Skip to main content
SpringerLink
Log in

Residential Relocation in a Metropolitan Area: A Case Study of the Seoul Metropolitan Area, South Korea

  • Chapter
  • First Online:
Spatial Econometric Interaction Modelling

Part of the book series: Advances in Spatial Science ((ADVSPATIAL))

  • 1611 Accesses

Abstract

Spatial interaction models have been utilized to model the movements of population. For instance, population migration, which generally refers to long distance movements such as interstate migration, has been widely investigated with this modeling framework. Recent research shows that a spatial interaction model can be significantly improved by incorporating network autocorrelation in its model specification. This new approach has been used in various types of population movement such as migration and commuting. However, network autocorrelation is rarely investigated for residential relocation, which refers to short distance movements within a small region such as a metropolitan area. This paper investigates the patterns of residential relocation with an empirical flow dataset in the Seoul metropolitan area, South Korea. Spatial interaction models are specified with an offset term and are estimated with Poisson and negative binomial regression. The eigenvector spatial filtering method is utilized to account for network autocorrelation in the models.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    An offset model specification is commonly utilized in modeling disease rates (e.g. Lawson et al. 2003).

  2. 2.

    Using contr.sum() function in R.

  3. 3.

    The test statistics of the likelihood ratio test for the Poisson models is 4,099,762 with 236 degrees of freedom. For the NB models, the test statistic is 3082.44 with 91 degrees of freedom.

References

  • Ball M (1986) The built environment and the urban question. Environ Plan D 4:447–464

    Article  Google Scholar 

  • Black WR (1992) Network autocorrelation in transport network and flow systems. Geogr Anal 24(3):207–222

    Article  Google Scholar 

  • Bolduc D, Dagenais MG, Gaudry MJI (1989) Spatially autocorrelated errors in origin–destination models: a new specification applied to aggregate mode choice. Transp Res B 23(5):361–372

    Article  Google Scholar 

  • Buchanan N, Barnett R (2006) Peripheral residential relocation and travel pattern change. Urban Policy Res 24(2):217–236

    Article  Google Scholar 

  • Burger M, van Oort F, Linders GJ (2009) On the specification of the gravity model of trade: zeros, excess zeros and zero-inflated estimation. Spat Econ Anal 4(2):167–190

    Article  Google Scholar 

  • Castro LJ, Rogers A (1981) Model migration schedules. International Institute for Applied Systems Analysis, Laxenburg

    Google Scholar 

  • Cho J (2006) Urban planning and urban sprawl in Korea. Urban Policy Res 23(2):203–218

    Article  Google Scholar 

  • Choi E (2004) The analysis of spatial characteristics on inter-regional migration. J Seoul Inst 5(3):49–66

    Google Scholar 

  • Choi E, Cho D (2005a) The characteristics of intra-urban migration in Seoul. J Korean Assoc Reg Geogr 11(2):169–186

    Google Scholar 

  • Choi E, Cho D (2005b) The economic barrier and characteristics of intra-urban migration in Seoul. J Seoul Inst 6(4):1–20

    Google Scholar 

  • Chun Y (2008) Modeling network autocorrelation within migration flows by eigenvector spatial filtering. J Geogr Syst 10:317–344

    Article  Google Scholar 

  • Chun Y (2013) Network autocorrelation and spatial filtering. In: Scherngell T (ed) The geography of networks and R&D collaborations. Springer, Berlin, pp 99–113

    Chapter  Google Scholar 

  • Chun Y (2014) Analyzing space-time crime incidents using eigenvector spatial filtering: an application to vehicle burglary. Geogr Anal 46(2):165–184

    Article  Google Scholar 

  • Chun Y, Griffith DA (2011) Modeling network autocorrelation in space-time migration flow data: an eigenvector spatial filtering approach. Ann Assoc Am Geogr 101(3):523–536

    Article  Google Scholar 

  • Chun Y, Kim H, Kim C (2012) Modeling interregional commodity flows with incorporating network autocorrelation in spatial interaction models: an application of the U.S. interstate commodity flows. Comput Environ Urban Syst 36(6):583–591

    Article  Google Scholar 

  • Clark WAV (1982) Recent research on migration and mobility: a review and interpretation. Prog Plan 18:1–56

    Article  Google Scholar 

  • Clark WAV, Onaka J (1983) Life cycle and housing adjustment as explanations of residential mobility. Urban Stud 20:47–57

    Article  Google Scholar 

  • Clark WAV, Deurloo MC, Dieleman FM (2000) Housing consumption and residential crowding in US housing markets. J Urban Aff 22(1):49–63

    Article  Google Scholar 

  • Clark WAV, Huanh Y, Withers S (2003) Does commuting distance matter? Commuting tolerance and residential change. Reg Sci Urban Econ 33:199–221

