# Detecting Tourism Typologies of Regional Destinations Based on Their Spatio-Temporal and Socioeconomic Performance: A Correlation-Based Complex Network Approach for the Case of Greece

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

_{2}index [31], promise to overcome these common restrictions describing the established indicators [9,14,36,42,43,44,45,46,47,48,49,50], but these composite indicators are also subjected to the restriction of uni-variable configuration and computational complexity due to higher demand in computations [9].

## 2. Methodology and Data

_{within communities}− m

_{between communities})]

_{1%}(i), Q

_{5%}(i), Q

_{10%}(i)), where each node (i) belongs to.

## 3. Results and Discussion

#### 3.1. Data Visualization

#### 3.2. Correlation Analysis

_{1}in Figure 3 and is configured by the rows 29:50 and the lines 29:50. This sub-table (A

_{1}) includes the seasonality patterns of the prefectures of Fokida (29), at central Greece, Kerkyra (30), Zakeenthos (31), Kefalonia (32), and Lefkada (33), at the Ionian Sea (west Greece), Achaia (34), Aitoloakarnania (35), and Heleia (36), at west Greece, Arkadia (37), Argolida (38), Korinthia (39), Lakonia (40), and Messenia (41), at the Peloponnese, Attiki (42), which is the metropolitan prefecture of Greece, Lesvos (43), Samos (44), Chios (45), Cyclades (46), and Dodecanese (47), at the Aegean Sea, and Heraklion (48), Lasithi (49), and Rethymno (50), on the island of Crete. As it can be observed in Figure 4, the geographical arrangement of these regions is based on the state of the adjacency (where prefectures are mainly located in central and south Greece), have geographical relevance, and their spatial distribution configures a "U"-shaped pattern.

_{2}, which includes the seasonality patterns of the prefectures of Xanthi (5), Thessaloniki (6), Hmathia (7), Kilkis (8), and Pella (9). All these regions have geographical relevance, as they are located in north-east Greece (Figure 4), as well as tourism-seasonality (functional) relevance, to the extent that they are in their majority positively correlated to all the other seasonality patterns of the Greek regions. A final distinguishable area that can be observed in Figure 3 is the cross-junction area A

_{3}, which includes the seasonality patterns of the neighbor prefectures of Kozani (13), Grevena (14), Kastoria (15), and Florina (16). These regions have also geographical relevance due to their neighboring location in north Greece (Figure 4) and their functional relevance regards their either negative or insignificant correlations to all other seasonality patterns of the Greek regions.

#### 3.3. Classification of Seasonality Patterns Based on Community Detection

_{1},p

_{2},p

_{3}), where the first coordinate (p

_{1},▪,▪) expresses the modularity class for the 1% level of significance, the second one (▪,p

_{2},▪) for 5% sig., and the third one (▪,▪,p

_{3}) for 10% significance. Therefore, based on the available ordered triplets p(i), which correspond to the i = 1,…, 51 modularity (seasonal) classes of the Greek prefectures, four (4) in number unique patterns (clusters) can be distinguished, which are expressed by the triplet-groups (0,0,1), (1,1,0), (1,1,1), and (2,2,0), as is shown in Figure 5. Amongst these patterns (clusters) of modularity classes, the first one configures a sigmoid (“S”-shaped) spatial pattern (Figure 6b) that consists of the prefectures of Drama (2), Xanthi (5), Thessaloniki (6), Imathia (7), Kilkis (8), Pella (9), Serres (11), Kastoria (15), Ioannina (17), at northern Greece, the prefectures of Karditsa (22), Trikala (24), and Viotia (26), at central Greece, and Chania (51), at the island of Crete. The second one includes a single prefecture, Kozani (13), which is located in north Greece (Figure 5 and Figure 6) and thus it configures a point or dot (“•”-shaped) spatial pattern (Figure 6c) at the central Macedonia region. As is evident, this community consists of the single element with the most unstable connectivity, as denoted by the shifting captured by the intersection consideration of communities between the 1%, 5%, and 10% significance levels. The third cluster configures a linear spatial pattern (Figure 6d) consisting of the prefectures of Grevena (14), Florina (16), at north Greece, Arta (18) and Evrytania (28), at central Greece, and the prefecture of Arkadia (37), at the region of Peloponnesus.

_{1}, p

_{2}, p

_{3}) that were generated configure diverse but also distinguishable spatial patterns in the geographical map of Figure 6. Within this framework of complexity, to further decompose and examine the multiplex information included in these modularity groups, we illustrate the line-plots of the overnight stay time-series per modularity group, as is shown in Figure 7. As it can be observed, modularity groups (0,0,1) and cluster (2,2,0) include seasonality patterns with more discrete periodical patterns, whereas groups (1,1,0) and (1,1,1) are described as more noisy patterns. In particular, the first modularity group (0,0,1), which corresponds to the “S”-shaped spatial pattern of Figure 6, includes cases that are described by a periodic pattern with an increasing trend. The case included in the second modularity group (1,1,0), which refers to the “dot”-shaped spatial pattern of Figure 6, is described by a noisy seasonal pattern with a decreasing trend, where linearity is more obvious in the time-series curve than periodicity. Also, the third modularity group (1,1,1), which corresponds to the "I"-shaped spatial pattern of Figure 6, includes cases that are described by a noisy periodical pattern that configures (on average) a bell-shaped curve with its convex part defined at the period 2003–2012. Finally, the fourth (and larger) modularity group (2,2,0), which corresponds to the “O”-shaped spatial pattern of Figure 6, includes cases that are described by an approximately cyclical pattern (i.e., a periodical with the same oscillation amplitude) that configures (on average) a U-shaped curve with its concave part also defined at the period 2003–2012. Overall, this analysis allows claiming that the four available modularity groups have, along with their spatial patterns, distinguishable seasonality patterns, on average.

