2.3. Methodology for Evaluating the Development Potential for Rural Tourism
Since the 1960s, numerous attempts have been made to develop a methodology for assessing tourism potential that would make it possible to identify the areas with the greatest potential for tourism development. They have been conducted in very different parts of the world, from Latin America, led by the Organization of American States (OAS) [
64], to Europe, applying common guidelines in countries with very different characteristics (Ecuador, Poland, Spain, etc.). Among the usual characteristics were the determination of the main resources that functioned as attractors, the inclusion of complementary factors, and resorting to experts or to the demand itself. However, in the scientific community, there has never been the necessary quorum for a single way of knowing the tourist attraction capacity offered by the territory to prevail, although certain coincidences can be observed when referring to the resources or heritage to be considered.
To date, there is no methodology for measuring the attractiveness of Extremadura for the practice of rural tourism; not even the proposal of the LEADER Observatory at the time was sufficiently well received [
65]. It is proposed, therefore, the design of a specific one for this territory and the tourist modality in question. To this end, it should be specified that it should comply as far as possible with the main requirements that are demanded of any methodology agreed upon in the literature.
The methodology presented in this article has been designed by the authors for the study area. However, it can be adapted to other territories, always bearing in mind that both variables and hierarchies and weightings should be adjusted to the specific conditions of the area of application.
In line with the above, it is proposed that it should be inclusive, considering the internal and external factors related to tourism and that it should be capable of interacting with supply and demand. Likewise, it is intended to be adapted to the Extremadura territory. This does not preclude its application to other areas by means of appropriate adaptations.
It also seeks operability since, given its relative simplicity, it can be applied quickly, especially if Geographic Information Systems are used for the calculation of certain variables and for further analysis through the implementation of the capabilities allowed by this type of software.
On the other hand, an attempt is made to confer the greatest possible objectivity, given that the inclusion of numerous variables stems from experience in the study of demand, which contributes to the realization of certain weightings. This is because not all variables have the same weight in the evaluation since it is conditioned by the opinion of the demand, by their tastes and preferences.
In addition, it pursues its feasibility, focused on the use of new techniques, but also the experience accumulated during more than two decades dedicated to the research of the tourism system in Extremadura.
Finally, this methodology is intended to be contributive, both in terms of its design and the analysis of the results obtained.
Its development has been conducted in four well-defined phases, which correspond to the selection of variables, their ranking, the construction of the table of equivalences, and, finally, the application of the proposed methodology to each of the municipalities that make up the study area. These four phases are conducted sequentially (
Figure 2), from the theoretical conception of the methodology to its implementation.
During Phase 1, the variables were selected by dividing them according to the group, type, and subtype to which they belong. In this sense, the starting point was the discrimination between a group of internal variables and another of external variables.
The first is made up of those variables that refer to the diversity of existing attractions, among which the relief, the hydrographic network, protected natural spaces, cultural and historical/artistic heritage, climatic comfort, and other factors of a variable nature stand out. Naturally, each of these types of attractions is divided into corresponding subtypes. This first group contributes 70% of the total score achieved by each of the municipalities that make up Extremadura.
The second group, much less numerous, is made up of tourist facilities, which concern the supply of both accommodation and complementary and other tourist services. Accessibility should also be considered as one of the main factors affecting the evolution of any destination. As with the internal variables, they are divided into subtypes, which serve to further specify the attractiveness of the territory in terms of supply and accessibility. Together, the external variables contribute 30% to the tourism potential of each municipality.
The choice of the selected variables was based on the existing literature and the preferences expressed by the demand. In addition, it has been accompanied by important fieldwork.
The different weight given to internal and external variables is since the former is a necessary condition for tourism development since it is already a cliché to point out that they are the raw material of tourism. On the other hand, the latter makes the difference between tourism exploitation or the maintenance of the mere potential provided by the former. At the same time, they are indispensable for the realization of this activity.