    Article  Google Scholar 

  • Curry L (1972) A spatial analysis of gravity flows. Reg Stud 6:131–47

    Article  Google Scholar 

  • Daniell J, Struyk R (1997) The evolving housing market in Moscow: indicators of housing reform. Urban Stud 34:235–254

    Article  Google Scholar 

  • Davis LK (2004) Reshaping Seoul: redevelopment, women and insurgent citizenship. Doctoral dissertation, Johns Hopkins University

    Google Scholar 

  • de Palma A, Motamedi K, Picard N, Waddell P (2005) A model of residential location choice with endogenous housing prices and traffic for the Paris region. Eur Transp 31:67–82

    Google Scholar 

  • Dieleman F (2001) Modeling residential mobility; a review of recent trends in research. J Hous Built Environ 16:249–265

    Article  Google Scholar 

  • Fischer MM, Griffith DA (2008) Modeling spatial autocorrelation in spatial interaction data: an application to patent citation data in the European Union. J Reg Sci 48:969–989

    Article  Google Scholar 

  • Flowerdew R, Aitkin M (1982) A method of fitting the gravity model based on the Poisson distribution. J Reg Sci 22(2):191–202

    Article  Google Scholar 

  • Forrest R, Murie A (1990) A dissatisfied state? Consumer preferences and council housing in Britain. Urban Stud 27:617–635

    Article  Google Scholar 

  • Gober P (1992) Urban housing demography. Prog Hum Geogr 16:171–189

    Article  Google Scholar 

  • Goh S, Lee K, Park JS, Choi MY (2012) Modification of the gravity model and application to the metropolitan Seoul subway system. Phys Rev E 86(2):026102

    Article  Google Scholar 

  • Golledge RG, Stimson RJ (1997) Spatial behaviour: a geographical perspective. Guilford Press, New York

    Google Scholar 

  • Greene RP, Pick JB (2006) Exploring the urban community. Pearson Prentice Hall, Upper Saddle River

    Google Scholar 

  • Greenwood M (1985) Human migration: theory, models and empirical studies. J Reg Sci 25(4):521–544

    Article  Google Scholar 

  • Greenwood MJ, Hunt GL (2003) The early history of migration research. Int Reg Sci Rev 26(1): 3–37

    Article  Google Scholar 

  • Griffith DA (2003) Spatial autocorrelation and spatial filtering: gaining understanding through theory and scientific visualization. Springer, Berlin

    Book  Google Scholar 

  • Griffith DA (2007) Spatial structure and spatial interaction: 25 years later. Rev Reg Stud 37(1): 28–38

    Google Scholar 

  • Griffith DA (2009) Modeling spatial autocorrelation in spatial interaction data: empirical evidence from 2002 Germany journey-to-work flows. J Geogr Syst 11:117–140

    Article  Google Scholar 

  • Griffith DA (2011a) Visualizing analytical spatial autocorrelation components latent in spatial interaction data: an eigenvector spatial filter approach. J Geogr Syst 35(2):140–149

    Google Scholar 

  • Griffith DA (2011b) Positive spatial autocorrelation impacts on attribute variable frequency distributions. Chil J Stat 2(2):3–28

    Google Scholar 

  • Griffith DA, Jones KG (1980) Explorations into the relationship between spatial structure and spatial interaction. Environ Plan A 12:187–201

    Article  Google Scholar 

  • Gu J, Liu F (1997) The problem of residential relocation, Shanghai in the next century: thinking on social development problems Shanghai ‘Nine-Fifth’ social development research group. Shanghai Social Science Academy, Shanghai, pp 396–402

    Google Scholar 

  • Helderman AC, Ham M, Mulder CH (2006) Migration and home ownership. Tijdschr Econ Soc Geogr 97(2):111–125

    Article  Google Scholar 

  • Jonas DV, Ben D, Veroniqu VA, Frank W (2012) Reducing car use: changing attitudes or relocating? The influence of residential dissonance on travel behavior. J Transp Geogr 22:1–9

    Article  Google Scholar 

  • Jun MJ (2012) The effects of Seoul’s new-town development on suburbanization and mobility: a counterfactual approach. Environ Plan A 44(9):2171–2190

    Article  Google Scholar 

  • Kim K (1993) Housing prices, affordability, and government policy in Korea. J Real Estate Financ Econ 6:55–71

    Article  Google Scholar 

  • Kim S (2009) Re-locating the national: spatialization of the nation past in Seoul. Policy Futures Educ 7(2):256–265

    Article  Google Scholar 

  • Kim C, Sang S, Chun Y, Lee W (2012) Exploring urban commuting imbalance by jobs and gender. Appl Geogr 32(2):532–545

    Article  Google Scholar 

  • Knox C (1993) Compulsory competitive tendering in Northern Ireland local government. Local Govern Stud 19(2):208–225

    Google Scholar 

  • Krizek KJ (2003) Residential relocation and changes in urban travel: does neighborhood-scale urban form matter? J Am Plann Assoc 69(3):265–281

