Modeling the Potential for Rural Tourism Development via GWR and MGWR in the Context of the Analysis of the Rural Lodging Supply in Extremadura, Spain
Abstract
:1. Introduction
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- Geostatistical models, specifically spatially weighted regressions (GWR) and multiscale geographically weighted regressions (MGWR), were used to determine the fit between the regressors (X1, X2..., Xn) and the predicted variable (Y). The former is understood to denote the types of tourism resources preferred by this type of demand. The latter, on the other hand, refers to the number of vacancies in rural tourism lodgings.
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- The use of this type of technique (GWR and MGWR) requires a series of preliminary investigations, in the course of which collinearity is eliminated by means of exploratory regressions.
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- The derivation of viable models enables the initial hypothesis to be corroborated or disproved.
2. Materials and Methods
2.1. Study Area
2.2. Materials
2.3. Geostatistical Analysis
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- The model must be linear;
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- The data used should not depend on any external factor;
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- Explanatory variables should not be related to each other;
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- The explanatory variables must have a negligible measurement error;
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- The residuals must add up to 0; and
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- The residuals must have homogeneous variance and follow a normal distribution.
3. Results
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- Kernel: adaptive; and
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- Bandwidth: AICc, CV, and BP following the neighbor criterion.
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Travelers | Overnight Stay- Ciones | EstanciaHalf | Grade of Occupation | Degree of Occupation F/Week | Establishment Mientos | Places | Employment | Overnight Stays /Plaza | |
---|---|---|---|---|---|---|---|---|---|
2001 | 30,192 | 66,548 | 2.09 | 23.31 | NA | 104 | 939 | 172 | 70.9 |
2005 | 65,815 | 146,220 | 2.13 | 18.88 | NA | 228 | 2668 | 397 | 54.8 |
2010 | 107,526 | 251,518 | 2.29 | 14.14 | 22.17 | 461 | 5496 | 719 | 45.8 |
2015 | 161,011 | 349,386 | 2.12 | 19.87 | 28.79 | 538 | 6515 | 795 | 53.6 |
2019 | 226,723 | 499,749 | 2.20 | 17.89 | 34.64 | 632 | 7609 | 1001 | 65,7 |
2020 | 110,065 | 271,571 | 2.47 | 13.42 | 22.17 | 466 | 5598 | 737 | 48,5 |
2021 | 184,975 | 433,665 | 2.34 | 15.51 | 28.04 | 664 | 7584 | 954 | 57.2 |
2022 * | 238,897 | 533,431 | 2.19 | 17.26 | 31.70 | 725 | 8355 | 1153 | 66.2 |
Internal Variables Considered (Type, Subtype and Code) | |||||
---|---|---|---|---|---|
Type | Subtype | Code | Type | Subtype | Code |
