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2025 Vol.43, Issue 6 Preview Page

Article

An Explanatory Analysis of Spatial Structure of Seoul Metropolitan Area Based on Individual Mobility Characteristics: An Application of Multivariate Spatial Statistics

개인 단위 모빌리티 특성 기반 수도권 공간구조 분석: 다변량 공간통계기법을 중심으로

Jiyun SHIM1
,
Jae Hyun LEE2*
심 지윤1
,
이 재현2*
1Researcher, Department of Transport Big Data, Korea Transport Institute, Sejong 30147, Korea
2Associate Professor, Department of Geography, Kyungpook National University, Daegu 41566, Korea
1한국교통연구원 교통빅데이터연구본부 연구원
2경북대학교 지리학과 부교수
*Corresponding Author
31 December 2025. pp. 743-760
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Abstract

The Seoul Metropolitan Area (SMA) operates as a functionally integrated region, yet reveals spatial disparities in population composition, industrial structure, and mobility behavior. Despite this, spatial understanding grounded in residents' daily activities and travel patterns—which could inform effective metropolitan planning—remains limited. This study examines the spatial autocorrelation of multidimensional mobility indicators to identify similarity and heterogeneity in residents’ travel patterns across the SMA. To this end, we utilize multidimensional mobility indicators that summarize individuals’ travel behaviors and conduct global and local Moran’s I analyses for both univariate and bivariate cases. The results reveal two contrasting spatial clusters: peripheral Gyeonggi regions characterized by long-distance, long-duration, monotonous, and elongated trips, and central Seoul characterized by short-distance, short- duration, complex, and circular trips. We find that the gravitational pull of central Seoul extends into surrounding Gyeonggi regions. These findings highlight practical evidence for developing policies that reflect the unique characteristics of regions within the Seoul Metropolitan Area.

Keywords
mobility analysis
multidimensional mobility indicator
seoul metropolitan area
spatial autocorrelation
spatial statistical analysis

수도권은 광역화된 하나의 경제 및 생활권으로 권역 내 위치한 도시 및 지역 간 상호작용이 활발한 지역이다. 그러나 이들 지역은 인구구조, 산업구조, 생활양식 등에서 상당한 차이가 나타나고 있다. 이에 따라 수도권 광역계획은 지역의 연계와 조화를 도모함과 동시에, 각 지역의 특성을 차별적으로 반영하여야 한다. 그러나, 이러한 계획 수립에 근거로 쓰일 수 있는 지역주민들의 일상생활 활동-통행 특성에 기반한 공간구조에 대한 이해는 미진한 실정이다. 따라서, 본 연구는 수도권 거주자의 모빌리티 특성을 공간적으로 분석하여 지역별 패턴의 유사성과 이질성을 이해하고자 하였다. 이를 위해 개개인의 복잡한 통행행태 특성을 요약하여 나타낼 수 있는 다차원 모빌리티 지표를 이용하여, 일변량 및 이변량에 대해 전역적, 국지적 Moran’s I 공간통계 분석을 수행하였다. 분석 결과, 장거리·장시간 통행 및 단순장종(長縱)형 패턴이 나타나는 경기 외곽 클러스터와, 단거리·단시간 통행 및 복잡원형형 패턴을 보이는 서울 중심 클러스터가 도출되었다. 이와 함께 수도권 중심부의 유인력이 인근 경기 지역까지 미치고 있음을 확인하였다. 본 연구는 공간적 자기상관 기반의 모빌리티 분석을 통해 수도권 내 지역 특성을 반영한 정책 수립의 실질적 근거를 제공할 수 있을 것이다.

키워드
모빌리티 분석
다차원 모빌리티 지표
수도권
공간적 자기상관
공간통계분석
References
1

Aguiar L. L., Manzato G. G., da Silva A. N. R. (2020), Combining Travel and Population Data Through a Bivariate Spatial Analysis to Define Functional Urban Regions, J. Transp. Geogr,, 82, 102565.

