Geotourism: From Theoretical Definition to Practical Analysis in the Sohodol Gorges Protected Area, Romania
Abstract
1. Introduction
2. Theoretical Background
3. Materials and Methods
3.1. Study Area
3.2. Data Sources
3.3. Analyzing Research Hypotheses
3.4. Variables Used in the Research
4. Results
4.1. Socio-Demographic Characteristics
4.2. Questionnaire Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
UNESCO | United Nations Educational, Scientific and Cultural Organization |
UGGps | UNESCO Global Geoparks |
GIS | Geographic Information System (Software) |
DMO | Destination Marketing Organization |
SM | Social media |
GGN | Global Network of Geoparks |
EGN | European Geoparks Network |
IUCN | International Union for Conservation of Nature |
EViews | Statistical analysis software package |
NIS | National Institute of Statistics |
PLS-SEM | Partial least squares structural equation modeling |
TLS | Terrestrial laser scanner |
DEM | Digital elevation model |
Appendix A
- I.
- Sample group data.
- Your gender?
- Male
- Female
- Which age category do you fit into?
- 18–30 years
- 31–45 years
- 46–65 years
- Over 65 years
- Your residence?
- Rural
- Urban
- Level of education?
- Secondary school studies
- High school studies
- Post-secondary studies
- Higher education (bachelor’s degree, master’s degree, doctorate)
- Your occupation?
- Student
- Employee
- Unemployed
- Self-employed
- Retiree
- II.
- Evaluation of the aspects related to the development of geotourism post-pandemic.
- 1.
- Economic impacts
Item. No. | Research Variable | Totally Disagree | Partially Disagree | Neutral | Partially Agree | Totally Agree |
---|---|---|---|---|---|---|
1. | Geotourism increases opportunities in the development of the local economy (employment and investment). | |||||
2 | Geotourism stimulates an increase in prices in accommodation units and traditional products. | |||||
3 | Geotourism diversifies businesses for locals. |
- 2.
- Impact on the environment
Item. No. | Research Variable | Totally Disagree | Partially Disagree | Neutral | Partially Agree | Totally Agree |
---|---|---|---|---|---|---|
1. | Geotourism causes damage to the natural environment of ecosystem development and the rural environment. | |||||
2 | The natural diversity of the protected area must be exploited and protected to reduce the impact on the environment. | |||||
3 | Geotourism must be developed in harmony with the natural and cultural environment of the protected area. |
- 3.
- Socio-cultural impacts
Item. No. | Research Variable | Totally Disagree | Partially Disagree | Neutral | Partial Agreement | Totally Agree |
---|---|---|---|---|---|---|
1. | The development of geotourism in Sohodol Gorges increases road traffic congestion. | |||||
2 | Geotourism in the Sohodol Gorges causes overcrowding of public and leisure spaces. | |||||
3 | The development of geotourism in the Sohodol Gorges would improve the quality of the roads and the agreement spaces. | |||||
4 | Geotourism in the Sohodol Gorges has a positive impact on the cultural identity of the local population. | |||||
5 | Geotourism intensifies social-cultural interactions between tourists and locals and between hotel managers and tourists. |
- 4.
- Where have you travelled post-COVID-19?
- In Romania
- Outside the country
- 5.
- How often have you travelled to Sohodol Gorges post-COVID-19?
- Only once
- 2–3 times
- More than 3 times
- 6.
- How did you most frequently travel after the COVID-19 pandemic in Sohodol Gorges?
- 7.
- What is the season when you visit the Sohodol Gorges?
- 8.
- What sources of information do you use to document yourself about the Sohodol Gorges?
- 9.
- What geotourism activities have you carried out post-COVID-19 in Sohodol Gorges?
- Nature and wildlife (wilderness camping, bird watching, wildlife watching)
- Adventure (Caving, Climbing)
- Walking and hiking (hiking, nature walk)
- Other
- 10.
- On a scale of 1 to 5, how likely are you to recommend Sohodol Gorges to others?
