All Issue

2026 Vol.15, Issue 3 Preview Page

Research Article

Changes in the Spatial Patterns of Depressive Symptoms before, during, and after the COVID-19 Pandemic in Korea: Evidence from Spatial Panel Models and Multiscale Geographically Weighted Regression

코로나19 전후 우울감의 공간 분포 변화: 공간패널모형과 다중 스케일 지리가중회귀의 적용

이재은1 · 임창민2 · 김영호3†

Jaeeun Lee1 ⋅ Changmin Im2 ⋅ Youngho Kim3†

1국립공주대학교 지리학과 석사과정
2국립공주대학교 지리학과 조교수
3고려대학교 지리교육과 교수
1Master's Student, Department of Geography, Kongju National University
2Assistant Professor, Department of Geography, Kongju National University
3Professor, Department of Geography Education, Korea University
30 September 2026. pp. 477~497
PDF Citation
Abstract
This study examined changes in the nationwide spatial patterns of depressive symptoms across the COVID-19 pandemic in Korea. Hotspots of depressive symptoms decreased during the pandemic. In the spatial lag model, the association between neighboring and local depressive symptoms was not significant during social distancing and re-emerged in 2023 and 2024. Once the spatial Durbin model controlled for the six covariates of neighboring districts, the association appeared only weakly before the pandemic and was sensitive to model specification. The annual unmet healthcare need rate showed the strongest association with depressive symptoms in all three periods. From the pandemic onward, walking, volunteering, high-risk drinking, and out-migration in neighboring districts were associated with local depressive symptoms. Residents’ activities and mobility therefore gained weight relative to neighboring depressive symptoms in the spatial association. In the multiscale geographically weighted regression, the association between walking practice and depressive symptoms was significant only in some districts before the pandemic but in all districts from the pandemic onward. The findings indicate the need for mental health policy to consider both district units and activity areas.
Keywords
Depressive symptoms
COVID-19
Spatial pattern
Spillover effect
Spatial panel model
Multiscale geographically weighted regression
본 연구는 우울감경험률의 전국 단위 공간 분포가 코로나19를 거치며 어떻게 달라졌는지 분석하였다. 우울감의 핫스팟은 코로나 시기에 줄었다. 이웃 지역의 우울감만 고려한 공간시차모형에서 인접 지역 간 연관은 거리두기 기간에 유의하지 않았다가 일상이 회복된 2023~2024년에 다시 유의해졌다. 이웃 지역의 건강, 의료, 사회, 인구 요인을 통제한 공간더빈모형에서는 우울감의 지역 간 연관이 코로나 전에만 약하게 나타났으며, 모형 설정에 민감하였다. 연간 미충족 의료율은 세 시기 모두 우울감과 가장 강하게 연관되었다. 코로나19 이후에는 이웃 지역의 활동(걷기, 자원봉사, 고위험 음주, 전출 등)이 자기 지역의 우울감과 연관되었다. 우울감의 지역 간 연관에서 이웃 지역의 우울감보다 주민의 활동과 이동이 차지하는 비중이 커진 것이다. 다중 스케일 지리가중회귀에서 걷기 실천율과 우울감의 연관은 코로나 전에는 일부 지역에서만 유의하였으나, 코로나19 이후에는 전국에서 유의하였다. 본 연구의 결과는 정신건강 정책에서 시군구 단위와 생활권 단위의 접근을 함께 검토할 필요가 있음을 보여준다.
키워드
우울감
코로나19
공간 분포
파급효과
공간패널모형
다중 스케일 지리가중회귀
References
  1. 김상원·이훈래, 2016, “수도권 도시유형별 내부 인구이동 영향요인 분석,” 한국산학기술학회논문지, 17(9), 737-744. 10.5762/KAIS.2016.17.9.737
  2. 김수현, 2013, “노인의 자원봉사 참여가 신체적 건강, 우울, 사회적 지지 및 삶의 의미에 미치는 영향,” 한국노년학, 33(1), 53-66.
  3. 김지훈·최경숙·이진영·유정아·윤지애, 2024, “20대 젊은 성인의 우울과 사회적 자본의 관련성: 2019년 지역사회 건강조사 자료 연구,” 신경정신의학, 63(1), 57-67.
  4. 민동후·이혜재, 2025, “지역의 교통 및 의료 환경에 대한 인식이 미충족의료에 미치는 영향: 재정자립도 수준별 비교를 중심으로,” 보건사회연구, 45(2), 396-421.
