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This article reports a study that applies the rough sets algorithm to tourism demand analysis. Empirical outcomes are a set of automated but practical decision rules for practitioners from data that have a high degree of vagueness. We also introduce two new measures of qualitative noneconomic factors, namely a leisure time index and climate index into the forecasting framework. On the basis of long-haul U.S. and U.K. tourism demand for Hong Kong, empirical results show that leisure time and climate have stronger impacts on tourist arrivals than economic factors. Comprehensible decision rules are generated and tourism demand forecasts attain an accuracy of up to 80%. The findings put forward the importance of qualitative non- economic factors in travel motivation theory and demand analysis.

a b s t r a c tThis study investigates the spatial associations of urban tourism phenomena by using GIS and statistical methods to examine the relationships between hotels and land use types, attractions, transportation facilities, and the economic variables of the tertiary planning units in which the hotels are located. Hong Kong is used as an example. The study first introduces the spatial characteristics of hotels and attractions development in Hong Kong. A geographical information system is then used to map hotels and investigate the characteristics of the land use, attractions, and transport facilities around hotels. The spatial relationships are then analyzed with a set of logistic regression models. The results reveal that commercial land type and the number of attractions around hotels are significantly related to the distribution of upper-grade hotels in Hong Kong. The determinants vary over time and the spatial structure changes accordingly. The analysis is important theoretically as it enriches the methodologies for analyzing the relationships between hotels and urban structure, and for conceptualizing and identifying tourism functional zones. It is important for practitioners as it provides useful information for selecting sites for hotels.