Australian Bureau of Statistics
8635.1.55.001 - Tourist Accommodation, Small Area Data, New South Wales, Mar 2013
Previous ISSUE Released at 11:30 AM (CANBERRA TIME) 28/06/2013
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4 The main source of coverage is from the Australian Automobile Association through AAA Tourism Pty Ltd. Updates are generally undertaken each quarter through information supplied to the ABS by AAA Tourism. Technical difficulties were experienced for the September and December quarters 2011 which resulted in the updates not occurring for these quarters. This has resulted in minimal impact on the STA data.
5 The AAA Tourism updates are supplemented by notification of new tourism developments and their likely opening dates in selected guides, major tourism journals, periodicals and newspapers. Periodic comparison with lists of accommodation establishments provided by the various tourism organisations and industry associations is also undertaken.
6 Data by star grade for states and territories are included in Tourist Accommodation, Small Area Data (cat. no. 8635.0.55.002 for national data and cat. no. 8635.1.55.001 - 8635.8.55.001 for state/territory data).
7 Small area statistics for 2013 are classified to the Australian Statistical Geography Standard (ASGS), 2011 Edition (cat. no. 1270.0.55.001) effective from March quarter 2012.
8 Small area data (SA2) are aggregated to tourism regions as defined by relevant state and territory tourism organisations. Tourism regions are reviewed annually and are subject to boundary and name changes. Where changes have occurred, care should be taken when making comparisons with previously published data at this level.
9 Details of the composition of tourism regions and maps of tourism regions are provided in the ABS publication Tourism Region Maps and Correspondence File (cat. no. 9503.0.55.001) available from the ABS web site <www.abs.gov.au>.
10 The survey does not have a sample component and the data are not subject to sampling variability. However, other inaccuracies collectively referred to as non-sampling error may affect the data. These non-sampling errors may arise from a number of sources, including:
11 Every effort has been made to reduce non-sampling error to a minimum by careful design and testing of questionnaires, and efficient operating procedures and systems used to compile statistics.
12 The quality and reliability of survey data can be affected by the degree of response to a survey however, it is rare to achieve a 100% response rate for any survey. The response rates for the Survey of Tourist Accommodation at state level are shown below.
13 Missing data items are replaced by imputed values based on reported data. Average quarterly movements are applied to previously reported data for each non-responding unit to estimate values for missing data items. Only if previously reported data are not available, will data from a similar unit be used as a 'donor' for the missing data items.
14 The imputation rates for Room nights occupied and Takings from accommodation for the most recent quarters at a national level are shown below.
15 Seasonal adjustment is a means of removing the estimated effects of normal seasonal variation from the original time series so that the effect of other influences on the series may be more clearly recognised. Seasonal adjustment procedures do not aim to remove the irregular or non-seasonal influences which may be present in any particular quarter. Irregular influences that are highly volatile can make it difficult to interpret the movement of the series even after adjustment for seasonal variation, and cannot be assumed to indicate changes in the trend.
16 The seasonally adjusted estimates in this publication have been produced using a concurrent methodology whereby the seasonal factors are revised each quarter to take into account the seasonality exhibited by the latest observation. A more detailed review is conducted annually.
17 From the March quarter 2008, the Survey of Tourist Accommodation collection implemented Autoregressive Integrated Moving Average (ARIMA) modelling techniques for the majority of applicable time series. The revision properties of the seasonally adjusted and trend estimates can be improved by the use of ARIMA modelling. ARIMA modelling relies on the characteristics of the series being analysed to project future period data. The projected values are temporary, intermediate values, that are only used internally to improve the estimation of the seasonal factors. The projected data do not affect the original estimates and are discarded at the end of the seasonal adjustment process.
18 For more information on the details of ARIMA modelling see the feature article 'Use of ARIMA modelling to reduce revisions' in the October 2004 issue of Australian Economic Indicators (cat. no. 1350.0). Any queries regarding the ARIMA modelling should be directed to Time Series Analysis on (02) 6252 6345 or email <email@example.com>.
