Commuting Analysis in the Seoul Metropolitan Statistical Area

Home , Gwacheon, Icheon, Namyangju, Pocheon, Uiwang

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

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 any Seoul-Incheon-Gyeonggi, thesocalled‘Sudokwon’(23million).Unfortunately, much biggerthanthatofSeoul(10million)butsmaller Seoul MSAisabout16.7million(about35%ofthetotalpopulation),which Icheon areallseparateindividualMSAs.Asaresult, thepopulationof Pyeongtack andAnseong,butalsoAnsanSiheung.Donducheon Hwaseong, andOsanare classifiedasanindependentMSA.Soare notonly in thatthepopulationofSeongnamwasclosetoonemillion.Suwon, Seongnam residentstoSeoulisabout29%.Thisasignificantlylarge number the SeoulMSAshowninFigure 1.Inparticular, thecommutingrateof tabulation analysisofcommutingflowdata theO/D(Origin andDestination) In ordertoanalyzecommutingpatterns, communities intheSeoulMSAare definedandclassified.Second,commuting Specifically, thecentralbusinessdistrict(CBD) aswellbedroom ways. First,commutingflowisdiscussedinconjunction withO/Dtabulation. commuting timeandpatterns. of lowpopulationdensity. row jrepresentsthenumberofcommuterswholiveini-th Population andHousingCensusin2005.InTable 1,thedataincolumniand working work orstudyatj-th and HousingCensussampledata the numberofcommutersinO/DmatrixTable 1,the2005Population 4. CommutingAnalysis 8) Korean Census(Population andHousing)2%SampleData2005CD,KoreanNational 7) Korean Census(PopulationandHousing)Data1980,1990,1995,2000, KoreanNational Based ontheabovecriteria,18SisinGyeonggiprovince wereincluded in In thischapter, intheSeoulMSAwasanalyzedtwo commutingpatterns Statistical Office. Statistical Office,http://www.nso.go.kr Guns SiGunGu in Gyeonggiprovince werenotincludedintheSeoulMSA,because SiGunGu is necessary,whichwereprovidedbytheKorean . Sincestudentsover12yearoldare includedin 8) were alsousedfortheanalysisof 7) from resident Mar. /Apr. SiGunGu SiGunGu ┃ 2009 ┃ , but Vol 14 to ┃ No 2 17 Current Issues 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 rates ofGwacheon- in GyeonggitheSeoulMSA.However, commuting interestingly,theinternal commuting constraintssuchascongestionormodeinSeoulthan households inmost of householdstohouses,thenumberhousesisgreater thanthenumberof household ismoredemandingtoliveclosetheCBD.According totheratio their housingdemandisverysensitivetoprices.Inaddition,single because economic theorythatlarge householdsmoveouttosuburbareas than3.02.Itisconsistentwithurban while thatinGyeonggiisquitemore However, theaveragehouseholdsizeinSeoulismuchsmallerthan3.02, as lowereconomicselfsufficiency. students commutewithinthosecitiesrespectively, whichcouldbeinterpreted Gyeonggi provinceintheSeoulMSA.Only36%and32%ofworkers Gyeonggi residentsin the SeoulMSA.Theaverageone-waymorning intheaverageone-waycommuting timeforSeouland big difference commuters walkingand using automobilehasbeenincreasing. Thereisno walking, automobile,publicbus,andsubway.In