  5. 박민진·김성아, 2022, “1인가구의 외로움과 사회적 고립 및 정신건강 문제의 특성과 유형: 서울시 1인가구를 중심으로,” 보건사회연구, 42(4), 127-141.
  6. 보건복지부, 2022, “사회적 거리두기 조치 약 2년 1개월만에 해제,” 보도자료(2022. 4. 15.).
  7. 오병록, 2014, “가구통행실태조사 자료를 이용한 통행특성 분석과 생활권 기준 설정 연구: 서울시를 중심으로,” 서울도시연구, 15(3), 1-18.
  8. 오영기·이병길·이상경, 2022, “서울시 상권 위계와 중심지체계 비교분석,” 한국지적정보학회지, 24(1), 65-76. 10.46416/JKCIA.2022.04.24.1.65
  9. 윤필한·우다래·박상신, 2022, “코로나바이러스감염증-19 상황에 따른 신체활동 감소 관련 요인,” 대한보건연구, 48(2), 99-110.
  10. 이상일·조대헌·이민파, 2016, “일변량 공간연관성통계량에 대한 비교 연구 (II): 국지적 Si 통계량을 중심으로,” 한국지리학회지, 5(3), 375-396. 10.25202/JAKG.5.3.12
  11. 이재경, 2015, “고위험음주율에 영향을 미치는 지역의 사회환경요인,” 한국사회복지학, 67(1), 165-187. 10.20970/kasw.2015.67.1.007
  12. 이진규·선우성문·최문정·김동하·박규민·고준석·장성만, 2021, “2019년 국내 우울증의 사회경제적 비용 산출,” 생물치료정신의학, 27(3), 237-244.
  13. 임유진·박미현, 2018, “1인가구 중년의 사회경제적 박탈이 우울에 미치는 영향,” 사회과학연구, 34(1), 187-206. 10.18859/ssrr.2018.02.34.1.187
  14. 전해정, 2016, “도시환경특성이 지역 암 사망자에 미치는 영향에 관한 연구: GIS와 지리적 가중회귀모형을 이용하여,” 한국지리학회지, 5(3), 345-355. 10.25202/JAKG.5.3.10
  15. 조대헌, 2018, “주택가격과 인구이동 간의 연관성에 관한 공간 분석: 수도권의 전세가격을 중심으로,” 한국지리학회지, 7(3), 449-462. 10.25202/JAKG.7.3.13
  16. 조미현·송재민, 2020, “서울 대도시권의 1인 가구 주거이동 및 유입지역 특성 분석: 직업과 주택 요인을 중심으로,” 국토계획, 55(4), 70-84. 10.17208/jkpa.2020.08.55.4.70
  17. 질병관리청, 2023, 「2022 지역건강통계 한눈에 보기」, 청주: 질병관리청.
  18. Berger, M., Zuba, M., and Simon, J., 2025, Urban-rural disparities in hospital admissions for depression in Austria: A spatial panel data analysis, Health Policy, 151, 105209. 10.1016/j.healthpol.2024.105209
  19. Brunsdon, C., Fotheringham, A.S., and Charlton, M.E., 1996, Geographically weighted regression: A method for exploring spatial nonstationarity, Geographical Analysis, 28(4), 281-298. 10.1111/j.1538-4632.1996.tb00936.x
  20. Choi, H. and Kim, H., 2017, Analysis of the relationship between community characteristics and depression using geographically weighted regression, Epidemiology and Health, 39, e2017025. 10.4178/epih.e2017025 28728351 PMC5668667
  21. Choi, J.A. and Kim, O., 2021, Factors influencing unmet healthcare needs among older Korean women, International Journal of Environmental Research and Public Health, 18(13), 6862. 10.3390/ijerph18136862 34206778 PMC8297300
  22. Comas, C., Martínez, A., and Blanch, A., 2025, Self-reported mental distress in the United States: A Bayesian analysis of the spatial structure over the COVID-19 pandemic across age groups, International Journal of Health Geographics, 24, 30. 10.1186/s12942-025-00418-6 41146281 PMC12560578
  23. Coviello, L., Sohn, Y., Kramer, A.D.I., Marlow, C., Franceschetti, M., Christakis, N.A., and Fowler, J.H., 2014, Detecting emotional contagion in massive social networks, PLoS ONE, 9(3), e90315. 10.1371/journal.pone.0090315 24621792 PMC3951248
  24. Cuijpers, P. and Smit, F., 2004, Subthreshold depression as a risk indicator for major depressive disorder: A systematic review of prospective studies, Acta Psychiatrica Scandinavica, 109(5), 325-331. 10.1111/j.1600-0447.2004.00301.x
  25. Curtis, S. and Jones, I.R., 1998, Is there a place for geography in the analysis of health inequality?, Sociology of Health and Illness, 20(5), 645-672. 10.1111/1467-9566.00123
  26. da Silva, A.R. and Fotheringham, A.S., 2016, The multiple testing issue in geographically weighted regression, Geographical Analysis, 48(3), 233-247. 10.1111/gean.12084
  27. Diez Roux, A.V., 2001, Investigating neighborhood and area effects on health, American Journal of Public Health, 91(11), 1783-1789. 10.2105/AJPH.91.11.1783
  28. Elhorst, J.P., 2014, Spatial Econometrics: From Cross-Sectional Data to Spatial Panels, Berlin: Springer. 10.1007/978-3-642-40340-8
  29. Fotheringham, A.S., Yang, W., and Kang, W., 2017, Multiscale geographically weighted regression (MGWR), Annals of the American Association of Geographers, 107(6), 1247-1265. 10.1080/24694452.2017.1352480
