Comparer des méthodes

	Gravity Model of Tourist Flows	Tourism Demand Forecasting
Domaine	Tourism Hospitality	Tourism Hospitality
Famille	Regression model	Regression model
Année d'origine	2014	2008
Auteur d'origine	Clive Morley; Jaume Rossello; Maria Santana-Gallego	Haiyan Song; Gang Li; Stephen F. Witt
Type	Spatial-interaction / bilateral-flow regression model	Predictive time-series and econometric demand models
Source fondatrice	Morley, C., Rossello, J., & Santana-Gallego, M. (2014). Gravity models for tourism demand: theory and use. Annals of Tourism Research, 48, 1-10.	Song, H., & Li, G. (2008). Tourism demand modelling and forecasting - A review of recent research. Tourism Management, 29(2), 203-220.
Alias	Tourism Gravity Equation, Bilateral Tourist Flow Model, Gravity Model of Tourism Demand, Spatial Interaction Model of Tourism	Tourist Arrivals Forecasting, SARIMA Tourism Forecasting, Tourism Demand Modelling and Forecasting, Econometric Tourism Forecasting
Apparentées	4	4
Résumé	The gravity model of tourist flows explains travel between an origin and a destination by analogy to Newton's law of gravitation: bilateral flows increase with the economic 'mass' of both the origin and the destination and decrease with the distance and cost of travel between them. Borrowed from international trade, the model has become a standard tool for analyzing the structural determinants of international tourism, capturing how population, income, distance, common language, shared borders, and historical or cultural ties shape who travels where. Clive Morley, Jaume Rossello, and Maria Santana-Gallego's 2014 Annals of Tourism Research paper grounded the tourism gravity equation in individual utility theory, while the broader trade literature — notably Anderson and van Wincoop's 'multilateral resistance' insight — showed how to specify and estimate it without bias.	Tourism demand forecasting predicts future tourist arrivals, overnight stays, or expenditure from historical data, supporting planning by destinations, airlines, hotels, and policymakers. The field spans two broad model families. Time-series models such as seasonal ARIMA (SARIMA) extrapolate the patterns embedded in the demand series itself — trend, seasonality, and autocorrelation — without explanatory variables. Econometric models such as autoregressive distributed lag models (ADLM) and error-correction models relate demand to drivers like income, relative prices, and exchange rates, allowing both forecasting and policy analysis. Haiyan Song and Gang Li's influential 2008 review in Tourism Management synthesized this literature, documenting the proliferation of methods since 2000 and emphasizing rigorous out-of-sample evaluation. Their work, with Stephen Witt, helped make tourism demand forecasting a methodologically mature subfield.