During phase 2, once the groups, types, and subtypes of variables have been decided, a hierarchical ranking procedure must be applied using 5 categories, each of which will be assigned a score. Thus, hierarchy 5, the most important, will have a score of 5; hierarchy 4, the second most important, will have 4 points; and so, down to hierarchy 1, the least important, which will be given a score of 1. Likewise, since each variable may have different importance in the group to which it belongs, they have been weighted at 10 levels with a score ranging from 0.1 for the least important subtypes to 1 for the most important. In other words, the scale has a range of 0.1; 0.2; 0.3; 0.4; 0.5; 0.6; 0.7; 0.8; 0.9 and 1, from the least important aspects to those of maximum interest. This weighting is based on the opinion of tourists.
Phase 3 corresponds to the elaboration of the equivalence table. That is the correspondence between a quantitative and qualitative assessment. To this end, the sums of the maximum and minimum values that can be reached by both internal and external variables are taken as a reference. In this way, the path of the variable is obtained, which makes it possible to establish 4 primary categories in each group of variables. These categories are A, B, C, and D; assuming that category A would be reached by obtaining an average equal to or higher than 4 in all the variables, category B would have an average between 3 and 3.999. In contrast, category C would be characterized by those municipalities with an average between 2 and 2.999, and category D would be those whose average values do not reach the value of 2.
These equivalences, valid for both the group of internal and external variables, allow for a combination of both in such a way that they give rise to 16 theoretical classes into which the municipalities can be grouped. Theoretically, they would be made up of the following categories:
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AA, when the average of the internal and external variables equals or exceeds 4. This is undoubtedly the best category because there is a balance between the internal capacity of attraction and the facilities that make it possible.
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AB, provided that the internal variables have an average higher than 4 and the external variables have an average between 3 and 3.999. In this category, there is a certain imbalance given that the internal potential of the attractions is high. However, it is not accompanied in the same way by the tourist facilities.
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AC, when the internal variables exceed an average of 4 and the external variables are between 2 and 2.999. In this case, there are important attractions, although tourist facilities are very scarce.
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AD, this category would imply a strong mismatch between heritage wealth and tourism facilities.
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In the BA category, this category corresponds to internal variables whose average ranges between 3 and 3.999, while external variables equal to or exceed 4.
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BB, provided there is a certain balance between internal and external attractions, presenting in both cases averages ranging between 3 and 3.999.
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BC, a category that includes those areas that have internal variables of intermediate value, with averages ranging from 3 to 3.999, and external variables that have very low values in all variables, ranging from 2 to 2.999 on average.
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BD, when the internal variables average values between 3 and 3.999 and the external variables do not reach the value of 2.
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CA is characterized by having averaged internal variables whose values range between 2 and 2.999, although the external variables are equal to or greater than 4.
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CB, in which the internal variables show averages that do not fluctuate between 2 and 2999, while the external variables range between 3 and 3.999.
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CC, a category reached when the internal and external variables average values ranging from 2 to 2.999.
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CD is a group characterized by having internal variables that vary between 2 and 2.999 on average, while the external variables do not reach the value of 2.
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DA is a class achieved when internal variables average less than 2 and external variables exceed 4.
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DB is a category characterized by internal variables with a low rating, less than 2 on average, and external variables whose average value ranges from 3 to 3.999.
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DC, a group in which the internal variables are deficient, averaging less than 2, while the external variables range from 2 to 2.999.
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DD is undoubtedly the worst category since both internal and external variables do not reach an average of 2.
As can be seen from these 16 categories, there is an important casuistry, despite the level of grouping into two groups of variables, internal and external, which does not prevent cases from being analyzed in isolation, taking into account specific variables. Apart from this, it is also possible to assume that some of these categories, although logical at a theoretical level, are difficult to observe in reality since most of the nuclei with very high potential may not reach them in all the attractions considered. This concerns both heritage and tourist facilities, from which it follows that it will be difficult to find categories with internal variables classified as A. Likewise, it is evident that if a place has excellent heritage, it will be difficult for it not to be accompanied by a first-rate offer, as would hypothetically be the case in an AD class, and, conversely, when it has little heritage, it will be difficult for it to have a significant volume of the offer, as would be the case in a DA category.