  30. Galea, S., Ahern, J., Rudenstine, S., Wallace, Z., and Vlahov, D., 2005, Urban built environment and depression: A multilevel analysis, Journal of Epidemiology and Community Health, 59(10), 822-827. 10.1136/jech.2005.033084 16166352 PMC1732922
  31. GBD 2019 Mental Disorders Collaborators, 2022, Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990-2019: A systematic analysis for the Global Burden of Disease Study 2019, The Lancet Psychiatry, 9(2), 137-150. 10.1016/S2215-0366(21)00395-3 35026139 PMC8776563
  32. Getis, A. and Ord, J.K., 1992, The analysis of spatial association by use of distance statistics, Geographical Analysis, 24(3), 189-206. 10.1111/j.1538-4632.1992.tb00261.x
  33. Grigoroglou, C., Munford, L., Webb, R.T., Kapur, N., Ashcroft, D.M., and Kontopantelis, E., 2020, Prevalence of mental illness in primary care and its association with deprivation and social fragmentation at the small-area level in England, Psychological Medicine, 50(2), 293-302. 10.1017/S0033291719000023 30744718 PMC7083582
  34. Ha, S.C. and Nam, E., 2022, Community welfare resources and late-life depression among older adults in Seoul: A multi-level analysis, International Journal of Geriatric Psychiatry, 37(3), https://doi.org/10.1002/gps.5690. 10.1002/gps.5690
  35. Hatfield, E., Cacioppo, J.T., and Rapson, R.L., 1993, Emotional contagion, Current Directions in Psychological Science, 2(3), 96-100. 10.1111/1467-8721.ep10770953
  36. Hausman, J.A., 1978, Specification tests in econometrics, Econometrica, 46(6), 1251-1271. 10.2307/1913827
  37. Hwang, J.H. and Kim, Y.J., 2023, Neighborhood effect on elderly depression in Republic of Korea, International Journal of Environmental Research and Public Health, 20(6), 5200. 10.3390/ijerph20065200 36982109 PMC10048868
  38. Jang, B.N., Youn, H.M., Lee, D.W., Joo, J.H., and Park, E.C., 2021, Association between community deprivation and practising health behaviours among South Korean adults: A survey-based cross-sectional study, BMJ Open, 11(6), e047244. 10.1136/bmjopen-2020-047244 34193495 PMC8246351
  39. Jeong, W. and Jeong, W., 2026, Trends in depressive episodes in pre-, intra-, and post-COVID-19 pandemic periods among South Korean adults, BMC Public Health, 26, 1473. 10.1186/s12889-026-27134-5 41896814 PMC13147646
  40. Joiner, T.E. and Katz, J., 1999, Contagion of depressive symptoms and mood: Meta-analytic review and explanations from cognitive, behavioral, and interpersonal viewpoints, Clinical Psychology: Science and Practice, 6(2), 149-164. 10.1093/clipsy.6.2.149
  41. Judd, L.L., Akiskal, H.S., and Paulus, M.P., 1997, The role and clinical significance of subsyndromal depressive symptoms (SSD) in unipolar major depressive disorder, Journal of Affective Disorders, 45(1-2), 5-18. 10.1016/S0165-0327(97)00055-4
  42. Kang, J., Park, J., Kim, H.J., Lee, H., Fond, G., Boyer, L., Rahmati, M., Nehs, C.J., Smith, L., and Yon, D.K., 2025, Association of unmet healthcare needs with depressive symptoms and COVID-19 pandemic-related factors among adults, 2009-2022: A nationwide study in Korea, Journal of Korean Medical Science, 40(47), e307. 10.3346/jkms.2025.40.e307 41365549 PMC12685598
  43. Kim, D., 2008, Blues from the neighborhood? Neighborhood characteristics and depression, Epidemiologic Reviews, 30(1), 101-117. 10.1093/epirev/mxn009
  44. Kim, E.S. and Nam, H.S., 2021, Factors related to regional variation in the high-risk drinking rate in Korea: Using quantile regression, Journal of Preventive Medicine and Public Health, 54(2), 145-152. 10.3961/jpmph.20.507 33845535 PMC8046605
  45. Kim, S.Y., Jeong, W., Park, E.C., Park, S., and Jang, S.I., 2021, Association between the severity of new-onset depression and unmet healthcare needs of South Korean adults, PLoS ONE, 16(8), e0256222. 10.1371/journal.pone.0256222 34411139 PMC8375986
  46. Kohn, R., Saxena, S., Levav, I., and Saraceno, B., 2004, The treatment gap in mental health care, Bulletin of the World Health Organization, 82(11), 858-866.
