• The Korean Journal of Community Living Science
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• v.19 no.4
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• pp.697-706
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• 2008

Generally, tourism environments are classified as macro or micro environments. Hotel revenues are influenced directly and indirectly by macro and micro environments. This study aims to examine the determinants of the change in macro-environments on hotel revenue. The explanatory variables in this research were wage, exchange rates (ER), consumer price index (CPI) and seasonality. The results of this study were as follows; The CPI and ER significantly affected the hotel food and beverage sales. The seasonality (summer and autumn) and wage significantly affected the hotel room sales. Wage and seasonality (summer and autumn) also significantly affected the total sale on the hotels.

• Korean Journal of Biological Psychiatry
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• v.17 no.3
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• pp.153-160
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• 2010

Objectives : Recurrence of mood episodes associated with a specific season has been described in various mood disorders. Seasonal change in mood and behavior as a lifetime trait is also observed in healthy individuals. This study aimed at comparing the lifetime trait of seasonal variations of mood and behavior between bipolar disorder patients and controls as well as investigating associated factors of seasonality. Methods : Subjects were ninety-four clinically stable patients with bipolar disorder and 188 age- and sex-matched healthy controls. Seasonality of mood and behavior was assessed retrospectively on lifetime basis using Seasonal Pattern Assessment Questionnaire(SPAQ). Results : The patient group showed a higher median global seasonality score(GSS) of SPAQ and a higher rate of seasonal affective disorder(SAD) compared to the control group(p < 0.0001). For subjects showing prominent seasonality, the seasonal symptom profile and seasonal pattern was similar in both patient and control groups. In addition to the diagnosis, female gender was shown to be a predictor of seasonality in the multiple linear regression analysis(p = 0.045). Conclusion : This study suggests that lifetime trait of seasonality may be related to the susceptibility of bipolar disorder.

• Ocean and Polar Research
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• v.23 no.4
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• pp.433-440
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• 2001

Marine organisms in Antarctica live in an environment which exhibits variability in physical processes over a wide range of temporal scales, from seconds to millennia. This time scale tends to be correlated with the spatial scale over which a given process operates, though this relationship is influenced by biology. The way organisms respond to variability in the physical environment depends on the time-scale of that variability in relation to life-span. Short-term variations are perceived largely as noise and probably have little direct impact on ecology. Of much greater importance to organisms in Antarctica are seasonal and decadal variations. Although seasonality has long been recognised as a key feature of polar environments, the realization that decadal scale variability is important is relatively recent. Long-term change has always been a feature of polar environments and may be a key factor in the evolution of the communities we see today.

• Korean Journal of Environmental Biology
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• v.37 no.3
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• pp.317-334
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• 2019

The present study was conducted to investigate the differences in managed farming practices, including low-intensive farming, duck farming, and golden apple snail farming, in a rice ecosystem by comparing terrestrial arthropod communities. A total of 75 species from 70 genera belonging to 43 families in 11 orders were identified from 9,622 collected arthropods. Araneae, Hemiptera, and Coleoptera were the richest taxa. Collembola was the most abundant, followed by Diptera, Hemiptera, and Araneae. Bray-Curtis similarity among the farming practices was very high (76.7%). The biodiversity of each farming practice showed a similar seasonality pattern. The richest species group was the predators, followed by the herbivores. The species richness and diversity of ecologically functional groups among the farming practices were not statistically significant, except for the abundance of predators in golden apple snail farming. The biodiversity seasonality of ecological functional groups in each farming practice showed similar patterns. The biomass of Araneae, Hemiptera, Coleoptera, and Diptera was greater than the other taxa, in general. The biomass of each ecological functional group showed little difference and the biomass fluctuation patterns in each farming practice were almost the same. Collectively, the community structures and biodiversity of terrestrial arthropods among the farming practices in the present study were not different. The present study may contribute to sustain rich biodiversity in irrigated rice fields and to advanced studies of food webs or energy flow structures in rice fields for ecological and sustainable agriculture.

• Journal of Korean Society of Transportation
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• v.29 no.5
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• pp.139-155
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• 2011

This study is to estimate the dynamic change of the regional railway passenger traffic and, based on the estimated, to forecast the future regional railway passenger traffic by using the Seasonal ARIMA model. The existing studies using ARIMA failed to consider seasonality nor the monthly or the quarterly data. It was attempted in this study to use the monthly regional railway passenger traffic data to propose a model that estimates dynamic change of demand. The authors employed the Seasonal ARIMA model previously developed and used (1) the numbers of monthly passenger data and (2) the monthly passenger-km data. The test results showed that the numbers of passengers in 2015 and 2020 would increase by 36% and 71%, respectively, compared to those in 2008. The numbers of passenger-kms in 2015 and 2020 would increase by 25% and 78%, respectively, compared to those in 2008.