general,theportionof used toexcludestudentcommuters.Theprimary commutingmethodsare and method.Inthisanalysis,the2%sampledatafrom theKoreancensuswas grown-ups stayingwiththeirparents. considerable numberofnewcouplesdelayingmarriage andmanyunmarried temporarily duetohigherhousingcosts.For example,there area senses. Secondly,itispossiblethattheaveragehouseholdsizegettinglarger area. Therefore,itisanappropriate timetorestructureoldhousesinsome households, whichcouldexplaintherelatively lowerhousingpricesinthat number ofhousesinGwanak- interpreted intwoways.First,houseshavebeenoversupply.Especiallythe housing inSeoul-Incheon-Gyeonggi.However, thiscouldbefurther in isaself-sufficiency consistent withLeeandLee(2008),whichsaidthere house rateintheSeoulMSAis1.34,whilethat1.44.Thisa The average household size of the Seoul MSA is around 3.02people. The averagehouseholdsizeoftheSeoulMSAisaround The secondpartofthecommutinganalysisisabout thecommutingtime Si SiGunGus and Uiwang- in theSeoulMSA.Theaveragehouseholdto Gu Si are relativelylowerthanothercitiesin is almosttwiceofthenumber Mar. /Apr. ┃ 2009 ┃ Vol 14 ┃ No 2 19 Current Issues Table 1. Commuting Flow in the Seoul MSA

SiGunGu Jongno Jung Yong– Seong– Gwang– Dong– Jung– Seong– Gang– Dobong Nowon san dong jin daemun nang buk buk Jongno 40,673 7,364 1,510 1,028 686 1,920 466 2,594 537 286 609 Jung 3,144 37,733 2,078 1,675 532 1,011 245 1,009 252 145 369 Yongsan 4,903 11,064 44,484 1,481 883 1,713 310 1,487 387 161 580 Seongdong 6,861 16,988 4,190 71,025 6,542 6,659 1,219 2,452 496 657 1,354 Gwangjin 6,423 10,137 2,110 10,710 78,726 6,914 2,666 2,647 400 473 2,432 Dongdaemun 9,951 11,444 2,832 6,317 4,827 96,509 4,866 7,122 1,329 1,172 4,388 Jungnang 7,235 9,732 2,369 5,926 8,937 15,815 87,352 4,445 1,061 1,691 10,338 Seongbuk 18,677 17,441 4,294 3,926 2,238 10,268 2,085 91,028 6,687 2,866 8,619 Gangbuk 11,248 12,008 3,028 2,764 1,484 6,381 1,542 14,007 59,458 6,823 8,007 Dobong 10,196 11,552 2,882 2,971 2,622 6,852 2,004 8,597 11,265 65,896 15,844 Nowon 14,124 16,718 5,037 5,246 5,782 11,661 7,597 12,173 5,922 8,385 130,315 Eunpyeong 15,433 14,139 4,618 2,174 1,234 3,245 607 3,614 575 650 1,279 Seodaemun 14,456 12,674 3,487 1,897 1,148 2,647 464 2,825 503 327 914 Mapo 9,051 15,507 6,373 2,224 1,641 2,705 478 2,874 292 302 1,181 Yangcheon 5,998 8,919 3,502 1,676 1,334 2,339 429 2,262 199 309 934 Gangseo 7,287 9,154 3,713 2,022 1,329 2,558 239 2,635 431 317 1,009 Guro 4,996 6,390 4,146 1,183 1,022 2,020 255 1,896 265 247 815 Geumcheon 2,204 2,789 2,156 586 388 954 212 596 53 128 203 Yeongdeungpo 5,767 9,460 5,929 1,430 1,072 2,433 321 1,880 257 315 728 Dongjak 6,505 9,672 7,138 2,103 1,817 878 489 2,414 463 387 1,064 Gwanak 6,038 9,215 5,654 2,411 2,214 2,733 429 2,330 351 248 749 Seocho 7,062 9,249 3,986 2,582 2,226 2,992 349 3,142 258 289 1,155 Gangnam 7,836 11,114 4,704 757 4,265 4,663 686 4,512 424 454 1,690 Songpa 6,443 9,801 3,599 5,423 7,019 4,726 1,031 3,575 512 613 1,882 Gangdong 6,194 7,269 2,630 