  47. Lee, E.J. and Kim, S.J., 2023, Prevalence and related factors of depression before and during the COVID-19 pandemic: Findings from the Korea National Health and Nutrition Examination Survey, Journal of Korean Medical Science, 38(10), e74. 10.3346/jkms.2023.38.e74 36918028 PMC10010913
  48. LeSage, J. and Pace, R.K., 2009, Introduction to Spatial Econometrics, Boca Raton: CRC Press. 10.1201/9781420064254
  49. Macintyre, S., Maciver, S., and Sooman, A., 1993, Area, class and health: Should we be focusing on places or people?, Journal of Social Policy, 22(2), 213-234. 10.1017/S0047279400019310
  50. Maza, A. and Hierro, M., 2022, Modelling changing patterns in the COVID-19 geographical distribution: Madrid’s case, Geographical Research, 60(2), 218-231. 10.1111/1745-5871.12521 PMC8652501
  51. Min, J., Kwon, D., Lee, W., Kang, C., Park, C., Heo, S., Bell, M.L., and Kim, H., 2022, Individual and regional characteristics associated with changes in mental health before and during the COVID-19 pandemic in South Korea, Scientific Reports, 12, 14189. 10.1038/s41598-022-18493-1 35986083 PMC9390104
  52. Moran, P.A.P., 1950, Notes on continuous stochastic phenomena, Biometrika, 37(1-2), 17-23. 10.1093/biomet/37.1-2.17
  53. Mutl, J. and Pfaffermayr, M., 2011, The Hausman test in a Cliff and Ord panel model, The Econometrics Journal, 14(1), 48-76. 10.1111/j.1368-423X.2010.00325.x
  54. Ord, J.K. and Getis, A., 1995, Local spatial autocorrelation statistics: Distributional issues and an application, Geographical Analysis, 27(4), 286-306. 10.1111/j.1538-4632.1995.tb00912.x
  55. Oshan, T.M., Smith, J.P., and Fotheringham, A.S., 2020, Targeting the spatial context of obesity determinants via multiscale geographically weighted regression, International Journal of Health Geographics, 19, 11. 10.1186/s12942-020-00204-6 32248807 PMC7132879
  56. Perlis, R.H., Lunz Trujillo, K., Safarpour, A., Quintana, A., Simonson, M.D., Perlis, J., Santillana, M., Ognyanova, K., Baum, M.A., Druckman, J.N., and Lazer, D., 2023, Community mobility and depressive symptoms during the COVID-19 pandemic in the United States, JAMA Network Open, 6(9), e2334945. 10.1001/jamanetworkopen.2023.34945 37755830 PMC10534266
  57. Prochazkova, E. and Kret, M.E., 2017, Connecting minds and sharing emotions through mimicry: A neurocognitive model of emotional contagion, Neuroscience and Biobehavioral Reviews, 80, 99-114. 10.1016/j.neubiorev.2017.05.013
  58. Rivera, K.M. and Mollalo, A., 2022, Spatial analysis and modelling of depression relative to social vulnerability index across the United States, Geospatial Health, 17(2), 1132. 10.4081/gh.2022.1132
  59. Rosenquist, J.N., Fowler, J.H., and Christakis, N.A., 2011, Social network determinants of depression, Molecular Psychiatry, 16(3), 273-281. 10.1038/mp.2010.13 20231839 PMC3832791
  60. Ryan, S.C., Desjardins, M.R., Runkle, J.D., Wertis, L., and Sugg, M.M., 2023, Evaluating co-occurring space-time clusters of depression and suicide-related outcomes before and during the COVID-19 pandemic, Spatial and Spatio-temporal Epidemiology, 47, 100607. 10.1016/j.sste.2023.100607 38042530 PMC10693657
  61. Schlosser, F., Maier, B.F., Jack, O., Hinrichs, D., Zachariae, A., and Brockmann, D., 2020, COVID-19 lockdown induces disease-mitigating structural changes in mobility networks, Proceedings of the National Academy of Sciences of the United States of America, 117(52), 32883-32890. 10.1073/pnas.2012326117 33273120 PMC7776901
