1. Introduction According to UNCHS1) data, the one-way commuting time in Seoul was about 60 minutes in 1998, while that in Tokyo was 45 minutes and that in New York was 32 minutes. In general, a city’s competitiveness is often measured by the commuting patterns and commuting time as well as some other economic variables such as Gross Regional Domestic Product(GRDP). Therefore, it is necessary to analyze the commuting not only in terms of time, but also flow. In order to explore commuting patterns, geographic classification is very Commuting Analysis in the Seoul Metropolitan Statistical Area important, which defines the spatial commuting boundary of a city. Thus, the Metropolitan Statistical Areas(MSAs) is used in this paper. It was defined by cross-commuting rates, population, and population density, which is quite similar to the criteria of a Standard Metropolitan Statistical Areas(SMSAs) in the United States. Then, the commuting inflows and outflows are considered using In-Out tabulation(often called origin/destination). From the In-Out tabulation matrix, the business and resident areas were able to be classified. Accordingly, the method of commuting is inspected across regions to see any potentially spatial constraint. For example, whether or not more people would use public transportation in the central business districts. This analysis in commuting behavior would be a great potential information source for regional development policies for suburban areas. 1) Global Urban Indicators of United Nations Centre for Human Settlements 14 KIET Industrial Economic Review 2. Literature Review Most researches on commuting patterns in Korea have been based on Current Issues administrative districts. Lee and McDonald(2003)2) investigated commuting patterns in Seoul using a 2% public sample of the Korean Population and Housing Census in 1995. The authors tried to show the commuting patterns and explain the factors of commuting time from individual characteristics such as gender, house ownership, occupation, industry, moving history, age, working status, marriage status, and number of workers. Kim(2007)3) extends Lee and McDonald’s work on commuting patterns and determinants of commuting time in seven metro cities from 1980 to 2000. He pointed out information technology usage, such as mobile phone usage, personal computer, and internet use as important factors of commuting time. However, these analyses are conducted with administrative wards such as Seoul or the seven metro-cities rather than economic wards such as Metropolitan Statistical Areas(MSAs). Therefore, Kim, Huh, and Lee(2008)4) defined the Korean MSAs by the cross commuting rates, population, and population density. The authors examined city distribution with newly defined MSAs and also analyzed the regional competitiveness among the 50 MSAs. Regarding commuting volume, Lee and Lee(2008)5) showed that the newly developed satellite cities around Seoul have established their own economic function and thus created urban sprawl in the Seoul MSA. Cho and Kim (2007)6) scrutinized commuting pattern of suburban areas around Seoul. 2) Lee, B. S. and McDonald, J. F., 2003, “Determinants of Commuting Time and Distance for Seoul Residents: The Impact of Family Status o the Commuting of Women,” Urban Studies 40 : pp. 1283-1302. 3) Kim, D., 2007, “Changes in Commuting Patterns and Determinants of Commuting Time: 7 Seven Korean Cities,” The Spatial Planning Review 53 : pp. 223-240. 4) Kim, D., M. Huh, and D. Lee, 2008, “Analysis of the Regional Economy by Defining Korean MSAs-on Urban Spatial Structure and Regional Competitiveness”, KIET Research Report 530. 5) Lee, Y. and S. Lee, 2008, “The Influence of New Town Development on the Changes of the Migration and Commuting Patterns in the Capital Region,” The Korean Geographical Society 43(4) : pp. 561-579. 6) Cho, H. and K. Kim, 2007, “Analysis of Accessibility Patterns for Commuting Trips in Seoul Metropolitan Area,” The Korean Geographical Society 42(6) : pp. 914-929. Mar. / Apr.┃2009┃Vol 14┃No 2 15 According to the results, the accessibility to Seoul had improved since 1990 but not since 1995. They also calculated the heavy commuting inflows from Gwacheon-Si, Seongnam-Si to Seoul but relatively light commuting inflows from Pocheon-Si. In addition, Kim(2007) pointed out rising reverse commuting rate which means that not only commuting inflow from suburban areas to Seoul, but also commuting outflows from Seoul to suburban areas increased . Further, he found higher internal commuting rates that means more people are likely to move close to work place. 