Submitted:
17 June 2026
Posted:
24 June 2026
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Abstract
Heritage tourism plays a central role in Spain’s cultural and territorial development, yet its spatial distribution and intensity remain unevenly understood. This study examines heritage tourism in Spain as a territorially differentiated phenomenon by analyzing provincial differences in tourism intensity, heritage tourism density, and cultural heritage concentration. Using official experimental statistics from the National Institute of Statistics of Spain, the study integrates mobile-phone geolocation data from 181,670,728 devices across 3,214 destinations, tourism expenditure information, and a Heritage Concentration Index constructed for architectural cultural heritage. Four one-way ANOVA models were applied to assess whether significant differences exist among Spain’s 52 provinces in the Internal Tourism Intensity Index, External Tourism Intensity Index, Heritage Tourism Density, and Heritage Concentration. Hochberg-adjusted post hoc comparisons were used to identify specific interprovincial differences while controlling for multiple comparisons. The results show statistically significant territorial differences across all four indicators, confirming that heritage tourism in Spain is not spatially homogeneous. These findings contribute to the literature by offering an integrated, data-driven approach to measuring heritage tourism at the provincial scale. They also provide practical evidence for designing differentiated tourism policies, improving destination management, and supporting more balanced heritage-based territorial planning.
Keywords:
heritage tourism
; cultural heritage
; tourism intensity
; heritage tourism density
; mobile phone data
; territorial differentiation
; Spain
; ANOVA
; Hochberg post hoc
; tourism mobility
1. Introduction
Heritage tourism is a key component of Spain’s tourism system and an important expression of the country’s cultural, historical, and territorial diversity. Spain’s architectural heritage, historical cities, monuments, cultural landscapes, and urban heritage areas attract national and international visitors and contribute to the symbolic and economic value of destinations. However, heritage tourism is not distributed evenly across the national territory. Some provinces concentrate high levels of cultural heritage, tourism mobility, and visitor pressure, while others remain less visible despite their historical and cultural relevance. This territorial imbalance is consistent with previous research showing that tourism development, cultural resources, and regional economic dynamics tend to vary significantly across space (Mathisen et al., 2022; Albaladejo et al., 2024). Understanding these differences is therefore essential for designing more balanced, evidence-based tourism policies.
In recent years, tourism research has increasingly relied on spatial indicators, geolocation technologies, and large-scale mobility data to examine how visitors move across destinations. These tools offer new possibilities for analyzing tourism intensity, visitor concentration, and territorial pressure beyond traditional surveys or aggregate arrival statistics. Geographic Information Systems and digital tools have already demonstrated their usefulness for heritage management, spatial interpretation, and destination planning (Hidalgo-Sánchez et al., 2024). Similarly, mobile-phone and geolocation-based data have become increasingly relevant for identifying large-scale mobility patterns and understanding the spatial structure of tourism demand (Padrón-Ávila & Hernández-Martín, 2020; Ruiz-Pérez et al., 2023). In the specific field of heritage tourism, this methodological shift is particularly important because cultural tourism flows are often difficult to separate from general tourism mobility. Visitors may engage with heritage sites directly, indirectly, or as part of broader destination experiences, which makes the territorial measurement of heritage tourism a complex but necessary task (Richards, 2018; Kalvet et al., 2020).
Previous studies have examined heritage tourism from different perspectives, including authenticity, destination image, cultural value, sustainability, economic development, and heritage management. Authenticity remains one of the central dimensions of cultural and heritage tourism experiences, shaping how visitors interpret and value heritage environments (Cheng et al., 2024; Yi et al., 2024). Other contributions have emphasized the role of cultural heritage in destination competitiveness, local identity, and sustainable territorial development (Noonan & Rizzo, 2017; Zubiaga et al., 2024). At the same time, recent studies highlight the increasing importance of digitalization, spatial technologies, and heritage-sensitive indicators for improving the management of cultural destinations (Egusquiza et al., 2021; Montalto et al., 2019; Wang et al., 2024). Nevertheless, despite these advances, there is still a need for empirical approaches capable of comparing heritage tourism dynamics across territories using integrated and comparable indicators.
This study addresses that gap by examining heritage tourism in Spain as a territorially differentiated phenomenon. Rather than assuming that heritage tourism operates uniformly at the national level, the article focuses on provincial differences in four indicators: the Internal Tourism Intensity Index, the External Tourism Intensity Index, Heritage Tourism Density, and the Heritage Concentration Index. This approach responds to the need for more robust and comparable frameworks for measuring cultural tourism and its territorial expressions (Kalvet et al., 2020; Zubiaga et al., 2024). It also contributes to ongoing debates on how cultural heritage, tourism mobility, and spatial concentration can be analyzed through data-driven methods in order to support more precise destination planning (Hidalgo-Sánchez et al., 2024; Zhang et al., 2024).
The empirical analysis uses official experimental statistics from the National Institute of Statistics of Spain, including mobile-phone geolocation data from 181,670,728 devices across 3214 destinations, complemented with tourism expenditure information and an index of architectural cultural heritage concentration. The study applies one-way ANOVA models and Hochberg-adjusted post hoc comparisons to identify statistically significant differences among Spain’s 52 provinces. This methodological design does not seek to establish causal relationships between heritage concentration, mobility, and expenditure. Instead, it provides evidence of territorial heterogeneity in heritage tourism indicators, allowing for a clearer understanding of how heritage tourism varies across provincial contexts.
The contribution of this article is threefold. First, it contributes to the literature on heritage tourism by framing it as a spatially uneven and territorially differentiated phenomenon, rather than as a homogeneous national activity. Second, it offers a data-driven methodological approach that integrates mobile-phone mobility data, tourism intensity indicators, heritage tourism density, and cultural heritage concentration at the provincial scale. Third, it provides practical evidence for policymakers and destination managers who need to design province-sensitive strategies for heritage tourism planning, visitor management, and territorial development. In this sense, the study aligns with recent calls for more evidence-based, heritage-sensitive, and territorially specific tools for sustainable cultural tourism management (Falk & Hagsten, 2024; Su, 2024; Zubiaga et al., 2024).
The remainder of the article is structured as follows. Section 2 develops the literature review and conceptual framework, focusing on heritage tourism, territorial differentiation, mobility indicators, and cultural heritage concentration. Section 3 presents the hypotheses derived from the literature. Section 4 describes the data sources, variables, and analytical procedure. Section 5 reports the ANOVA and Hochberg-adjusted post hoc results. Section 6 discusses the findings in relation to previous research and their implications for heritage tourism planning. Finally, Section 7 presents the conclusions, limitations, and future research directions.
2. Literature Review and Conceptual Framework
Heritage tourism is widely understood as a multidimensional form of tourism in which historical, cultural, architectural, symbolic, economic, and experiential dimensions interact. The value of heritage destinations is not limited to the physical presence of monuments or historical sites; it also depends on how residents and visitors interpret authenticity, identity, memory, and cultural meaning. In this sense, perceived authenticity plays a central role in strengthening local identity, cultural resilience, and the value assigned to historical environments (Yi et al., 2024). Similarly, cultural ecosystem services and visitor preferences are increasingly relevant for understanding how architectural heritage can be preserved, interpreted, and used sustainably (Valença Pinto et al., 2024; Moon & An, 2024).
The complexity of heritage tourism also lies in the variety of factors that shape tourism attitudes toward cultural destinations. Historical relevance, aesthetic value, architectural quality, spirituality, environmental conditions, economic opportunities, and managerial capacity all influence how heritage sites are perceived and consumed (Acharjya & Acharjya, 2024). This complexity is consistent with the broader role attributed to cultural heritage in sustainable development agendas, where heritage is not only a tourism resource but also a component of territorial identity, social continuity, and long-term development (Cusumano, 2024). Attributes such as uniqueness, tradition, and cultural continuity remain central to the creation of meaningful tourism experiences (Chakraborty & Ghosal, 2024).
