Spatial Autocorrelation Analysis of Tourist Arrivals Using Municipal Data: a Serbian Example

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Spatial Autocorrelation Analysis of Tourist Arrivals Using Municipal Data: a Serbian Example ISSN 0354-8724 (hard copy) | ISSN 1820-7138 (online) Spatial autocorrelation analysis of tourist arrivals using municipal data: A Serbian example Uglješa StankovA*, Tanja ArmenskiA, Michal KlaucoB, Vanja PavlukovićA, Marija CimbaljevićA, Nataša Drakulić-KovačevićC Received: February 17, 2016 | Revised: April 27, 2017 | Accepted: May 28, 2017 DOI: 10.18421/GP21.02-04 Abstract Spatial autocorrelation methodologies can be used to reveal patterns and temporal changes of differ- ent spatial variables, including tourism arrivals. The research adopts a GIS-based approach to spatially analyse tourist arrivals in Serbia, using Global Moran’s I and Anselin’s Local Moran’s I statistics applied on the level of municipalities. To assess feasibility of this approach the article discusses spatial changes of tourist arrivals in order to identify potentially significant trends of interest for tourism development policy in Serbia. There is a significant spatial inequality in the distribution of tourism arrivals in Serbia that is not adequately addressed in tourism development plans. The results of global autocorrelation suggest the existence of low and decreasing spatial clustering for domestic tourist arrivals and high, rel- atively stable spatial clustering for international tourists. Local autocorrelation statistics revealed differ- ent of domestic and international tourism arrivals. In order to assess feasibility of this approach these results are discussed in their significance to tourism development policy in Serbia. Keywords: Spatial autocorrelation, GIS, tourist arrivals, Serbia Introduction Tourist flows have been studied at different state Spatial inequality of tourism development is pro- levels, from micro-level to the - level of a destina- nounced in Serbia. To date, no tourism development tion. For example, Jansen-Verbeke (1995) used inter- strategy has systematically dealt with this issue. Tour- regional and intra-regional analysis of tourist flows ism, as a sector with significant spillover into the rest within Europe to assess particular destinations with- of the economy, can be of paramount importance for in Europe. Hence, in order to carry out an adequate regional development. Serbia, likewise other develop- research in the context in Serbia, the authors needed ing countries, embrace tourism to jumpstart its so- a quantitative technique for detecting and analyzing cio-economic development and to uphold sustainable temporal and spatial patterns across whole country’s regional development. In the more recent studies, spa- territory. The main spatial trends that are to be not- tial statistical techniques were used more extensively ed in tourism context are the clusters of popular and to analyze tourism development inequality and tour- unpopular places. To ensure viability of the approach, ism flow patterns in various countries (see Klepers, the level of clustering should be related to the exist- Rozite, 2009; Chhetri, et al., 2013; Yang, Wong, 2013; ing administrative division of the country. As a solu- Yang, et al., 2013; Kang, et al., 2014; Xing‐zhu, Qun, tion, the authors adopted a GIS approach for spatial 2014). analysis using spatial autocorrelation analysis based A University of Novi Sad, Faculty of Sciences, Department of Geography, Tourism and Hotel Management, Trg Dositeja Obradovića 3, 21000 Novi Sad, Serbia; B Slovak Environment Agency, Tajovského 28, 975 90 Banská Bystrica, Slovakia. C Kovacevic-engineering doo, Nemanjina 35, 26320 Banatski Karlovac, Serbia. * Corresponding author: Uglješa Stankov, e-mail: [email protected] 106 Geographica Pannonica • Volume 21, Issue 2, 106–114 (June 2017) Uglješa Stankov, Tanja Armenski, Michal Klauco, Vanja Pavluković, Marija Cimbaljević, Nataša Drakulić-Kovačević on the data gathered on the municipality level. The first aim was to investigate trends of spatial cluster- Subotica ing of municipalities by the level of tourist arrivals be- N tween 2001 and 2013. The second aim was to indicate VOJVODINA local deviations from global pattern of spatial associ- Novi Sad ation, that is, significant local clusters or outliers and how they have changed over the time. For achieving Belgrade aims of the research Global Moran’s I and Anselin’s BELGRADE Local Moran’s I statistics are applied using Geograph- REGION ic Information System (GIS) software tools. This paper extends the literature by using contem- Kragujevac SOUTH-WEST porary methodological framework within which spa- SERBIA tial changes of tourist arrivals can be studied with re- SOUTH-EAST SERBIA spect to statistically significant global and local spatial Niš associations in the variables. Unlike other similar