• The Journal of Economics, Marketing and Management
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• v.9 no.2
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• pp.1-10
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• 2021

Purpose: In this work, we examine several time series models to predict internet and mobile transaction amount in South Korea, whereas Jeong (2020) has obtained the optimal forecasts for online shopping transaction amount by using time series models. Additionally, optimal forecasts based on the model considered can be calculated and applied to the Corona 19 situation. Research design, data, and methodology: The data are extracted from the online shopping trend survey of the National Statistical Office, and homogeneous and comparable in size based on 46 realizations sampled from January 2007 to October 2020. To achieve the goal of this work, both multiplicative ARIMA model and Holt-Winters Multiplicative seasonality method are taken into account. In addition, goodness-of-fit measures are used as crucial tools of the appropriate construction of forecasting model. Results: All of the optimal forecasts for the next 12 months for two online shopping transactions maintain a pattern in which the slope increases linearly and steadily with a fixed seasonal change that has been subjected to seasonal fluctuations. Conclusions: It can be confirmed that the mobile shopping transactions is much larger than the internet shopping transactions for the increase in trend and seasonality in the future.

• ALGAE
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• v.20 no.4
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• pp.325-331
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• 2005

Populations of Campylaephora borealis (Nakamura) Seo, Cho et Boo and C. crassa (Okamura) Nakamura show a year-around occurrence of all life-history stages. Such a concurrency of life-history stages produces problems in recognizing species in the field. Here, we invesitgated the morphological variation and life-history stages of both species using a statistical character analysis. Life-history stage was correlated with the seawater temperature in C. borealis, whereas it was dependant on biomass in C. crassa. Thalli had dichotomous branches with adaxial branchlets. The statistics showed that the seasonal change in morphology of C. borealis was significantly different from that of C. crassa in seven qualitative characters and five quantitative characters (p < 0.001), although six quantitative features including tetrasporangial size were similar in both species. The morphological difference between the two species may be due to the annual variation of branchlet number and the variance of branch subangle.

• Journal of Preventive Medicine and Public Health
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• v.43 no.5
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• pp.436-444
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• 2010

Objectives: Infectious diseases are known to be affected by climate change. We investigated if the infectious diseases were related to meteorological factors in Korea. Methods: Scrub typhus, hemorrhagic fever with renal syndrome (HFRS), leptospirosis, malaria and Vibrio vulnificus sepsis among the National Notifiable Infectious Diseases were selected as the climate change-related infectious diseases. Temperature, relative humidity and precipitation were used as meteorological factors. The study period was from 2001 through 2008. We examined the seasonality of the diseases and those correlations with meteorological factors. We also analyzed the correlations between the incidences of the diseases during the outbreak periods and monthly meteorological factors in the hyper-endemic regions. Results: All of the investigated diseases showed strong seasonality; malaria and V. vulnificus sepsis were prevalent in summer and scrub typhus, HFRS and leptospirosis were prevalent in the autumn. There were significant correlations between the monthly numbers of cases and all the meteorological factors for malaria and V. vulnificus sepsis, but there were no correlation for the other diseases. However, the incidence of scrub typhus in hyper-endemic region during the outbreak period was positively correlated with temperature and humidity during the summer. The incidences of HFRS and leptospirosis had positive correlations with precipitation in November and temperature and humidity in February, respectively. V. vulnificus sepsis showed positive correlations with precipitation in April/May/July. Conclusions: In Korea, the incidences of the infectious diseases were correlated with meteorological factors, and this implies that the incidences could be influenced by climate change.

• Environmental and Resource Economics Review
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• v.29 no.4
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• pp.419-442
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• 2020

The majority of the natural gas demand in South Korea is mainly determined by the heating demand. Accordingly, there is a distinct seasonality in which the gas demand increases in winter and decreases in summer. Moreover, the degree of sensitiveness to temperature on gas demand has changed over time. This study firstly introduces changing temperature response function (TRF) to capture effects of changing seasonality. The temperature effect (TE), estimated by integrating temperature response function with daily temperature density, represents for the amount of gas demand change due to variation of temperature distribution. Also, this study presents an innovative way in forecasting daily temperature density by employing functional principal component analysis based on daily max/min temperature forecasts for the five big cities in Korea. The forecast errors of the temperature density and gas demand are decreased by 50% and 80% respectively if we use the proposed forecasted density rather than the average daily temperature density.

• Journal of Cadastre & Land InformatiX
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• v.47 no.2
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• pp.19-37
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• 2017

Visible Infrared Imaging Radiometer Suite Day-Night Band (VIIRS-DNB) data provides a much higher capability for observing and quantifying nighttime light (NTL) brightness in comparison with Defense Meteorological Satellite-Operational Linescan System (DMSP-OLS) data. In South Korea, there is little research on the detection of NTL brightness change using VIIRS-DNB data. This study analyzed the spatial distribution and change of NTL brightness between 2013 and 2016 using VIIRS-DNB data, and detected its spatial relation with possible influencing factors using regression models. The intra-year seasonality of NTL brightness in 2016 was also studied by analyzing the deviation and change clusters, as well as the influencing factors. Results are as follows: 1) The higher value of NTL brightness in 2013 and 2016 is concentrated in Seoul and its surrounding cities, which positively correlated with population density and residential areas, economic land use, and other factors; 2) There is a decreasing trend of NTL brightness from 2013 to 2016, which is obvious in Seoul, with the change of population density and area of industrial buildings as the main influencing factors; 3) Areas in Seoul, and some surrounding areas have high deviation of the intra-year NTL brightness, and 71% of the total areas have their highest NTL brightness in January, February, October, November and December; and 4) Change of NTL brightness between summer and winter demonstrated a significantly positive relation with snow cover area change, and a slightly and significantly negative relation with albedo change.