Relief | Mountains and their foothills | VI1 | Summer thermal comfort | Average maximum temperature from June to September | VI22 |
Saws | VI2 | Others | Big game hunting + 1000 hectares. | VI23 | |
Piedemontes | VI3 | Long-distance trails | VI24 | ||
Lakesides and valleys | VI4 | Short-distance trails | VI25 | ||
Sedimentary basins/plains and peneplains | VI5 | Local trails | VI26 | ||
Hydrography | Rivers (up to 2 km) | VI6 | Optimal visiting period (demand preferences) | VI27 | |
Reservoirs | VI7 | Singularity (pairwise) | VI28 | ||
Bathing areas | VI8 | Attractiveness according to demand (questionnaire) | VI29 | ||
Waterfalls | VI9 | Proximity to main tourist attractions (EOH) | VI30 | ||
Protected natural areas | National Park | VI10 | Proximity to tourist area (EOTR) | VI31 | |
Park or Nature Reserve | VI11 | Population size | VI32 | ||
Natural Monument | VI12 | Vegetation species | VI33 | ||
ZEPA | VI13 | Geosites | VI34 | ||
ZEC | VI14 | Viewpoints | VI35 | ||
Cultural/historical-artistic heritage | Distance to World Heritage City (time) | VI15 | Observation points | VI36 | |
Historic-artistic site | VI16 | Greenways | VI37 | ||
Assets of Cultural Interest (Monuments) | VI17 | ||||
Museums and collections | VI18 | ||||
Livestock trails at 2 km | VI19 | ||||
Castles | VI20 | ||||
Archaeological sites | VI21 | ||||
Considered external variables (type, subtype, and code) | |||||
Type | Subtype | Code | Type | Subtype | Code |
Hotel accommodation Accommodation (beds) | Hotel | VE1 | Others | Activity companies (no.) | VE13 |
Hotel–Apartment | VE2 | Tourist guides (no.) | VE14 | ||
Hostel | VE3 | Tourist offices | VE15 | ||
Pension | VE4 | Interpretation centers | VE16 | ||
Non-hotel accommodation (vacancies) | Tourist apartment | VE5 | Wharfs | VE17 | |
Camping | VE6 | Accessibility | Highway | VE18 | |
Rural lodging (vacancies) | Rural hotel | VE7 | National highway | VE19 | |
Rural house | VE8 | Autonomous highway | VE20 | ||
Rural apt. | VE9 | Main bus stations | VE21 | ||
Restoration | Three- and four-fork restaurants (seats) | VE10 | Train stations | VE22 | |
One- and two-fork restaurants (seats) | VE11 | Airport | VE23 | ||
Café-bar (no.) | VE12 |
Search Criteria | Cut | Testing | No. of Accepted | % Accepted |
---|---|---|---|---|
R2 adjusted minimum | >0.3 | 261,800 | 44,362 | 16.94 |
Maximum coefficient p value | <0.05 | 261,800 | 1269 | 0.48 |
Maximum variance inflation factor | <5.0 | 261,800 | 242,401 | 92.59 |
Minimum p value of Jarque–Bera | >0.1 | 261,800 | 0 | 0 |
Minimal spatial autocorrelation | >0.1 | 42 | 38 | 90.48 |
Percentages | Inflation Factor of Variance (VIF) | Violations | Repetitions | Models | % of Occurrence | |||
---|---|---|---|---|---|---|---|---|
Variable * | Significance | Negative | Positive | |||||
VI1 | 21.84 | 36.13 | 63.87 | 6.58 | 19,399 | 389 | 1269 | 30.65% |
VI2 | 11.45 | 95.48 | 4.52 | 3.11 | 0 | 326 | 1269 | 25.69% |
VI3 | 23.11 | 0.88 | 99.12 | 2.37 | 0 | 308 | 1269 | 24.27% |