#### 3.4. Socio-Economic Determination of the Modularity Seasonal Groups

## 4. Further Analysis and Overall Assessment

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

TOURISM SEASONALITY | |||||||
---|---|---|---|---|---|---|---|

A. CONCEPTUALIZATION | B. MODELING | C. IMPLEMENTATION | |||||

A1. Definition | A2. Space of Embedding | B1. Variable Complexity | B2. Data | B3. Attribute/Aspect | B4. Models | B5. Approach | C1. Geographical Scale |

1. Tourism Demand [1,6,9,12,14,15,16,18,20,21,22,28,30,31,32,34,37,38,39,40,41,44,45], | 1. Socioeconomic [1,9,12,15,16,17,18,20,21,22,28,32,37,41,42,44,45] | 1. Uni-variable (one attribute) [6,14,32,33,40,41,43,45] | 1. Visitors [6,22,30,31,34,39] | 1. Concentration [9,39,40,42,45] | 1. Indicators [6,12,14,18,19,22,28,29,30,31,32,34,35,36,38,39,40,41,45] | 1. Single discipline [6,12,13,14,29,30,31,32,35,38,39,40,41,42,43] | |

2. Multivariable (many attributes) [9,15,16,20,21,22,28,31,34] | 2. Arrivals [13,15,16,32,33,34,41,42] | 2. Synergy [5] | 2. Measures/metrics [9,14,21,31,36,40,43] | 2.Multidisciplinary [9,15,16,17,21,22,28,33,34,44,45] | |||

3. Overnight-stays [9,12,14,17,18,29,31,32,36,38,40,45] | 3. Traditions [4,6,12,18] | 3. Econometric [15,17,21,22,44,45] | |||||

4. Income [16]. | 4. Tourism-capacity [42]. | ||||||

5. Occupancy [12,16,17,18,31,35] | 5. Competitiveness [22,23,32,42] | ||||||

6. Number of trips [28] | 6. Attractiveness [19,29,44] | ||||||

7. Staff [31,45] | 7. Economic structure/configuration [15,44,45] | ||||||

8. Prices [31] | 8. Type of tourism product [6,12,17,18,28,30,31,32,35,37,39,40,44] | ||||||

2. Time References [1,6,9,11,12,15,17,20,27,29,33,35,40,43,45] | 2. Temporal (time dimension) [9,13,18,29,33,34,35,38,39,41] | 1. Uni-variable [13,29,35,38,39] | 9. Daily [34,35,41] | 9. Scale [4,5,17,27,36,40] | 4. Measures/metrics [5,13,35,36] | 1. Single discipline [6,12,29,35,38,39,40,43] | |

10. Weekly [34,41,43] | 10.Variability [4,5,15,28,35,36,41] | 5. Time-series [9,13,29,33,35] | |||||

11. Monthly [6,9,12,13,15,16,18,28,29,30,31,33,35,38,39,40,41,43,45] | 11. Periodicity [4,13,15,35,36,38,40,41] | 6. TALC [42,49]. | 2. Multidisciplinary [9,15,17,33,45] | ||||

12.Annual [14,19,21,35,40] | 12. Cyclical performance [9,14,29,35,36] | 7. Pattern recognition [9,29,35,36,38,39] | |||||

3. Spatial References [6,9,12,14,15,16,17,18,19,20,21,25,28,30,32,34,37,39,40,42,43] | 3. Geography (spatial dimension) [9,12,14,15,16,17,18,19,29,31,34,40,42,43] | 1. Uni-variable (one destination) [12,18,30,32,33,34,35,41] | 13. Location [12,13,18,21,30,32,34,35,41,42] | 13. Geographical scale [6,9,19,28,42,44] | 7. Pattern recognition [12,14,18,42] | 1. Single discipline [6,12,14,29,30,39,40,42,43] | 1. Local [13,17,21,30,32,34,35,41,42] |

2. Multivariable (many destinations) [6,9,14,15,16,17,19,28,34,36,38,39,40,43,44,45] | 14. Destination [9,14,15,16,17,22,28,31,33,34,36,38,41,42,43,44,45] | 14. Geomorphology [9,16] | 8. Classification [6,9,14,19,22,44] | 2. Multidisciplinary [9,15,16,17,21,28,34] | 2.Urban [34] | ||

3. Climate [15,16],19] | 3. Regional [6,9,16,19,28,29,31,34,38,39,40,43,44,45] | ||||||

15. Accessibility [16,30] | 4. National [12,18,22,33,34,40] | ||||||

5. International [14,15,36] |

**Table A2.**The seasonality variables participating in the analysis and correspond to the 51 Greek prefectures.

Prefecture | Variable Code | Prefecture | Variable Code | Prefecture | Variable Code | Prefecture | Variable Code |
---|---|---|---|---|---|---|---|

ACHAIA | 34 | EVROS | 3 | KEFALONIA | 32 | PIERIA | 10 |

AITOLOAKARNANIA | 35 | EVRYTANIA | 28 | KERKYRA | 30 | PREVEZA | 20 |

ARGOLIDA | 38 | FLORINA | 16 | KILKIS | 8 | RETHYMNO | 50 |

ARKADIA | 37 | FOKIDA | 29 | KORINTHIA | 39 | RODOPI | 1 |

ARTA | 18 | FTHIOTIDA | 25 | KOZANI | 13 | SAMOS | 44 |

ATTIKI | 42 | GREVENA | 14 | LAKONIA | 40 | SERRES | 11 |

CHALKIDIKI | 12 | HELEIA | 36 | LARISSA | 21 | THESPOTIA | 19 |

CHANIA | 51 | HERAKLION | 48 | LASITHI | 49 | THESSALONIKI | 6 |

CHIOS | 45 | HMATHIA | 7 | LEFKADA | 33 | TRIKALA | 24 |

CYCLADES | 46 | IOANNINA | 17 | LESVOS | 43 | VIOTIA | 26 |

DODECANESE | 47 | KARDITSA | 22 | MAGNESIA | 23 | XANTHI | 5 |

DRAMA | 2 | KASTORIA | 15 | MESSENIA | 41 | ZAKEENTHOS | 31 |

EVIA | 27 | KAVALA | 4 | PELLA | 9 |

**Table A3.**The socio-economic and geographical (SEG) variables * used to determine the socio-economic profiles of the modularity groups of Figure 5.