  62. Seo, Y.M. and Park, K.S., 2023, The effect of COVID-19 outbreak and incidence on the health-related behaviors and depression of Gyeongnam residents in Republic of Korea, Medicina, 59(9), 1672. 10.3390/medicina59091672 37763791 PMC10535272
  63. Seok, J.W., Kim, K., Kim, J.U., and Yim, M.H., 2025, Association between unmet healthcare needs and depression in older adults: Evidence from the Korean National Health and Nutrition Examination Survey, Healthcare, 13(20), 2635. 10.3390/healthcare13202635 41154314 PMC12562491
  64. Tamura, M., Hattori, S., Tsuji, T., Kondo, K., Hanazato, M., Tsuno, K., and Sakamaki, H., 2021, Community-level participation in volunteer groups and individual depressive symptoms in Japanese older people: A three-year longitudinal multilevel analysis using JAGES data, International Journal of Environmental Research and Public Health, 18(14), 7502. 10.3390/ijerph18147502 34299953 PMC8306052
  65. Thornicroft, G., Chatterji, S., Evans-Lacko, S., Gruber, M., Sampson, N., Aguilar-Gaxiola, S., Al-Hamzawi, A., Alonso, J., Andrade, L., Borges, G., Bruffaerts, R., Bunting, B., Caldas de Almeida, J.M., Florescu, S., de Girolamo, G., Gureje, O., Haro, J.M., He, Y., Hinkov, H., Karam, E., Kawakami, N., Lee, S., Navarro-Mateu, F., Piazza, M., Posada-Villa, J., Torres de Galvis, Y., and Kessler, R.C., 2017, Undertreatment of people with major depressive disorder in 21 countries, British Journal of Psychiatry, 210(2), 119-124. 10.1192/bjp.bp.116.188078 27908899 PMC5288082
  66. Tsimpida, D., Tsakiridi, A., Daras, K., Corcoran, R., and Gabbay, M., 2024, Unravelling the dynamics of mental health inequalities in England: A 12-year nationwide longitudinal spatial analysis of recorded depression prevalence, SSM - Population Health, 26, 101669. 10.1016/j.ssmph.2024.101669 38708408 PMC11066558
  67. Villarreal-Zegarra, D., Al-kassab-Córdova, A., Otazú-Alfaro, S., and Cabieses, B., 2025, Socioeconomic and spatial distribution of depressive symptoms and access to treatment in Peru: A repeated nationwide cross-sectional study from 2014 to 2021, SSM - Population Health, 29, 101724. 10.1016/j.ssmph.2024.101724 39723109 PMC11667185
  68. Watanabe, R., Kondo, K., Saito, T., Tsuji, T., Hayashi, T., Ikeda, T., and Takeda, T., 2019, Change in municipality-level health-related social capital and depressive symptoms: Ecological and 5-year repeated cross-sectional study from the JAGES, International Journal of Environmental Research and Public Health, 16(11), 2038. 10.3390/ijerph16112038 31181718 PMC6604020
  69. WHO(World Health Organization), 2022, WHO Guidelines on Mental Health at Work, Geneva: World Health Organization.
  70. WHO(World Health Organization), 2026, Depressive disorder (depression), Fact sheet, https://www.who.int/news-room/fact-sheets/detail/depression
Information
  • Publisher :The Association of Korean Geographers
  • Publisher(Ko) :한국지리학회
  • Journal Title :Journal of the Association of Korean Geographers
  • Journal Title(Ko) :한국지리학회지
  • Volume : 15
  • No :3
  • Pages :477~497
  • DOI :https://doi.org/10.25202/JAKG.15.3.7
Journal Informaiton Journal of the Association of Korean Geographers Journal of the Association of Korean Geographers
Online Submission
  • NRF
  • KISTI Current Status
  • KISTI Cited-by
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close