However, heritage tourism does not operate uniformly across space. The distribution of cultural resources, tourism infrastructure, visitor flows, and destination visibility tends to produce territorial differences. Some places become highly consolidated heritage destinations, while others remain underused or less visible despite their cultural value. This territorial unevenness is important because heritage tourism can support urban revitalization, economic resilience, and more responsible forms of tourism development, but these benefits depend on local conditions and management capacity (Nag & Mishra, 2024). Emotional attachment to heritage, including psychological ownership, may also influence preservation attitudes and community engagement, reinforcing the social dimension of heritage tourism (Lin et al., 2024).
From an economic and cultural perspective, heritage tourism has been linked to destination competitiveness, local identity, and the symbolic positioning of territories (Noonan & Rizzo, 2017). Yet, development outcomes cannot be reduced to economic indicators alone. Tourism development is also connected with broader dimensions of quality of life, including education, employment, health, social cohesion, and cultural heritage (Beltramo et al., 2024). This is especially relevant in heritage-rich destinations, where tourism pressure may create tensions between conservation, visitor experience, local life, and long-term sustainability (Matyska, 2024; Navarrete-Hernandez et al., 2024).
A persistent challenge in heritage tourism research is the absence of standardized and comparable frameworks for measuring its territorial performance. Existing indicator systems, such as the European Tourism Indicators System, have contributed to sustainability assessment, but they remain insufficient for capturing the specific complexity of cultural and heritage tourism as a subsector (GSTC, 2019; Kalvet et al., 2020). This limitation is particularly relevant because heritage tourism often overlaps with general tourism flows. Visitors may travel for leisure, business, events, urban experiences, or coastal tourism while simultaneously consuming cultural heritage, making it difficult to isolate heritage tourism through conventional statistics (Richards, 2018; Pedersen, 2002).
The literature therefore points to the need for more heritage-sensitive indicators capable of capturing the interaction between cultural resources, tourist mobility, visitor concentration, and territorial pressure. Policymakers and researchers have emphasized that methodological clarity is still limited regarding which indicators and data sources are most appropriate for evaluating the contribution of heritage tourism to destinations (Pedersen, 2002; Kalvet et al., 2020). This problem becomes even more relevant when comparing territories, since data availability, reliability, and scale vary significantly across destinations (Mohamed et al., 2024).
Heritage inventories are also essential in this process. Far from being merely administrative records, inventories help document built heritage assets, establish preservation priorities, and support evidence-based planning (Soomro, 2024). However, inventories alone do not explain how heritage resources relate to tourism mobility or visitor concentration. For this reason, the measurement of heritage tourism requires integrated approaches that combine heritage concentration, tourism intensity, and mobility data. This need aligns with recent calls for more specialized indicators to guide destination planning, conservation strategies, and long-term competitiveness (Montalto et al., 2019; Zubiaga et al., 2024).
Tourism is inherently spatial. Visitor flows concentrate in some territories and disperse across others depending on accessibility, destination image, infrastructure, attractiveness, and the symbolic value of places. Tourism geography reflects this tension between agglomeration and dispersion: economies of scale tend to attract visitors to consolidated destinations, while the search for authenticity can encourage geographical diffusion toward less saturated areas (Albaladejo et al., 2024). These dynamics are particularly relevant in heritage tourism, where historical environments, cultural landscapes, and architectural assets may create strong territorial attractions.
The increasing availability of geolocation and mobile-phone data has transformed the study of tourism mobility. Traditional observation methods are no longer sufficient to capture large-scale spatial patterns, especially in destinations where visitor flows are complex and dynamic. Geolocation-based techniques provide new opportunities for analyzing long-term mobility behavior, visitor concentration, tourism pressure, and the spatial structure of demand (Padrón-Ávila & Hernández-Martín, 2020; Ruiz-Pérez et al., 2023). Mobile-phone traces allow researchers to examine tourism movements at a scale that was previously difficult to achieve using surveys or accommodation statistics alone (State et al., 2013).
In this context, tourism intensity becomes a useful concept for comparing how strongly tourism activity is expressed across territories. Internal tourism intensity reflects the movement and presence of domestic visitors, while external tourism intensity captures the weight of non-resident visitors. Both dimensions are important because domestic and international tourism do not necessarily follow the same territorial logic. International mobility can act as a structural force behind tourism development and economic policymaking, while domestic mobility may respond more strongly to proximity, accessibility, regional identity, and internal travel patterns (Robin et al., 2022; Choe & Lugosi, 2022). Other mobility studies have shown that tourist density interacts with urban dynamics, residential volatility, and spatial concentration, reinforcing the need to analyze tourism as a territorial phenomenon (Valente & Medina-Ariza, 2024).
Cultural heritage concentration is a central dimension for understanding territorial differences in heritage tourism. Heritage buildings, monuments, historical districts, and culturally significant structures attract visitors not only because of their architectural value, but also because of their symbolic, social, and experiential meanings (Cheung & Wong, 2024). Authenticity continues to influence perceived value in cultural heritage tourism, even if it does not always determine the intensity of immersion or visitor engagement (Liu et al., 2024). This suggests that heritage concentration should be treated as a territorial attribute that contributes to destination differentiation, rather than as a simple count of historical assets.
At the same time, heritage concentration is not evenly distributed. Historical urban development, political investment, conservation policies, cultural recognition, and international designations can create strong differences between territories. Recent studies on the spatial distribution of cultural heritage resources show that heritage assets tend to follow uneven territorial patterns, shaped by historical trajectories and institutional recognition (Zhang et al., 2024). Digital tools, spatial applications, and heritage-oriented technologies have further reinforced the need to analyze cultural resources in relation to their geographical context (Hidalgo-Sánchez et al., 2024; Wang et al., 2024).
The concentration of heritage resources also creates management challenges. High-value heritage environments may attract visitors, but they may also generate pressure on conservation systems, interpretation infrastructure, mobility networks, and local communities (Falk & Hagsten, 2024). Effective heritage management therefore requires diagnostic tools capable of identifying vulnerabilities and guiding conservation strategies, particularly in destinations where architectural heritage is exposed to intensive tourism use (Rusnak et al., 2024). In this sense, heritage concentration should be analyzed together with tourism density and mobility indicators, since the cultural value of a territory and the intensity of its tourism use are not necessarily balanced.
Heritage tourism density provides a useful lens for understanding the relationship between heritage concentration and the population or visitors present in a territory. While tourism intensity captures the magnitude of tourism activity, density helps identify how heritage-related pressure may vary across places. This distinction is important because territories with similar tourism volumes may experience different levels of heritage pressure depending on their cultural assets, resident population, visitor concentration, and spatial configuration.
Sustainable cultural tourism requires the integration of economic, environmental, and sociocultural dimensions (Rawal et al., 2023). In heritage destinations, this means balancing visitor attraction with conservation, interpretation, accessibility, and community well-being. Cultural institutions also need to strengthen communication, interpretation, and visitor engagement strategies in order to improve awareness and support more responsible heritage use (Folgado-Fernández et al., 2024). Festivals, cultural events, and heritage-based activities can intensify the social and cultural functions of historical spaces, but they may also increase pressure on fragile environments if management strategies are not adapted to local conditions (Amer, 2022).
For this reason, heritage tourism density should be understood as part of a broader territorial management problem. It can help identify areas where tourism use is concentrated in relation to heritage resources, as well as territories where cultural potential may be underdeveloped. This perspective supports more differentiated planning approaches, particularly in countries such as Spain, where cultural heritage is widely distributed but tourism demand remains spatially uneven. It also responds to the need for data-driven tools capable of supporting sustainable, evidence-based, and territorially sensitive tourism policies (Egusquiza et al., 2021; Zubiaga et al., 2024).
The literature reviewed shows that heritage tourism is shaped by authenticity, cultural value, mobility, spatial concentration, sustainability, and destination management. However, it also reveals a methodological and empirical gap. Although many studies have examined heritage tourism from cultural, experiential, economic, or sustainability perspectives, fewer have compared heritage tourism indicators across territories using integrated data on mobility, tourism intensity, heritage density, and cultural heritage concentration. This gap is particularly relevant in Spain, where heritage resources and tourism flows are both highly significant but unevenly distributed across provinces.
