Published on 01 January 2004 |
International Passenger Survey, 1999
View DatasetDescription
The <i>International Passenger Survey</i> (IPS) aims to collect data on both credits and debits for the travel account of the Balance of Payments, provide detailed visit information on overseas visitors to the United Kingdom (UK) for tourism policy, and collect data on international migration.<br> There are two versions of the IPS data for 1999, the reduced version used for fuller queries but restricted in the number of variables and the main data for expert users which contain all analysable variables.<br>Reduced dataset - the depositor recommends using this dataset for fuller queries as it contains most of the important analysable information and, due to their nature, will be much easier to understand and tabulate. Although the variable list is the same from year to year, care must be taken with these files when trying to perform time series operations as codes can also vary from year to year for some variables.<br>Main dataset - the depositor recommends that only expert users who are very familiar with the coding and weighting structures use this dataset as limited support is available. Some considerable understanding of the data is required before meaningful analyses can be made, care must be taken when performing time series operations as codes can vary from year to year and not all variables from one years dataset are used in other years. The data covers four subject areas, AIRMILES (held as a complete year) ALCOHOL, QREGTOWN and QCONTACT (held quarterly in four files per subject area). These can be joined together using the variables YEAR, SERIAL, FLOW and QUARTER. The weighting of IPS data is complex and done in several stages. When working with the system weights, great care should be taken to read the documentation concerning weighting procedures as not all records are treated in exactly the same way (this does not apply to the smaller dataset).<br><br><br>
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Publication Details
Subfield
Artificial Intelligence
Field
Computer Science
Domain
Physical Sciences
Confidence Score
37%
Source
Scholar Data Model