4,749 7,024 4,452 1,187 3,020 369 394 1,848 Seongnam 5,650 8,840 3,518 3,787 3,998 3,056 689 2,589 30 435 1,325 Uijeongbu 5,369 4,860 1,956 2,023 1,319 4,748 1,613 4,491 3,301 6,832 8,902 Anyang 4,006 5,950 3,361 1,372 1,068 1,507 220 1,587 233 248 43 Bucheon 6,474 6,789 4,576 1,574 1,551 2,182 277 1,692 407 322 1,145 Gwangmyeong 2,630 3,293 2,036 817 464 1,320 152 791 215 71 342 Goyang 15,114 18,051 5,506 2,345 1,696 2,891 528 3,536 357 514 1,526 Gwacheon 822 1,179 526 223 188 117 45 269 108 37 79 Guri 1,634 2,124 610 1,786 3,246 3,107 3,308 986 201 213 1,860 Namyangju 3,097 3,990 1,578 2,302 3,890 5,645 5,542 2,112 439 791 5,352 Gunpo 1,504 2,502 1,469 484 385 717 63 668 83 64 421 Uiwang 677 1,224 780 289 194 357 45 224 99 36 94 Hanam 815 1,085 403 1,002 1,266 741 230 507 29 60 354 Yongin 3,043 4,357 2,061 1,176 1,540 1,545 280 1,502 112 203 458 Paju 1,671 1,758 620 235 247 424 44 537 117 104 197 Gimpo 971 128 622 367 285 337 34 434 32 34 123 Gwangju 644 618 308 1,758 563 386 138 302 42 38 124 Yangju 776 911 385 452 275 687 271 634 543 866 1,285 Pocheon 199 77 52 71 50 224 45 234 201 131 607 Inflow 293,801 366,426 166,816 170,349 169,217 237,039 131,052 208,231 99,595 104,534 223,143 Outflow 79,088 66,781 108,906 175,475 196,577 199,629 218,811 226,881 169,720 191,686 307,549 InOut ratio 3.71 5.49 1.53 0.97 0.86 1.19 0.60 0.92 0.59 0.55 0.73 Population 156,018 128,443 217,708 327,566 366,746 373,232 413,760 442,426 339,147 368,716 604,161 Household 55,748 47,760 79,340 112,942 127,302 132,478 138,595 147,948 113,037 116,787 191,785 house 39,016 31,621 55,186 75,489 64,901 81,268 80,203 105,674 79,241 94,553 179,848 Householdsize 2.80 2.69 2.74 2.90 2.88 2.82 2.99 2.99 3.00 3.16 3.15 HHHouse Ratio 1.43 1.51 1.44 1.50 1.96 1.63 1.73 1.40 1.43 1.24 1.07 Source : Census 2005.

20 KIET Industrial Economic Review Table 1. endugo964106574814437823678,4 ,4 ,9 6, 3,995 7,740 83,745 3,657 7,882 4,413 4,851 6,587 4,120 996 Yeongdeungpo Hos ai .3 .6 .6 .6 .8 .1 .4 .2 .4 1.95 1.54 1.62 1.74 1.41 1.28 1.26 1.46 1.46 1.33 HHHouse Ratio wnmen 0 ,1 ,3 ,2 ,1 1441,4 ,7 ,8 ,7 4, 2,378 2,887 7,974 10,444 11,424 1,610 2,228 2,236 1,515 409 Gwangmyeong oshlsz .7 .8 .4 .9 .2 .0 .0 .6 .6 2.60 2.96 2.86 2.90 3.00 3.02 3.19 2.74 2.88 3.07 Householdsize ogamn63235245605012839309145793,923 739 1,495 3,079 359 1,208 540 610 2,405 2,335 653 Dongdaemun emho 7 ,7 ,6 ,1 ,2 ,7 0326643435743,093 5,794 3,473 6,634 60,332 6,976 1,124 1,214 1,869 1,072 277 Geumcheon edeu ,9 1201,6 ,1 ,1 ,8 4 ,6 ,0 ,2 4,51 1,222 1,401 5,664 748 1,985 1,917 1,410 10,961 71,250 8,492 Seodaemun endn 1 ,0 ,4 8 3 ,6 7 ,3 ,1 5 5,659 953 1,014 3,635 479 1,263 637 588 2,148 2,403 314 Seongdong agho ,6 ,1 ,5 5471,7 ,6 ,4 07 ,7 ,2 5, 2,528 3,879 20079 2,945 9,361 19,075 95,427 6,953 5,114 1,163 Yangcheon upen 0881,7 007140174227835751741045,4 1,054 1,744 5,715 833 2,207 1,784 1,470 10,037 13,772 90,878 Eunpyeong aynj 1 ,2 3 6 4 4 312 4 2 2,735 329 643 1321 73 641 346 361 931 1,023 415 Namyangju