During phase 4, the absolute values of each variable in each of the municipalities are replaced by the real score that corresponds to it according to the hierarchy that characterizes it. In addition, the appropriate calculations are made to determine the internal and external potential of each of the municipalities.
The selection and inclusion of the variables in types and subtypes, as well as their hierarchy and weighting, corresponding to phases 1 and 2, is of vital importance for the design of this specific methodology. For its configuration, it has been taken into account the existence of previous methodologies that consider part of these variables as primordial factors for making a tourist area attractive. In relation to this, 6 types of internal variables and another 6 types of external variables are proposed, in both cases well differentiated and supported by the literature [
64,
65].
Among the internal variables, the most attractive for rural tourism is the relief. It forms the most important landscape areas where some general physical-environmental processes can also be recognized [
64].
In this sense, a distinction is made between mountains and their foothills, mountain ranges, foothills, riverbanks, and valleys, and, finally, sedimentary basins, as well as plains and peneplains. Each of these landscape domains has been hierarchized according to the Euclidean distance so that the places within a radius of 5 km of these domains are considered the most attractive and become less interesting as they become more distant. In fact, they become the lowest scoring when they are more than 30 km away.
On the other hand, each of these subgroups has been weighted with a different weight since a mountain area does not have the same value for the tourist as a flat area. This conclusion is reached after resorting to another methodology centered on consulting the demand by pairs of photos and analyzing its results by means of a Hierarchical Analysis Process. Accordingly, the mountains and their foothills were considered the areas that made the territory more attractive and were therefore weighted with a value of 1. On the other hand, the mountain ranges, of lesser importance than the mountains, were weighted by 0.7, the foothills by 0.5, while the sedimentary shores and basins and the plains and peneplains had a weight of 0.3 and 0.1, respectively.
Another major attraction for rural tourism is hydrography, where rivers, reservoirs, bathing areas, and waterfalls have been highlighted as key subtypes, all of which have been mentioned in the literature as factors of tourist attraction.
The hierarchization of these has been carried out, taking into account different aspects, depending on the subgroup. In this sense, the categorization of watercourses has been carried out following the criterion applied in the catalog of geographical objects of the BTN100 [
62]. According to this, watercourses are grouped into rivers of principal interest, secondary interest, tertiary interest, and lesser interest, with their absence and a high distance to them remaining as hierarchy 1.
On the other hand, the reservoirs have been ranked according to the type of navigation they allow, considering the data available from the Hydrographic Confederations. Logically, based on this, those reservoirs in which all types of navigation are allowed, without any additional restriction, have been ranked with the highest score. They lose weight as restrictions are imposed, until ending with the lowest contribution when navigation is not allowed. The justification for this choice is due to the importance of river cruises as a booming activity in the autonomous community. In addition, some reservoirs are beginning to develop sports activities linked to the aquatic environment.
Along with these, the bathing areas approved by the Junta de Extremadura are also taken into account, to which different hierarchies have been assigned according to the distance to the population centers so that the closest ones obtain a hierarchy 5 and those that are more than 10 km away obtain the lowest rating, in line with summer tourism, in which these facilities acquire a very positive rating for rural tourists. At the same time, they also indicate that one of the motivations for selecting one destination or another is the greater proximity to this type of infrastructure.
Finally, the quality of the landscape projected by the hydrographic resource is taken into account, for which waterfalls are used as places where spectacular views can be enjoyed. However, the ranking of these is also based on distance. The weighting of the variables that bring together the hydrography is done by giving reservoirs and bathing areas a weighting of 0.7 and 0.8, respectively, because during the summer, they are the most attractive and even necessary for the practice of rural tourism due to the prevailing climate. Meanwhile, rivers and waterfalls have a weighting of 0.5 and 0.1, respectively, considering their lower capacity to attract and attract tourists.
