic conditions but low scores in most criteria, Neyestanak is suitable for educational camping, astronomical tourism, and desert tours due to its proximity to the desert areas of Isfahan Province. Adding simple facilities such as nature guides, an observation platform, and overnight camps would help this caravanserai’s potential for tourism development. Mahyar needs major changes for tourism development. With relatively good accessibility but low service scores, it can be used as a stopover and local tourist destination. Amin Abad, with a high score in climatic conditions but a severe weakness in attractions and services, is suitable for specific tours such as scientific ecotourism. By developing access and services, it could be transformed into an area for scientific and educational camping. Maranjab is not suitable for regular tourism due to its lack of access, services, and unsuitable climate. Given its proximity to the desert areas of the province, it could be transformed into a destination for survival tours, adventure tours such as off-roading, desert climbing, and survival skills training.

5. Discussion

This study underscores the critical role of spatial analysis in assessing the tourism development potential of caravanserais within the broader context of heritage tourism. As highlighted by Istoc [1] and Richards [2], heritage tourism has emerged as a growing segment in the global tourism industry, given that heritage sites have been integrated into tourism development strategies [10]. However, as Wong [92] emphasizes, the development of heritage tourism must carefully navigate the inherent tensions between heritage conservation and tourism exploitation. Balancing economic exploitation with long-term preservation is crucial [93,94]. This necessitates careful planning and the adaptive reuse of heritage sites, aligning with the emphasis on sustainable community development and empowerment advocated by Mo et al. [14], Vasavada and Kour [15], and Nkwanyana and Nzama [16].

Spatial analysis emerges as a critical tool in this endeavor, providing a robust framework for understanding and optimizing the development of heritage tourism. By comprehensively assessing the geographical and environmental potential of each site, spatial analysis enables informed decision making that minimizes negative impacts and maximizes the positive contributions of tourism. This aligns with the findings obtained by Rezvani et al. [95], who demonstrated the efficacy of spatial analysis techniques in enhancing decision-making processes within the tourism planning domain.

The findings of this study underscore the importance of a multi-criterion approach in assessing the tourism development potential of caravanserais. The framework developed in this study, utilizing five primary criteria (network connectivity and access, tourist attractions, climatic conditions, geomorphological features and hazards, and facilities and services), provides valuable insights into the strengths and challenges of each site. This approach aligns with the emphasis on understanding the spatial distribution and characteristics of heritage sites, as highlighted by Che et al. [34], Vulevic et al. [35], and other researchers. As the results show, not all criteria are in optimal condition for each heritage site. While a site may excel in one or more criteria, it can exhibit vulnerabilities in others. Among all criteria, particular attention must be paid to vulnerabilities. This aligns with the emphasis on evaluating the vulnerability of heritage sites to natural hazards, as highlighted by Garrote et al. [55] and Valagussa et al. [57]. This analysis helps to identify sites that are vulnerable to natural disasters and assess their potential for tourism business development. The results of such assessments highlight the essential need to develop and implement mitigation strategies to minimize these risks.

5.1. Policy Implications

The findings have implications for decision makers and planners. First, this study emphasizes the importance of a spatially informed approach to tourism planning. By identifying areas with high suitability, policymakers can prioritize development efforts, direct investments towards the most promising locations, and minimize potential negative impacts on the environment and local communities. Second, to ensure that heritage tourism development aligns with environmental and cultural sensitivities, policy should prioritize zoning regulations that designate specific areas for different types of tourism development based on suitability assessments, as emphasized by this text’s focus on considering the specific conditions at each location to prevent incompatible development. Third, to capitalize on the potential for creating niche markets and diversifying heritage tourism products, as highlighted by this text, policies should encourage and support the development of diverse tourism products tailored to specific locations and their unique characteristics. This could be achieved through policy incentives such as grants to encourage entrepreneurs to develop innovative and sustainable tourism offerings. Fourth, to ensure sustainable tourism development and minimize negative impacts on heritage sites, policies should establish and enforce carrying capacity limits for heritage sites, such as caravanserais. These limits will help protect these sites from overcrowding and ensure their long-term preservation. Fifth, to effectively evaluate the success of heritage tourism policies and make necessary adjustments, data on tourist flows, visitor impacts, and the effectiveness of tourism management strategies must be consistently collected and analyzed.

