Analyzing and Forecasting Tourism Demand: A Rough Sets Approach

Abstract

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.

The Impact of Climate on Outbound Tourism in Hong Kong: Resident’s Mode of Transport

“…When there is a deviation of temperature from the optimal, there will be impact on tourism demand. In another study, Goh, Law and Mok (2008) measure the impact of leisure time and climate on annual Hong Kong inbound tourism demand, by using both the rough set algorithm approach and econometric analysis consisting of both quantitative economic factors and qualitative non-economic factors. Focusing on the longhaul US and UK demand for Hong Kong tourism, this study showed that leisure time and climate have stronger impacts on tourist arrivals than do economic factors.…”

“…Wind has the lowest weight. Goh et al (2008) and Goh (2012) applied the TCI index to Hong Kong data. They constructed a climate index for Hong Kong, including following climate variables: a daytime comfort index comprising maximum daily temperature and minimum daily relative humidity; a daily comfort index composed of daily temperature and daily relative humidity; and ratings for precipitation in Hong Kong, duration of sunshine, and wind speed.…”

“…The average temperature is measured in degrees Celsius; Humidity is measured in percentage; Duration of sunshine is measured in hours per day; Wind speed is measured in kilometers per hour (Lise and Tol, 2002;Goh et al, 2008;Goh, 2012).…”

Modeling Seasonal Variation in Tourism Flows with Climate Variables

“…Using regression analysis for the estimation procedure, the authors found that climate variables (represented by temperature and precipitation) will have an increasingly strong effect on tourism demand. In another study, Goh, law, and Mok (2008) used both the rough sets algorithms approach and econometric analysis consisting of both quantitative economic factors and qualitative noneconomic factors to measure the impact of leisure time and climate on annual Hong Kong inbound tourism demand. Focusing on long-haul US and UK tourism demand for Hong Kong, their study showed that leisure time and climate have stronger impacts on tourist arrivals than the economic factors.…”

“…To measure the impact of climate variables on tourism, some studies (Goh et al, 2008;Matzarakis, 2001aMatzarakis, , 2001bMieczkowski, 1985;Skinner & De Dear, 2001) have attempted to construct a tourism climate index to capture weather information relevant to specific tourist activities at a particular destination. Key variables considered in these studies are temperature, hours of sunshine, humidity, wind speed, and solar radiation.…”