The consideration of tourist demand for protected natural areas means that these acquire an important weight when it comes to measuring the attractiveness of the territory. This group was divided into subtypes with different types of protection, including national parks, natural parks or reserves, natural monuments, SPAs, and SCIs (SACs), according to the catalog of geographical objects of the BTN100 [
62].
Each of these areas is classified into five different categories according to the distance criterion, which means that the nearest centers are the most highly valued. In fact, they are considered of maximum importance if they are located within a radius of less than 5 km, reaching the minimum when they exceed 30 km. However, in the case of the national park, this distance is doubled because there are studies that corroborate a much higher attraction capacity than the rest of the protected areas.
This variety of protected areas is weighted differently because of their current attraction capacity, which is expected to continue with this trend. In this sense, the national park is the most important figure, and therefore its weighting is set at 0.8, followed at some distance by the park or nature reserve, with a value of 0.5, and the natural monument, weighted at 0.4, while the ZEPA and ZEC are weighted at 0.3 and 0.2, respectively.
Along with natural areas, the demand for rural tourism in Extremadura attaches great importance to cultural and historical heritage. In fact, in the case of those who stay in a rural establishment, more than 92% recognize that they also practice cultural tourism. This circumstance, together with the presence of a notable heritage of this type in the community, including, of course, the rural nuclei, imposes this type of attraction as a necessary factor to evaluate the tourist potential. Specifically, 7 subtypes are contemplated, very well defined, and with an important acceptance on the part of the demand.
The most important element is considered to be the travel time to the cities that have the distinctive seal of World Heritage Site awarded by UNESCO. In this sense, the centers that are less than 15 min away enjoy the greatest attraction, only to decline to a minimum when the journey time exceeds one hour by car. The rest of the heritage present in the territory does not reach its attractiveness. However, it should be noted that the historical sites, the assets of cultural interest in their facet of monuments, the main museums, and collections, as well as the cattle trails, castles, and archaeological sites, are considered. All of them have been ranked according to distance, giving the highest hierarchy to those centers that are less than 5 km away and the lowest to those that are more than 30 km away.
Naturally, not all of the components of cultural heritage have the same weighting when it comes to determining tourism potential. In this case, the distance to heritage cities has a weighting of 0.5, followed by historical-artistic sites (0.3), while the rest of the components have a weighting of 0.2, except for cattle trails, which only have a weighting of 0.1.
The literature has also emphasized the consideration of climate as a conditioning factor in tourism development. In fact, there are numerous climatic comfort indexes which have been taken into account when developing the evaluation methodology. In this sense, and given the extension of the area analyzed, only the average maximum temperature averaged between the months of June and September has been considered. Logically and consequently, with the temperatures reached during this time of the year, the highest hierarchy has been given to the lowest thermal average, while the places where the thermal records are higher have the lowest hierarchy. In addition, given the importance of temperature for tourism during the peak season, its weighting is high (0.8), inferring the enormous importance given to this aspect.
Although the five types and their corresponding subtypes are important for determining the tourist potential of any territory in Extremadura, it is worth adding others, very varied but which also contribute to making it attractive, even if only for a very concrete and specific segment of tourism. These include the existence of big game hunting reserves since the literature has shown the importance of hunting activity in the region, although it is still little exploited from the tourist point of view, with a low weighting of 0.2. The presence of trails, long-distance, short-distance, and local trails, all with a weighting of 0.1, is also considered, as they are a complementary activity demanded by hikers, although not normally by tourists.
The optimal visitation period is another factor considered since there are areas whose physical and heritage conditions can attract tourists all year round, thus acquiring the highest hierarchy. On the contrary, when there is not even an optimal season, it is considered with the lowest hierarchy. Given the importance of this period, its weighting is 0.7.