Certainly not | 1 | 2 | 3 | 4 | 5 | Very likely |
Appendix B. The Results of the Regression Analysis for Hypothesis 1
Dependent Variable: IMPACT_ECONOMIC_OPPORTUNIES | ||||
Method: Least Squares | ||||
Date: 27 September 2024 | ||||
Time: 18:15 | ||||
Sample: 1400 | ||||
Included observations: 400 | ||||
Variable | Coefficient | Error Standard | t-Statistic | Probability (Prob.) |
C | 1.172782 | 0.362161 | 3.23829 | 0.0013 |
AGE_CATEGORY | −0.115688 | 0.080973 | −1.42873 | 0.1539 |
STUDY_LEVEL | 0.568877 | 0.095113 | 5.98103 | 0.0000 |
BUSINESS_ECONOMIC_IMPACT | 0.228818 | 0.044190 | 5.17807 | 0.0000 |
R-squared | 0.153663 | Mean dependent var | 3.850000 | |
Adjusted R-squared | 0.147251 | S.D. dependent var | 1.462137 | |
S.E. of regression | 1.350202 | Akaike info criterion | 3.448335 | |
Sum squared resid | 721.9256 | Schwarz criterion | 3.488249 | |
Log-likelihood | −685.6669 | Hannan–Quinn criterion | 3.464141 | |
F-statistic | 23.96620 | Durbin–Watson stat | 1.667800 | |
Prob (F-statistic) | 0.000000 |
Appendix C. The Results of the Regression Analysis for Hypothesis 2
Dependent Variable: IMPACT_ECONOMIC_INCREASE_PRICES | ||||
Method: Least Squares | ||||
Date: 27 September 2024 | ||||
Time: 18:23 | ||||
Sample: 1400 | ||||
Included observations: 400 | ||||
Variable | Coefficient | Error Standard | t-Statistic | Probability |
C | 4.127483 | 0.254953 | 16.18916 | 0.0000 |
SEX | −0.737659 | 0.384837 | −1.916808 | 0.0560 |
ENVIRONMENT_PROVENIENCE | 0.151587 | 0.151206 | 1.002521 | 0.3167 |
SEX*ENVIRONMENT_ORIGIN | 0.29441 | 0.227758 | 1.292642 | 0.1969 |
R-squared | 0.032613 | Mean dependent var | 4.2575 | |
Adjusted R-squared | 0.025284 | S.D. dependent var | 1.113235 | |
S.E. of regression | 1.099071 | Akaike info criterion | 3.036758 | |
Sum squared resid | 478.3512 | Schwarz criterion | 3.076672 | |
Log-likelihood | −603.3516 | Hannan–Quinn criterion | 3.052564 | |
F-statistic | 4.450009 | Durbin–Watson stat | 1.537746 | |
Prob (F-statistic) | 0.00433 |
Appendix D. The Results of the Regression Analysis for Hypothesis 3
Dependent Variable: IMPACT_ENVIRONMENT_DAMAGE | ||||
Method: Least Squares | ||||
Date: 27 September 2024 | ||||
Time: 18:47 | ||||
Sample: 1400 | ||||
Included observations: 400 | ||||
Variable | Coefficient | Error Standard | t-Statistic | Probability |
C | 2.797111 | 0.199719 | 14.00525 | 0.0000 |
AGE_CATEGORY | −0.047056 | 0.079297 | −0.593412 | 0.5532 |
OCCUPATION | 0.091445 | 0.057501 | 1.590315 | 0.1126 |
IMPACT_ENVIRONMENT_HARMONY | 0.314804 | 0.038487 | 8.179547 | 0.0000 |
R-squared | 0.162231 | Mean dependent var | 4.3375 | |
Adjusted R-squared | 0.155884 | S.D. dependent var | 0.990478 | |
S.E. of regression | 0.910009 | Akaike info criterion | 2.659226 | |
Sum squared resid | 327.9343 | Schwarz criterion | 2.699104 | |
Log-likelihood | −527.8452 | Hannan–Quinn criterion | 2.675032 | |
F-statistic | 25.56129 | Durbin–Watson stat | 1.630223 | |
Prob (F-statistic) | 0.000000 |
Appendix E. The Results of the Regression Analysis for Hypothesis 4
Dependent Variable: IMPACT_ENVIRONMENT_PROTECT_DIVERSITY | ||||
Method: Least Squares | ||||
Date: 27 September 2024 | ||||
Time: 19:02 | ||||
Sample: 1400 | ||||
Included observations: 400 | ||||
Variable | Coefficient | Error Standard | t-Statistic | Probability |
C | 2.175053 | 0.28059 | 7.751705 | 0.0000 |
ENVIRONMENT_PROVENIENCE | 0.100527 | 0.108541 | 0.92616 | 0.3549 |
EDUCATION_LEVEL | 0.534794 | 0.069907 | 7.650077 | 0.0000 |
R-squared | 0.14236 | Mean dependent var | 4.295 | |
Adjusted R-squared | 0.13804 | S.D. dependent var | 1.107267 | |
S.E. of regression | 1.028007 | Akaike info criterion | 2.900592 | |