10.1016/j.jtrangeo.2019.102565
2

Anselin L. (1995), Local Indicators of Spatial Association—LISA, Geogr. Anal., 27(2), 93-115.

10.1111/j.1538-4632.1995.tb00338.x
3

Anselin L., Syabri I., Smirnov O. (2002), Visualizing Multivariate Spatial Correlation with Dynamically Linked Windows, In Proceedings, CSISS Workshop on New Tools for Spatial Data Analysis, Santa Barbara, 2, CA.

4

Cao J., Li Q., Tu W., Wang F. (2019), Characterizing Preferred Motif Choices and Distance Impacts, PLOS ONE, 14(4), e0215242.

10.1371/journal.pone.021524230990848PMC6467417
5

Chea H., Shin J. (2015), Exploration of Changes in Social Diversity in the Seoul Metropolitan Region, J. Assoc. Korean Geogr., 4(1), 139-154.

10.25202/JAKG.4.1.9
6

Cho S., Lee G. (2017), Spatial Mismatch Between the Elderly Populated Areas and Elderly Demanding Facilities Using Spatial Statistics, J. Korean Urban Geog. Soc., 20(2), 99-112.

10.21189/JKUGS.20.2.8
7

Choo S., Lee H., Shin H. (2013), Analyzing Changes in Travel Behavior of the Elderly Using Travel Diary Survey Data in Seoul Metropolitan Area, Korea Spat. Plan. Rev., 31-45.

10.15793/kspr.2013.76..003
8

Fillekes M. P., Giannouli E., Kim E. K., Zijlstra W., Weibel R. (2019), Towards a Comprehensive Set of GPS-based Indicators Reflecting the Multidimensional Nature of Daily Mobility for Applications in Health and Aging Research, Int. J. Health Geogr., 18, 1-20.

10.1186/s12942-019-0181-031340812PMC6657041
9

García-Albertos P., Picornell M., Salas-Olmedo M. H., Gutiérrez J. (2019), Exploring the Potential of Mobile Phone Records and Online Route Planners for Dynamic Accessibility Analysis, Transp. Res. A: Policy Pract., 125, 294-307.

10.1016/j.tra.2018.02.008
10

Gutiérrez J., García-Palomares J. C., Romanillos G., Salas-Olmedo M. H. (2017), The Eruption of Airbnb in Tourist Cities: Comparing Spatial Patterns of Hotels and Peer-to-peer Accommodation in Barcelona, Tour. Manag., 62, 278-291.

10.1016/j.tourman.2017.05.003
11

Jang S., Yi C. (2021), Imbalance Between Local Commuting Accessibility and Residential Locations of Households by Income Class in the Seoul Metropolitan Area, Cities, 109, 103011.

10.1016/j.cities.2020.103011
12

Jeon B. Y., Yi C., Song H. J. (2024), An Analysis of Access by Public Service Facilities Considering the Walking Speed of the Elderly Population: Focused on Daejeon Metropolitan City, J. Korea Acad.-Ind. Cooper. Soc., 25(1), 283-294.

10.5762/KAIS.2024.25.1.283
13

Jiang S., Ferreira J., González M. C. (2012), Clustering Daily Patterns of Human Activities in the City, Data Mining and Knowledge Discovery, 25, 478-510.

10.1007/s10618-012-0264-z
14

Jin C., Lee H., Choo S. (2012), Developing Trip Generation Models Using Spatial Regression Analysis: A Case for Seoul, Korea, Korea Spat. Plan. Rev., 73, 131-143.

10.15793/kspr.2012.73..008
15

Jin T., Cheng L., Wang K., Cao J., Huang H., Witlox F. (2022), Examining Equity in Accessibility to Multi-tier healthcare Services Across Different Income Households Using Estimated Travel Time, Transp. Policy. 121, 1-13.