stud- Tourism clusters ies, we used municipality level data for the analysis of tourist arrivals. Usually, for this kind of tourism stud- Belgrade region Kosovo and Metohija ies the researchers used global and local distribution of South-West Serbia cities or point data and examined their spatial aggre- Vojvodina gation analysis (Yüncü, et al., 2016). However, munic- South-East Serbia 150 ipality level data appears to be a compromise between km data availability, data aggregation level, analytical ca- pabilities and displaying opportunities of the results for Figure 1. Main tourism clusters according to Tourism Development Strategy (2005-2015) and major cities in effective decision making. Comparing to studies that Serbia. employ similar methodology, this paper analyses the topic in the context of a European country as there is Domestic tourist arrivals in Serbia showed a sig- no similar research concerning this region. Further- nificant declining trend in the reported period. Af- more, in order not to miss statistically significant re- ter the significant fall in 2002, domestic tourist arriv- sults in case of local autocorrelation analysis, temporal als suffered another “shock” during global economic statistical tests are presented in the isolation at different crises (Najdić, Sekulović, 2012). Domestic tourist ar- level of significance, together with results of false dis- rivals decreased from 1.88 million in 2001 to 1.27 in covery rate (FDR) corrected results. 2013. On the other hand, international tourist arriv- als have fared better. The significant jump recorded in 2007 was followed by small decline during the glob- Methodology al economic crisis (Čerović, et al., 2015). However, the strong increasing trend of international arrivals has The case area been continued from 2010 (see Figure 2). The 2005 Tourism Development Strategy divided the country into four main spatial tourism clusters: Vo- Data collection and processing jvodina, Belgrade region, South-West Serbia and For the purpose of this research, already aggregat- South-East Serbia (Official Gazette of the Republic of ed data for domestic and international tourist arrivals Serbia, 2006). The suggestions of four tourism clusters were collected for every municipality in Serbia for the were based on the rational strongholds and various period of 13 years (2001-2013). The data is obtained from kinds of economic activities that could be developed Statistical Office of the Republic of Serbia, the only -offi in some parts of the country (Ilić, 2013). Bordering ar- cial and hence trustful provider of statistical data. eas can be considered as parts of two bordering clus- The authors used 160 polygons that represent ter- ters (see Figure 1). There are two main basic markets ritories of municipalities and cities - units of admin- for tourism demand: (1) Belgrade, Novi Sad and oth- istrative division of Serbia without territory of Koso- er cities dominantly visited by international tourists vo and Metohija. GIS data were analyzed using Esri’s (primarily motivated by business reasons) and (2) spas ArcGIS suite version 10.2. and mountains that together make up more than half of the tourist traffic. Spas and mountains are visited Global and local autocorrelation analysis dominantly by domestic tourists (Ministry of Trade, Spatial autocorrelation can be defined “as the corre- Tourism and Telecommunications of the Republic of lation of a variable with itself over space” (Burt, et al., Serbia, 2014). 2009). Spatial autocorrelation is concerned with es- Geographica Pannonica • Volume 21, Issue 2, 106–114 (June 2017) 107 Spatial autocorrelation analysis of tourist arrivals using municipal data: A Serbian example 2500000 2000000 1500000 1000000 Tourist arrivals 500000 0 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Years Domestic tourist arrivals International tourist arrivals Total tourist arrivals Linear (Domestic tourist arrivals) Linear (International tourist arrivals) Linear (Total tourist arrivals) Figure 2. Tourist arrivals in Serbia from 2001 to 2013. tablishing whether the presence of a variable in one random spatial pattern. To evaluate statistical signifi- region makes the presence of that variable in neigh- cance of Moran’s I z-score and p-value were used. The bouring regions more, or less, likely (Thomas, Hug- critical value of z-score is dependable on desired level gett, 1980). Spatial autocorrelation can be positive and of confidence. If standardize percentile of normal dis- negative. If similar values of a variable tend to cluster tribution α is set to 0.1 then z0.05 =1.64 for 90 percent in space, the geographical distribution of that varia- confidence intervals. For 95 percent confidence inter- ble can be described as positively spatially auto-cor- vals when α=0.05 then percentile of normal distribution related. Negative spatial
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