The spillover effect of attractions

Zhou, Bo; Yang, Bi; Li, Hengyun; Qu, Hailin

doi:10.5367/te.2016.0541

Cited by 40 publications

(31 citation statements)

References 27 publications

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“…The modelling strategy in these studies is to study the spillover effects by assuming spatial correlation structures based on geographical proximity. (Bo et al, 2017;Deller, 2010;Li et al, 2016;Marcouiller et al, 2004;Yang and Fik, 2014;Yang and Wong, 2012). This is consistent with Tobler's Law which states "everything is related to everything else, but near things are more related than distant things" (Tobler, 1970).…”

Section: Motivationsupporting

confidence: 81%

“…Positive spillovers are known as "spatially complementary", while negative spillovers are known as "spatially competitive". This description is a modification of statements by (Bo et al, 2017;Yang and Fik, 2014;Yang and Wong, 2012). These positive spillovers reflect cross regional collaboration while negative spillovers reflect the cross regional competion.Spillover effects, with respect to tourism, can be defined from a number of perspectives.…”

Section: Spatial Dependence In Tourism (Tourism Spillovers)mentioning

confidence: 98%

“…These spillovers can be either positive or negative. The positive spillovers are known as spatial complementary effects while negative spillovers are known as spatial competitive effects (Bo et al, 2017;Patuelli et al, 2013).…”

Section: List Of Figuresmentioning

confidence: 99%

“…In tourism research, there is a considerable body of literature devoted to investigating the spillovers between geographic neighbours. The modelling strategy in the literature is to study the spillover effects by using spatial correlation structures based on geographical proximity (Bo et al, 2017;Chhetri et al, 2008;Deller, 2010;Li et al, 2016;Marcouiller et al, 2004;Yang and Fik, 2014;Yang and Wong, 2012). The tourism models that acknowledge the spatial spillovers across regions have great importance to regional economies.…”

Section: List Of Figuresmentioning

confidence: 99%

“…These positive spillovers reflect cross regional collaboration while negative spillovers reflect the cross regional competion.Spillover effects, with respect to tourism, can be defined from a number of perspectives. It could be from the tourists' flow spillover, which is the influence or the impact of tourists' flow in one region on the tourism activity in neighboring regions (Yang and Fik, 2014), or tourist attraction spillovers; which are defined as the tourist attractions in one region influencing the tourism in neigbouring regions (Bo et al, 2017). It could also be both; tourists' flow spillover effects and the tourist attractions spillover effects.…”

Section: Spatial Dependence In Tourism (Tourism Spillovers)mentioning

confidence: 99%

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Natural amenities and tourism employment: A spatial analysis

Naranpanawa¹

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This thesis examines the spatial linkages between natural amenities and tourism employment when spatial spillover effects are taken into account under three neighbourhood structures. To the author's knowledge, tourism employment and natural amenity spillovers have not previously been examined in a non-geographic spatial context.To address this research gap and explore the non-geographic spatial spillovers in tourism employment and natural amenities, spatial models are developed with different neighbourhood structures. In addition, the thesis examines: tourism clusters; the measurement of cluster proximity; and characteristics of tourism employment within and across clusters. This thesis developed an empirical model at the local government level for Queensland, Australia. The model incorporates 13 natural amenity variables and four other variables that explain regional tourism employment. The analysis is conducted for 74 local governments in Queensland using cross-sectional data compiled for 2011. Three specifications of a Spatial Durbin Model (SDM) are implemented. The specifications use three alternative weight matrices to reflect three different proximity dimensions or neighbourhood structures. A non-spatial linear model (NSLM) estimated by least squares is used as the base model. The three weight matrices specifications are proportional to: a) geographic contiguity, to capture geographic spillovers; b) the share of employment in the tourism industry; and c) the proportion of tourism employment to total employment. The last two are measures of economic distance.To identify tourism employment clusters, the study uses a K-mean cluster analysis technique. Factor analysis is used to summarise 17 variables selected from a review of theoretical models of tourism employment. Based on computed factor scores, the main cluster attributes are identified.The results suggest that independent of the specification (spatial and non-spatial), internet penetration, regional population, the number of regional parks and state forests and World Heritage areas are statistically significant. This is taken as evidence of their importance as factors that influence tourism employment in a region. Most importantly, spillovers exist; not only between traditional geographic neighbours, but also between neighbours of economic proximity where neighbours are those that have a similar profile in their share of tourism employment.iv When the spatial autocorrelation parameter is negative and significant between geographic neighbours, it reflects competition; however, when it is positive and significant between economic neighbours (regions with similar share of tourism employment) it indicates a collaborative effect between the regions with similar tourism performance. The results further suggest non-geographic proximity can explain tourism employment clusters.Characteristics such as "urbanness", communications capacity, natural attractions, level of agriculture and percentage of indigenous population are shared within clusters.The conclusion is ...

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Section: Motivationsupporting

confidence: 81%

Section: Spatial Dependence In Tourism (Tourism Spillovers)mentioning

confidence: 98%

Section: List Of Figuresmentioning

confidence: 99%

Section: List Of Figuresmentioning

confidence: 99%

Section: Spatial Dependence In Tourism (Tourism Spillovers)mentioning

confidence: 99%

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Natural amenities and tourism employment: A spatial analysis

Naranpanawa¹

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Analysing domestic tourism flows at the provincial level in Spain by using spatial gravity models

Álvarez‐Díaz

d’Hombres

Ghisetti

et al. 2020

Journal of Tourism Research

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Domestic tourism represents a large share of the total tourism volume in Spain, but it is still an under‐researched topic. This study focuses on the determinants of domestic flows in Spain at provincial level. The prior assumption is that domestic tourism demand may be affected by specific local conditions that previous studies, mostly based on more aggregate data, would hardly capture. A gravity model and various spatial econometric models are estimated assuming alternative spatial weighting matrices. Results suggest that income and relative prices affect tourism demand in Spanish provinces as well as weather, natural amenities, infrastructures, and recreational activities.

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