10.1016/j.tranpol.2022.03.014
16

Jung N. J., Kang J. Y. (2024), Measuring Spatial Accessibility to Hospitals for Infants, Children, Adolescents, and the Elderly Population Using 2SFCA: A Case Study of Chuncheon-si, Gangwon-do, J. Cadastre & Land Information, 54(1), 49-61.

17

Kim H., An Y. (2018), A Study on the Identification of the Center of the Seoul Metropolitan Area and Methodology Based on Commuting, J. Korean Reg. Sci. Assoc., 34(2), 49-64.

18

Kim J. (2024), Optimal Location Modeling for Small Senior Welfare Centers in Seoul: Multi-Objective Optimization Model Considering Spatial Efficiency and Equity (Doctoral dissertation), Graduate School of Seoul National University, Seoul, South Korea.

19

Kim J. Y., Song T. J., Seong H. M., Kim D. H. (2019), A Study on the Reliability of Traffic Demand Prediction Based on Big Data, The Korea Transport Institute, 1-177.

20

Kim K. (2003), Exploration of Spatial Autocorrelation and Application of Spatial Regression Analysis, Korean J. Policy Analysis and Evaluation, 13(1), 273-294.

21

Kim K. (2015), A Study on an Assessment of Spatial Equity about Opportunities to Use Public Childcare Services: Focused on National/Public Childcare Facilities in Seoul (Doctoral dissertation), Graduate School of Seoul National University, Seoul, South Korea.

22

Kim S. (2023), Changes in Spatial Equity of the Korean Intercity Rail Network in the 2010s (Doctoral dissertation), Graduate School of Seoul National University, Seoul, South Korea.

23

Kim S. A., Lee J., Park S. J., An S., Kim H. (2023), Analyzing Urban Characteristics Affecting Public Transportation Use of the Elderly in Seoul: Using Transportation Card Data, J. Korean Assoc. Prof. Geogr., 57(4), 377-390.

10.22905/kaopqj.2023.57.4.2
24

Kong J. H., Lee E., Chung J. H., Kim J. (2021), A Study on the Categorization of Worker’s Activity Behaviors and the Relationship Between Land Use and Transportation Infrastructure Level in Seoul, J. Korean Soc. Transp., 39(4), Korean Society of Transportation, 430-446.

10.7470/jkst.2021.39.4.430
25

Lee H., Noh S. (2020), Advanced Statistical Analysis: Theory and Practice, Moonwoosa, Goyang-si, South Korea, 228-234, 594-604.

26

Lee J. (2020), An Analysis of the Elderly’s Travel Pattern Based on Individual Traffic Type Classification: Focused on Seoul Smart Card Data (Master’s thesis), Graduate School of Ewha Womans University, Seoul, South Korea.

27

Martin H., Wiedemann N., Reck D. J., Raubal M. (2023), Graph-based Mobility Profiling, Computers, Environment and Urban Systems, 100, 101910.

10.1016/j.compenvurbsys.2022.101910
28

Moran P. A. (1950), Notes on Continuous Stochastic Phenomena, Biometrika, 37(1/2), 17-23.

10.1093/biomet/37.1-2.17
29

Park S., Woo M. (2022), A Study on Urban Spatial Structure and Major Factors Associated with Jobs-housings Balances: Focusing on the Job-housings Index Reflecting Commuting Distance and Spatial Regression Analysis, Proceedings of the Fall Joint Conference of the Korean Regional Development Association, 556-574.

30

Park W., Lee W. D., Joh C. H. (2013), Time-use and Travel Pattern of Residents in the Seoul Metropolitan Area Using Activity-based Analysis, Proceedings of the 69th Conference of the Korean Soc. of Transp., 69, 354-358.

31

Shan Z., Li H., Pan H., Yuan M., Xu S. (2022), Spatial Equity of PM2.5 Pollution Exposures in High-density Metropolitan Areas Based on Remote Sensing, LBS and GIS Data: A Case Study in Wuhan, China, Int. j. environ. res. public health, 19(19), 12671.