Sum squared resid | 419.5488 | Schwarz criterion | 2.930528 | |
Log-likelihood | −577.1185 | Hannan–Quinn criterion | 2.912447 | |
F-statistic | 32.94915 | Durbin–Watson stat | 1.626902 | |
Prob (F-statistic) | 0.00000 |
Appendix F. The Results of the Regression Analysis for Hypothesis 5
Dependent Variable: SOCIO_CULTURAL_TRAFFIC_IMPACT | ||||
Method: Least Squares | ||||
Date: 27 September 2024 | ||||
Time: 19:05 | ||||
Sample: 1400 | ||||
Included observations: 400 | ||||
Variable | Coefficient | Error Standard | t-Statistic | Probability |
C | 2.906420 | 0.230304 | 12.61995 | 0.0000 |
SEX | −0.094251 | 0.090553 | −1.040844 | 0.2986 |
STUDY_LEVEL | 0.418111 | 0.059604 | 7.014863 | 0.0000 |
R-squared | 0.116632 | Mean dependent var | 4.395000 | |
Adjusted R-squared | 0.112182 | S.D. dependent var | 0.949330 | |
S.E. of regression | 0.894498 | Akaike info criterion | 2.622364 | |
Sum squared resid | 317.6503 | Schwarz criterion | 2.652300 | |
Log-likelihood | −521.4727 | Hannan–Quinn criterion | 2.634219 | |
F-statistic | 26.20816 | Durbin–Watson stat | 1.618870 | |
Prob (F-statistic) | 0.000000 |
Appendix G. The Results of the Regression Analysis for Hypothesis 6
Covariance Analysis: Ordinary | ||||
Date: 27 September 2024 | ||||
Time: 19:34 | ||||
Sample: 1400 | ||||
Included observations: 400 | ||||
Covariance | CATEGORY_ AGE | IMPACT_SOCIO _CULTURAL_ INFRASTRUCTURE | IMPACT_SOCIO _OVERAGGREGATION | OCCUPATION |
Correlation | ||||
CATEGORY_AGE | 0.775994 | |||
1.000000 | ||||
IMPACT_SOCIO_ CULTURAL_ INFRASTRUCTURE | 0.221056 | 1.320494 | ||
0.218376 | 1.000000 | |||
IMPACT_SOCIO _OVERAGGREGATION | 0.102194 | 0.397756 | 0.858994 | |
0.125170 | 0.373468 | 1.000000 | ||
OCCUPATION | 0.815788 | 0.395412 | 0.132587 | 1.489775 |
0.758731 | 0.281917 | 0.117205 | 1.000000 |
Appendix H. The Results of the Regression Analysis for Hypothesis 7
Dependent Variable: SOCIO_CULTURAL_IMPACT_CULTURAL_IDENTITY | ||||
Method: Least Squares | ||||
Date: 27 September 2024 | ||||
Time: 22:35 | ||||
Sample: 1400 | ||||
Included observations: 400 | ||||
Variable | Coefficient | Error Standard | t-Statistic | Probability |
C | 1.960059 | 0.393704 | 4.978508 | 0.0000 |
ENVIRONMENT_PROVENIENCE | 0.395232 | 0.152297 | 2.595136 | 0.0098 |
STUDY_LEVEL | 0.338976 | 0.098088 | 3.455823 | 0.0006 |
R-squared | 0.056826 | Mean dependent var | 3.840000 | |
Adjusted R-squared | 0.052074 | S.D. dependent var | 1.481515 | |
S.E. of regression | 1.442425 | Akaike info criterion | 3.578000 | |
Sum squared resid | 825.9941 | Schwarz criterion | 3.607957 | |
Log-likelihood | −712.6000 | Hannan–Quinn criterion | 3.589535 | |
F-statistic | 11.95956 | Durbin–Watson stat | 1.645415 | |
Prob (F-statistic) | 0.000009 |
Appendix I. The Results of the Regression Analysis for Hypothesis 8
Covariance Analysis: Ordinary | ||
Date: 27 September 2024 | ||
Time: 22:40 | ||
Sample: 1400 | ||
Included observations: 400 | ||
Covariance | IMPACT_SOCIO_CULTURAL _INTERACTIONS | OCCUPATION |
Correlation | ||
IMPACT_SOCIO_ CULTURAL_INTERACTIONS | 1.192344 | |
1.000000 | ||
OCCUPATION | −0.120437 | 1.489775 |
−0.090365 | 1.000000 |
Appendix J. The Results of the Regression Analysis for Hypothesis 9
Covariance Analysis: Ordinary | |||
Date: 27 September 2024 | |||
Time: 22:44 | |||
Sample: 1400 | |||
Included observations: 400 | |||
Covariance | AGE_CATEGORY | FREQUENCY_ SOHODOL_ GORGES | TRAVEL_MODE |
Correlation | |||
AGE_CATEGORY | 0.775994 | ||
1.000000 | |||
FREQUENCY_ SOHODOL_GORGES | 0.191600 | 0.494400 | |
0.309334 | 1.000000 | ||
TRAVEL_MODE | 0.015906 | 0.055500 | 0.893594 |
0.019102 | 0.083499 | 1.000000 |
