Analysis of the effect of weather determinants on lodging demands using big data processing

Weather conditions determine the variation in the floating population at tourist sites and, consequently, the number of persons who stay overnight; therefore, it is necessary to select those determinants that are most influenced by the weather. Our aim is to construct a multiple linear regression model and system based on big data processing tools to process large volumes of meteorological and population data with the ultimate goal of supporting predicted accommodation congestion with high prediction accuracy. First, we transform the floating population data into accommodation congestion by estimating the resident population during periods of high demand. Second, we conduct a multiple linear regression analysis with variable removal steps and a residual analysis. Moreover, to verify our regression model, we use two prediction accuracy measures: the mean absolute percentage error and the R-squared value. The final part of the paper describes the construction of a big data processing system that was built to compute the series of prediction operations.