• Journal of the Korean Geographical Society
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• v.37 no.4
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• pp.462-464
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• 2002

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1531-1538
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• 2014

The causes of cancer are diverse, complex, and only partially understood. Many factors including health behaviors, socioeconomic environments and geographical locations can directly damage genes or combine with existing genetic faults within cells to cause cancerous mutations. Collecting the cancer data and reporting the statistics, therefore, are important to help identify health trends and establish normal health changes in geographical areas. In this article, we analyzed cancer data and demon-strated how spatial patterns of the age-standardized rate and health indicators can be examined visually and simultaneously using linked micromap plots. As a result of data analysis, the age-standardized rate has positive correlativity with thyroid and breast cancer, but the rate has negative correlativity with smoking and drinking. In addition, the regions with high age-standardized rate are located in southwest and the areas of high population density while the standardized mortality ratio is higher in southwest and northeast where there are lots of rural areas.

• Journal of Preventive Medicine and Public Health
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• v.38 no.2
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• pp.147-153
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• 2005

Objectives: Studies on the geographical differences in mortality tend to use a census population, rather than a registration population, as the denominator of mortality rates in South Korea. However, an administratively determined registration population would be the logical denominator, as the geographical areas for death certificates (numerator) have been determined by the administratively registered residence of the deceased, rather than the actual residence at the time of death. The purpose of this study was to examine the differences in the total number of a district population, and the associated district-specific mortality indicators, when two different measures as a population denominator (census and registration) were used. Methods: Population denominators were obtained from census and registration population data, and the numbers of deaths (numerators) were calculated from raw death certificate data. Sex- and 5-year age-specific numbers for the populations and deaths were used to compute sex- and age-standardized mortality rates (by direct standardization methods) and standardized mortality ratios (by indirect standardization methods). Bland-Altman tests were used to compare district populations and district-specific mortality indicators according to the two different population denominators. Results : In 1995, 9 of 232 (3.9%) districts were not included in the 95% confidence interval (CI) of the population differences. A total of 8 (3.4%) among 234 districts had large differences between their census and registration populations in 2000, which exceeded the 95% CI of the population differences. Most districts (13 of 17) exceeding the 95% CI were rural. The results of the sex- and age-standardized mortality rates showed 15 (6.5%) and 16 (6.8%) districts in 1995 and 2000, respectively, were not included in the 95% CI of the differences in their rates. In addition, the differences in the standardized mortality ratios using the two different population denominators were significantly greater among 14 districts in 1995 and 11 districts in 2002 than the 95% CI. Geographical variations in the mortality indicators, using a registration population, were greater than when using a census population. Conclusion: The use of census population denominators may provide biased geographical mortality indicators. The geographical mortality rates when using registration population denominators are logical, but do not necessarily represent the exact mortality rate of a certain district. The removal of districts with large differences between their census and registration populations or associated mortality indicators should be considered to monitor geographical mortality rates in South Korea.

• Proceedings of the KGS Conference
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• 2002.05a
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• pp.144-147
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• 2002

• Proceedings of the KGS Conference
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• 2004.11a
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• pp.43-43
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• 2004

• Food Quality and Culture
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• v.3 no.1
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• pp.20-26
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• 2009

The purpose of this study was to investigate the characteristics of dining-out customers by their behaviors based on geographical regions and visiting days in relation to restaurant attraction at ski resorts in Korea. The data were collected from six ski resorts. Three of the ski resorts were located in Kyunggi Province close to a metropolitan area, namely Seoul. The other three resorts were located in Kangwon Province, which is considered to be distant from the Seoul metropolitan area. A total of 599 usable questionnaires were utilized in the data analysis. Descriptive statistics and a cross tabulation analysis with chi square were used to examine the demographic characteristics of the respondents and the significant differences between geographical regions as well as between weekdays and weekends. The responding customers consisted of 57.3% (n=343) males and 42.7% (n=256) females. With respect to age, 15.5% were less than 20 yr., 44.6% were $20{\sim}30$ yr., and 28.0% were $30{\sim}40$ yr. The most recognizable occupations were student (32.9%) followed by office worker (33.2%). Twenty-seven percent of the respondents had less than one year of skiing experience and the majority (32.9%) had more than $1{\sim}3$ yr of experience. The major findings obtained from this study include statistically significant differences in the customers' demographical characteristics of age, occupation, skiing experience, and residential area according to the geographical regions of Kyunggi Province and Kangwon Province (p<0.05). All six of the customers' behavioral attributes, including transportation, reason to visit, staying days, purpose of visit, spending expenses, and usage of discount programs, showed significant differences between geographical groups (p<0.05). Finally, restaurant attraction was associated with the purpose of visiting and spending expenses by customers at the ski resorts (p<0.001).

