Spatializing an Artist-Resident Community Area at a Building-Level: A Case Study of Garosu-Gil, South Korea

This study integrated a focus on geographical, physical, and commercial characteristics to explore the commercial gentrification phenomenon and its related statistical summaries in the area of Garosu-gil in Seoul’s Sinsa-dong ward. In particular, this study first collected parcel and building data and corresponding attribute information and mapped the resulting datasets in a geographic information system (GIS) environment. We then examined gentrification issues per building and conducted statistical analyses to investigate spatial patterns of commercial gentrification, which were used to develop criteria for determining degrees of gentrification. Third, this study conducted correlation and regression analyses to quantify the strength of the linear relationship between pairs of variables associated with primary factors contributing to commercial gentrification, and used a geographically weighted regression model (GWR) to help understand and predict spatial relationships between significant variables. The results showed positive correlations between several variables and commercial gentrification in the study area, namely neighborhood-convenience facilities, building ages, store rents, new franchise and restaurant businesses, distance to subways, and the presence of multiple roads. Based on its finding, there are key contributions of this study as follows. The first significant contribution of this study is developing measurement of gentrification levels that can be used by policy makers at each of four stages of the gentrification process. Furthermore, this paper develops a comprehensive approach for spatially identifying gentrifying neighborhoods across multiple time periods in 2- and 3-dimensions. It eventually helps urban planners implement preventative or supportive programs to protect lower-income residents and small businesses and thereby engender more sustainable community development.

[1]  Residential Displacement: Extent, Nature, and Effects , 1982 .

[2]  Yannis A. Phillis,et al.  Urban sustainability assessment and ranking of cities , 2017, Comput. Environ. Urban Syst..

[3]  Sandro Galea,et al.  The Present and Future of Cities , 2019, Urban Health.

[4]  Andrew T. Crooks,et al.  Constructing and implementing an agent-based model of residential segregation through vector GIS , 2010, Int. J. Geogr. Inf. Sci..

[5]  J. Maantay,et al.  Brownfields to Greenfields: Environmental Justice Versus Environmental Gentrification , 2018, International journal of environmental research and public health.

[6]  J. Patch The embedded landscape of gentrification , 2004 .

[7]  Jina Park,et al.  Stage Classification and Characteristics Analysis of Commercial Gentrification in Seoul , 2018, Sustainability.

[8]  Samuel C. Shaw,et al.  Retail Gentrification and Race: The Case of Alberta Street in Portland, Oregon , 2011 .

[9]  J. Brueckner,et al.  Gentrification and Neighborhood Housing Cycles: Will America's Future Downtowns Be Rich? , 2005, The Review of Economics and Statistics.

[10]  K. Chapple,et al.  Retail Trade as a Route to Neighborhood Revitalization , 2009 .

[11]  Paul M. Torrens,et al.  Modeling gentrification dynamics: A hybrid approach , 2007, Comput. Environ. Urban Syst..

[12]  A. Stewart Fotheringham,et al.  Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity , 2010 .

[13]  M. Charlton,et al.  Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis , 1998 .

[14]  D. Ley,et al.  Immigration, Polarization, or Gentrification? Accounting for Changing House Prices and Dwelling Values in Gateway Cities , 2002 .

[15]  Seungjae Lee,et al.  Fast and Accurate Visual Place Recognition Using Street‐View Images , 2017 .

[16]  Li Yin,et al.  The Dynamics of Residential Segregation in Buffalo: An Agent-based Simulation , 2009 .

[17]  Yushim Kim,et al.  Gentrification and Displacement: Modeling a Complex Urban Process , 2018, Housing Policy Debate.

[18]  I. Ellen,et al.  Pathways to Integration: Examining Changes in the Prevalence of Racially Integrated Neighborhoods , 2012 .

[19]  Camille Z. Charles,et al.  Neighborhood Racial-Composition Preferences: Evidence from a Multiethnic Metropolis , 2000 .

[20]  Ji Youn (Rose) Kim Cultural entrepreneurs and urban regeneration in Itaewon, Seoul , 2016 .

[21]  Soyoung Han,et al.  Mapping the Distribution Pattern of Gentrification near Urban Parks in the Case of Gyeongui Line Forest Park, Seoul, Korea , 2017 .

[22]  John R. Hipp Segregation Through the Lens of Housing Unit Transition: What Roles Do the Prior Residents, the Local Micro-Neighborhood, and the Broader Neighborhood Play? , 2012, Demography.

[23]  Cheng Liu,et al.  An abstract model of gentrification as a spatially contagious succession process , 2016, Comput. Environ. Urban Syst..

[24]  P. Marcuse Abandonment, gentrification, and displacement: the linkages in New York City , 2013 .

[25]  Cuz Potter,et al.  Urban regeneration and gentrification: Land use impacts of the Cheonggye Stream Restoration Project on the Seoul's central business district , 2013 .

[26]  Neil Smith,et al.  Gentrification and the Rent Gap , 1987 .

[27]  D. O'Sullivan Geographical information science: critical GIS , 2006 .

[28]  C. Parker,et al.  A voice that could not be ignored: community GIS and gentrification battles in San Francisco , 2002 .

[29]  J. Ord,et al.  Local Spatial Autocorrelation Statistics: Distributional Issues and an Application , 2010 .

[30]  P. Marcuse Comment on Elvin K. Wyly and Daniel J. Hammel's “Islands of Decay in Seas of Renewal: Housing Policy and the Resurgence of Gentrification” , 1999 .

[31]  A. Getis The Analysis of Spatial Association by Use of Distance Statistics , 2010 .

[32]  L. Lees The geography of gentrification , 2012 .

[34]  Raja Sengupta,et al.  Agent‐Based Simulation of Urban Residential Dynamics and Land Rent Change in a Gentrifying Area of Boston , 2008, Trans. GIS.