VI4 | 6.88 | 77.95 | 22.05 | 1.64 | 0 | 244 | 1269 | 19.23% |
VI5 | 50.03 | 100 | 0 | 1.97 | 0 | 272 | 1269 | 21.43% |
VI7 | 17.99 | 90.26 | 9.74 | 1.4 | 0 | 388 | 1269 | 30.58% |
VI8 | 95.75 | 0 | 100 | 2.24 | 0 | 252 | 1269 | 19.86% |
VI10 | 3.97 | 9.22 | 90.78 | 1.95 | 0 | 172 | 1269 | 13.55% |
VI11 | 100 | 0 | 100 | 1.31 | 0 | 513 | 1269 | 40.43% |
VI12 | 0.59 | 50.15 | 49.85 | 1.49 | 0 | 18 | 1269 | 1.42% |
VI13 | 36.2 | 0 | 100 | 1.19 | 0 | 535 | 1269 | 42.16% |
VI15 | 22.13 | 52.03 | 47.97 | 2.47 | 0 | 301 | 1269 | 23.72% |
VI16 | 100 | 0 | 100 | 1.96 | 0 | 307 | 1269 | 24.19% |
VI17 | 27.93 | 19.12 | 80.88 | 1.63 | 0 | 444 | 1269 | 34.99% |
VI28 | 100 | 0 | 100 | 2.27 | 0 | 51 | 1269 | 4.02% |
VI33 | 43.27 | 0 | 100 | 1.35 | 0 | 586 | 1269 | 46.18% |
VE18 | 48.22 | 99.77 | 0.23 | 3.16 | 0 | 702 | 1269 | 55.32% |
VE19 | 12.82 | 3.47 | 96.53 | 1.62 | 0 | 222 | 1269 | 17.49% |
Model | R2 Adj | AICc | JB | VIF | SA | Model |
---|---|---|---|---|---|---|
(1) | 0.32 | 3849 | 0.00 | 1.46 | 0.37 | +VI8 ** + VI11 *** + VI16 *** + VI28 *** |
(2) | 0.31 | 3856 | 0.00 | 1.59 | 0.69 | −VI5 + VI11 *** + VI16 *** + VI28 *** |
(3) | 0.31 | 3856 | 0.00 | 1.11 | 0.47 | +VI10 +VI11 *** + VI16 *** + VI28 *** |
(4) | 0.32 | 3850 | 0.00 | 1.51 | 0.40 | +VI8 ** + VI10 + VI11 *** + VI16 *** + VI28 *** |
(5) | 0.32 | 3860 | 0.00 | 5.83 | 0.99 | −VI1 ** − VI2 + VI3 − VI4 − VI5 + VI8 ** + VI10 * VI11 *** − VI12 + VI16 *** + VI17 + VI28 *** + VI33 − VE8 * + VE19 |
Variable | Coefficient | Std. Error | t-Statistic | Probability | Robust_SE | Robust_t | Robust_Pr | VIF |
---|---|---|---|---|---|---|---|---|
Intercept | −33.16 | 5.171 | −6.41 | 0.000000 * | 7.38 | −4.49 | 0.000012 * | ------- |
VI8 | 5.44 | 1.717 | 3.17 | 0.001659 * | 2.40 | 2.27 | 0.023983 * | 1.46 |
VI11 | 6.00 | 1.554 | 3.86 | 0.000140 * | 2.26 | 2.66 | 0.008137 * | 1.09 |
VI16 | 5.15 | 1.504 | 3.43 | 0.000694 * | 1.38 | 3.75 | 0.000219 * | 1.18 |
VI28 | 10.70 | 1.535 | 6.97 | 0.000000 * | 1.84 | 5.81 | 0.000000 * | 1.33 |
Variable | Coefficient | Std. Error | t-Statistic | Probability | Robust_SE | Robust_t | Robust_Pr | VIF |
---|---|---|---|---|---|---|---|---|
Intercept | −36.82 | 5.985 | −6.15 | 0.000000 * | 8.34 | −4.41 | 0.000016 * | ------- |
VI8 | 5.06 | 1.745 | 2.90 | 0.003916 * | 2.38 | 2.13 | 0.033719 * | 1.51 |
VI10 | 2.18 | 1.797 | 1.21 | 0.225489 | 1.90 | 1.15 | 0.251148 | 1.05 |
VI11 | 6.25 | 1.566 | 3.99 | 0.000087 * | 2.30 | 2.72 | 0.006785 * | 1.10 |
VI16 | 5.16 | 1.503 | 3.43 | 0.000679 * | 1.38 | 3.73 | 0.000231 * | 1.18 |
VI28 | 10.70 | 1.535 | 6.97 | 0.000000 * | 1.85 | 5.77 | 0.000000 * | 1.33 |
Parameters | Conceptualization of Distance | ||
---|---|---|---|
AICc | BP Neighbors | CV | |
Residuals square | 403,427 | 174 | 435,921 |
Effective number | 25.9 | 5.5 | 12.2 |
Sigma | 33.4 | 8.4 | 34.1 |
AICc | 3841.8 | 118.3 | 3848.9 |
R2 | 0.394 | 0.961 | 0.354 |
R2 adjusted | 0.353 | 0.890 | 0.326 |
Model configuration | Dependent variable: Rural lodging places | Independent variables: VI8–VI10–VI11–VI16–VI28 |