The present study responds to this gap by framing heritage tourism in Spain as a territorially differentiated phenomenon. The conceptual argument is that heritage tourism should not be analyzed only at the national level, because the spatial distribution of tourism intensity, heritage tourism density, and cultural heritage concentration may vary significantly from one province to another. This perspective does not assume causal relationships among these dimensions; rather, it proposes that their territorial variation is itself a relevant empirical question for understanding heritage tourism planning and management.
Accordingly, this study uses mobile-phone geolocation data, tourism expenditure information, and a heritage concentration index to compare Spain’s 52 provinces. The analysis focuses on four dimensions of territorial differentiation: Internal Tourism Intensity, External Tourism Intensity, Heritage Tourism Density, and Cultural Heritage Concentration. By doing so, the study contributes to the development of a more precise and evidence-based understanding of heritage tourism as a spatially uneven system.
3. Hypotheses Development
The literature reviewed suggests that heritage tourism is not evenly distributed across territories. Tourism mobility, cultural heritage concentration, destination visibility, accessibility, and visitor pressure tend to vary according to the historical, cultural, and spatial characteristics of each destination (Albaladejo et al., 2024; Richards, 2018; Zubiaga et al., 2024). In this study, Spain is approached not as a homogeneous heritage tourism space, but as a territorially differentiated system in which provinces may present distinct patterns of internal tourism intensity, external tourism intensity, heritage tourism density, and cultural heritage concentration.
Internal tourism intensity reflects the movement and presence of domestic visitors within the national territory. Previous research indicates that domestic tourism mobility is shaped by proximity, accessibility, urban centrality, regional identity, and the availability of cultural and leisure resources (Choe & Lugosi, 2022; Padrón-Ávila & Hernández-Martín, 2020). Since these conditions differ across Spanish provinces, internal tourism intensity is expected to show significant territorial variation. Therefore, the following hypothesis is proposed:
H1.
Internal Tourism Intensity differs significantly across Spanish provinces.
External tourism intensity captures the presence of non-resident visitors and their relative weight in provincial tourism dynamics. International tourism flows are often concentrated in destinations with stronger connectivity, higher visibility, consolidated tourism infrastructure, and internationally recognized cultural or urban attractions (Robin et al., 2022; Valente & Medina-Ariza, 2024). In the Spanish context, these conditions are not evenly distributed across provinces. Accordingly, the study proposes the following hypothesis:
H2.
External Tourism Intensity differs significantly across Spanish provinces.
Heritage tourism density refers to the relationship between heritage concentration and the population present in a given territory. This indicator is relevant because territories with similar levels of tourism activity may experience different levels of pressure depending on their heritage resources, visitor concentration, and spatial structure. Sustainable cultural tourism research emphasizes the need to understand these territorial pressures in order to balance visitor use, conservation, and local well-being (Rawal et al., 2023; Falk & Hagsten, 2024; Kalvet et al., 2020). Therefore, the following hypothesis is proposed:
H3.
Heritage Tourism Density differs significantly across Spanish provinces.
Cultural heritage concentration is also expected to vary across territories. Heritage assets are shaped by historical trajectories, urban development, conservation policies, institutional recognition, and cultural investment, all of which tend to generate uneven spatial distributions (Hidalgo-Sánchez et al., 2024; Zhang et al., 2024). Since Spanish provinces differ in the number, diversity, historical significance, and recognition of their architectural heritage resources, the following hypothesis is proposed:
H4.
Cultural Heritage Concentration differs significantly across Spanish provinces.
Together, these hypotheses support the central argument of the study: heritage tourism in Spain should be understood as a territorially differentiated phenomenon. The purpose of the empirical analysis is not to establish causal relationships among the indicators, but to test whether statistically significant differences exist across provinces in each of the four dimensions under study.
4. Materials and Methods
This study adopts a quantitative, territorial, and comparative research design. The empirical strategy was developed to test the four hypotheses derived from the literature by examining whether statistically significant differences exist among Spanish provinces in selected heritage tourism indicators. The analysis focuses on four dimensions: Internal Tourism Intensity, External Tourism Intensity, Heritage Tourism Density, and Cultural Heritage Concentration.
Given the purpose of the study, one-way ANOVA was selected as the main statistical technique. This procedure is appropriate for comparing mean differences across more than two independent groups, in this case Spain’s 52 provinces. When significant differences were detected, Hochberg-adjusted post hoc comparisons were applied to identify specific interprovincial differences while controlling multiple comparisons. This design is consistent with the objective of identifying territorial heterogeneity, rather than estimating causal or predictive relationships.
The study used official experimental statistical databases developed by the National Institute of Statistics of Spain (INE, 2026a, 2026b, 2026c, 2026d). The main dataset corresponds to mobile-phone geolocation data designed to measure tourism flows at the national scale. This dataset recorded the geolocated positions of 181,670,728 mobile devices across 3214 destinations during 2021, providing a large-scale empirical basis for analyzing tourist presence, mobility concentration, and spatial distribution.
To complement the mobility information, the study incorporated INE data on tourism expenditure by foreign visitors for the period 2018–2022. This source provides information on average daily spending at the provincial level. The use of expenditure data made it possible to construct synthetic indicators of tourism intensity by combining visitor presence and spending information. The 2018–2022 period was used to obtain a more stable expenditure reference and reduce the influence of annual fluctuations.
The unit of territorial aggregation was the Spanish province. The 3214 geolocated areas included in the INE mobility dataset were aggregated into Spain’s 52 provinces. This provincial aggregation allows for a clear territorial comparison and is consistent with the study’s objective of identifying differences in heritage tourism indicators across subnational units.
Four indicators were constructed for the empirical analysis: the Internal Tourism Intensity Index, the External Tourism Intensity Index, Heritage Tourism Density, and the Heritage Concentration Index. These indicators were selected because they capture complementary dimensions of heritage tourism territorial differentiation: domestic mobility, international mobility, heritage-related density, and architectural heritage concentration.
The Internal Tourism Intensity Index (IITI) was calculated as follows: IITI = NAP × GD where IITI refers to the Internal Tourism Intensity Index, NAP represents the number of distinct overnight stay areas used by the resident population, and GD refers to average daily spending per visitor. This indicator captures the interaction between domestic tourism mobility and spending intensity.
The External Tourism Intensity Index (IITE) was calculated as follows: IITE = E × GD where IITE refers to the External Tourism Intensity Index, E represents the non-resident population staying in the area, and GD refers to average daily spending per visitor. This indicator captures the interaction between non-resident tourist presence and spending intensity.
Heritage Tourism Density (DTP) was calculated as follows: DTP = CP/F where DTP refers to Heritage Tourism Density, CP represents the Heritage Concentration Index, and F refers to the total population staying in the area. This indicator was designed to capture the relative density of heritage concentration in relation to the population present in each territorial unit.
The Heritage Concentration Index (CP) was developed to estimate the spatial relevance of architectural cultural heritage across the 3214 geolocated areas. CP was constructed as a standardized expert-assisted scoring index ranging from 1 to 10, where 1 indicates minimal architectural heritage concentration and 10 indicates maximum architectural heritage concentration. The index was based on four criteria: number of historical sites and monuments, historical and cultural significance, diversity and variety of architectural expressions, and recognition or distinctions.
The Heritage Concentration Index was estimated through an AI-assisted content evaluation procedure combined with human supervision. A large-scale natural language processing model was used to support the classification and scoring of architectural heritage concentration across the 3214 locations included in the dataset. The model was provided with standardized evaluation criteria based on the four dimensions described in Table 1.
For each location, the model generated a score from 1 to 10 according to the concentration and relevance of architectural cultural heritage. The prompts and evaluation criteria were kept constant throughout the process to ensure procedural consistency. Human supervision was then applied to verify coherence, identify potential classification inconsistencies, and ensure that the scoring process remained aligned with the conceptual definition of architectural heritage concentration.
