평일 시간활동패턴을 활용한 도시 인구의 유사노출집단 분류
Determination of Similar Exposure Groups Using Weekday Time Activity Patterns of Urban Populations
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참고문헌
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초록
Objectives: Determining the time activity patterns of urban populations is critical when performing an exposure assessment. The purposes of this study were to classify urban populations in Korea by their time activity patterns and to identify factors that influence these patterns. Methods: The time activity patterns of 31,634 and 20,263 individuals were obtained from two national databases collected in 2004 and 2009, respectively. The two largest metropolitan cities in Korea, Seoul and Busan, were selected for this analysis. For each city, multivariate linear regressions were performed to determine factors affecting the time spent in a residence and in transit. We also used cluster analysis to classify each urban population by activity pattern. Results: Nine distinctive activity patterns were identified in the Seoul and Busan populations, respectively, and the resulting classified population groups had specific characteristics. The identified patterns were similar for Seoul and Busan. The most significant factors affecting time spent in a residence were employment status, age, marriage status, education, and gender. Gender, education, employment status, and monthly income were significant factors affecting time spent in transit. Conclusion: These results indicate that, in addition to region, exposure scientists in Korea should consider classifying populations based on age, gender, and occupation.
Abstract
Objectives: Determining the time activity patterns of urban populations is critical when performing an exposure assessment. The purposes of this study were to classify urban populations in Korea by their time activity patterns and to identify factors that influence these patterns. Methods: The time activity patterns of 31,634 and 20,263 individuals were obtained from two national databases collected in 2004 and 2009, respectively. The two largest metropolitan cities in Korea, Seoul and Busan, were selected for this analysis. For each city, multivariate linear regressions were performed to determine factors affecting the time spent in a residence and in transit. We also used cluster analysis to classify each urban population by activity pattern. Results: Nine distinctive activity patterns were identified in the Seoul and Busan populations, respectively, and the resulting classified population groups had specific characteristics. The identified patterns were similar for Seoul and Busan. The most significant factors affecting time spent in a residence were employment status, age, marriage status, education, and gender. Gender, education, employment status, and monthly income were significant factors affecting time spent in transit. Conclusion: These results indicate that, in addition to region, exposure scientists in Korea should consider classifying populations based on age, gender, and occupation.
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