Robust principal component analysis and geographically weighted regression: Urbanization in the Twin Cities Metropolitan Area of Minnesota

Debarchana Ghosh, Steven M. Manson

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

In this paper, we present a hybrid approach, robust principal component geographically weighted regression (RPCGWR), in examining urbanization as a function of both extant urban land use and the effect of social and environmental factors in the Twin Cities Metropolitan Area (TCMA) of Minnesota. We used remotely sensed data to treat urbanization via the proxy of impervious surface. We then integrated two different methods, robust principal component analysis (RPCA) and geographically weighted regression (GWR) to create an innovative approach to model urbanization. The RPCGWR results show significant spatial heterogeneity in the relationships between proportion of impervious surface and the explanatory factors in the TCMA. We link this heterogeneity to the "sprawling" nature of urban land use that has moved outward from the core Twin Cities through to their suburbs and exurbs.

Original languageEnglish (US)
Pages (from-to)15-25
Number of pages11
JournalURISA Journal
Volume20
Issue number1
StatePublished - Dec 1 2008

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Land use
Principal component analysis

Keywords

  • Geographically weighted regression
  • Land use
  • Robust principal component analysis
  • Urbanization

Cite this

Robust principal component analysis and geographically weighted regression : Urbanization in the Twin Cities Metropolitan Area of Minnesota. / Ghosh, Debarchana; Manson, Steven M.

In: URISA Journal, Vol. 20, No. 1, 01.12.2008, p. 15-25.

Research output: Contribution to journalArticle

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