ennm5728025254881747643227220620,874 2,036 2,712 4,342 796 1,754 828 564 2,522 2,880 507 Seongnam agog382892195357119332831441135,982 1,143 1,454 2,843 393 1,139 577 553 2,139 2,829 308 Gangdong wcen2 6 7 4 713234965582,999 538 635 469 243 173 67 148 274 361 27 Gwacheon oshl 4,4 1,7 3,5 4,7 7,1 3,3 718139,25 87,188 135,436 178,612 148,672 136,259 119,270 145,845 Household aga 4 ,3 ,4 ,9 9 ,4 2 ,5 ,5 ,9 21,096 3,894 3,254 6,051 929 1,847 994 1,095 3,647 6,033 542 Gangnam yogmncensocheon seo cheon mun pyeong enbk963453358490138574541901035,472 1,083 1,970 4,554 567 1,368 940 874 3,335 3,495 966 Seongbuk ouain4761335333074427589746292246398, 252,446 406,299 538,997 474,247 373,057 343,593 447,611 Population ugag55194192535613847245156935,209 933 1,536 2,455 407 1,358 596 583 1,972 1,984 555 Jungnang nu ai .0 .0 .5 .6 .7 .0 .1 .9 .8 .2 1. 0.62 0.78 1.49 0.91 0.90 0.67 0.66 1.05 1.10 0.60 InOut ratio ienb 2 ,8 ,7 0 5 4 3 ,6 ,0 8 2,147 283 1,000 1,366 231 946 459 301 1,379 1,189 725 Uijeongbu SiGunGu wnjn322242174255135403281491286,000 1,208 1,459 3,208 460 1,305 515 432 2,197 2,214 372 Gwangjin age ,6 ,8 ,4 12413746752012,2 ,9 ,1 5, 2,713 3,712 4,199 713 20,629 2,071 1,050 6,705 2,520 113,704 283 21,224 8,646 1,086 5,686 630 1,469 486 2,254 Gangseo 2,436 730 Gangbuk oga 7 ,3 ,4 8 5 ,1 5 ,9 ,0 ,6 4,072 1,063 2,004 3,996 450 1,215 759 686 4,043 2,337 472 Yongsan oho 44 61 606 64 44 42 36 60 0 56 0 10 26 47 14 Pocheon uho 1 ,1 ,9 ,4 ,5 2413091,6 ,6 ,6 5,097 2,468 4,468 12,864 3,099 12,471 9,751 7,346 4,899 3,119 616 Bucheon wnj 629211816175 1 0 8 2,144 184 404 415 56 157 116 128 211 249 56 Gwangju wnk683434922472498156211,2 354180419,47 108,014 13,514 13,226 6,281 8,135 2,489 2,437 4,902 3,453 648 Gwanak oga 8 ,9 ,4 ,9 ,5 ,4 ,0 2187,5 ,8 17,424 9,380 73,954 12,188 2,801 4,447 2,052 1,494 4,145 3,094 588 Dongjak oog55273236434513438327138713,337 781 1,328 3,217 368 1,324 495 453 2,366 2,703 545 Dobong oga5439028183891516639425318611,064 1,896 2,563 3,984 676 1,511 859 813 2,851 3,990 514 Songpa ogo892591552225522017175411,902 431 775 1,701 230 512 275 202 1,565 2,589 809 Jongno eco504532839510518510659343347280,247 4,702 4,393 5,993 1,096 1,825 1,035 955 2,803 4,503 510 Seocho oag1,6 1831,0 ,3 ,5 ,4 ,1 2972611106,2 1,130 2,691 12,927 1,110 3,348 6,354 3,033 11,906 11,803 11,368 Goyang ufo 2,6 7,3 9,5 4,4 8,9 1,5 3,6 206,164 133,066 211,356 282,098 249,348 195,156 178,836 225,865 Outflow oo 9 ,3 ,3 1 ,1 ,7 3 ,1 ,6 ,1 7,407 1,117 2,468 4,710 639 1,870 1,011 717 3,334 3,538 798 Nowon nag4417023211110843562759943629011,410 2,900 4,356 5,919 6,217 4,395 1,088 1,141 2,342 1,720 454 Anyang iag132447161362869094512,761 591 964 960 846 672 143 156 467 274 123 Uiwang aa 245179 9215 5 6 8 882 188 165 454 51 241 79 91 147 465 62 Hanam up 8 3 4 8 3 ,7 ,7 ,7 ,7 ,5 3,946 1,254 1,276 2,275 1,970 2,075 438 389 745 737 180 Gunpo ogn261241313236906824014110512,004 1,035 1,441 2,460 678 940 386 332 1,311 1,224 276 Yongin ip 8 2 ,7 ,6 ,2 8 2 ,2 5 8 803 185 254 2,228 227 981 