19 Smoothing the seasonally adjusted series reduces the impact of the irregular component of the seasonally adjusted series and creates the trend estimates. The trend estimates are derived by applying a 7-term Henderson moving average to the quarterly seasonally adjusted series. The Henderson moving average used in the middle of the time series is symmetric but, as the end of a time series is approached, asymmetric forms of the symmetric moving average are applied. Unlike the weights of the symmetric 7-term Henderson moving average, the asymmetric weights have been tailored to suit the particular characteristics of individual series.
20 While these techniques enable trend estimates for the latest period to be produced, the process does result in revisions to the trend estimates in recent quarters, particularly as additional original estimates become available. For further information refer to Information Paper: A Guide to Interpreting Time Series - Monitoring Trends, 2003 (cat. no. 1349.0) available at the ABS web site <www.abs.gov.au>.
CONFIDENTIALISATION OF DATA
21 Under the Census and Statistics Act, when releasing statistics the ABS is required to do this in a manner that is "not likely" (in a legal sense) to enable the identification of a particular person or organisation. A number of techniques are used to do this, including suppression of information. To ensure provider confidentiality in the Survey of Tourist Accommodation, the ABS uses a computerised process known as Disclosure Avoidance Analysis System (DAAS) to confidentialise the entire tourist accommodation dataset each quarter. This process not only ensures that data are suppressed to ensure individual establishments cannot be identified, but also suppresses data in other (consequential) cells to ensure data cannot be derived through deduction from the information available.
USER AGGREGATION OF DATA
22 The aggregation of data by users across time periods should be undertaken with caution, due to the possibility of non-inclusion of confidentialised data (see the above section for more information about confidentialisation). Where one or more cells contributing to a total have been confidentialised (ie, contains the value of n.p.), the resulting aggregated total will be incorrect. However, some broader levels of data may not be affected by confidentialised cells.
23 Where data can be aggregated (ie, no confidentialised cells are included) for calendar and financial year/s purposes, the data items Establishments, Rooms, Persons employed and Bed spaces should not be aggregated. For these items it is recommended that for calendar years, the value of the December quarter is used, and for financial years, the value of the June quarter is used.
24 Any data items that have been derived from other items collected in the survey cannot be aggregated (ie, all those with labels ending in 'rate' or commencing with 'average'). These items must be re-derived based on the aggregation of each of the quarterly items collected in the survey used in the derivation of the rate or average (see Glossary for formulas).
25 Users are cautioned against deriving any non-standard aggregations (eg, aggregation of selected star grading such as 4-star and 5-star; aggregation of selected geographical areas such as capital city areas and balance of state; aggregation of selected activities such as hotels and motels combined). This is because data are confidentialised based on the standard data item structure.
EFFECTS OF ROUNDING
26 Where figures have been rounded, discrepancies may occur between totals and the sum of the component items.
27 Estimates of movement shown in this publication are obtained by taking the difference of unrounded estimates. The movement is then rounded to one decimal place. Therefore where a discrepancy occurs between the reported movement and the difference of the rounded estimates, the reported movement will be more accurate.
28 Other ABS publications and products which may be of interest are outlined below. All publications released from 1998 onwards are available on the ABS web site www.abs.gov.au.
Tourism Region Maps and Correspondence Files, Australia (cat. no. 9503.0.55.001) (annual)
Tourist Accommodation, Australia, Expanded Scope Collection (cat. no. 8635.0.55.001) (irregular)
Short-term Visitor Arrivals Estimates, Australia (cat. no. 3401.0.55.001) (issued monthly)
Overseas Arrivals and Departures, Australia (cat. no. 3401.0) (issued monthly)
Australian National Accounts, Tourism Satellite Account (cat. no. 5249.0) (annual)
29 The catalogue of current publications and other products is available from the ABS web site www.abs.gov.au. The ABS also issues release advice on the web site which detail products to be released both in the coming week and the next six months.
ABS DATA AVAILABLE ON REQUEST
30 As well as the statistics included in this publication, the ABS has other relevant data available on request. Inquiries should be made to the National Information and Referral Service on 1300 135 070.
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This page last updated 25 September 2013