Introduction

Health tourism has become one of the most dynamic segments of the global tourism industry because it combines medical services, destination attractiveness, mobility, and digital information-seeking behavior. For Iran, this sector has particular strategic importance due to the country’s medical capacities, cost advantages, cultural resources, and potential to generate foreign exchange, employment, and regional development. However, the development of health tourism requires more than investment in hospitals and accommodation facilities; it also depends on understanding where potential demand emerges, which destinations are perceived as attractive, and how information flows between regions. Traditional data sources such as surveys, official reports, and interviews provide useful insights, but they often suffer from delay, high cost, limited spatial coverage, and inability to capture real-time behavioral signals (Zayed et al., 2023). Therefore, this study aims to map and analyze Iran’s inter-provincial health tourism network using Google Trends data from 2020 to 2025. The study seeks to identify key destination provinces, reveal online search-flow patterns among provinces, and offer practical insights for sustainable health tourism planning.

Literature Review

The use of Google Trends in tourism research has expanded significantly because search data can serve as a proxy for tourist interest, destination image, and demand forecasting. Previous studies have shown that Google Trends can improve tourism demand prediction, particularly when combined with statistical or machine-learning models (Bangwayo-Skeete & Skeete, 2015; Önder & Gunter, 2015). However, Google Trends data should be interpreted carefully because they represent relative search interest rather than absolute search volume. The 0–100 scale indicates normalized search intensity, meaning that the highest observed search value becomes 100 and other values are calculated relative to that peak. This makes the data useful for comparison across regions and time periods but not for measuring the exact number of users or actual travelers (Springer et al., 2023; Zayed et al., 2023).

Network analysis provides another important theoretical and methodological foundation for this research. In tourism studies, network analysis enables researchers to model destinations as nodes and relationships among them as edges, thereby identifying hubs, bridges, clusters, and structural inequalities within tourism systems (Baggio & Valeri, 2021; Seok et al., 2021). Centrality measures such as in-degree, out-degree, betweenness, closeness, and eigenvector centrality help determine which destinations attract attention, which provinces act as intermediaries, and which regions occupy influential positions in the network. Community-detection algorithms such as Louvain further reveal regional clusters based on connection patterns (Blondel et al., 2008). Despite the growing use of both Google Trends and network analysis in tourism research, few studies have integrated these approaches to examine health tourism demand in Iran at the inter-provincial level. This gap is important because health tourism demand is not evenly distributed; it is shaped by geography, infrastructure, medical reputation, cultural affinity, accessibility, and digital visibility (Lukose et al., 2024).

Methodology

This study employed an exploratory quantitative design based on digital trace data. Google Trends data were collected for the period 2020–2025 using a Python-based application programming interface. The keyword “health tourism” was combined with the names of Iran’s 31 provinces to extract relative search interest values. The resulting dataset was organized as an adjacency matrix in which provinces represented nodes and inter-provincial search intensities represented weighted directed edges. A directed network was appropriate because search flows are asymmetric: users in one province may search for health tourism services in another province without an equivalent reverse flow.

Data preprocessing included replacing missing values with zero, converting all values into numeric format, removing intra-provincial self-relations, and excluding very weak connections to reduce noise. Since Google Trends does not provide absolute search counts, the study interprets the data as indicators of latent demand and online search intention, not as direct evidence of actual patient movement or realized health-tourism trips. Network analysis was then conducted using degree centrality, in-degree centrality, out-degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. The Louvain algorithm was used to identify regional clusters. In addition, descriptive statistics, Spearman correlation, and independent t-tests were used to examine search intensity patterns and relationships between provinces.

Results and Findings

The analysis revealed a relatively sparse but meaningful health tourism search network in Iran. The final network consisted of 31 nodes and 131 directed edges, with a density of 0.1409. This low density indicates that health tourism search behavior is selective and concentrated rather than evenly distributed across all provinces. The average degree was 8.45, suggesting that each province was connected, on average, to approximately eight or nine other provinces. The average clustering coefficient was 0.4603, indicating a moderate tendency toward regional grouping.

Tehran emerged as the dominant health tourism hub, with the highest in-degree centrality of 1.0, meaning that it received search attention from all other provinces. This confirms Tehran’s central position in Iran’s health tourism network and reflects its concentration of specialized medical infrastructure, digital visibility, and perceived service quality. The strongest search flow was from Alborz to Tehran, with a weight of 43, further highlighting Tehran’s role as the main destination for nearby provinces. Khuzestan had the highest betweenness centrality, with a value of 0.3454, identifying it as the most important bridge province in the network. This means that Khuzestan plays a key intermediary role in connecting different regional search flows. Other provinces with important central positions included Fars, Gilan, Isfahan, Mazandaran, Lorestan, and Alborz.

Discussion

The findings show that Iran’s health tourism network is not a fully integrated national system but a selective and regionally clustered structure. Tehran’s dominance indicates that digital demand is strongly concentrated around the capital, which may create opportunities for specialized investment but also risks overcentralization. Khuzestan’s high betweenness centrality suggests that bridge provinces should not be treated merely as secondary destinations; they can play a strategic role in connecting regional markets, distributing demand, and improving network efficiency. The presence of distinct regional clusters also implies that health tourism planning should not rely on a uniform national strategy. Instead, policymakers should design cluster-based development programs, strengthen cooperation among neighboring provinces, and create complementary medical-tourism routes.