10.3390/ijerph19191267136231971PMC9566263
32

Shin H., Woo M. (2021), The Impact of Compact City Indicators and Commuting Network on Commuting Time: Focused on Suburban Cities in the Seoul Metropolitan Area, J. Korean Reg. Sci. Assoc., 37(2), 49-61.

33

Statistics Korea (2023), Gross Regional Domestic Product (GRDP) by Province and Economic Activity [Data set], KOSIS (Korean Statistical Information Service), https://kosis.kr/statHtml/statHtml.do?orgId=101&tblId=DT_1C91, 2025. 10. 01.

34

Statistics Korea (2023), Number of Establishments and Employees by Region, Industry, and Enterprise Size [Data set], KOSIS (Korean Statistical Information Service), https://kosis.kr/statHtml/statHtml.do?orgId=101&tblId=DT_1K52D03, 2025. 10. 01.

35

Statistics Korea (2025), Population by Sex and Administrative District (Si/Gun/Gu) [Data set], KOSIS (Korean Statistical Information Service), https://kosis.kr/statHtml/statHtml.do?orgId=101&tblId=DT_1B040A3, 2025. 10. 01.

36

Tobler W. R. (1970), A Computer Movie Simulating Urban Growth in the Detroit Region, Econ. Geogr., 46(sup1), 234-240.

10.2307/143141
37

Won M. (2022), Mobility Report: March Issue [In Korean], Korea Transport DataBase (KTDB). https://www.ktdb. go.kr/www/selectBbsNttView.do?key=42&bbsNo=7&nttNo=3687, 2025. 09. 24.

38

Won M., Kim D., Jang D. I., Kim J. (2023), Mobility Analysis of People in South Korea Using Pseudonymous Mobile Phone Data, The Korea Transport Institute, 231-326.

39

Won M., Shim J., Choi S., Lee J. H. (2024), Development of Multi-dimensional Mobility Indicators (MDMI) for Agent-based Travel Pattern Classification, J. Korean Soc. Transp., 42(6), Korean Society of Transportation, 674-693.

10.7470/jkst.2024.42.6.674
40

Xu Y., Xue J., Park S., Yue Y. (2021), Towards a Multidimensional View of Tourist Mobility Patterns in Cities: A Mobile Phone Data Perspective, Comput. Environ. Urban Syst.,, 86, 101593.

10.1016/j.compenvurbsys.2020.101593
41

Yang Y., Samaranayake S., Dogan T. (2023), A Clustering-based Approach to Quantifying Socio-demographic Impacts on Urban Mobility Patterns, Environ. Plan. B Urban Anal. City Sci., 50(9), 2452-2469.

10.1177/23998083231159909
42

Yun H., Park M. (2014), Multivariate Spatial Autocorrelation on the Number of Tourists and Natural Amenities, J. Tour. Manag. Res., 58, 135-150.

43

Zamprogno M. M., Esztergár-Kiss D. (2024), Applying Cluster Analysis for the Investigation of Travel Behavior and User Profiles, Period. Polytech. Transp. Eng., 52(4), 362-371.

10.3311/PPtr.25663
44

Zhou Y., Yang C., Chen M., Liu Y., Yuan Q. (2023), Commuting Versus Consumption: The Role of Core City in a Metropolitan Area, Cities, 141, 104495.

10.1016/j.cities.2023.104495
Information
  • Publisher :Korean Society of Transportation
  • Publisher(Ko) :대한교통학회
  • Journal Title :Journal of Korean Society of Transportation
  • Journal Title(Ko) :대한교통학회지
  • Volume : 43
  • No :6
  • Pages :743-760
  • Received Date : 2025-07-01
  • Revised Date : 2025-07-17
  • Accepted Date : 2025-09-02
  • DOI :https://doi.org/10.7470/jkst.2025.43.6.743
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