The uniqueness of the territory, on the other hand, as with the vegetation species, was obtained using the methodology of combining pairs of photos and also treating the results with a Hierarchical Analysis Process. From the result of the demand opinions, 5 different hierarchies were established, and both variables were weighted by 0.7.
On the other hand, their attractiveness was assessed by means of a questionnaire, with 5 possible answers (excellent, very good, good, normal, and unattractive), which were assimilated to each of the hierarchies in descending order. The weighting chosen, given its importance, was 1.
Proximity to the main tourist points defined by the INE in the Hotel Occupancy Survey (EOH) also occupies a prominent place since there are studies that reveal the diffusion of tourists staying in them toward rural environments [
50]. This variable was ranked according to the distance of displacement so that those centers that were close had a higher hierarchy than those that were farther away. It was also weighted by 0.4.
In line with the previous variable, the distance to the main tourist area was also considered, following a similar pattern, based on travel time and, therefore, proximity. Likewise, the weighting of this variable, given its importance in attracting visitors from the surrounding area, was 0.6.
The size of the population also has an impact on the tourism potential of an area. There are two reasons for this. The first is that the legislation itself imposes a demographic limit to be able to install rural tourism lodgings in the population center, although not in the municipal district. The second is due to the fact that the nuclei with a greater number of inhabitants are not the preferred ones for rural tourists. In this sense, this variable is hierarchized according to population intervals in such a way that the maximum hierarchy corresponds to the nuclei that do not exceed 2000 inhabitants, while the minimum coincides with those that exceed 20,000.
Geosites are also part of the attractiveness of an area since the analysis of the activities carried out by the demand includes a percentage of it dedicated to geotourism. However, since it is not a majority activity, its weighting is limited to 0.2 and is ranked according to the distance to each of these attractions.
Finally, viewpoints, observation points, and greenways are considered attractive to demand, although their weighting is limited. Specifically, a weighting of 0.1 is established and is ranked according to distance.
The 37 variables analyzed serve to calculate the internal tourism potential that characterizes each of the Extremadura municipalities and, as mentioned above, contribute 70% to the calculation of the total attraction capacity.
On the other hand, the external variables referring to tourist facilities are centered on two large blocks. On the one hand, there is the supply of accommodation and complementary services, and on the other, accessibility. Their contribution to tourism potential is limited to 30%.
The supply of lodgings is divided into the three main groups indicated in the literature and tourism legislation: hotel lodgings, extra-hotel lodgings, and, of course, rural lodgings. Each of these types has been divided into the main subtypes that each of them brings together, ignoring the different categories into which they are divided. At the same time, they have been hierarchized in the appropriate manner, taking into account the number of vacancies existing in Extremadura as of 31 November 2021, the most recent data available.
On the other hand, they have been weighted differently, taking into account that there are accommodations that have a higher valuation due to the interest they awaken for rural tourists, a condition extracted from the surveys carried out on demand. Thus, the maximum weighting is given to hotels, to all the variety of rural establishments (hotels, houses, and apartments), and to tourist apartments, which have burst into many tourist spots. On the other hand, the lowest hierarchy corresponds to the lack of vacancies in each of the types of accommodation considered.
Taking this aspect into account, rural establishments are weighted with 1; hotels, on the other hand, have a weighting of 0.6; at a lower level is the variety of hotel-apartment and tourist camps, weighted with 0.4; while hostels and guesthouses are weighted with 0.2.
Restaurants, considered in this case as restaurants and establishments with café-bar category, have also been hierarchized according to the number of existing seats and reserved for the lowest hierarchy the lack of such establishments. In addition, restaurants have been divided according to their category, creating two specific subtypes, those of 3 and 4 forks on the one hand and those of 1 and 2 on the other, setting the weighting at 0.6 for each case. The number of coffee-bars has been ranked according to the number of establishments, although their weight has been reduced to 0.1.
