Disaggregating National Tourism Data to Regional Areas

Full text for this resource is not available from the Research Repository.

Whitelaw, Paul A and Jago, Leo Kenneth (2008) Disaggregating National Tourism Data to Regional Areas. Technical Report. CRC for Sustainable Tourism, Queensland.

Abstract

This paper reports on the development and application of a methodology to convert large scale data such as the International Visitor Survey (IVS) and the National Visitor Survey (NVS) into a format that can be used at the local level. A technique was developed whereby the large regional data such as Statistical Local Area (SLA) from Tourism Research Australia (TRA) (IVS and NVS) can be disaggregated to smaller local areas within the region such as an Urban Centre (UC). Whilst the mathematics are relatively straightforward, the process is somewhat complex because it requires detailed local area data (Census) from the ABS as well as the larger area data (IVS and NVS) from the TRA. This data can be enriched by incorporating other local area data such as road traffic counts, retail and accommodation statistics and other local area data that is reliable. The data can also be used to create ‘new’ tourism geographies, such as a string of townships along a highway or coastline. The most significant contribution of this disaggregation technique is that it can bring a wide body of data including all that which is incorporated in the IVS and NVS as well as Census and other geographically based data into a highly focused analysis of a small, local area; an activity which has not been feasible with existing public datasets.

Dimensions Badge

Altmetric Badge

Item type Monograph (Technical Report)
URI https://vuir.vu.edu.au/id/eprint/2173
DOI 9781920965839
Official URL http://www.crctourism.com.au/WMS/Upload/Resources/...
Subjects Historical > Faculty/School/Research Centre/Department > School of Hospitality Tourism and Marketing
Keywords ResPubID: 15177, national tourism data, tourism industry, Australia
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login