    Article  Google Scholar 

  • Kwon Y, Lee J (1995) Spatial pattern of migration in the Seoul metropolitan area. J Korea Plann Assoc 30(4):21–39

    Google Scholar 

  • La Grange A, Jung H (2004) The commodification of land and housing: the case of South Korea. Hous Stud 19(4):557–580

    Article  Google Scholar 

  • Ladd H, Ludwig J (1997) Federal housing assistance, residential relocation, and educational opportunities: evidence from Baltimore. Am Econ Rev 87(2):272–277

    Google Scholar 

  • Lawson AB, Browne WJ, Vidal Rodeiro CL (2003) Disease Mapping with WinBUGS and MLwiN. Wiley, Chichester

    Book  Google Scholar 

  • Lee H, Lee S (2008) The Influence of new town development on the changes of the migration and commuting patterns in the capital region. J Korean Geogr Assoc 43(4):561–579

    Google Scholar 

  • LeSage J, Pace R (2008) Spatial econometric modeling of origin–destination flows. J Reg Sci 48(5):941–967

    Article  Google Scholar 

  • Liu Y, Shen J (2013) Spatial patterns and determinants of skilled internal migration in China, 2000–2005. Pap Reg Sci. doi:10.1111/pirs.12014

    Google Scholar 

  • Longino CF (1980) Residential relocation of older people metropolitan and nonmetropolitan. Res Aging 2(2):205–216

    Google Scholar 

  • Ludwig J, Duncan G, Pinkston J (2000) Neighborhood effects on economic self-sufficiency: evidence from a randomized housing-mobility experiment. Georgetown University, unpublished paper

    Google Scholar 

  • Mulder CH (1993) Migration dynamics: a life course approach. Thesis Publishers, Amsterdam

    Google Scholar 

  • Nijkamp P, Van Wissen L, Rima A (1993) A household life cycle model for residential relocation behaviour. Socioecon Plann Sci 27(1):35–53

    Article  Google Scholar 

  • Pacione M (2001) Urban geography: a global perspective. Routledge, London

    Book  Google Scholar 

  • Patuelli R, Griffith DA, Tiefelsdorf M, Nijkamp P (2011) Spatial filtering and eigenvector stability: space-time models for German unemployment data. Int Reg Sci Rev 34(2):253–80

    Article  Google Scholar 

  • Patuelli R, Mussoni M, Candela G (2013) The effects of World Heritage Sites on domestic tourism: a spatial interaction model for Italy. J Geogr Syst 15(3):369–402

    Article  Google Scholar 

  • Rogers A, Castro LJ (1981) Model migration schedule. International Institute for Applied Systems Analysis, Laxenburg

    Google Scholar 

  • Rossi PH (1955) Why families move: A study in the social psychology of urban residential mobility. Free Press, Glencoe

    Google Scholar 

  • Smith N (1996) The new urban frontier: gentrification and the revanchist city. Routledge, London

    Google Scholar 

  • Strassmann WP (2001) Residential mobility: contrasting approaches in Europe and the United States. Hous Stud 16(1):7–20

    Article  Google Scholar 

  • Sullivan O, Murphy MJ (1984) Housing pathways and stratification: some evidence from a British national survey. J Soc Policy 13(02):147–165

    Article  Google Scholar 

  • Teixeira C, Murdie RA (1997) The role of ethnic real estate agents in the residential relocation process: a case study of Portuguese homebuyers in suburban Toronto. Urban Geogr 18(6):497–520

    Article  Google Scholar 

  • Tiefelsdorf M, Griffith DA (2007) Semiparametric filtering of spatial autocorrelation: the eigenvector approach. Environ Plan A 39(5):1193–1221

    Article  Google Scholar 

  • White P (1985) Levels of intra-urban migration in Western European cities. Espace, populations, sociétés 3(1):161–169

    Article  Google Scholar 

  • Wilson AG (1974) Urban and regional models in geography and planning. Wiley, London

    Google Scholar 

  • Wu F (2004) Intraurban residential relocation in Shanghai: modes and stratification. Environ Plan A 36(1):7–26

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Economic, Political and Policy Science, The University of Texas at Dallas, Richardson, TX, USA

    Monghyeon Lee & Yongwan Chun

Authors
  1. Monghyeon Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yongwan Chun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Monghyeon Lee .

Editor information

Editors and Affiliations

  1. Department of Economics, University of Bologna, Rimini, Italy

    Roberto Patuelli

  2. Università Cattolica del Sacro Cuore, Rome, Italy

    Giuseppe Arbia

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Lee, M., Chun, Y. (2016). Residential Relocation in a Metropolitan Area: A Case Study of the Seoul Metropolitan Area, South Korea. In: Patuelli, R., Arbia, G. (eds) Spatial Econometric Interaction Modelling. Advances in Spatial Science. Springer, Cham. https://doi.org/10.1007/978-3-319-30196-9_17

Download citation

Publish with us

Policies and ethics

Search

Navigation