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Objectives: Main objective of this study was to estimate crematory facility demand of Suwon city, using Seasonal ARIMA Model. This study aims to estimate the demand based on the number of currently operating crematory facilities, dividing into inward district and outward district of Suwon city. Methods: As the construction of crematory facilities is greatly in need of supply along with the increasing funerals, it requests more accurate estimation of the demand in specific area. This study employed Seasonal ARIMA Model which is useful to deal with time series data with small size and various patterns, instead of the Gompertz curve and logistic curve frequently used in the past. This study did analyses in four steps, discrimination of stationarity (balance for average and dispersion), identification of functions (ACF, PACF), model diagnostic (estimation of parameter), and decision (forecasting of crematory facilities). Results: First, the demand of cremation in inward district of Suwon was estimated to be 4,051 persons in 2017 and to be 5,129 persons in 2022 using the ARIMA (0,1,1)(1,1,0)S = 12 Model, which is to be increased 31.8 percent points compared to that of 2016. Second, the demand of cremation in outward district of Suwon was estimated to be 6,731 persons in 2017 and 7,060 persons in 2022 using the ARIMA (0,0,3)(1,1,1)S = 12 Model. Altogether the users of crematory facilities were estimated to be 10,782 persons in 2017 and 12,189 persons in 2022, which shows increasing trend of 3 to 4 percent points every year. Conclusions: This study proved that Seasonal ARIMA Model is a proper tools to estimate the crematory facility demand in specific area. Demand and supply of crematory facilities should be estimated based on reliable statistics and data. Suwon city should provide more facilities to meet the increasing need of cremation. For this, Suwon city should input more investment for improving of quality of facilities and coordination of the number.

Estimation of Crematory Facility Demand in Suwon City Using Seasonal ARIMA Model