4,123 1,063 1,077 721 182 Gimpo agu7932293 2 3 3251410440 100 144 295 53 130 128 30 279 382 719 Yangju os 0,5 1919,2 1,2 3,7 6035,1 5718,6 1 89,762 85,791 50,018 96,053 139,678 117,621 93,425 81,911 109,251 house ao3031,1 5342332822951191,6 ,8 ,7 4,971 1,673 1,983 11,460 1,109 2,935 2,862 2,313 75,354 12,513 3,043 Mapo nlw145917412487150610061038116736891 306,869 121,667 190,388 190,066 165,076 204,867 197,411 134,529 Inflow uo713124496113907,2 ,2 6194383946,254 3,964 4,398 16,159 7,126 79,521 3,930 6,131 1,793 4,449 437 3,102 422 791 1,205 101 Guro 341 279 189 1,572 1,315 244 Jung au14612610437412115963117630 187 341 926 135 271 471 397 1,064 1,266 1,496 Paju ui2356571117379 6 8 1 1,740 215 387 964 98 387 187 151 517 556 223 Guri Eun – Seodae Commuting FlowinSeoulMSA(continued) – Mapo Yang – Gang – Guro Geum enp a ak jak deungpo – Yeong – Dong Mar. /Apr. – Gwan 5 0,1 3,3 372,795 530,833 409,519 251 ┃ 3,7 0,9 125,833 204,392 138,179 6 2009 – 1,9 8,4 202,335 284,841 216,990 8241743325,859 177,473 68,274 Seocho ┃ Vol 14 ┃ 46799,212 04,657 No 2 21 1.27 2.96 378 477 091 014 35 61 01 7 3 Current Issues Table 1. Commuting Flow in Seoul MSA(continued) Gang– Gang– Seong– Ui jeong– Gwang– Gwa– SiGunGu Songpa Anyang Bucheon Go yang Guri nam dong nam bu myung cheon Jongno 4,013 569 259 589 304 337 198 70 814 120 62 Jung 5,031 708 346 718 82 203 193 67 348 89 83 Yongsan 8,066 1,201 417 1,025 84 740 791 141 934 374 97 Seongdong 17,375 3,953 1,884 2,243 329 412 558 192 779 259 529 Gwangjin 21,745 8,746 3,615 3,948 543 687 376 136 816 360 1,105 Dongdaemun 11,593 2,865 1,690 1,684 1,164 551 577 146 937 194 1,114 Jungnang 15,516 4,337 2,458 2,252 1,733 538 570 153 677 119 3,618 Seongbuk 12,446 2,498 1,139 1,611 1,932 748 585 143 1,435 187 714 Gangbuk 9,664 1,726 800 1,176 2,700 514 400 94 976 236 363 Dobong 10,253 2,140 807 1,127 5,604 465 327 128 781 197 696 Nowon 19,765 4,301 2,126 2,029 6,362 721 733 163 1,311 320 1,517 Eunpyeong 11,536 1,916 734 1,583 867 938 946 320 12,252 213 162 Seodaemun 8,981 1,400 582 1,162 317 697 1,078 165 4,952 319 107 Mapo 10,672 1,710 473 985 332 986 1,198 412 3,993 247 198 Yangcheon 10,510 1,520 496 1,226 293 1,826 5,618 2,039 2,398 336 95 Gangseo 11,570 2,175 661 1,496 294 1,860 5,966 950 5,167 355 129 Guro 11,008 1,305 413 1,175 181 2,864 5,503 5,024 1,504 351 62 Geumcheon 5,616 716 180 783 111 4,030 1,108 3,965 757 331 30 Yeongdeungpo 11,767 1,885 562 1,293 133 2,569 2,463 1,443 2,150 377 65 Dongjak 20,105 2,957 657 2,273 307 2,763 1,586 1,125 1,210 992 164 Gwanak 30,124 4,486 970 3,351 219 3,530 2,289 1,460 1,698 1,120 154 Seocho 29,102 3,429 1,125 3,982 247 2,018 830 370 1,041 1,075 248 Gangnam 148,028 7,674 1,884 7,459 263 1,383 523 354 935 889 339 Songpa 40,914 147,179 10,426 12,668 281 1,303 655 179 915 546 851 Gangdong 19,904 25,443 101,517 7,127 443 690 388 169 646 315 751 Seongnam 43,446 19,773 4,142 279,146 456 3,001 745 230 658 1,440 533 Uijeongbu 5,775 1,344 642 831 99,927 246 310 149 1,488 69 894 Anyang 13,467 