Code | Variable’s Symbol | Description | Source |
---|---|---|---|

SEG.1 | LAT | Latitude, defined by the geographical center of the prefecture. | [63] |

SEG.2 | LONG | Longitude, defined by the geographical center of the prefecture. | [63] |

SEG.3 | RSI | The Relative Seasonality Index of the prefectures’ seasonality patterns | [9] |

SEG.4 | GINI | The Gini coefficient of the prefectures’ seasonality patterns | [9] |

SEG.5 | ROAD DENSITY | The road density (road length/area) of each prefecture (measured in km/km^{2}). | [64,65,66,67,68] |

SEG.6 | ROAD LENGTH | The road length of each prefecture (measured in km). | [64,65,66,67,68] |

SEG.7 | COASTAL | Dummy variable capturing coastal configuration (1 = coastal perfectures; 0 = non-coastal perfectures). | [67] |

SEG.8 | ISLAND | Dummy variable capturing island configuration. | [59] |

SEG.9 | INLAND | Dummy variable capturing inland configuration. | [59] |

SEG.10 | RAIL | The length of the rail network. | [48,68] |

SEG.11 | PORTS | The number of ports. | [59,68] |

SEG.12 | AIRPORTS | The number of airports. | [59,68] |

SEG.13 | AREA | The geographical area (measured in km^{2}). | [59] |

SEG.14 | POP | The regional population (2011 national census). | [65] |

SEG.15 | URB | The urbanization level (i.e., the proportion of the capital city’s population to the regional population). | [65] |

SEG.16 | GDP | Gross Domestic Product. | [65] |

SEG.17 | Human Capital | Defined by the proportion of labor-force (i.e., population between 18 and 65 years old) to the total population. | [1] |

SEG.18 | A_{SEC} | The specialization (% of the GDP) in the primary (A) sector. | [66,69] |

SEG.19 | B_{SEC} | The specialization (% of the GDP) in the secondary (B) sector. | [66,69] |

SEG.20 | C_{SEC} | The specialization (% of the GDP) in the tertiary (C) sector. | [66,69] |

SEG.21 | TOURISM GDP | The specialization (% of the GDP) in the tourism sector. | [66] |

SEG.22 | TILLING LAND | The proportion of the tilling-land areas to the total regional area. | [67] |

SEG.23 | FORESTS | The proportion of forest-areas to the total regional area. | [67] |

SEG.24 | INLAND WATERS | The proportion of the inland-water-areas to the total regional area. | [67] |

SEG.25 | INDUSTRIAL AREA | The proportion of the industrial-areas to the total regional area. | [67,69] |

SEG.26 | LAND AREA | The proportion of (non-mountainous) land-areas to the total regional area. | [67] |

SEG.27 | SEMI MOUNTAIN AREA | The proportion of the semi-mountain areas to the total regional area. | [67] |

SEG.28 | MOUNTAIN AREA | The proportion of the mountain-areas to the total regional area. | [67] |

SEG.29 | MOUNT ACTIVITIES | The number of mountain-activities (e.g., walking paths, mount sports, climb fields). | [67] |

SEG.30 | CLIMB FIELDS | The number of climb-fields. | [67] |

SEG.31 | MOUNT ROUTES | The number of mountain-routes. | [67] |

SEG.32 | RAFTING POINTS | The number of rafting-points. | [67] |

SEG.33 | CANYONING POINTS | The number of canyoning-points. | [67] |

SEG.34 | SKI CENTERS | The number ski-centers. | [67] |

SEG.35 | SKI ROUTES LENGTH | The length of the ski-routes (measured in km). | [67] |

SEG.36 | RESTAURANTS | The number of restaurants. | [67] |

SEG.37 | NATURA AREA | The geographical area of the Natura parks (i.e., environmentally protected areas). | [67] |

SEG.38 | WOODLANDS PARKS | The number of woodland-parks. | [67] |

SEG.39 | HOTELS | The number of hotels. | [66] |

SEG.40 | CAMPING | The number of camping sites. | [66] |

SEG.41 | BLUE FLAG | The number of beaches that are granted a blue flag. | [66] |

SEG.42 | BEACHES | The number of organized beaches. | [66] |

SEG.43 | ANC MONUMENTS | The number of ancient monument sites. | [66] |

SEG.44 | UNESCO MONUMENTS | The number of UNESCO monument sites. | [66] |

SEG.45 | HOTEL BEDS | The number of hotel beds (bed capacity). | [66] |

SEG.46 | ROOMS | The number of rooms to let (non-hotel accommodation). | [66] |

SEG.47 | ROOMS BEDS | The number of rooms’ beds (non-hotel accommodation capacity). | [66] |

SEG.48 | ACCOMMODATION BEDS | The number of other types of accommodation beds. | [66] |

SEG.49 | CULTURAL RESOURCES | The number of cultural-resources sites. | [67] |

SEG.50 | BEACHES LENGTH | The length of beaches. | [67] |

SEG.51 | SAND BEACHES LENGTH | The length of sand beaches. | [67] |

**Table A4.**“Min-max” table showing the minimum and maximum performance of the modularity-groups for the available socio-economic and geographical (SEG) attributes.

