A regional analysis of tourist flows within Europe

Jansen‐Verbeke, Myriam; Spee, R.J.A.P.

doi:10.1016/0261-5177(95)93433-w

Cited by 46 publications

(23 citation statements)

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“…Asymmetrical movements of inter-regional tourist flows have been witnessed in Europe (e.g. Williams and Zelinsky 1970;Høivik and Heiberg 1980;Pearce 1987b;Jansen-Verbeke 1995), France (e.g. Pearce 1987a;Limtanakool 2007), Germany (e.g.…”

Section: Discussionmentioning

confidence: 99%

A Reformulated Directional Bias of Tourist Flow

Zhong

Zhang

2011

Tourism Geographies

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Conventional directional bias is unable to identify the interactions and relationships between emitted tourist flows and attracted tourist flows, while arguably contradicting the actual emitting/attracting functions for any region, especially in systematic analysis. A newly coined FTR (Functional Tourism Region) based on a regional perspective and a new directional bias derived from conceptual, geographical and technical contexts are proposed. This paper proposes that the new directional bias is defined in terms of two characteristics. The first is the pointing of tourist flow as well as its pointing types, and the second is the inertial state of pointing. Pointing types are classified into three basic types: city-pointing, seasidepointing and sun-lust-pointing. A case study of Chinese rural residents' domestic overnight travel pattern demonstrates that the new definition of directional bias may yield useful qualitative and quantitative information. Results indicate that city-pointing and seaside-pointing constitute the main pattern, while sun-lust-pointing increasingly contributes to the evolution of the whole tourist flow system. Increasing regional disparity is also detected in that China's eastern economic belt continuously enjoys tourism profits. Finally, it is postulated that the new directional bias of tourist flow can also be applied to other non-work flows that also have origins and destinations.

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Section: Discussionmentioning

confidence: 99%

A Reformulated Directional Bias of Tourist Flow

Zhong

Zhang

2011

Tourism Geographies

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“…These indicators were used for the calculation of percentages in the research so far (Lundberg, 1974;de Albuquerque, McElroy, 1992;Harrison, 1992;Štoković, Kolić, 1994;Verbeke, Spee, 1995;McElroy de Albuquerque, 1998;Medlik, 2003;Matzler et al, 2005;Castellani et al, 2007;Castellani, Sala, 2010;Dumbrovská, Fialová, 2014;Mikulić et al, 2014;2016) and they were used to calculate, determine and rank the following for tourist intensity in destinations: the tourist intensity rate (TIR), the tourist penetration rate (TPR), the tourism density rate (TDR) which calculates tourism density according to the total number of tourist arrivals and overnight stays, and coefficient of tourist functionality (CTF).…”

Section: U Rječniku Turizmamentioning

confidence: 99%

“…Osim ovoga pokazatelja u analizi intenziteta i opterećenosti destinacije turizmom upotrebljavali su se brojni drugi pokazatelji poput složenih indikatora održivosti turizma (Blancas i dr., 2010.;Torres-Delgado, López Palomeque, 2014. ), stope turističkog intenziteta, stope turističke penetracije, gustoće i koncentracije (Lundberg, 1974.;de Albuquerque, McElroy, 1992.;Harrison, 1992.;Verbeke, Spee, 1995.;McElroy, de Albuquerque, 1998.;Matzler i dr., 2005. Castellani i dr., 2007Castellani, Sala, 2010.…”

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“…jebljeni su za izračunavanje stopa kojima se u dosadašnjim istraživanjima (Lundberg, 1974.;de Albuquerque, McElroy, 1992.;Harrison, 1992.;Štoković, Kolić, 1994.;Verbeke, Spee, 1995.;McElroy, de Albuquerque, 1998.;Medlik, 2003;Matzler i dr., 2005.;Castellani i dr., 2007;Castellani, Sala, 2010.;Dumbrovská, Fialová, 2014.;Mikulić i dr, 2014.;2016. ) procjenjivao, određivao i rangirao intenzitet turizma u destinacijama, a to su: stopa turističkog intenziteta (TIR), stopa turističke penetracije (TPR), stopa turističke gustoće (TDR) i koeficijent turističke funkcionalnosti (CTF).…”