This procedure made it possible to transform heterogeneous qualitative information on architectural heritage into a comparable territorial indicator. However, CP should be interpreted as a standardized scoring index, not as a direct official inventory of heritage assets. Its purpose is to support territorial comparison and the construction of heritage tourism density metrics within the analytical framework of this study.
To facilitate territorial comparison, the 3214 geolocated areas were aggregated into Spain’s 52 provinces. This aggregation allows the study to compare mean differences across provinces using ANOVA. Table 2 reports the number of geolocated areas included in each province. These values also define the provincial sample sizes used in the descriptive and inferential analysis.
The statistical analysis was conducted in RStudio using R. First, the dataset was reviewed for missing values, variable consistency, and provincial aggregation accuracy. Descriptive statistics were then calculated for each province and indicator, including the number of geolocated areas, mean values, standard deviations, and standard errors.
Four independent one-way ANOVA models were estimated, one for each dependent variable: IITI, IITE, DTP, and CP. In all models, province was used as the grouping factor. The general analytical structure was as follows: Indicatorij = μ + Provincei + εij where Indicatorij represents the value of the corresponding tourism or heritage indicator for area j in province i, μ is the overall mean, Provincei is the effect of belonging to province i, and εij is the residual error term.
When the ANOVA results were statistically significant, Hochberg-adjusted post hoc comparisons were applied to identify which provinces differed from one another. This correction was selected to control multiple comparisons while retaining adequate statistical power. Statistical significance was evaluated at the 95% confidence level.
The assumptions of ANOVA were assessed through residual inspection and tests of variance homogeneity. Given the large and uneven number of geolocated areas across provinces, particular attention was paid to the interpretation of statistical significance, effect sizes, and post hoc comparisons. Therefore, the results are interpreted as evidence of territorial differentiation among provinces, not as proof of causal relationships between heritage concentration, tourist mobility, and expenditure.
Table 3.
Summary of sampling design, data sources, and analytical procedures.
| Variable | Indicator |
|---|---|
| Tracked mobile phones | 181,670,728 |
| Year of mobility tracking | 2021 |
| Provinces analyzed | 52 |
| Total geolocated areas | 3214 |
| Tourism expenditure database years | 2018–2022 |
| Primary data source | National Institute of Statistics of Spain (INE), Experimental Statistics |
| Territorial unit of analysis | Spanish provinces |
| Main statistical method | One-way ANOVA |
| Post hoc procedure | Hochberg-adjusted post hoc comparisons |
| Data analysis software | RStudio (R) |
5. Results
This section presents the results of the one-way ANOVA models used to examine whether the four heritage tourism indicators differ significantly across Spanish provinces. The analysis was conducted separately for the Internal Tourism Intensity Index (IITI), the External Tourism Intensity Index (IITE), Heritage Tourism Density (DTP), and the Heritage Concentration Index (CP). When significant differences were identified, Hochberg-adjusted post hoc comparisons were used to examine specific interprovincial differences. Effect sizes were also calculated using eta squared (η2) and omega squared (ω2) to assess the magnitude of territorial differences beyond statistical significance.
Table 4.
One-way ANOVA results for heritage tourism indicators.
| Indicator | df Between | df Within | SS Between | SS Within | MS Between | MS Within | F Value | p-Value | η2 | ω2 |
|---|---|---|---|---|---|---|---|---|---|---|
| Internal Tourism Intensity Index (IITI) | 51 | 3162 | 2.304 × 1010 | 1.712 × 1011 | 4.517 × 108 | 5.414 × 107 | 8.344 | <0.001 | 0.119 | 0.104 |
| External Tourism Intensity Index (IITE) | 51 | 3162 | 9.744 × 1014 | 5.685 × 1015 | 1.911 × 1013 | 1.798 × 1012 | 10.627 | <0.001 | 0.146 | 0.133 |
| Heritage Tourism Density (DTP) | 51 | 3162 | 3.760 × 10−5 | 8.662 × 10−4 | 7.372 × 10−7 | 2.739 × 10−7 | 2.691 | <0.001 | 0.042 | 0.026 |
| Heritage Concentration Index (CP) | 51 | 3162 | 793.190 | 5.235 × 103 | 15.553 | 1.656 | 9.394 | <0.001 | 0.132 | 0.118 |
The one-way ANOVA showed statistically significant differences in the Internal Tourism Intensity Index (IITI) across Spanish provinces, F(51, 3162) = 8.344, p < 0.001. The effect size was moderate, η2 = 0.119 and ω2 = 0.104, indicating that province explains a relevant proportion of the variance in internal tourism intensity. This result supports H1 and confirms that domestic tourism intensity is not evenly distributed across the Spanish territory.
The descriptive results show clear territorial variation. Provinces such as Araba/Álava, Albacete, Navarra, Madrid, Guadalajara, Bizkaia, Gipuzkoa, Burgos, and Salamanca presented some of the highest mean values, while provinces such as Ceuta, Sevilla, Granada, Córdoba, Jaén, Huelva, and Cádiz showed lower average values. These differences suggest that domestic tourism intensity follows a heterogeneous provincial pattern rather than a uniform national distribution.
Hochberg-adjusted post hoc comparisons confirmed significant differences between several high- and low-intensity provinces. This supports the interpretation of IITI as a territorially differentiated indicator of domestic tourism mobility and expenditure.
Table 5.
Descriptive statistics for the Internal Tourism Intensity Index by province.
| Variable | F Value | Pr (>F) | Destination | Mean | SE |
|---|---|---|---|---|---|
| Internal Tourism Intensity Index by province | 8.343 | <2 × 10−16 *** | Barcelona | 7943 | 413 |
| Valencia/València | 7260 | 573 | |||
| Madrid | 11,897 | 430 | |||
| Cádiz | 4190 | 861 | |||
| Granada | 3380 | 844 | |||
| Jaén | 3635 | 966 | |||
| Sevilla | 3248 | 663 | |||
| Córdoba | 3629 | 906 | |||
| Huelva | 3896 | 1135 | |||
| Málaga | 3753 | 823 | |||
| Almería | 4155 | 1073 | |||
| Huesca | 4519 | 1322 | |||
| Zaragoza | 5827 | 879 | |||
| Teruel | 4386 | 1785 | |||
| Asturias | 6944 | 873 | |||
| Santa Cruz de Tenerife | 5009 | 966 | |||
| Palmas, Las | 5545 | 975 | |||
| Cantabria | 5043 | 1011 | |||
| Albacete | 12,230 | 1366 | |||
| Guadalajara | 10,836 | 1472 | |||
| Toledo | 9292 | 850 | |||
| Ciudad Real | 6834 | 1135 | |||
| Burgos | 10,130 | 1343 | |||
| Salamanca | 9687 | 1262 | |||
| Soria | 4165 | 2327 | |||
| Zamora | 4973 | 1569 | |||
| León | 7906 | 1020 | |||
| Segovia | 8435 | 1502 | |||
| Ávila | 7275 | 1569 | |||
| Girona | 7250 | 886 | |||
| Lleida | 6086 | 1051 | |||
| Tarragona | 8365 | 867 | |||
| Ceuta | 2906 | 3004 | |||
| Castellón/Castelló | 6381 | 1122 | |||
| Alicante/Alacant | 5558 | 683 | |||
| Badajoz | 4455 | 920 | |||
| Cáceres | 4787 | 1062 | |||
| Coruña, A | 5074 | 798 | |||
| Lugo | 5018 | 1244 | |||
| Ourense | 4989 | 1281 | |||
| Pontevedra | 6275 | 920 | |||
| Murcia | 7451 | 906 | |||
| Navarra | 11,715 | 913 | |||
| Gipuzkoa | 10,273 | 1001 | |||
| Araba/Álava | 14,502 | 1785 | |||
| Bizkaia | 10,663 | 855 | |||
| Rioja, La | 7310 | 1472 | |||
| Balears, Illes | 5269 | 906 | |||
| Cuenca | 8389 | 1416 | |||
| Palencia | 6282 | 1569 | |||
| Melilla | 6868 | 2781 |
The ANOVA results showed statistically significant differences in the External Tourism Intensity Index (IITE) among Spanish provinces, F(51, 3162) = 10.627, p < 0.001. The effect size was moderate, η2 = 0.146 and ω2 = 0.133, indicating that province accounts for a meaningful share of the variance in external tourism intensity. This finding supports H2 and confirms that non-resident tourism intensity varies significantly across provinces.