MODULARITY GROUPS | |||||
---|---|---|---|---|---|

Variable Code | Variable Name | (0,0,1) | (1,1,0) | (1,1,1) | (2,2,0) |

GEOGRAPHIC | |||||

SEG1 | LAT | MAX | MAX | MIN | |

SEG2 | LONG | MAX | MIN | MAX | |

SEG3 | COASTAL | MIN | MIN | MAX | |

SEG4 | ISLAND | MIN | MIN | MAX | |

SEG5 | INLAND | MIN | MAX | ||

SEG6 | AREA | MIN | MAX | MIN | |

SEG7 | TILLING LAND | MAX | MIN | MIN | |

SEG8 | FORESTS | MAX | MIN | ||

SEG9 | INLAND WATERS | MIN | MAX | MIN | |

SEG10 | LAND AREA | MAX | MIN | ||

SEG11 | SEMI MOUNTAIN AREA | MIN | MAX | MIN | MIN |

SEG12 | MOUNTAIN AREA | MAX | MIN | ||

SEASONALITY | |||||

SEG13 | RSI | MIN | MIN | MAX | |

SEG14 | GINI | MIN | MIN | MIN | MAX |

TRANSPORT | |||||

SEG15 | ROAD DENSITY | MIN | MAX | ||

SEG16 | ROAD LENGTH | ||||

SEG17 | RAIL | MAX | MIN | ||

SEG18 | PORTS | MIN | MIN | MIN | MAX |

SEG19 | AIRPORTS | MAX | MIN | ||

DEMOGRAPHIC | |||||

SEG20 | POP | MAX | MIN | ||

SEG21 | URB | MAX | MIN | MAX | MAX |

SEG22 | HUMAN CAPITAL | MAX | MIN | ||

PRODUCTIVITY | |||||

SEG23 | GDP | MAX | MIN | ||

SEG24 | A_{SEC} | MIN | MAX | ||

SEG25 | B_{SEC} | MIN | MAX | MIN | MIN |

SEG26 | C_{SEC} | MIN | MAX | MAX | |

SEG27 | TOURISM GDP | MAX | MIN | ||

SEG28 | INDUSTRIAL AREA | MIN | MAX | MIN | MIN |

TOURISM | |||||

SEG29 | HOTELS | MIN | MIN | MAX | |

SEG30 | HOTEL BEDS | MIN | MIN | MAX | |

SEG31 | ROOMS | MIN | MIN | MAX | |

SEG32 | ROOMS BEDS | MIN | MIN | MAX | |

SEG33 | ACCOMMODATION BEDS | MIN | MIN | MAX | |

SEG34 | CAMPING | MIN | MIN | MIN | MAX |

SEG35 | RESTAURANTS | MIN | MIN | MAX | |

SEG36 | MOUNT ACTIVITIES | MAX | MIN | MIN | |

SEG37 | CLIMB FIELDS | MAX | MIN | MAX | MAX |

SEG38 | MOUNT ROUTES | MAX | MIN | MIN | |

SEG39 | RAFTING POINTS | ||||

SEG40 | CANYONING POINTS | MIN | MAX | ||

SEG41 | SKI CENTERS | MAX | MIN | MAX | MIN |

SEG42 | SKI ROUTES LENGTH | MAX | MIN | ||

ENVIRONMENTAL | |||||

SEG43 | NATURA AREA | MAX | MIN | MAX | MAX |

SEG44 | WOODLANDS PARKS | MAX | MIN | ||

SEG45 | BLUE FLAG BEACHES | MIN | MIN | MAX | |

SEG46 | BEACHES | MIN | MIN | MIN | MAX |

SEG47 | BEACHES LENGTH | MIN | MIN | MIN | MAX |

SEG48 | SAND BEACHES LENGTH | MIN | MIN | MIN | MAX |

CULTURAL | |||||

SEG49 | ANC MONUMENTS | MIN | MAX | ||

SEG50 | UNESCO MONUMENTS | MIN | MIN | MAX | |

SEG51 | CULTURAL RESOURCES | MIN | MAX |

## References

- Polyzos, S. Regional Development; Kritiki: Athens, Greece, 2019; ISBN 9789602187302. [Google Scholar]
- Mastronardi, L.; Cavallo, A. The Spatial Dimension of Income Inequality: An Analysis at Municipal Level. Sustainability
**2020**, 12, 1622. [Google Scholar] [CrossRef] [Green Version] - Vo, D.H.; Nguyen, T.C.; Tran, N.P.; Vo, A.T. What Factors Affect Income Inequality and Economic Growth in Middle-Income Countries? J. Risk Financial Manag.
**2019**, 12, 40. [Google Scholar] [CrossRef] [Green Version] - Charles-Edwards, E.; Bell, M. Seasonal Flux in Australia’s Population Geography: Linking Space and Time. Popul. Space Place
**2013**, 21, 103–123. [Google Scholar] [CrossRef] - Romão, J.; Saito, H. A spatial analysis on the determinants of tourism performance in Japanese Prefectures. Asia-Pac. J. Reg. Sci.
**2017**, 1, 243–264. [Google Scholar] [CrossRef] [Green Version] - Batista e Silva, F.; Kavalov, B.; Lavalle, C. Socio-Economic Regional Microscope Series—Territorial Patterns of Tourism Inten-Sity and Seasonality in the EU; Publications Office of the European Union: Luxembourg, 2019. [Google Scholar] [CrossRef]
- Ulbrich, P.; de Albuquerque, J.P.; Coaffee, J. The Impact of Urban Inequalities on Monitoring Progress towards the Sustainable Development Goals: Methodological Considerations. ISPRS Int. J. Geo-Inf.
**2018**, 8, 6. [Google Scholar] [CrossRef] [Green Version] - Băndoi, A.; Jianu, E.; Enescu, M.; Axinte, G.; Tudor, S.; Firoiu, D. The Relationship between Development of Tourism, Quality of Life and Sustainable Performance in EU Countries. Sustainability
**2020**, 12, 1628. [Google Scholar] [CrossRef] [Green Version] - Tsiotas, D.; Krabokoukis, T.; Polyzos, S. Detecting Interregional patterns in tourism-seasonality of Greece: A principal components analysis approach. Reg. Sci. Inq.
**2020**, 12, 91–112. [Google Scholar] - Krabokoukis, T.; Polyzos, S. An Investigation of Factors Determining the Tourism Attractiveness of Greece’s Prefectures. J. Knowl. Econ.
**2020**. [Google Scholar] [CrossRef] - Saarinen, J.; Rogerson, C.M.; Hall, C.M. Geographies of tourism development and planning. Tour. Geogr.
**2017**, 19, 307–317. [Google Scholar] [CrossRef] - Butler, R.W. Seasonality in tourism: Issues and implications. In Tourism: The State of the Art; Seaton, A., Ed.; Wiley: Chichester, UK, 1994; ISBN 978-0471950929. [Google Scholar]
- Gil-Alana, L.A. International Arrivals in the Canary Islands: Persistence, Long Memory, Seasonality and other Implicit Dynamics. Tour. Econ.
