Modelling spatial tourism and hospitality employment clusters using geographical information systems

Chhetri, A 2014, Modelling spatial tourism and hospitality employment clusters using geographical information systems, Doctor of Philosophy (PhD), Mathematical and Geospatial Sciences, RMIT University.


Document type: Thesis
Collection: Theses

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Title Modelling spatial tourism and hospitality employment clusters using geographical information systems
Author(s) Chhetri, A
Year 2014
Abstract Tourism is considered a mechanism for regional development with the potential to increase economic growth in regional and remote areas. Tourism however tends to spatially cluster to areas of resource advantage (i.e. tourism attractions) or of strategic importance such as transportation hubs. This is often referred to as the ‘agglomeration effect’, where related firms locate near one another to take advantage of pooled market and pooled resource supply allied to reduced service costs. An integrated cluster based approach is arguably well suited to assist regional development policies to gear towards achieving the geostrategic dispersal of tourism across regional and remote areas. The advocacy of cluster theory as a policy tool for tourism development however requires a thorough examination before it can be transmuted into public policy.

Drawing on cluster theory as the theoretical base, this thesis develops a spatial framework to examine the geographic clustering of tourism and hospitality employment in Victoria, Australia. It aims to answer four interrelated research questions: i) what industries represent the tourism and hospitality sector and what are its key components; ii) where are the tourism and hospitality-related employment clusters in Victoria; iii) what are the location-specific factors that drive the clustering of tourism and hospitality employment and iv) what strategic geo-targeted cluster-based policy can be formulated to improve evidence to guide destination planning?

Using the 4-digit Australian and New Zealand Standard Industrial Classification (ANZSIC) data, the sub-industries that ‘explicitly’ relate to tourism and hospitality were first individually identified and then the numbers of people working in these industries were aggregated. Results show that total employment in the tourism and hospitality sector in Australia for 2006 is 7.74 per cent. ‘Cafes and Restaurants’ (22.34%) are the largest employer of labour force, followed closely by ‘Takeaway Food Services’ (20.53%) and ‘Accommodation’ (16 %).

Using principal component analysis, four key ‘components’ that define and characterise the underlying structure of the tourism and hospitality industry are identified. These include ‘Tourism Operational Services’; ‘Hospitality Services’; ‘Entertainment Services’; and ‘Infrastructure Operational Facilities Services’. The highly correlated component analysis indicates the functional interdependence of inter-related industries within the sector. The results show that the ‘tourism operational services’ are more widely distributed across the state, whilst ‘hospitality services’ are more concentrated in Melbourne and other regional cities/towns. The Melbourne central business district emerged as a hub for the ‘entertainment services’; however there are also other pockets in regional areas that offer such services. Employment in ‘infrastructure operational facility services’ is largely segregated around airports and transit hubs to support tourist movements. There is evidence of a high concentration of employment along the coast and a few isolated pockets particularly along the Great Ocean Road, Phillip Island and areas around the alpine region.

The Local Indicator of Spatial Autocorrelation (LISA) technique was employed to identify five established spatial tourism and hospitality clusters in Victoria, consisting of a total of 28 Statistical Local Areas. Spatial econometric techniques were drawn on to identify location-specific factors that stimulate clustering of tourism and hospitality employment. Five contextual factors of geographic space that positively and significantly impact on T&H employment clustering were identified in Victoria. These include: the tourism potential index, proximity to coast, density of road network, gross regional product and index of economic resources. These variables collectively explained about 55 percent of variability in T&H employment. The tests indicated the improved fit for the added variable (i.e. spatially lagged dependent variable) and confirmed the significance of spatial autoregressive coefficient, suggesting the better fit of the spatial lag model over ordinary least square or spatial error model. The significant effect of the spatial lag variable also suggests the prevalence of a ‘spill-over effect’, meaning a higher concentration of T&H employment in an area exerts positive externalities on its neighbouring areas. These spatial clusters could potentially act as growth foci to create synergy and foster spill-over effects through sharing of resources between inter-related and interdependent firms operating supply chains within the tourism sector.

The adoption of an integrated cluster-based spatial planning framework was shown to have the potential to permit tourism-led economic growth to be better spatially dispersed across Victoria. It is anticipated that through further investment in these employment clusters, the quality of tourism service can be improved, the tourism destinations can be better connected, the labour supply and tourism infrastructure can be better utilised and shared. The benefits associated with economies of scale and agglomeration will strengthen the competitive advantage and strategic positioning of Victoria as a leading international tourist destination. However, the successful implementation of clustering as a policy would require a stronger government stimulus to ignite the synergy towards creating vibrant tourism and hospitality employment clusters of global significance.
Degree Doctor of Philosophy (PhD)
Institution RMIT University
School, Department or Centre Mathematical and Geospatial Sciences
Keyword(s) Tourism and hospitality sector
Cluster theory
Geographical information system (GIS)
Spatial autocorrelation
Spatial econometric models
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