5.2. Limitations and Future Studies

The developed maps not only show the most suitable areas but also provide insights into appropriate tourism activities considering the specific conditions at each location. For example, caravanserais located in areas with high scores in “Network Connectivity and Access” and “Facilities and Services” (Figure 3) might be suitable for developing resort-style tourism, while those with high scores in “Tourist Attractions” and “Geomorphological Features and Hazards” (Figure 3 and Figure 6) might be more appropriate for adventure tourism or ecotourism. However, this was not the focus and aim of the current study, so we did not focus on it. Future studies might use the results to discuss the caravanserais’ suitability for particular tourism activities or products. This will help not only to enhance tourists’ experiences but also to create niche markets and diversify tourism products and activities in particular areas. Moreover, this study utilized a GIS and WLC as the MCDM method for suitability assessment. While effective, WLC has limitations, such as its sensitivity to the assigned weights. Exploring other MCDM techniques, such as AHP or TOPSIS, could provide a more comprehensive and robust evaluation. Future research studies could compare the results obtained with different MCDM methods to enhance the accuracy and robustness of suitability maps for heritage tourism development. Furthermore, this study is limited in scope, as it only explores the potential for tourism development. Given this limitation, future studies must rigorously investigate the carrying capacity of heritage sites, such as caravanserais, to ensure sustainable tourism development. While acknowledging the inherent risk of damage to heritage sites during tourism development, future research should prioritize methodologies for determining visitor thresholds that minimize negative impacts on the cultural and physical integrity of these invaluable assets.

Considering sustainability as a significant issue, adaptive reuse strategies should respect the historical function and architectural integrity of heritage sites while minimizing environmental impacts. So, future research studies could explore how green infrastructure and vernacular architecture can enhance visitor comfort without compromising authenticity, particularly in areas with extreme climatic conditions. Similarly, the feasibility and long-term impacts of low-impact infrastructure, such as ecolodges, must be assessed, especially in rich natural or cultural settings lacking services. Furthermore, future studies should focus on developing frameworks for monitoring and carrying capacity management. Such research studies are essential to ensuring that heritage tourism serves as a tool for cultural preservation and responsible economic development, rather than a driver of destruction and commodification.

6. Conclusions

This study aimed to assess the tourism development potential of eight caravanserais recently inscribed on the UNESCO World Heritage List within Isfahan Province, Iran. By employing a multi-criteria evaluation framework that integrated GIS-based spatial analysis with a comprehensive assessment of key factors, this research study provides valuable insights for stakeholders and decision makers. The findings reveal a diverse spatial distribution of tourism development potential across the study area. While a significant portion of the region demonstrates high suitability, characterized by excellent network connectivity, a rich concentration of attractions, favorable climatic conditions, and well-developed infrastructure, challenges exist in certain areas. Notably, climatic conditions, particularly in terms of temperature and precipitation, emerged as a significant constraint for some locations. The analysis underscores the need for a nuanced understanding of the specific characteristics and constraints of each caravanserai. Tailored development strategies should be formulated for each site, considering its unique strengths and weaknesses. For instance, improving access and enhancing service infrastructure could significantly enhance the tourism potential in some caravanserais.

Spatial analysis results can play a key role in the sustainable development of heritage sites, as these analyses help more accurately examine the characteristics and potential of each site. By assessing geographical features, climatic conditions, geological characteristics, and infrastructure, spatial analysis can identify the strengths and weaknesses of each location, thereby providing better decisions for the sustainable development of heritage tourism. Climatic and geological criteria help identify the vulnerabilities of sites to natural disasters, assisting in risk management decisions. This not only allows for the efficient use of existing resources but also enables the development of environmentally friendly plans that align with local needs, considering the specific conditions of each site. As a result, spatial analysis, by integrating different criteria, helps balance economic exploitation with the conservation of cultural heritage, ensuring sustainable and viable development processes.