**2010**, 16, 287–302. [Google Scholar] [CrossRef] - Ferrante, M.; Magno, G.L.L.; De Cantis, S. Measuring tourism seasonality across European countries. Tour. Manag.
**2018**, 68, 220–235. [Google Scholar] [CrossRef] - Duro, J.A.; Turrión-Prats, J. Tourism seasonality worldwide. Tour. Manag. Perspect.
**2019**, 31, 38–53. [Google Scholar] [CrossRef] [Green Version] - Sæþórsdóttir, A.D.; Hall, C.M.; Stefánsson, Þ. Senses by Seasons: Tourists’ Perceptions Depending on Seasonality in Popular Nature Destinations in Iceland. Sustainability
**2019**, 11, 3059. [Google Scholar] [CrossRef] [Green Version] - Corluka, G.; Mikinac, K.; Milenkovska, A. Classification of tourist season in coastal tourism. UTMS J. Econ.
**2016**, 7, 71–83. [Google Scholar] - Butler, R.W. Seasonality in Tourism: Issues and Implication. In Seasonality in Tourism; Baum, T., Lundtorp, S., Eds.; Elsevier Ltd.: Oxford, UK, 2001; ISBN 9780080436746. [Google Scholar]
- Fang, Y.; Yin, J. National Assessment of Climate Resources for Tourism Seasonality in China Using the Tourism Climate Index. Atmosphere
**2015**, 6, 183–194. [Google Scholar] [CrossRef] [Green Version] - De Almeida, A.L.; Kastenholz, E. Towards a Theoretical Model of Seasonal Tourist Consumption Behaviour. Tour. Plan. Dev.
**2018**, 16, 533–555. [Google Scholar] [CrossRef] - Choe, Y.; Kim, H.; Joun, H.-J. Differences in Tourist Behaviors across the Seasons: The Case of Northern Indiana. Sustainability
**2019**, 11, 4351. [Google Scholar] [CrossRef] [Green Version] - Liu, Y.; Li, Y.; Parkpian, P. Inbound tourism in Thailand: Market form and scale differentiation in ASEAN source countries. Tour. Manag.
**2018**, 64, 22–36. [Google Scholar] [CrossRef] - Gómez-Vega, M.; Picazo-Tadeo, A.J. Ranking world tourist destinations with a composite indicator of competitiveness: To weigh or not to weigh? Tour. Manag.
**2019**, 72, 281–291. [Google Scholar] [CrossRef] - Niavis, S.; Tsiotas, D. Decomposing the price of the cruise product into tourism and transport attributes: Evidence from the Mediterranean market. Tour. Manag.
**2018**, 67, 98–110. [Google Scholar] [CrossRef] - Tsiotas, D.; Niavis, S.; Sdrolias, L. Operational and geographical dynamics of ports in the topology of cruise networks: The case of Mediterranean. J. Transp. Geogr.
**2018**, 72, 23–35. [Google Scholar] [CrossRef] - Niavis, S.; Tsiotas, D. Assessing the tourism performance of the Mediterranean coastal destinations: A combined efficiency and effectiveness approach. J. Destin. Mark. Manag.
**2019**, 14, 100379. [Google Scholar] [CrossRef] - Romão, J.; Guerreiro, J.; Rodrigues, P.M.M. Territory and Sustainable Tourism Development: A Space-Time Analysis on European Regions. Region
**2017**, 4, 1–17. [Google Scholar] [CrossRef] [Green Version] - Fernández-Morales, A.; Cisneros-Martínez, J.D.; McCabe, S. Seasonal concentration of tourism demand: Decomposition analysis and marketing implications. Tour. Manag.
**2016**, 56, 172–190. [Google Scholar] [CrossRef] - Cuccia, T.; Rizzo, I. Tourism seasonality in cultural destinations: Empirical evidence from Sicily. Tour. Manag.
**2011**, 32, 589–595. [Google Scholar] [CrossRef] - Lundtorp, S.; Rassing, C.R.; Wanhill, S. The off-Season is ‘No Season’: The Case of the Danish Island of Bornholm. Tour. Econ.
**1999**, 5, 49–68. [Google Scholar] [CrossRef] - Martín, J.M.M.; Fernández, J.A.S. Comprehensive evaluation of the tourism seasonality using a synthetic DP2 indicator. Tour. Geogr.
**2019**, 21, 284–305. [Google Scholar] [CrossRef] - Andriotis, K. Seasonality in Crete: Problem or a Way of Life? Tour. Econ.
**2005**, 11, 207–224. [Google Scholar] [CrossRef] - Assaf, A.G.; Barros, C.P.; Gil-Alana, L.A. Persistence in the Short- and Long-Term Tourist Arrivals to Australia. J. Travel Res.
**2010**, 50, 213–229. [Google Scholar] [CrossRef] - Þórhallsdóttir, G.; Ólafsson, R. A method to analyse seasonality in the distribution of tourists in Iceland. J. Outdoor Recreat. Tour.
**2017**, 19, 17–24. [Google Scholar] [CrossRef] - De Cantis, S.; Ferrante, M.; Vaccina, F. Seasonal Pattern and Amplitude—A Logical Framework to Analyse Seasonality in Tourism: An Application to Bed Occupancy in Sicilian Hotels. Tour. Econ.
**2011**, 17, 655–675. [Google Scholar] [CrossRef] - Magno, G.L.L.; Ferrante, M.; De Cantis, S. A new index for measuring seasonality: A transportation cost approach. Math. Soc. Sci.
**2017**, 88, 55–65. [Google Scholar] [CrossRef] - Koenig-Lewis, N.; Bischoff, E.E. Seasonality research: The state of the art. Int. J. Tour. Res.
**2005**, 7, 201–219. [Google Scholar] [CrossRef] - Fernández-Morales, A. Decomposing seasonal concentration. Ann. Tour. Res.
**2003**, 30, 942–956. [Google Scholar] [CrossRef] - Cisneros-Martínez, J.D.; Fernández-Morales, A. Cultural tourism as tourist segment for reducing seasonality in a coastal area: The case study of Andalusia. Curr. Issues Tour.
**2013**, 18, 765–784. [Google Scholar] [CrossRef] - Duro, J.A. Seasonality of hotel demand in the main Spanish provinces: Measurements and decomposition exercises. Tour. Manag.