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“…In fact, it was according to Defert's coefficient and rate of tourist functionality that six groups or categories of tourist operative indicators were derived, and these were proposed and used in the ensuing decades as one of the key indicators of spatial and socio-economic tourism identity at various spatial levels (Keogh, 1984;Pearce, 1987;Smith, 1989;Lozato-Giotart, 1992;Potts, Uysal, 1992;Medlik, 1997). Besides this indicator in the analysis of the intensity and encumbrance caused by tourism in a destination, numerous other indicators were used, such as complex indicators of tourism sustainability (Blancas et al, 2010;Torres-Delgado, López Palomeque, 2014), tourist intensity rate, tourist penetration, density and concentration (Lundberg, 1974;de Albuquerque, McElroy, 1992;Harrison, 1992;Verbeke, Spee, 1995;McElroy de Albuquerque, 1998;Matzler et al, 2005;Castellani et al, 2007;Castellani, Sala, 2010) as well as the influence of tourist activity on a locality evaluated via the number of tourist beds in a spatial unit (Dumbrovská, Fialová, 2014).…”

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Intenzitet turizma u vodećim hrvatskim turističkim gradovima i općinama

Vojnović

2018

Geoadria

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Tourist intensity in Croatia’s leading tourist destinations will be analysed here. 25 tourist towns and municipalities were selected, with the criteria that they achieved more than one million overnight stays in 2016. According to geographical distribution there are ten such locations in Istria, six in the Kvarner region, eight in Dalmatia, and one in central Croatia. In 2016, these leading tourist towns and municipalities had 48% of tourist beds for commercial accommodation in their offers, and collectively achieved 57% of all tourist arrivals and 54% of all overnight stays in the Republic of Croatia. In this analysis of tourist intensity the following indicators were used: the total number of tourist beds, the total number of tourist arrivals, the total number of tourist overnight stays, and an approximation of the population number in municipalities and cities at the end of 2016 according to the Croatian Bureau of Statistics, as well as the area of municipalities and cities according to the data of the State Geodetic Administration. The data cited here were used in order to calculate the tourist intensity rate (TIR), the tourist penetration rate (TPR) according to the total number of total tourist overnight says, the tourism density rate (TDR) which calculates tourism density according to the total number of tourist arrivals and overnight stays, and coefficient of tourist functionality (CTF). The results of this analysis derived from the evaluation of every different percentage all showed that the greatest tourist intensity among all the leading towns and municipalities in the Republic of Croatia are in the municipalities of Funtana and Tar-Vabriga on the west coast of Istria, as well as the Municipality of Medulin in the furthest south of Istria. According to these percentages, the smallest intensity is in the largest cities such as Zagreb, Šibenik and Zadar. Field research and an interview with the representative of the tourist board of the Municipality of Funtana showed that tourist intensity did not endanger the sustainability of this municipality, that it strengthened the local economy by encouraging employment, stimulated the preservation of traditions, and that there were no conflicting relationships between tourists and the local population.

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Exploratory Space–time Analysis of Inbound Tourism Flows to China Cities

Xing-zhu

Wang

2013

Journal of Tourism Research

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The integration of space and time generates much closer interactions between geography and other social sciences in general while providing new perspectives for the role of geography in tourism flow studies. This paper aims to apply exploratory space-time analysis to provide insights into inbound tourism flow patterns in China's cities over the period of 2000-2009 using city-level Geographical Information System datasets. First, this paper ascertains that there exists geographic neighborhood effect at city level by testifying significantly positive spatial autocorrelation. The hot spots and temporal stability are identified. The hot spots are locally concentrated in metropolitan areas. Local Markov matrix has also shown significant transitions with negative or positive influence of geographic neighbors upon a city. Specifically, three-dimensional surface is created to visualize tourism flows. The spatial inequalities have the pattern of 'the east more dense than the west, the south more dense than the north'. There simultaneously exist spatial agglomeration and spatial dispersion. The intensity of spatial agglomeration has become stronger. At the same time, smaller peaks of tourism flow centers developed around the main cities and spread toward surrounding cities. These potential tourism flow centers have gradually emerged and grown larger. Furthermore, the space-time covariance matrix uncovers correlation, which shows strong regional integration over years. Eight hundred and ninety links are identified and visualized. Two hundred and sixty nine links forming 10 spatial clusters represent strong positive correlation. One hundred and thirtyfour links are negative correlation, which is about 15.06 per cent of all links. In conclusion, our comprehensive evidences offer deeper insights and have important policy implications. These enable the tourism-oriented governmental agencies, as well as the tourism industry professionals, to better understand the changes of inbound tourism flows in China's cities and relevant tourism partners/competitors for cities.

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A regional analysis of tourist flows within Europe

Cited by 46 publications

References 1 publication

A Reformulated Directional Bias of Tourist Flow

A Reformulated Directional Bias of Tourist Flow

Intenzitet turizma u vodećim hrvatskim turističkim gradovima i općinama

Exploratory Space–time Analysis of Inbound Tourism Flows to China Cities

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