2,466 677 4,235 189 159,561 1,549 3,580 937 6,759 66 Bucheon 9,402 1,609 508 1,667 363 3,005 247,562 2,128 3,219 335 121 Gwangmyeong 6,951 822 204 997 144 4,601 3,151 62,972 951 214 11 Goyang 12,946 1,942 692 1,770 1,532 1,413 3,026 446 247,186 275 154 Gwacheon 2171 290 148 568 27 1,782 96 27 73 10,583 20 Guri 4,909 2,586 1,836 1,069 575 215 77 85 438 44 44,493 Namyangju 8,932 5,082 3,026 2,391 1,678 306 282 70 532 96 15,274 Gunpo 5,322 886 189 1,727 43 19,366 766 834 453 1,682 54 Uiwang 3,437 494 100 1,066 31 14,512 188 270 199 2,219 33 Hanam 3,392 4,459 7,473 1,648 59 92 84 29 159 38 217 Yongin 22,076 6,074 1,180 30,638 210 2,303 540 187 575 636 176 Paju 1,435 276 117 189 526 235 217 64 15,450 33 1 Gimpo 1,669 95 97 254 56 209 1,249 144 1,730 35 0 Gwangju 4,321 2,806 1,615 14,540 12 304 119 64 110 69 108 Yangju 943 298 144 229 9,617 23 114 36 901 10 115 Pocheon 145 56 42 38 2,421 41 9 8 116 0 121 Inflow 656,043 288,197 159,053 407,968 142,991 244,588 295,543 90,731 324,601 34,458 75,644 Outflow 284,484 315,167 237,445 495,255 198,460 321,873 441,165 160,810 434,771 29,047 95,830 InOut ratio 2.31 0.91 0.67 0.82 0.72 0.76 0.67 0.56 0.75 1.19 0.79 Population 510,221 579,040 445,339 934,984 398,870 612,423 838,801 320,268 866,846 56,711 187,414 Household 187,294 187,877 143,517 309,511 128,483 192,632 271,865 103,108 277,619 17,712 59,137 house 135,226 137,781 94,563 200,322 104,559 157,688 208,872 87,459 224,635 12,473 44,481 Householdsize 2.72 3.08 3.10 3.02 3.10 3.18 3.09 3.11 3.12 3.20 3.17 HHHouse Ratio 1.39 1.36 1.52 1.55 1.23 1.22 1.30 1.18 1.24 1.42 1.33

22 KIET Industrial Economic Review Table 1. endugo146419279831113146 6206164 86 65 144 1,123 331 958 207 199 634 164 Yeongdeungpo wnmen 787309 0 2 0 7 42 160,810 22 44 172 500 326 905 92 380 827 67 Gwangmyeong Hos ai .3 .8 .6 .0 .8 .4 .6 .2 .9 1.01 0.99 1.12 1.06 1.04 1.08 1.60 1.16 1.18 1.03 HHHouse Ratio ogamn92148 7 6 8 3 4 4 1 199,629 513 146 342 139 184 862 377 81 184 912 Dongdaemun oshlsz .6 .5 .8 .8 .5 .7 .4 .6 .3 2.98 3.23 3.26 3.24 3.17 3.25 3.18 3.28 3.15 3.26 Householdsize emho 2 ,3 0 5 0 5 0 54 133,066 43 45 107 351 203 453 0 308 1,030 120 Geumcheon edeu 2 6 6116385429 512178,836 142 65 97 462 825 653 161 86 169 224 Seodaemun endn 7 5 760911515691127175,475 267 121 629 135 155 911 600 87 158 778 Seongdong agho 7 9 0 310069199209 5 249348 150 225,865 99 464 240 396 1,929 264 689 533 1,020 2,109 53 627 100 163 396 87 173 281 162 Yangcheon Eunpyeong aynj 0,1 44 ,4 9 0 1 6 9 ,2 204,003 3,223 397 867 119 308 790 2,049 45 44 107,913 Namyangju ennm8360491821,4 4 8 4335 4 495,255 242 55 14,373 287 442 17,145 1,822 429 600 813 Seongnam agog15 4 163619017142484 6 237,445 265 40 2,418 144 177 1,960 6,356 31 245 1257 Gangdong wcen1 2 1 7383 51 129,047 11 18 95 9 36 318 27 318 328 18 Gwacheon oshl 3,1 5844,5 84222017,8 0486,7 47,0 63,376 60,418 76,388 212,061 38,492 43,859 85,814 130,614 Household enbk912113327245282826107226,881 1,027 286 288 278 455 762 312 133 211 951 Seongbuk