**2016**, 52, 52–63. [Google Scholar] [CrossRef] [Green Version] - Rosselló, J.; Sansó, A. Yearly, monthly and weekly seasonality of tourism demand: A decomposition analysis. Tour. Manag.
**2017**, 60, 379–389. [Google Scholar] [CrossRef] - Terkenli, T.S. Human Activity in Landscape Seasonality: The Case of Tourism in Crete. Landsc. Res.
**2005**, 30, 221–239. [Google Scholar] [CrossRef] - Ahas, R.; Aasa, A.; Mark, Ü.; Pae, T.; Kull, A. Seasonal tourism spaces in Estonia: Case study with mobile positioning data. Tour. Manag.
**2007**, 28, 898–910. [Google Scholar] [CrossRef] - Connell, J.; Page, S.J.; Meyer, D. Visitor attractions and events: Responding to seasonality. Tour. Manag.
**2015**, 46, 283–298. [Google Scholar] [CrossRef] [Green Version] - Cisneros-Martínez, J.D.; McCabe, S.; Morales, A.F. The contribution of social tourism to sustainable tourism: A case study of seasonally adjusted programmes in Spain. J. Sustain. Tour.
**2018**, 26, 85–107. [Google Scholar] [CrossRef] - World Bank. World Development Indicators: Travel and Tourism. 2020. Available online: http://wdi.worldbank.org/table/6.14 (accessed on 18 December 2020).
- INSETE. The Contribution of Tourism to the Greek Economy. 2020. Available online: https://insete.gr/bi/ (accessed on 18 December 2020).
- Tsiotas, D. The imprint of tourism on the topology of maritime networks: Evidence from Greece. Anatolia
**2016**, 28, 52–68. [Google Scholar] [CrossRef] - Polyzos, S.; Tsiotas, D.; Kantlis, A. Determining the Tourism Developmental Dynamics of the Greek Regions, by using TALC Theory, Tourismos: An International Multidisciplinary. J. Tour.
**2013**, 8, 159–178. [Google Scholar] - Kalantzi, O.; Tsiotas, D.; Polyzos, S. The contribution of tourism in national economies: Evidence of Greece. EJBSS
**2016**, 5, 41–64. [Google Scholar] - Barabási, A.-L. Network science. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci.
**2013**, 371, 20120375. [Google Scholar] [CrossRef] [PubMed] - Newman, M.E.J. Networks: An Introduction; Oxford University Press: Oxford, UK, 2010. [Google Scholar]
- Boccaletti, S.; Bianconi, G.; Criado, R.; del Genio, C.; Gómez-Gardeñes, J.; Romance, M.; Sendiña-Nadal, I.; Wang, Z.; Zanin, M. The structure and dynamics of multilayer networks. Phys. Rep.
**2014**, 544, 1–122. [Google Scholar] [CrossRef] [Green Version] - Tsiotas, D. Detecting different topologies immanent in scale-free networks with the same degree distribution. Proc. Natl. Acad. Sci. USA
**2019**, 116, 6701–6706. [Google Scholar] [CrossRef] [Green Version] - Baggio, R.; Valeri, M. Network science and sustainable performance of family businesses in tourism. J. Fam. Bus. Manag.
**2020**. [Google Scholar] [CrossRef] - Valeri, M.; Baggio, R. Italian tourism intermediaries: A social network analysis exploration. Curr. Issues Tour.
**2020**, 1–14. [Google Scholar] [CrossRef] - Valeri, M.; Baggio, R. Social network analysis: Organizational implications in tourism management. Int. J. Organ. Anal.
**2020**. [Google Scholar] [CrossRef] - Tsiotas, D.; Tselios, V. Understanding the uneven spread of COVID-19 in the context of the global interconnected economy. arXiv
**2021**, arXiv:2101.11036. [Google Scholar] - Hellenic Statistical Authority—ELSTAT 2019a. Number of Monthly Overnight-Stays in the Greek Prefectures for the Period 1998–2018. Available online: www.statistics.gr (accessed on 18 December 2020).
- Walpole, R.E.; Myers, R.H.; Myers, S.L.; Ye, K. Probability & Statistics for Engineers & Scientists, 9th ed.; Prentice Hall Publications: New York, NY, USA, 2012; ISBN 9780321629111. [Google Scholar]
- Fortunato, S. Community detection in graphs. Phys. Rep.
**2010**, 486, 75–174. [Google Scholar] [CrossRef] [Green Version] - Blondel, V.D.; Guillaume, J.-L.; Lambiotte, R.; Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp.
**2008**, 2008, P10008. [Google Scholar] [CrossRef] [Green Version] - Google Maps. Google Mapping Services. 2020. Available online: www.google.gr/maps?hl=el (accessed on 18 December 2020).
- Tsiotas, D. Links between network topology and socio-economic framework of railway transport: Evidence from Greece. J. Eng. Sci. Technol. Rev.
**2017**, 10, 175–187. [Google Scholar] [CrossRef] - Hellenic Statistical Authority—ELSTAT. Population and Social Conditions. 2019. Available online: https://www.statistics.gr/el/statistics/pop (accessed on 3 June 2020).
- Hellenic Statistical Authority—ELSTAT. Economy, Indices. 2019. Available online: https://www.statistics.gr/el/statistics/eco (accessed on 3 June 2020).
- Hellenic Statistical Authority—ELSTAT. Environment and Energy. 2019. Available online: https://www.statistics.gr/el/statistics/env (accessed on 3 June 2020).
- Polyzos, S.; Tsiotas, D. The contribution of transport infrastructures to the economic and regional development. Theor. Empir. Res. Urban Manag.
**2020**, 15, 5–23. [Google Scholar] - Polyzos, S.; Tsiotas, D. Measuring structural changes of the Greek economy during the period of economic crisis. Manag. Res. Pract.
**2020**, 12, 5–24. [Google Scholar]