• Journal of The Korean Society of Clinical Toxicology
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• v.20 no.2
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• pp.45-50
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• 2022

Purpose: This study utilizes the NEDIS (National Emergency Department Information System) database to suggest a predictive model for snakebite and envenomation in Korea by analyzing the geographical distribution and seasonal variation of snakebite patients visiting the ER. Methods: This was a retrospective study on snakebite patients visiting the ER using the NEDIS database from January 2014 to December 2019. The subjects include patients with the KCD (Korea Standard Classification of Disease) disease code T63.0 (Toxic effect of contact with snake venom). Geographical location, patient gender, patient age, date of ER visit, treatment during the ER stay, and disposition were recorded to analyze the geographical distribution and seasonal variation of snakebite patients in Korea. Results: A total of 12,521 patients were evaluated in this study (7,170 males, 54.9%; 5,351 females, 40.9%). The average age was 58.5±17.5 years. In all, 7,644 patients were admitted with an average admission time of 5.04±4.7 days, and 2 patients expired while admitted. The geographical distribution was Gyeongsang 3,370 (26.9%), Cheonra 2,692 (21.5%), Chungcheong 2,667 (21.3%), Seoul Capital area 1,999 (16.0%), Kangwon 1,457 (11.6%), and Jeju 336 (2.7%). The seasonal variation showed insignificant incidences in winter and higher severity in spring and summer than in fall: winter 27 (0.2%), spring 2,268 (18.1%), summer 6,847 (54.7%), and fall 3,380 (27.0%). Conclusion: Patients presenting with snakebites and envenomation in the emergency room were most common in the Gyeongsang area and during summer. The simple seasonal model predicted that 436 snakebites and 438 envenomation cases occurred in July and August. The results of this study can be applied to suitably distribute and stock antivenom. Appropriate policies can be formed to care for snakebite patients in Korea.

• Journal of The Geomorphological Association of Korea
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• v.20 no.3
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• pp.109-118
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• 2013

The purpose of this study is to clarify the optimum locational environment of Neolithic Age and Bronze Age dwelling sites in Yeongjong-do, Incheon in terms of geographical characteristics. To make this possible, the micro-landform location environment of individual dwelling sites has been analyzed targeting 145 dwelling sites of Neolithic Age and 47 dwelling sites of Bronze Age in which quantitative analysis is possible among the dwelling sites verified in the investigation area until now. As a result, the micro-landform of hills where Neolithic Age and Bronze Age dwelling sites are crowded the most has been determined as the upper side hollow (64 dwelling sites, 44.1%) and the crest slope (39 dwelling sites, 83%), respectively. This means that the optimum locational environment of dwelling sites has been changed as the times have progressed from Neolithic Age to Bronze Age in the investigation area and the location of micro-landform of hills also where dwelling sites are located also has relatively moved up a little seen in terms of geographical perspective.

• Journal of the Korean Geographical Society
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• v.35 no.3
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• pp.489-501
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• 2000

옛 인간의 삶을 정확하게 이해하기 위해서는 자연환경과 인간생활의 상호관계를 파악하는 일이 매우 중요하다. 송국리를 비롯한 부여지방에는 다른 지역에 비해 월등히 많은 청동기 시대의 유물.유적이 분포하고 있다. 본 논문에서는 부여지방의 청동기시대 유물.유적지의 지형적 특징을 살펴보고 이를 당시의 자연환경 특히 해수변동과 관련지어 설명하였다. 그럼으로써 자연환경이 인간의 삶에 어떠한 영향을 주었는가를 구체적으로 알 수 있었다.

• Journal of the Economic Geographical Society of Korea
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• v.23 no.2
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• pp.166-181
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• 2020

This paper attempts to compare and analyze the intensity, trend, and regional gap of income inequality, capitalizing upon the Age-Period-Cohort model which considers age, time and cohort effects, with the 1998-2018 Korea Labor Panel (KLIPS) survey data for respondents living in the Capital and Non-Capital Regions. The main analysis results are as follows. First, in the case of both cohort and age effects, those in their 50~60s, including the so-called baby boomers and '386 generation' living in the Capital Region, have relatively lower income inequality effect compared to that of other age groups and cohorts in the Non-Capital Region. Second, the micro-individual characteristics cannot be ignored to account for a regional gap in income inequality, but rather the effects of structural and institutional omitted variables and the social discrimination effects of individual characteristics variables are more significant in explaining it. Overall, intra-and inter-cohort income inequalities appear to overlap.