ouain4607200213971237696122211576206, 195,776 242,241 689,691 122,337 143,987 270,042 426,087 Population ugag390198 9 1 4 3 8 4 8 218,811 981 249 582 231 349 815 699 80 109 3,910 Jungnang aga 1 6 8 8 ,9 0 7 4 818284,484 188 78 742 278 200 3,194 585 280 264 317 Gangnam nu ai .8 .3 .5 .6 .2 .7 .0 .2 .7 1.32 0.87 0.92 0.90 0.97 0.72 0.86 0.55 0.63 0.68 InOut ratio ienb ,0 36 4 6 ,2 2 31,1 ,3 198,460 9,136 11,714 93 124 1,025 268 241 60 93 2,209 Uijeongbu SiGunGu wnjn9511151041381014541747196,577 457 127 594 124 170 1,308 1,014 115 161 975 Gwangjin agu 6 63 0 1 7 1 4 6 ,1 169,720 1,216 361 141 119 274 610 200 36 96 567 Gangbuk age 1 9 1 7 ,5 ,4 ,8 8 310282,098 120 53 187 4,282 1,144 1,057 174 117 491 216 Gangseo oga 5 9 79 6 6 3 0 25 108,906 56 22 108 237 164 561 95 87 194 155 Yongsan oho 5 62 002 4 80566,310 58,075 647 25 0 70 23 66 0 0 756 Pocheon uho 9 2 4 5 ,5 ,6 ,0 5 4 2 441,165 321 143 354 3,703 1,060 1,352 152 244 720 191 Bucheon wnj 8 24 ,3 ,3 875,1 35 101,055 55 23 58,110 7 38 3,036 1,433 45 52 189 Gwangju wnk289439282043849291810284,841 120 108 279 459 338 2,004 298 339 904 208 Gwanak oga 7 9 2 0 ,6 2 5 7 79 216,990 96 77 272 356 223 1,268 306 229 392 174 Dongjak oog86159 5 7 1 7 2 ,3 ,5 191,686 2,352 1,338 228 173 419 479 251 97 145 896 Dobong ogo194 43 5 1 3104 679,088 36 47 130 83 115 252 36 64 48 119 Jongno eco193823142841722393 0 202335 109 32 399 232 107 2,814 174 293 348 119 Seocho oag292127171121,8 ,1 2 0 7 434,771 875 603 224 2,817 16,485 1,192 167 237 241 299 Goyang oga80401935635628271961710315,167 190 137 1,916 257 298 3,576 3,516 179 400 820 Songpa ufo 0,0 3,5 2726,5 3,1 1,9 404110572, 101,055 94,054 113,996 331,413 62,058 72,712 139,054 204,003 Outflow oo ,5 7 5 8 ,3 2 8 3 ,7 ,8 307,549 2,787 1,072 330 280 422 1,238 381 151 174 2,555 Nowon nag141,2 ,5 6 ,5 2 1 5 56 321,873 66 55 555 518 321 3,057 267 7,458 15,224 174 Anyang iag3 ,1 3594 1 54 6 472,712 34 0 166 46 65 812 46 23,519 4,119 34 Uiwang aa 5 42 963415 01881 2 62,058 128 10 1,828 10 50 471 29,693 21 74 459 Hanam up 15,4 ,9 711 1151806 139,054 62 0 148 125 51 1315 87 2,890 56,445 41 Gunpo ogn14735040174114164761 0 331,413 105 12 4,776 136 134 177,481 450 540 773 184 Yongin ip 47 13 6 9 1832 32 94,054 22 43 27 61,853 194 169 33 11 78 34 Gimpo agu161 910758 34,0 ,0 72,576 2,005 43,609 43 87 765 120 69 0 11 176 Yangju os 2,5 2403,3 41915877,9 6745,3 73147 47,341 56,630 56,754 73,499 195,877 24,119 37,933 72,490 127,056 house ao202514116574769 512195,156 122 55 90 776 704 655 111 144 225 210 Mapo nlw18838,8 9845,9 3,1 1,6 4649,0 62,990 93,200 84,624 110,567 238,310 53,591 39,854 87,786 138,863 Inflow ug8 62 3336 4113 3 66,781 137 33 111 34 62 333 43 21 66 86 Jung uo15652415983292123 6211,356 86 32 162 952 362 938 105 234 695 145 Guro au2 34 0187,4 3 44822113,996 202 408 34 232 78,643 118 20 45 43 21 Paju ui9029 460407 450158195,830 821 125 510 84 75 430 660 34 94 9,072 Guri aguju yangju Nam – Gunpo Commuting FlowinSeoulMSA(continued) Ui wang aa ogi auGimpo Paju Yongin Hanam Gwang – Yangju Mar. /Apr. 0 5,0 142,189 152,007 304 Pocheon ┃ 2009 16,627,149 ┃ 4,101,474 5,507,139 7 66,310 576 Vol 14 047,688 10 Total ┃ 87,415 No 2 ,337 23 1.34 3.02 Current Issues Table 2. Average One-way Commuting Time & Main Way in the Seoul MSA