**Figure 2.**Line-plots showing the time-series patterns of the available 51 seasonality-variables, which are shown in (

**left**) metric and (

**right**) semi-log scale. Each case (seasonality-variable) expresses a seasonality pattern of tourism overnight-stays (shown at the y-axis) that corresponds to one of the 51 prefectures in Greece. Seasonality-variables are of length 252, i.e., including 252 monthly records from January 1998 to December 2018. The available data were granted upon request by the Hellenic Statistical Authority [59] to be used under an exclusive license, for this study.

**Figure 3.**Het-maps showing the results of the correlation analysis applied to the available seasonality-variables (see Table A2 in the Appendix A), where case (

**a**) shows correlations of sig. = 0.01, (

**b**) of sig. = 0.05, and (

**c**) of sig. = 0.10. At these correlation tables, only significant correlations are shown in color. (

**d**) Distinguishable areas (A

_{1}, A

_{2}, and A

_{3}) of correlated variables in the correlation tables.

**Figure 4.**Maps showing the geographical location of the (

**a**) A

_{1}, (

**b**) A

_{2}, and (

**c**) A

_{3}areas, which are observed in the correlation analysis of Figure 3.

**Figure 5.**Geographical distribution and tabulation of the seasonality patterns that correspond to the Greek prefectures and are produced by the modularity classification for (

**a**) 1%, (

**b**) 5%, and (

**c**) 10% levels of significance. Case (

**d**) shows the aggregate patterns including the unique triplets of the (

**a**), (

**b**), and (

**c**) modularity classes. Dashed lines illustrate shapes of spatial patterns.

**Figure 6.**Maps showing the geographical distribution of the available 51 seasonality patterns that are grouped (according to the modularity classification shown in Figure 5) to the modularity (triplet-) groups, as follows: (

**a**) aggregate, including all 4 available groups, (

**b**) (0,0,1), of an “S”-shaped spatial pattern, (

**c**) (1,1,0), of a “dot”-shaped spatial pattern, (

**d**) (1,1,1), of an “I”-shaped spatial pattern, and (

**e**) (2,2,0), of an “O”-shaped spatial pattern.

**Figure 7.**Line plots with the time-series of the available 51 seasonality patterns that are grouped (according to the modularity classification shown in Figure 6) to the triplet-groups: (

**a**) (0,0,1) of the “S”-shaped spatial pattern, (

**b**) (1,1,0) of the “dot”-shaped spatial pattern, (

**c**) (1,1,1) of the “I”-shaped spatial pattern, and (

**d**) (2,2,0) of the “O”-shaped spatial pattern.

**Figure 8.**Error-bar plots representing 95% confidence intervals (95%CIs) for the mean values of (

**a**) the Gini coefficient and (

**b**) the RSI (Relative Seasonal Index).

**Figure 9.**Maps showing the geographical distribution of the Principal Components Analysis max-filtering groups (PCM) that are computed in the paper of [9], where (

**a**) is the aggregate group, including all 5 available PCM groups, (

**b**) is the PCM#1 group of an “O”-shaped spatial pattern, (

**c**) is the PCM#2 group of a “U”-shaped spatial pattern, (

**d**) is the PCM#3 group of an “I”-shaped spatial pattern, (

**e**) is the PCM#4 group of a “dot”-shaped spatial pattern, and (

**f**) is the PCM#5 group of a “dot”-shaped spatial pattern.

**Figure 10.**Maps showing the geographical distribution of the Principal Components Analysis min-filtering groups (PCm) that are computed in the paper of [9], where (

**a**) is the aggregate group, including all 7 available PCm groups, (

**b**) is the PCm#1 groups of a “dot”-shaped spatial pattern, (

**c**) is the PCA#2 group of an “O”-shaped spatial pattern, (

**d**) and (

**e**) are the PCA#3 and PCA#4 groups of a “U”-shaped spatial patterns, (

**f**) is the PCA#5 group of an “S”-shaped spatial pattern, and (

**g**) and (

**h**) are the PCA#6 and PCA#7 groups of an “I”-shaped spatial patterns.

**Table 1.**The semiology of the modularity groups (see Figure 5) resulted from the analysis.

Modularity Groups (Size) | Socio-Economic and Geographical Semiology | |
---|---|---|