The highest mean values were observed in provinces such as Araba/Álava, Madrid, Bizkaia, Navarra, Gipuzkoa, Soria, Rioja, Salamanca, Albacete, and Burgos. In contrast, lower values were found in provinces such as Ceuta, Málaga, Granada, Jaén, Sevilla, Córdoba, and Cádiz. Because IITE is a synthetic indicator combining non-resident presence and expenditure, the ranking should not be interpreted as a simple measure of international arrivals. Instead, it reflects differentiated territorial patterns associated with mobility, spending intensity, and provincial aggregation.
Hochberg-adjusted post hoc comparisons identified significant differences between several high- and low-intensity provinces, reinforcing the existence of interprovincial variation in external tourism intensity.
Table 6.
Descriptive statistics for the External Tourism Intensity Index by province.
| Variable | F Value | Pr (>F) | Destination | Mean | SE |
|---|---|---|---|---|---|
| External Tourism Intensity Index by province | 10.63 | <2 × 10−16 | Barcelona | 1823028 | 75310 |
| Valencia/València | 1532587 | 104385 | |||
| Madrid | 2734620 | 78333 | |||
| Cádiz | 1105750 | 156935 | |||
| Granada | 960729 | 153806 | |||
| Jaén | 1046567 | 176062 | |||
| Sevilla | 1063852 | 120900 | |||
| Córdoba | 1082682 | 165047 | |||
| Huelva | 1169247 | 206898 | |||
| Málaga | 960507 | 149912 | |||
| Almería | 1095857 | 195583 | |||
| Huesca | 1191949 | 240824 | |||
| Zaragoza | 1408491 | 160262 | |||
| Teruel | 1615593 | 325204 | |||
| Asturias | 1407142 | 159130 | |||
| Santa Cruz de Tenerife | 1146976 | 176062 | |||
| Palmas, Las | 1102608 | 177600 | |||
| Cantabria | 1202483 | 184180 | |||
| Albacete | 2002384 | 248990 | |||
| Guadalajara | 1632934 | 268170 | |||
| Toledo | 1659500 | 154828 | |||
| Ciudad Real | 1443004 | 206898 | |||
| Burgos | 1947569 | 244805 | |||
| Salamanca | 2036210 | 229954 | |||
| Soria | 2249885 | 424014 | |||
| Zamora | 1110540 | 285870 | |||
| León | 1742044 | 185943 | |||
| Segovia | 1551974 | 273700 | |||
| Ávila | 1835648 | 285870 | |||
| Girona | 1559860 | 161420 | |||
| Lleida | 1452509 | 191550 | |||
| Tarragona | 1805984 | 158021 | |||
| Ceuta | 626013 | 547400 | |||
| Castellón/Castelló | 1326658 | 204478 | |||
| Alicante/Alacant | 1294935 | 124495 | |||
| Badajoz | 1187107 | 167606 | |||
| Cáceres | 1320239 | 193535 | |||
| Coruña, A | 1151927 | 145436 | |||
| Lugo | 1295141 | 226645 | |||
| Ourense | 1297350 | 233412 | |||
| Pontevedra | 1463047 | 167606 | |||
| Murcia | 1741997 | 165047 | |||
| Navarra | 2612464 | 166312 | |||
| Gipuzkoa | 2370186 | 182467 | |||
| Araba/Álava | 3436895 | 325204 | |||
| Bizkaia | 2708526 | 155871 | |||
| Rioja, La | 2080172 | 268170 | |||
| Balears, Illes | 1145835 | 165047 | |||
| Cuenca | 1160627 | 258047 | |||
| Palencia | 1762073 | 285870 | |||
| Melilla | 1534556 | 506794 |
Heritage Tourism Density (DTP) was calculated as the ratio between the Heritage Concentration Index and the total population staying in each area. The one-way ANOVA showed statistically significant differences in DTP across Spanish provinces, F(51, 3162) = 2.691, p < 0.001. The effect size was small, η2 = 0.042 and ω2 = 0.026, suggesting that territorial differences in heritage tourism density are statistically significant but more limited in magnitude than those observed for tourism intensity or heritage concentration. This result supports H3 and indicates that heritage tourism density differs significantly across provinces.
The descriptive results show that some provinces present higher heritage tourism density values than others. Provinces such as Ceuta, Málaga, Sevilla, Coruña, Granada, Almería, Palmas, Las, Castellón/Castelló, Alicante/Alacant, and Zaragoza showed relatively higher mean values. By contrast, Rioja, La, Soria, Segovia, Burgos, Albacete, Navarra, Araba/Álava, Teruel, Salamanca, and Palencia presented lower values.
These differences indicate that heritage-related territorial pressure is not homogeneous. Provinces with relatively high DTP may experience a stronger concentration of heritage value in relation to the population present, while provinces with lower DTP may reflect lower heritage concentration, larger population bases, or different spatial configurations of heritage resources.
Hochberg-adjusted post hoc comparisons confirmed significant differences among several provinces, supporting the interpretation of DTP as a relevant indicator for identifying territorial variation in heritage tourism density.
Table 7.
Descriptive statistics for Heritage Tourism Density by province.
| Variable | F Value | Pr (>F) | Destination | Mean | SE |
|---|---|---|---|---|---|
| Heritage Tourism Density by province | 10.63 | <2 × 10−16 | Barcelona | 0.000519 | 2.94 × 10−5 |
| Valencia/València | 0.000637 | 4.07 × 10−5 | |||
| Madrid | 0.000563 | 3.06 × 10−5 | |||
| Cádiz | 0.000643 | 6.12 × 10−5 | |||
| Granada | 0.000705 | 6.00 × 10−5 | |||
| Jaén | 0.000625 | 6.87 × 10−5 | |||
| Sevilla | 0.000779 | 4.72 × 10−5 | |||
| Córdoba | 0.000654 | 6.44 × 10−5 | |||
| Huelva | 0.000513 | 8.07 × 10−5 | |||
| Málaga | 0.000857 | 5.85 × 10−5 | |||
| Almería | 0.000707 | 7.63 × 10−5 | |||
| Huesca | 0.000619 | 9.40 × 10−5 | |||
| Zaragoza | 0.000668 | 6.25 × 10−5 | |||
| Teruel | 0.000439 | 1.27 × 10−5 | |||
| Asturias | 0.000552 | 6.21 × 10−5 | |||
| Santa Cruz de Tenerife | 0.000539 | 6.87 × 10−5 | |||
| Palmas, Las | 0.000694 | 6.93 × 10−5 | |||
| Cantabria | 0.000629 | 7.19 × 10−5 | |||
| Albacete | 0.000391 | 9.72 × 10−5 | |||
| Guadalajara | 0.000535 | 1.05 × 10−5 | |||
| Toledo | 0.000532 | 6.04 × 10−5 | |||
| Ciudad Real | 0.000475 | 8.07 × 10−5 | |||
| Burgos | 0.000390 | 9.55 × 10−5 | |||
| Salamanca | 0.000442 | 8.97 × 10−5 | |||
| Soria | 0.000298 | 1.65 × 10−5 | |||
| Zamora | 0.000633 | 1.12 × 10−5 | |||
| León | 0.000554 | 7.26 × 10−5 | |||
| Segovia | 0.000388 | 1.07 × 10−5 | |||
| Ávila | 0.000480 | 1.12 × 10−5 | |||
| Girona | 0.000565 | 6.30 × 10−5 | |||
| Lleida | 0.000559 | 7.48 × 10−5 | |||
| Tarragona | 0.000498 | 6.17 × 10−5 | |||
| Ceuta | 0.001146 | 2.14 × 10−5 | |||
| Castellón/Castelló | 0.000691 | 7.98 × 10−5 | |||
| Alicante/Alacant | 0.000671 | 4.86 × 10−5 | |||
| Badajoz | 0.000575 | 6.54 × 10−5 | |||
| Cáceres | 0.000536 | 7.55 × 10−5 | |||
| Coruña, A | 0.000771 | 5.68 × 10−5 | |||
| Lugo | 0.000522 | 8.84 × 10−5 | |||
| Ourense | 0.000512 | 9.11 × 10−5 | |||
| Pontevedra | 0.000560 | 6.54 × 10−5 | |||
| Murcia | 0.000476 | 6.44 × 10−5 | |||
| Navarra | 0.000405 | 6.49 × 10−5 | |||
| Gipuzkoa | 0.000498 | 7.12 × 10−5 | |||
| Araba/Álava | 0.000416 | 1.27 × 10−4 | |||
| Bizkaia | 0.000495 | 6.08 × 10−5 | |||
| Rioja, La | 0.000280 | 1.05 × 10−4 | |||
| Balears, Illes | 0.000645 | 6.44 × 10−5 | |||
| Cuenca | 0.000575 | 1.01 × 10−4 | |||
| Palencia | 0.000439 | 1.12 × 10−4 | |||
| Melilla | 0.000577 | 1.98 × 10−4 |
The Heritage Concentration Index (CP) was developed as a standardized expert-assisted scoring index of architectural cultural heritage concentration. The ANOVA results showed statistically significant differences in CP across Spanish provinces, F(51, 3162) = 9.394, p < 0.001. The effect size was moderate, η2 = 0.132 and ω2 = 0.118, indicating that province explains a meaningful proportion of the variance in architectural heritage concentration. This finding supports H4 and confirms that cultural heritage concentration varies significantly across the Spanish territory.