Appendix K. The Results of the Regression Analysis for Hypothesis 10
Dependent Variable: POST_PANDEMIC_DESTINATIONS | ||||
Method: Least Squares | ||||
Date: 27 September 2024 | ||||
Time: 22:46 | ||||
Sample: 1400 | ||||
Included observations: 400 | ||||
Variable | Coefficient | Error Standard | t-Statistic | Probability |
C | 1.093413 | 0.039309 | 27.81605 | 0.0000 |
SEX | 0.018869 | 0.021504 | 0.877426 | 0.3808 |
INFORMATION_SOURCES | −0.010934 | 0.00755 | −1.44819 | 0.1484 |
R-squared | 0.006668 | Mean dependent var | 1.047500 | |
Adjusted R-squared | 0.001663 | S.D. dependent var | 0.212972 | |
S.E. of regression | 0.212795 | Akaike info criterion | −0.249504 | |
Sum squared resid | 17.97683 | Schwarz criterion | −0.219568 | |
Log-likelihood | 52.90073 | Hannan–Quinn criterion | −0.237649 | |
F-statistic | 1.332411 | Durbin–Watson stat | 1.103095 | |
Prob (F-statistic) | 0.265018 |
Appendix L. The Results of the Regression Analysis for Hypothesis 11
Dependent Variable: GEOTOURISTIC_ACTIVITIES | ||||
Method: Least Squares | ||||
Date: 27 September 2024 | ||||
Time: 22:48 | ||||
Sample: 1400 | ||||
Included observations: 400 | ||||
Variable | Coefficient | Error Standard | t-Statistic | Probability |
C | 2.139280 | 0.294078 | 7.274542 | 0.0000 |
SEASON | 0.231376 | 0.082983 | 2.788254 | 0.0056 |
STUDY_LEVEL | 0.081910 | 0.064853 | 1.263018 | 0.2073 |
R-squared | 0.024020 | Mean dependent var | 2.940000 | |
Adjusted R-squared | 0.019103 | S.D. dependent var | 0.986831 | |
S.E. of regression | 0.977359 | Akaike info criterion | 2.799547 | |
Sum squared resid | 379.2268 | Schwarz criterion | 2.829483 | |
Log-likelihood | −556.9094 | Hannan–Quinn criterion | 2.811402 | |
F-statistic | 4.885293 | Durbin–Watson stat | 1.540314 | |
Prob (F-statistic) | 0.008017 |
Appendix M. The Results of the Regression Analysis for Hypothesis 12
Dependent Variable: RECOMMEND_DESTINATION | ||||
Method: Least Squares | ||||
Date: 27 September 2024 | ||||
Time: 22:52 | ||||
Sample: 1400 | ||||
Included observations: 400 | ||||
Variable | Coefficient | Error Standard | t-Statistic | Probability |
C | 4.129074 | 0.178917 | 23.07815 | 0.0000 |
OCCUPATION | 0.075608 | 0.032228 | 2.346054 | 0.0195 |
TRAVEL_MODE | 0.006353 | 0.041612 | 0.152674 | 0.8787 |
R-squared | 0.013693 | Mean dependent var | 4.4125 | |
Adjusted R-squared | 0.008725 | S.D. dependent var | 0.789876 | |
S.E. of regression | 0.786422 | Akaike info criterion | 2.364826 | |
Sum squared resid | 245.5287 | Schwarz criterion | 2.394762 | |
Log-likelihood | −469.9653 | Hannan–Quinn criterion | 2.376681 | |
F-statistic | 2.755881 | Durbin–Watson stat | 1.665161 | |
Prob (F-statistic) | 0.064769 |
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Number | Variables |
---|---|
H1 | Age_category Study_level Economic_impact_opportunities Business_economic_impact |
H2 | Sex Economic_impact_price_increase Provenance_environment |
H3 | Age_category Occupation Environmental_impact_damage Impact_environment_harmony |
H4 | Provenance_environment Study_level Impact_environment_protection_diversity |
H5 | Sex Study_level Impact_socio_cultural_traffic |
H6 | Occupation Age_category Impact_socio_cultural_infrastructure Impact_socio_overcrowding |
H7 | Socio_cultural_impact_cultural_identity Provenance_environment Study_level |
H8 | Sex Occupation Impact_socio_cultural_interactions |
H9 | Age_category Travel_mode Sohodol_Gorges_frequency |
H10 | Sex Information_sources Post_pandemic_destinations |
H11 | Season Education_level Geotourism_activities |
H12 | Occupation Travel_mode Destination_recommendation |
