“…The results from clustering were compared with the two previous form typology studies in Seoul to understand their performance differences (Table S4 and Table S5 in the Supplementary Material). The study area of this study covers 70.2% of Seoul, while previous studies by Kim (2003) and SDI (2009) cover only a small proportion of Seoul: 0.3% and 6.9%, respectively. The overlap results show that the present study is more consistent with the study made by Kim (2003) than the study of SDI (2009), suggesting the good performance of this research since the study of the former was considered more systematic (Figure S8 in the Supplementary Material).…”
Section: Verification and Comparisonmentioning
confidence: 82%
“…The final study area comprised 1701 grid cells (see Figure 1). Figure 1.Mapping of different study areas (Kim, 2003; SDI, 2009). …”
Section: Methodsmentioning
confidence: 99%
“…The typologies herein were also compared with those of Kim (2003) and SDI (2009) based on a manual approach. Typologies from both studies were overlapped in ArcGIS and then analyzed.…”
Section: Methodsmentioning
confidence: 99%
“…Aside from those studies on individual typology, Seoul’s urban forms have been classified as typologies more comprehensively by two studies. Kim (2003) identified seven typologies for eight 500 m × 500 m areas based on typicality, density, and housing type. Among the typologies, two are in city centers, and five in mid or high-density residential typologies.…”
Seoul, the capital city of South Korea, has diverse urban forms developed through its complex history. Previous studies show limitations of strong subjectivity and difficulty in scalability in identifying typical Seoul urban forms with expert knowledge. Data-driven approach offers an opportunity to address those challenges, but previous studies often focused on direct applications of clustering algorithms to a given area with diverse methods and workflows, lacking a systematic framework. This study addressed these issues by developing a new form clustering framework to systematically identify form typologies at a large scale and demonstrated its application in Seoul. With a 500 m × 500 m grid as the basic spatial unit and twelve urban form attributes as learning features, 14 clusters were identified using the Gaussian mixture model. These clusters were further translated into form typologies following a semantic typology naming system, with representative form samples identified. The resulting typologies were then verified and validated through comparisons with previous studies. Their relationships with zoning classes were also examined, emphasizing their role in urban planning and design. Results suggest this new framework is an effective and promising way to identify urban form typologies in complex urban environments to better support urban planning and management.
“…The results from clustering were compared with the two previous form typology studies in Seoul to understand their performance differences (Table S4 and Table S5 in the Supplementary Material). The study area of this study covers 70.2% of Seoul, while previous studies by Kim (2003) and SDI (2009) cover only a small proportion of Seoul: 0.3% and 6.9%, respectively. The overlap results show that the present study is more consistent with the study made by Kim (2003) than the study of SDI (2009), suggesting the good performance of this research since the study of the former was considered more systematic (Figure S8 in the Supplementary Material).…”
Section: Verification and Comparisonmentioning
confidence: 82%
“…The final study area comprised 1701 grid cells (see Figure 1). Figure 1.Mapping of different study areas (Kim, 2003; SDI, 2009). …”
Section: Methodsmentioning
confidence: 99%
“…The typologies herein were also compared with those of Kim (2003) and SDI (2009) based on a manual approach. Typologies from both studies were overlapped in ArcGIS and then analyzed.…”
Section: Methodsmentioning
confidence: 99%
“…Aside from those studies on individual typology, Seoul’s urban forms have been classified as typologies more comprehensively by two studies. Kim (2003) identified seven typologies for eight 500 m × 500 m areas based on typicality, density, and housing type. Among the typologies, two are in city centers, and five in mid or high-density residential typologies.…”
Seoul, the capital city of South Korea, has diverse urban forms developed through its complex history. Previous studies show limitations of strong subjectivity and difficulty in scalability in identifying typical Seoul urban forms with expert knowledge. Data-driven approach offers an opportunity to address those challenges, but previous studies often focused on direct applications of clustering algorithms to a given area with diverse methods and workflows, lacking a systematic framework. This study addressed these issues by developing a new form clustering framework to systematically identify form typologies at a large scale and demonstrated its application in Seoul. With a 500 m × 500 m grid as the basic spatial unit and twelve urban form attributes as learning features, 14 clusters were identified using the Gaussian mixture model. These clusters were further translated into form typologies following a semantic typology naming system, with representative form samples identified. The resulting typologies were then verified and validated through comparisons with previous studies. Their relationships with zoning classes were also examined, emphasizing their role in urban planning and design. Results suggest this new framework is an effective and promising way to identify urban form typologies in complex urban environments to better support urban planning and management.
“…On the other hand, the proportion of mothers in their early 20s has considerably decreased from 40.5% in 1984 to 5.2% in 2010 and that of mothers in their late 20s also decreased from 54.6% in 1970 to 31.4% in 2010 [ 33 , 34 ]. These trends might be caused by a high unemployment rate and the older age at marriage [ 35 ]. Therefore, solutions to address unemployment and residence problems for young people should be priorities of the Korean government.…”
PurposeThe aim of this study was to estimate the burden of breast cancer that can be attributed to rapid lifestyle changes in South Korea in 2013-2030.Materials and MethodsAn age-period-cohort model was used to estimate the incidence and mortality. The Global Burden of Disease Study Group methodwas used to calculate the years of life lost and years lived with disability in breast cancer patients using a nationwide cancer registry. The population attributable riskswere calculated using meta-analyzed relative risk ratios and by assessing the prevalence of risk factors.ResultsWomen’s reproductive/lifestyle changes, including advanced maternal age at first childbirth (from 37 to 85 disability-adjusted life years [DALYs] per 100,000 person-years), total period of breastfeeding (from 22 to 46 DALYs per 100,000 person-years), obesity (from 37 to 61 DALYs per 100,000 person-years), alcohol consumption (from 19 to 39 DALYs per 100,000 person-years), oral contraceptive use (from 18 to 27 DALYs per 100,000 person-years), and hormone replacement therapy use (from 2 to 3 DALYs per 100,000 person-years) were identified as factors likely to increase the burden of breast cancer from 2013 to 2030. Approximately, 34.2% to 44.3% of the burden of breast cancer could be avoidable in 2030 with reduction in reproductive/lifestyle risk factors.ConclusionThe rapid changes of age structure and lifestyle in South Korea during the last decade are expected to strongly increase the breast cancer burden over time unless the risk factors can be effectively modified.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