The descriptive statistics indicate that provinces such as Valencia/València, Zaragoza, Sevilla, Alicante/Alacant, Málaga, Santa Cruz de Tenerife, Asturias, Madrid, Palmas, Las, and Barcelona presented comparatively higher CP values. In contrast, provinces such as Palencia, Cuenca, Albacete, Ávila, Zamora, Ourense, Teruel, Rioja, La, Salamanca, and Lugo presented lower mean scores.
These results are consistent with the idea that architectural heritage concentration is shaped by uneven historical, cultural, urban, and institutional trajectories. The findings do not imply that provinces with lower CP lack heritage value; rather, they indicate that the standardized concentration score differs across the provincial structure analyzed in this study.
Hochberg-adjusted post hoc comparisons identified significant interprovincial differences, supporting the existence of territorial heterogeneity in architectural cultural heritage concentration.
Table 8.
Descriptive statistics for the Heritage Concentration Index by province.
| Variable | F value | Pr (>F) | Destination | mean | SE |
|---|---|---|---|---|---|
| Heritage Concentration Index by province | 10.63 | <2 × 10−16 | Barcelona | 6.64 | 0.0723 |
| Valencia/València | 7.54 | 0.1002 | |||
| Madrid | 6.72 | 0.0752 | |||
| Cádiz | 6.33 | 0.1506 | |||
| Granada | 6.43 | 0.1476 | |||
| Jaén | 6.31 | 0.1689 | |||
| Sevilla | 7.33 | 0.1160 | |||
| Córdoba | 6.53 | 0.1584 | |||
| Huelva | 6.19 | 0.1985 | |||
| Málaga | 6.89 | 0.1438 | |||
| Almería | 6.15 | 0.1877 | |||
| Huesca | 5.77 | 0.2311 | |||
| Zaragoza | 7.40 | 0.1538 | |||
| Teruel | 5.71 | 0.3120 | |||
| Asturias | 6.76 | 0.1527 | |||
| Santa Cruz de Tenerife | 6.84 | 0.1689 | |||
| Palmas, Las | 6.68 | 0.1704 | |||
| Cantabria | 6.49 | 0.1767 | |||
| Albacete | 5.52 | 0.2389 | |||
| Guadalajara | 5.84 | 0.2573 | |||
| Toledo | 6.21 | 0.1486 | |||
| Ciudad Real | 6.05 | 0.1985 | |||
| Burgos | 5.90 | 0.2349 | |||
| Salamanca | 5.74 | 0.2206 | |||
| Soria | 6.30 | 0.4068 | |||
| Zamora | 5.68 | 0.2743 | |||
| León | 6.54 | 0.1784 | |||
| Segovia | 5.75 | 0.2626 | |||
| Ávila | 5.59 | 0.2743 | |||
| Girona | 6.45 | 0.1549 | |||
| Lleida | 6.45 | 0.1838 | |||
| Tarragona | 6.40 | 0.1516 | |||
| Ceuta | 6.83 | 0.5252 | |||
| Castellón/Castelló | 6.26 | 0.1962 | |||
| Alicante/Alacant | 7.33 | 0.1194 | |||
| Badajoz | 5.95 | 0.1608 | |||
| Cáceres | 6.31 | 0.1857 | |||
| Coruña, A | 6.13 | 0.1395 | |||
| Lugo | 5.74 | 0.2175 | |||
| Ourense | 5.70 | 0.2240 | |||
| Pontevedra | 6.62 | 0.1608 | |||
| Murcia | 6.50 | 0.1584 | |||
| Navarra | 6.29 | 0.1596 | |||
| Gipuzkoa | 6.41 | 0.1751 | |||
| Araba/Álava | 5.82 | 0.3120 | |||
| Bizkaia | 6.03 | 0.1496 | |||
| Rioja, La | 5.72 | 0.2573 | |||
| Balears, Illes | 6.29 | 0.1584 | |||
| Cuenca | 5.52 | 0.2476 | |||
| Palencia | 5.45 | 0.2743 | |||
| Melilla | 6.57 | 0.4863 |
The results support the four hypotheses proposed in the study. Significant differences were found across Spanish provinces for internal tourism intensity, external tourism intensity, heritage tourism density, and cultural heritage concentration.
Table 9.
Summary of hypothesis testing.
| Hypothesis | Indicator | ANOVA Result | Effect Size | Outcome |
|---|---|---|---|---|
| H1 | Internal Tourism Intensity Index (IITI) | F(51, 3162) = 8.344, p < 0.001 | η2 = 0.119; ω2 = 0.104 | Supported |
| H2 | External Tourism Intensity Index (IITE) | F(51, 3162) = 10.627, p < 0.001 | η2 = 0.146; ω2 = 0.133 | Supported |
| H3 | Heritage Tourism Density (DTP) | F(51, 3162) = 2.691, p < 0.001 | η2 = 0.042; ω2 = 0.026 | Supported |
| H4 | Heritage Concentration Index (CP) | F(51, 3162) = 9.394, p < 0.001 | η2 = 0.132; ω2 = 0.118 | Supported |
Overall, the results confirm that heritage tourism indicators in Spain are territorially differentiated. The evidence shows that tourism intensity, heritage tourism density, and heritage concentration do not follow a homogeneous provincial pattern.
6. Discussion
The purpose of this study was to examine heritage tourism in Spain as a territorially differentiated phenomenon. Drawing on large-scale mobile-phone geolocation data, tourism expenditure information, and a Heritage Concentration Index, the analysis tested whether four heritage tourism indicators differed significantly across Spain’s 52 provinces. The results support the four hypotheses and provide empirical evidence of territorial heterogeneity in internal tourism intensity, external tourism intensity, heritage tourism density, and cultural heritage concentration.