There is another offer, which is also very attractive to tourists because it allows them to obtain information and carry out a variety of activities. The hierarchies have been established according to the number of companies in some cases and distances in others. Specifically, activity companies and tour guides have been ranked according to their number, again taking the situation in the region as a reference. On the other hand, tourist offices, interpretation centers and wharves are ranked according to distance so the nearest centers are ranked higher than those farther away. The weighting of activity companies is 1, while that of tourist guides is limited to 0.5, as are tourist offices and interpretive centers.
Accessibility, in general, makes up the other large block of external variables measured through communication routes and other infrastructures. Obviously, the highway stands out with the highest weighting (1), followed by national and regional highways, with values between 0.6 and 0.3. On the other hand, bus and train stations and the airport, given their shortcomings and limitations, have a low weighting of 0.1 for land transport and 0.2 for air transport. In some cases, this is due to the fact that very few tourists travel by bus and train, which, apart from being a real adventure, is not competitive either in terms of price or travel time. The airplane, unfortunately, has an enormous limitation as far as destinations are concerned, so it cannot have a higher weighting either. Nevertheless, we hope to soon have to change these weightings, at least in the case of the train.
In summary, the weightings corresponding to the internal variables considered add up to a score of 15, while the external variables compute 12.2. This can be understood by multiplying by 0.7 the sum of the weights corresponding to the internal variables, which implies 70%, and doing the same with the sum of the weights given to the external variables, multiplying them in this case by 0.3. Naturally, if both are added together, they should represent the theoretical 100%, although if we test by multiplying by 0.7 and 0.3 to obtain the appropriate percentages, we can see that this is not the case, as shown below:
where
a is the sum of the weights assigned to the internal variables multiplied by 0.7 to represent 70% and
b is the sum of the weights set for the external variables multiplied by 0.3 to represent 30%. This makes it necessary to establish a correction coefficient (c) so that they really represent 70% and 30%, respectively. This factor is obtained very simply since it is no more than a simple proportion:
Substituting
b for
c in the above formulation 0.3 (0.368852459) shows that the values obtained do represent the 70–30 ratio established for each set of variables.
The maximum and minimum values corresponding to the hierarchy are obtained by multiplying by 5 (hierarchy 5) and by 1 (hierarchy 1), which allows us to establish the path of the internal and external variables. It follows that the maximum theoretical internal potential will be 52.5 and the minimum 10.5; mutatis mutandis, in the external variables, would correspond to 22.5 and 4.5.
Both the variables proposed for internal variables (
Table 1) and external variables (
Table 2), their ranking and weightings, as well as the calculations of maximum and minimum values, make up the proposed methodology. These tables can be consulted in more detail in the
Supplementary Material, which includes the hierarchy and weighting of each of the variables included in the calculations.
The explanation of the proposed methodology ends with a table of equivalences (
Table 3), where the path of internal and external variables is used to establish 4 different categories for each one of them. This makes it easier with a few simple codes to know in a generic way how attractive the attractiveness is of each municipality.
Considering the values obtained, in order to have an A category in the internal variables, the sum of these variables must be equal to or greater than 42.000 points; category B, on the other hand, would be limited to scores between 31.500 and 41.999. Category C would be made up of values between 21.000 and 31.4999, and D if the sum of these variables is between 20.999 and 10.5.
On the other hand, the values that the external variables must reach must add up to at least 18.000 points to obtain category A; between 13.500 and 17.999 to obtain category B; while if the sum fluctuates between 9 and 13.499 it would correspond to a category C; and it would be D in the case of having values between 8.999 and 4.500.
Once the methodology has been presented, it can be concluded that it meets the requirements of the initial approach, so that all that remains is its application and analysis, both of which can be achieved easily if we consider that the set of procedures followed is contained in a spreadsheet.