Hypothesis | Statistical Method Used |
---|---|
H1. Younger and more educated tourists are more likely to perceive geotourism as generating positive economic opportunities and promoting local business diversification in the Sohodol Gorges. | Multiple regression analysis |
H2. Female tourists and those from rural areas are more likely to perceive geotourism as increasing prices for accommodation and traditional local products. | Multiple regression analysis |
H3. Younger tourists and those working in environmental or tourism-related fields are more likely to perceive geotourism as harmful to the natural environment and emphasize the need for sustainable development. | Multiple regression analysis |
H4. Urban tourists and those with higher levels of education are more likely to support biodiversity protection in geotourism development within protected areas. | Multiple regression analysis |
H5. Female tourists and those with higher levels of education are more likely to perceive road traffic congestion caused by geotourism as negatively impacting the quality of the tourist experience. | Multiple regression analysis |
H6. Older tourists are more likely to perceive overcrowding in public spaces and inadequate infrastructure as negative impacts of geotourism compared to younger tourists, who are more tolerant of these issues. | Pearson correlation coefficient |
H7. Urban tourists and those with higher education are more likely to perceive geotourism as positively contributing to the preservation and promotion of local cultural identity. | Multiple regression analysis |
H8. Tourists working in professions that involve frequent social interaction are more likely to perceive geotourism as intensifying meaningful socio-cultural interactions with locals. | Pearson correlation coefficient |
H9. Older tourists are slightly more likely to visit the Sohodol Gorges more frequently, while younger tourists tend to prefer more independent modes of travel in the post-pandemic context. | Pearson correlation coefficient |
H10. Female tourists are more likely to choose safe, well-documented post-pandemic destinations using official online sources, while male tourists prefer adventurous destinations and rely more on informal sources. | Multiple correlation coefficient |
H11. Tourists with higher levels of education are more likely to engage in educational geotourism activities in spring and autumn, while less educated tourists prefer recreational or adventure activities in summer. | Multiple correlation coefficient |
H12. Tourists in liberal or creative professions and those traveling with family or in organized groups are more likely to recommend the Sohodol Gorges compared to solo travelers or those from technical fields. | Multiple correlation coefficient |
Ref. No. | Question | Variable |
---|---|---|
1. | Your gender? | Sex |
2. | What age group do you fall into? | Age_category |
3. | Where are you from? | Medium_provenance |
4. | Level of completed studies? | Study_level |
5. | Your occupation? | Occupation |
Economic impacts | ||
6. | Economic impacts [geotourism increases opportunities in the development of the local economy (employment and investment)] | Economic_impact_opportunities |
7. | Economic impacts [geotourism stimulates an increase in prices in accommodation units and traditional products] | Economic_impact_price_increase |
8. | Economic impacts [geotourism diversifies businesses for locals] | Business_economic_impact |
Impacts on the environment | ||
9. | Environmental impacts [geotourism causes damage to the natural environment of ecosystem development and rural environment] | Environmental_impact_damage |