The significant differences found in the Internal Tourism Intensity Index support H1 and indicate that domestic tourism mobility and spending intensity are unevenly distributed across Spanish provinces. This finding is consistent with previous studies showing that tourism mobility is shaped by accessibility, territorial centrality, destination attractiveness, and the spatial structure of demand (Padrón-Ávila & Hernández-Martín, 2020; Ruiz-Pérez et al., 2023). The results suggest that internal tourism cannot be interpreted as a uniform national flow. Instead, it reflects differentiated provincial dynamics that may be associated with domestic travel habits, urban structure, regional accessibility, and the distribution of cultural and leisure resources.
The results for the External Tourism Intensity Index support H2 and show that non-resident tourism intensity also varies significantly across provinces. This finding aligns with research highlighting the role of international mobility, connectivity, and destination visibility in shaping tourism concentration (Robin et al., 2022; Valente & Medina-Ariza, 2024). However, the provincial pattern observed in this study also suggests that external tourism intensity may not be fully explained by traditional assumptions about internationally consolidated destinations. Since the indicator combines non-resident presence and spending, the results should be interpreted as evidence of territorial differentiation rather than as a direct measure of international destination competitiveness.
The significant differences in Heritage Tourism Density support H3. This finding is particularly relevant from a management perspective because it suggests that heritage-related pressure differs across provinces. Tourism density is not only a matter of visitor volume; it also depends on the relationship between heritage concentration and the population present in each area. This result is consistent with the literature on sustainable cultural tourism, which emphasizes the need to balance visitor use, heritage conservation, and local well-being (Rawal et al., 2023; Falk & Hagsten, 2024; Kalvet et al., 2020). Provinces with higher DTP values may require more specific planning tools to manage heritage use, while provinces with lower values may require strategies aimed at improving visibility, accessibility, or heritage-based tourism development.
The findings for the Heritage Concentration Index support H4 and confirm that architectural cultural heritage concentration is not evenly distributed across Spanish provinces. This result is consistent with studies showing that cultural heritage resources follow spatially uneven patterns shaped by historical trajectories, urban development, institutional recognition, and cultural investment (Hidalgo-Sánchez et al., 2024; Zhang et al., 2024). The territorial variation in CP reinforces the need to analyze heritage tourism beyond national-level aggregates. Provinces differ not only in tourist flows, but also in the concentration and diversity of heritage resources that may support cultural tourism development.
Taken together, these results contribute to the literature by showing that heritage tourism in Spain should be understood as a spatially uneven system. The study does not claim that heritage concentration causes tourism intensity, nor that tourism density directly determines expenditure. Such relationships would require correlation, regression, spatial econometric models, or longitudinal designs. Instead, the contribution lies in demonstrating that the selected indicators differ significantly across provinces and that these differences can be measured through an integrated, data-driven framework.
The findings also have practical implications. For policymakers and destination managers, the results suggest that heritage tourism planning should not rely on homogeneous national strategies. Provinces with high tourism intensity may need tools for visitor management, mobility planning, and heritage conservation. Provinces with high heritage concentration but lower tourism intensity may benefit from interpretation strategies, digital promotion, accessibility improvements, and product development. Provinces with high heritage tourism density may require closer monitoring of visitor pressure and conservation risks. In this sense, the study provides evidence for province-sensitive heritage tourism planning and more balanced territorial development.
7. Conclusions
This study examined heritage tourism in Spain through the lens of territorial differentiation. Using official experimental statistics from the National Institute of Statistics of Spain, the analysis integrated mobile-phone geolocation data, tourism expenditure information, and a Heritage Concentration Index to compare four indicators across Spain’s 52 provinces: Internal Tourism Intensity, External Tourism Intensity, Heritage Tourism Density, and Cultural Heritage Concentration.
The results show statistically significant differences across provinces in all four indicators. Internal tourism intensity and external tourism intensity are not evenly distributed, confirming that tourism mobility and spending-related indicators follow differentiated provincial patterns. Heritage Tourism Density also differs significantly, indicating that heritage-related territorial pressure varies across Spain. Likewise, the Heritage Concentration Index shows significant differences among provinces, confirming the uneven spatial distribution of architectural cultural heritage concentration.
The main contribution of this study is to provide an integrated empirical approach for analyzing heritage tourism as a territorially differentiated phenomenon. Rather than treating heritage tourism in Spain as a homogeneous national activity, the study shows that provincial differences matter. This perspective contributes to the literature on cultural tourism measurement, tourism mobility, and heritage-based territorial planning by combining large-scale mobility data with heritage indicators and ANOVA-based comparison.
From a practical perspective, the findings support the need for province-sensitive strategies in heritage tourism management. Tourism policies should consider the specific territorial profile of each province, including its tourism intensity, heritage density, and cultural heritage concentration. This is especially important for balancing visitor experience, heritage preservation, local development, and sustainable destination management.
The study has several limitations. First, the analysis identifies differences among provinces but does not test causal relationships between heritage concentration, mobility, and expenditure. Second, the Heritage Concentration Index is a standardized expert-assisted scoring index and should not be interpreted as an official inventory of heritage assets. Third, the use of mobile-phone data and provincial aggregation may hide local variations within provinces. Fourth, the use of expenditure data from 2018–2022 and mobility data from 2021 requires cautious interpretation because the two sources do not refer to exactly the same temporal frame.
Future research should extend this approach by applying correlation analysis, regression models, spatial econometrics, longitudinal designs, or machine learning techniques to explain the relationships among heritage concentration, tourism mobility, visitor pressure, and economic outcomes. Further studies could also compare Spain with other heritage-rich countries or analyze smaller territorial units in order to capture local-level dynamics that provincial aggregation may obscure.
Overall, the findings confirm that heritage tourism in Spain is not territorially homogeneous. Its intensity, density, and cultural heritage concentration vary across provinces, and these differences should be considered when designing evidence-based, sustainable, and territorially balanced tourism policies.
8. Future Research Directions
The findings of this study open several avenues for future research on heritage tourism, territorial differentiation, and data-driven tourism analysis. Since the present study focused on identifying statistically significant differences across provinces, future work should move toward explanatory and predictive approaches capable of modeling the relationships among heritage concentration, tourism mobility, visitor pressure, and economic outcomes.
First, future studies could incorporate socioeconomic indicators such as employment, income distribution, business activity, public investment, and residents’ quality of life. This would make it possible to assess how different forms of heritage tourism intensity are associated with broader territorial development processes. Such an approach would also help clarify whether provinces with higher heritage concentration or tourism density obtain stronger socioeconomic benefits, or whether these benefits depend on complementary factors such as infrastructure, governance, accessibility, and local absorptive capacity.
Second, further research should examine tourist spending behavior in greater detail. The present study used average daily expenditure as an input for constructing tourism intensity indicators, but it did not model the determinants of expenditure. Future analyses could incorporate visitor characteristics such as country of origin, age, travel motivation, length of stay, type of accommodation, and travel party composition. This would allow researchers to better understand how economic value is generated and distributed across heritage destinations.
Third, future research could apply correlation analysis, regression models, spatial econometrics, longitudinal models, or machine learning techniques to explain the relationships among the indicators analyzed in this study. These methods would make it possible to move beyond the identification of territorial differences and examine whether heritage concentration, mobility patterns, accessibility, or tourism density help explain variations in tourism performance across provinces.
Fourth, the territorial scale of analysis should be refined. Although the provincial level provides a useful basis for comparison, it may hide important local differences within provinces. Future studies could examine municipalities, historical districts, cultural routes, or specific heritage sites in order to capture more precise spatial dynamics. This would be particularly useful for identifying areas exposed to visitor pressure, underused heritage resources, or emerging heritage tourism corridors.
Fifth, future studies should explore the relationship between heritage tourism density and sustainable destination management. High-density heritage environments may generate opportunities for economic activity, but they may also increase pressure on conservation systems, mobility infrastructure, local communities, and visitor experience. Research in this direction could help destinations design more balanced strategies for capacity management, heritage interpretation, visitor dispersion, and conservation planning.