3. Seoul Metropolitan Statistical Area Delineation As introduced in the previous chapter, the Seoul MSA was defined based on the following criteria : 1) the central city is supposed to be a Si having at least 50,000 people, 2) any peripheral cities are to be adjacent SiGunGus to the central city, having at least 100 people per square kilometer of population density, and 3) the cross commuting rates between the central city and its peripheral cities are supposed to be at least 10%. Basically, these criteria are quite similar to the criteria for the US MSA. Figure 1. Seoul MSA 16 KIET Industrial Economic Review Based on the above criteria, 18 Sis in Gyeonggi province were included in the Seoul MSA shown in Figure 1. In particular, the commuting rate of Seongnam residents to Seoul is about 29%. This is a significantly large number Current Issues in that the population of Seongnam was close to one million. Suwon, Hwaseong, and Osan are classified as an independent MSA. So are not only Pyeongtack and Anseong, but also Ansan and Siheung. Donducheon and Icheon are all separate individual MSAs. As a result, the population of the Seoul MSA is about 16.7 million(about 35% of the total population), which is much bigger than that of Seoul(10 million) but much smaller than that of Seoul-Incheon-Gyeonggi, the so called ‘Sudokwon’(23 million). Unfortunately, any Guns in Gyeonggi province were not included in the Seoul MSA, because of low population density. 4. Commuting Analysis In order to analyze commuting patterns, the O/D(Origin and Destination) tabulation analysis of commuting flow data7) from resident SiGunGu to working SiGunGu is necessary, which were provided by the Korean Population and Housing Census in 2005. In Table 1, the data in column i and row j represents the number of commuters who live in the i-th SiGunGu, but work or study at j-th SiGunGu. Since students over 12 year old are included in the number of commuters in the O/D matrix in Table 1, the 2005 Population and Housing Census sample data8) were also used for the analysis of the commuting time and patterns. In this chapter, commuting patterns in the Seoul MSA was analyzed in two ways. First, commuting flow is discussed in conjunction with O/D tabulation. Specifically, the central business district(CBD) as well as bedroom communities in the Seoul MSA are defined and classified. Second, commuting 7) Korean Census (Population and Housing) Data 1980, 1990, 1995, 2000, Korean National Statistical Office, http://www.nso.go.kr 8) Korean Census (Population and Housing) 2% Sample Data 2005 CD, Korean National Statistical Office. Mar. / Apr.┃2009┃Vol 14┃No 2 17 time and commuting modes are compared between Seoul and Gyeonggi in the Seoul MSA. According to Table 1, the InOut ratio of SiGunGus shows that Jongno-Gu, Jung-Gu, Yongsan-Gu, Yeungdeungpo-Gu, Gangnam-Gu, and Seocho-Gu are higher than 1.5, which means that the commuting inflows are much bigger than commuting outflows. In other words, those six Gus have higher levels of job density and thus could be interpreted as the CBDs of the Seoul MSA. In particular, Jongno-Gu, Jung-Gu, and Yongsan-Gu are traditional CBDs having relatively small resident populations. In general, this traditional CBD is a center of north areas of the Han river, because the most commuting inflows are from Sungbuk-Gu, Eunpyung-Gu, and even Goyang-Si. As a financial center, Yeungdeungpo-Gu has a high commuting inflow from the western parts of the Seoul MSA including Yangcheon-Gu, Gangseo-Gu, Bucheon-Si, and Goyang-Si. Especially most financial headquarters such as banks, stock management companies, and even Korea Stock Exchange are located at Yeouido. The other CBD is Gangnam-Gu and Seocho-Gu which is newly developed commercial areas having more than 800 thousand people in both Gus. Interestingly, the Gangnam-Gu and Seocho-Gu is becoming the CBD not only for southern Seoul but for the entire Seoul MSA, because of the high volume of commuting inflows from all over the SiGunGus in the Seoul MSA. For example, the number of commuters from even Goyang-Si is about 13,000. Therefore, the Seoul MSA has changed from mono-centric to poly-centric. On the other hand, Jungnang-Gu, Gangbuk-Gu, Dobong-Gu, Eunpyung-Gu, Yangcheon-Gu, Gangseo-Gu, Gwanak-Gu, and Gandong-Gu are classified as residential areas rather than commercial areas, because the InOut ratios of those eight Gus are lower than 0.7. In Gyeonggi province, Gwacheon, Paju, Gimpo, Yangju, and Pocheon have high InOut ratios, which means that those local economies are either relatively well developed or independently developed. In Table 1, the number of commuters in the main diagonal represents internal commuters who reside and work in the same SiGunGu. The average internal commuting rate in Seoul is about 42%, while that in Gyeonggi in the Seoul MSA is about 53%. This indicates that there are more 18 KIET Industrial Economic Review commuting constraints such as congestion or commuting mode in Seoul than in Gyeonggi in the Seoul MSA. However, interestingly, the internal commuting rates of Gwacheon-Si and Uiwang-Si are relatively lower than other cities in Current Issues Gyeonggi province in the Seoul MSA.