Places | Places | ||||||||
---|---|---|---|---|---|---|---|---|---|
Municipality | R2 | Real | Dear | Waste | Municipality | R2 | Real | Dear | Waste |
Viandar de la Vera | 0.35 | 0 | 91 | −91 | Jaraíz de la Vera | 0.34 | 148 | 71 | 77 |
Gargantilla | 0.33 | 8 | 92 | −84 | Acebo | 0.32 | 143 | 66 | 77 |
Piornal | 0.34 | 5 | 84 | −79 | Burguillos del Cerro | 0.15 | 106 | 23 | 83 |
Guijo de Santa Bárbara | 0.35 | 20 | 98 | −78 | Villanueva de la Vera | 0.35 | 180 | 91 | 89 |
Talaveruela de la Vera | 0.35 | 10 | 85 | −75 | Jerte | 0.34 | 206 | 117 | 89 |
Segura de Toro | 0.33 | 22 | 92 | −70 | Baños de Montemayor | 0.34 | 157 | 67 | 90 |
Valdastillas | 0.34 | 18 | 83 | −65 | Alange | 0.11 | 104 | 8 | 96 |
Rebollar | 0.34 | 16 | 80 | −64 | Pinofranqueado | 0.33 | 168 | 61 | 107 |
Plasencia | 0.33 | 19 | 77 | −58 | Montánchez | 0.11 | 133 | 26 | 107 |
Descargamaría | 0.33 | 8 | 64 | −56 | Torrejón el Rubio | 0.30 | 137 | 19 | 118 |
Abadía | 0.33 | 0 | 56 | −56 | Valencia de Alcantara | 0.37 | 250 | 104 | 146 |
La Garganta | 0.34 | 8 | 62 | −54 | Navaconcejo | 0.34 | 294 | 124 | 170 |
Cabrero | 0.34 | 6 | 57 | −51 | Hervás | 0.34 | 269 | 93 | 176 |
Jarilla | 0.33 | 18 | 68 | −50 | Jarandilla de la Vera | 0.35 | 268 | 90 | 178 |
Variable | Est. | SE | t(Est/SE) | p-Value |
---|---|---|---|---|
Intercept | 0.000 | 0.042 | 0.000 | 1.000 |
VI8 | 0.149 | 0.051 | 2.903 | 0.004 |
VI10 | 0.052 | 0.043 | 1.214 | 0.225 |
VI11 | 0.176 | 0.044 | 3.990 | 0.000 |
VI16 | 0.156 | 0.045 | 3.432 | 0.001 |
VI28 | 0.336 | 0.048 | 6.972 | 0.000 |
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Sánchez-Martín, J.M.; Hernández-Carretero, A.M.; Rengifo-Gallego, J.I.; García-Berzosa, M.J.; Martín-Delgado, L.M. Modeling the Potential for Rural Tourism Development via GWR and MGWR in the Context of the Analysis of the Rural Lodging Supply in Extremadura, Spain. Systems 2023, 11, 236. https://doi.org/10.3390/systems11050236
Sánchez-Martín JM, Hernández-Carretero AM, Rengifo-Gallego JI, García-Berzosa MJ, Martín-Delgado LM. Modeling the Potential for Rural Tourism Development via GWR and MGWR in the Context of the Analysis of the Rural Lodging Supply in Extremadura, Spain. Systems. 2023; 11(5):236. https://doi.org/10.3390/systems11050236
Chicago/Turabian StyleSánchez-Martín, José Manuel, Ana María Hernández-Carretero, Juan Ignacio Rengifo-Gallego, María José García-Berzosa, and Luz María Martín-Delgado. 2023. "Modeling the Potential for Rural Tourism Development via GWR and MGWR in the Context of the Analysis of the Rural Lodging Supply in Extremadura, Spain" Systems 11, no. 5: 236. https://doi.org/10.3390/systems11050236
APA StyleSánchez-Martín, J. M., Hernández-Carretero, A. M., Rengifo-Gallego, J. I., García-Berzosa, M. J., & Martín-Delgado, L. M. (2023). Modeling the Potential for Rural Tourism Development via GWR and MGWR in the Context of the Analysis of the Rural Lodging Supply in Extremadura, Spain. Systems, 11(5), 236. https://doi.org/10.3390/systems11050236