10. | Environmental impacts [the natural diversity of the protected area must be exploited and protected to reduce environmental impacts] | Impact_environment_protection_diversity |
11. | Impacts on the environment [geotourism must be developed in harmony with the natural and cultural environments of the protected area] | Impact_environment_harmony |
Socio-cultural impacts | ||
12. | Socio-cultural impacts [the development of geotourism in the Sohodol Gorges increases road traffic congestion] | Impact_socio_cultural_traffic |
13. | Socio-cultural impacts [geotourism in Sohodol Gorges causes overcrowding of public and leisure spaces] | Impact_socio_supraaglomerarr |
14. | Socio-cultural impacts [the development of geotourism in the Sohodol Gorges would improve the quality of roads and recreational spaces] | Impact_socio_cultural_infrastructure |
15. | Socio-cultural impacts [geotourism in the Sohodol Gorges has a positive impact on the cultural identity of the local population] | Impact_socio_cultural_identity_cultural |
16. | Socio-cultural impacts [geotourism intensifies socio-cultural interactions between tourists and locals and between hotel managers and tourists] | Impact_socio_cultural_interactions |
17. | Where have you traveled post-COVID-19 pandemic? | Post_pandemic_destinations |
18. | How often have you traveled to Sohodol Gorges post-COVID-19 pandemic? | Frequency_Sohodol_Gorges |
19. | How did you most commonly travel in the Sohodol Gorges following the COVID-19 pandemic? | Travel_mode |
20. | What is the season to visit the Sohodol Gorges? | Season |
21. | What sources of information do you use to document yourself about the Sohodol Gorges? | Information_sources |
22. | What geotourism activities have you carried out post-COVID-19 pandemic in Sohodol Gorges? | Geotourism_activities |
23. | On a scale of 1 to 5, how likely are you to recommend Sohodol Gorges to others? | Recommend_destination |
Characteristics | Categories | n | % |
---|---|---|---|
Sex | Male | 177 | 44.25 |
Female | 223 | 55.75 | |
Age range | 18–30 | 75 | 18.75 |
31–45 | 167 | 41.75 | |
46–65 | 120 | 30 | |
>65 | 38 | 9.5 | |
Residence | Rural | 153 | 38.25 |
Urban | 247 | 61.75 | |
Education | Secondary-school studies | 45 | 11.25 |
High school studies | 22 | 5.5 | |
Post-secondary studies | 8 | 2 | |
Higher education (bachelor’s degree, master’s degree, doctorate) | 325 | 81.25 | |
Occupation | Student | 64 | 16 |
Employed | 243 | 60.75 | |
Unemployed | 11 | 2.75 | |
Self-employed | 37 | 9.25 | |
Retiree | 45 | 11.25 |
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Niță, A.; Drăguleasa, I.-A.; Constantinescu, E.; Bonea, D. Geotourism: From Theoretical Definition to Practical Analysis in the Sohodol Gorges Protected Area, Romania. Geographies 2025, 5, 53. https://doi.org/10.3390/geographies5040053
Niță A, Drăguleasa I-A, Constantinescu E, Bonea D. Geotourism: From Theoretical Definition to Practical Analysis in the Sohodol Gorges Protected Area, Romania. Geographies. 2025; 5(4):53. https://doi.org/10.3390/geographies5040053
Chicago/Turabian StyleNiță, Amalia, Ionuț-Adrian Drăguleasa, Emilia Constantinescu, and Dorina Bonea. 2025. "Geotourism: From Theoretical Definition to Practical Analysis in the Sohodol Gorges Protected Area, Romania" Geographies 5, no. 4: 53. https://doi.org/10.3390/geographies5040053
APA StyleNiță, A., Drăguleasa, I.-A., Constantinescu, E., & Bonea, D. (2025). Geotourism: From Theoretical Definition to Practical Analysis in the Sohodol Gorges Protected Area, Romania. Geographies, 5(4), 53. https://doi.org/10.3390/geographies5040053