Sixth, comparative research with other heritage-rich countries could strengthen the international relevance of this approach. Applying similar indicators to countries such as Italy, France, Portugal, Greece, or the United Kingdom would allow researchers to compare territorial patterns of heritage tourism intensity, density, and concentration. Such benchmarking could provide useful evidence on different governance models, policy instruments, and planning strategies for cultural tourism.
Finally, future research should further develop the measurement of cultural heritage concentration. The Heritage Concentration Index proposed in this study offers a useful starting point for territorial comparison, but it could be refined through expert panels, official heritage inventories, GIS-based spatial validation, and mixed-method approaches. Improving the robustness and replicability of this type of indicator would contribute to more precise, transparent, and internationally comparable heritage tourism research.
Overall, future research should build on the present study by moving from territorial comparison toward explanation, prediction, and policy evaluation. This would contribute to a more complete understanding of how heritage resources, tourism mobility, visitor behavior, and territorial development interact in heritage destinations.
9. Patents
Not applicable. No patents resulted from the work reported in this manuscript.
Supplementary Materials
The following supporting information can be downloaded at the website of this paper posted on Preprints.org. Table S1: Provincial descriptive statistics for the Internal Tourism Intensity Index (IITI), External Tourism Intensity Index (IITE), Heritage Tourism Density (DTP), and Heritage Concentration Index (CP); Table S2: Hochberg-adjusted post hoc comparisons among Spanish provinces; Appendix S1: Criteria and scoring protocol used for the construction of the Heritage Concentration Index (CP).
Author: Contributions: Conceptualization, A.-R.G.-P.; methodology, A.-R.G.-P.; software, A.-R.G.-P.; validation, A.-R.G.-P.; formal analysis, A.-R.G.-P.; investigation, A.-R.G.-P.; resources, A.-R.G.-P.; data curation, A.-R.G.-P.; writing—original draft preparation, A.-R.G.-P.; writing—review and editing, A.-R.G.-P; visualization, A.-R.G.-P.; supervision, A.-R.G.-P.; project administration, A.-R.G.-P.; funding acquisition, not applicable. Author has read and agreed to the published version of the manuscript.
Funding
This research received no external funding. The APC was funded by [please specify: the author/Universidad UTE/University of Valencia/other source].
Institutional Review Board Statement
Not applicable. This study did not involve experiments with human participants or animals. The research was based on secondary, aggregated, and anonymized statistical data obtained from official experimental statistics of the National Institute of Statistics of Spain (INE), together with derived territorial indicators constructed for analytical purposes.
Informed Consent Statement
Not applicable. The study did not involve direct interaction with human participants, individual-level surveys, interviews, experiments, or identifiable personal data.
Data Availability Statement
The data supporting the findings of this study were obtained from official experimental statistical sources published by the National Institute of Statistics of Spain (INE). The processed dataset generated for this study, including the constructed indicators used in the ANOVA models, may be made available by the corresponding author upon reasonable request. The Heritage Concentration Index (CP) is a derived research indicator developed by the author for the purposes of this study.
Acknowledgments
The author acknowledges the National Institute of Statistics of Spain (INE) for making available the experimental statistical data used in this research. During the preparation of this manuscript, the author used OpenAI’s ChatGPT [please specify version/model if required] for language editing, text refinement, and support in structuring selected sections of the manuscript. AI-assisted tools were also used to support the standardized evaluation procedure involved in the construction of the Heritage Concentration Index (CP). The author reviewed, edited, and validated all outputs and take full responsibility for the content of this publication.
Conflicts of Interest
The author declares no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
Abbreviations
The following abbreviations are used in this manuscript:
| Abbreviation | Meaning |
| ANOVA | Analysis of Variance |
| CP | Heritage Concentration Index |
| DTP | Heritage Tourism Density |
| GD | Average Daily Spending per Visitor |
| GIS | Geographic Information Systems |
| IITI | Internal Tourism Intensity Index |
| IITE | External Tourism Intensity Index |
| INE | National Institute of Statistics of Spain |
| NAP | Number of Distinct Overnight Stay Areas |
| SE | Standard Error |
Appendix A
Appendix A.1. Criteria Used for the Heritage Concentration Index
The Heritage Concentration Index (CP) was developed to estimate the relative concentration of architectural cultural heritage across the territorial units included in the study. The index was constructed using a standardized scoring procedure based on four criteria: number of historical sites and monuments, historical and cultural significance, diversity and variety of architectural expressions, and recognition or distinctions. Each territorial unit was assigned a score from 1 to 10, where 1 indicated minimal architectural heritage concentration and 10 indicated maximum architectural heritage concentration.
Table A1.
Criteria used to construct the Heritage Concentration Index (CP).
| Criterion | Description |
|---|---|
| Number of historical sites and monuments | Total number of historical buildings, monuments, archaeological sites, and culturally significant structures present in the area. |
| Historical and cultural significance | Degree of historical relevance, antiquity, cultural symbolism, and association with historically significant events. |
| Diversity and variety. | Variety of architectural styles, cultural influences, and artistic manifestations within the area. |
| Recognition and distinctions | Presence of national or international designations, recognitions, or certifications, including UNESCO recognition and heritage listings. |
Appendix A.2. Analytical Model
The empirical analysis was based on four independent one-way ANOVA models. In each model, the province was used as the grouping factor, and one of the four indicators was used as the dependent variable: Internal Tourism Intensity Index (IITI), External Tourism Intensity Index (IITE), Heritage Tourism Density (DTP), and Heritage Concentration Index (CP). When statistically significant differences were identified, Hochberg-adjusted post hoc comparisons were applied to identify specific interprovincial differences.
Appendix B
Appendix B.1. Supplementary Post Hoc Results
The full matrix of Hochberg-adjusted post hoc comparisons is provided as Supplementary Material. These comparisons support the interpretation of territorial differentiation across Spanish provinces and complement the ANOVA results reported in the main text.
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Table 1.
Criteria used to estimate the Heritage Concentration Index (CP).
| Criterion | Description |
|---|---|
| Number of historical sites and monuments | Total number of historical buildings, monuments, archaeological sites, and culturally significant structures present in the area. |
| Historical and cultural significance | Degree of historical relevance, antiquity, cultural symbolism, and association with historically significant events. |
| Diversity and variety | Variety of architectural styles, cultural influences, and artistic manifestations within the area. |
| Recognition and distinctions | Presence of national or international designations, recognitions, or certifications, including UNESCO recognition and heritage listings. |
Table 2.
Number of geolocated areas per province in the INE mobility dataset.
| Province | Number of Areas | Province | Number of Areas |
|---|---|---|---|
| Barcelona | 317 | Zamora | 22 |
| Valencia/València | 165 | León | 52 |
| Madrid | 293 | Segovia | 24 |
| Cádiz | 73 | Ávila | 22 |
| Granada | 76 | Girona | 69 |
| Jaén | 58 | Lleida | 49 |
| Sevilla | 123 | Tarragona | 72 |
| Córdoba | 66 | Ceuta | 6 |
| Huelva | 42 | Castellón/Castelló | 43 |
| Málaga | 80 | Alicante/Alacant | 116 |
| Almería | 47 | Badajoz | 64 |
| Huesca | 31 | Cáceres | 48 |
| Zaragoza | 70 | Coruña, A | 85 |
| Teruel | 17 | Lugo | 35 |
| Asturias | 71 | Ourense | 33 |
| Santa Cruz de Tenerife | 58 | Pontevedra | 64 |
| Palmas, Las | 57 | Murcia | 66 |
| Cantabria | 53 | Navarra | 65 |
| Albacete | 29 | Gipuzkoa | 54 |
| Guadalajara | 25 | Araba/Álava | 17 |
| Toledo | 75 | Bizkaia | 74 |
| Ciudad Real | 42 | Rioja, La | 25 |
| Burgos | 30 | Balears, Illes | 66 |
| Salamanca | 34 | Cuenca | 27 |
| Soria | 10 | Palencia | 22 |
| Valladolid | 45 | Melilla | 7 |
| TOTAL, AREAS n = 3214 | |||
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