MAX ^{(a)} | MIN ^{(b)} | |

Group (0,0,1)(13 prefectures) | Northern and eastern location; urbanization; specialization in winter tourism activities; environmental wealth. | Area; seasonality; ports; camping; beaches. |

Group (1,1,0)(1 prefecture) | Northern and west location; rich geomorphological configuration; mainland geomorphology; rich rail and airport configuration; high secondary sector specialization; high mountainous activities. | Coastal or island area; seasonality; poor road density and roads; low primary and tertiary sector specialization; low tourism profile; low environmental wealth, low cultural resources profile. |

Group (1,1,1)(5 prefectures) | Mainland geomorphology; urbanization; high tertiary sector specialization; high environmental wealth. | Coastal or island area; poor geomorphological configuration; seasonality; poor ports and airports configuration; population and human capital; low income; low secondary sector specialization; low tourism profile; low beach environment; low cultural resources profile. |

Group (2,2,0)(32 prefectures) | Southern and eastern location; coastal or island area, high seasonality; rich road density and ports configuration; high urbanization; high primary and tertiary sector specialization; high tourism profile; high environmental quality; high capacity of cultural resources. | Poor geomorphological configuration; poor rail configuration; low secondary sector specialization; low mountainous activities. |

^{(a)}as defined by the max values of Table A4 (see Appendix A).

^{(b)}as defined by the min values of Table A4 (see Appendix A).

**Table 2.**Multi-block comparative table showing the intersection results $PC\#i\cap MOD\#j$ (window: Intersection Frequencies) between i-th PCA and j-th modularity groups, with j = (2,2,0), (0,0,1), (1,1,1), or (1,1,0), their percentage (%) relevance to the PCA/MOD groups (window: Relevance to PCA/MOD Groups), and a metric test (window: Differences in Relevance) implying which (PCA/MOD) group is dominant for each intersection.

Modularity Group | |||||||||||||||||||

Group Size | |||||||||||||||||||

32 | 13 | 5 | 1 | 32 | 13 | 5 | 1 | 32 | 13 | 5 | 1 | 32 | 13 | 5 | 1 | ||||

Group Label | |||||||||||||||||||

(2,2,0) | (0,0,1) | (1,1,1) | (1,1,0) | (2,2,0) | (0,0,1) | (1,1,1) | (1,1,0) | (2,2,0) | (0,0,1) | (1,1,1) | (1,1,0) | (2,2,0) | (0,0,1) | (1,1,1) | (1,1,0) | ||||

Common Cases Between MOD and PCA Groups (intersection) | |||||||||||||||||||

Group Size | Group Label | ◂Relevance to PCA Groups | Intersection Frequencies | ▴Relevance to MOD Groups | Differences in Relevance (MOD-PCA) | ||||||||||||||

PCA Group | #1 (max filtering) | 38 | PCM#1 | 84.2% | 13.2% | 2.6% | 32 | 5 | 1 | 100% | 38.5% | 20.0% | 15.8% | 25.3% | 17.4% | ||||

7 | PCM#2 | 100% | 7 | 53.8% | −46.2% | ||||||||||||||

4 | PCM#3 | 100% | 4 | 80.0% | −20.0% | ||||||||||||||

1 | PCM#4 | 100% | 1 | 100% | |||||||||||||||

1 | PCM#5 | 100% | 1 | 7.7% | −92.3% | ||||||||||||||

#2 (min filtering) | 1 | PCM#1 | 100% | 1 | 7.7% | −92.3% | |||||||||||||

7 | PCM#2 | 85.7% | 14.3% | 6 | 1 | 18.8% | 20.0% | −67.0% | 5.7% | ||||||||||

13 | PCM#3 | 69.2% | 30.8% | 9 | 4 | 28.1% | 30.8% | −41.1% | |||||||||||

14 | PCM#4 | 7.1% | 14.3% | 14.3% | 1 | 2 | 2 | 3.1% | 15.4% | 40.0% | −4.0% | 1.1% | 25.7% | ||||||

8 | PCM#5 | 50.0% | 37.5% | 12.5% | 4 | 3 | 1 | 12.5% | 23.1% | 100% | −37.5% | −14.4% | 87.5% | ||||||

5 | PCM#6 | 20.0% | 40.0% | 40.0% | 1 | 2 | 2 | 3.1% | 15.4% | 40.0% | −16.9% | −24.6% | |||||||

3 | PCM#7 | 66.7% | 33.3% | 2 | 1 | 6.3% | 7.7% | −60.4% | −25.6% | ||||||||||

Lagend | 0% | 0–20% | 20–40% | 40–60% | 60–80% | ≥80%% | PCA < 0 | MOD > 0 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Tsiotas, D.; Krabokoukis, T.; Polyzos, S.
Detecting Tourism Typologies of Regional Destinations Based on Their Spatio-Temporal and Socioeconomic Performance: A Correlation-Based Complex Network Approach for the Case of Greece. *Tour. Hosp.* **2021**, *2*, 113-139.
https://doi.org/10.3390/tourhosp2010007

**AMA Style**

Tsiotas D, Krabokoukis T, Polyzos S.
Detecting Tourism Typologies of Regional Destinations Based on Their Spatio-Temporal and Socioeconomic Performance: A Correlation-Based Complex Network Approach for the Case of Greece. *Tourism and Hospitality*. 2021; 2(1):113-139.
https://doi.org/10.3390/tourhosp2010007

**Chicago/Turabian Style**

Tsiotas, Dimitrios, Thomas Krabokoukis, and Serafeim Polyzos.
2021. "Detecting Tourism Typologies of Regional Destinations Based on Their Spatio-Temporal and Socioeconomic Performance: A Correlation-Based Complex Network Approach for the Case of Greece" *Tourism and Hospitality* 2, no. 1: 113-139.
https://doi.org/10.3390/tourhosp2010007