Commuting Way Commuting Time(min) Seoul Resident(%) Gyeonggi Resident(%) Walk 14.31 20.7 18.3 Automobile 38.94 29.1 46.1 Bus(Public) 43.87 21.1 19.9 Commuter Bus 48.23 1.7 3.1 Express Bus 34.40 0.4 0.7 Subway 50.10 23.8 8.7 Train 92.29 0.1 0.1 Taxi 20.00 0.6 0.3 Bicycle 21.15 0.9 0.6 Misc 26.78 1.7 2.1 Source : Korean Census (Population and Housing) 2% Sample Data, 2005.

commuting time of residents living in Seoul and Gyeonggi are about 37.51 and 37.37 minutes respectively. In Table 2, commuters using train or subway as the main commuting method seem like long distance commuters. According to Kim(2007), the proportion of commuters using automobiles has been increasing but that of commuters using public buses has been decreasing since 1980. The percentage of commuters walking to their work place is about 20.7% which is a bit higher than Kim(2007)’s data(18%), possibly because Kim’s data included students over 12 year old which increased the number of people walking to a nearby place. Interestingly, the commuting method in Seoul compared to Gyeonggi in the Seoul MSA is quite different. More commuters in Gyeonggi in the Seoul MSA used automobiles to go to work, while a very small percentage of commuters in Gyeonggi in the Seoul MSA used subways.

5. Conclusion

This paper illustrates the commuting patterns in the Seoul MSA. It could be summarized in several points. First, urban sprawl has been ongoing and thus many suburban areas have been developed. However, large portions of suburban areas have been developed as residential areas rather than self-sufficient economic areas. Second, the Seoul MSA has been poly-centric.

24 KIET Industrial Economic Review The traditional centers(Jongno-Gu, Jung-Gu, and Yongsan-Gu) have moved to the financial center(Youngdeungpo-Gu) and new business centers

(Gangnam-Gu and Seocho-Gu). Third, the average one-way commuting time Current Issues is about 38 minutes in 2005, which is improved comparing to the commuting time in the UNCHS. However, it is necessary to discuss the average commuting time in more detail later to evaluate whether it is efficiently short or not. Fourth, the number of commuters using automobiles has been increasing, especially commuters from Gyeonggi to Seoul in the Seoul MSA, which increases congestion cost and demands of more social overhead capital investment. This commuting analysis implicates the development of some other large MSAs in following aspects: the location decision for commercial areas and resident areas, transportation, and infrastructure investment plans.

Dongsoo Kim Associate Research Fellow Research Center for Balanced National Development [email protected]

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