文档介绍：Robust ponent Analysis and Geographically Weighted Regression:
Urbanization in the Twin Cities Metropolitan Area (TCMA)
Debarchana Ghosh
(PHD Student)
Prof. Steven M. Manson
University of Minnesota
Department of Geography
414 Social Sciences
267 - 19th Avenue South
Minneapolis, MN 55455
USA
(612) 625-6080
(612) 624-1044 (fax)
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Robust ponent Analysis and Geographically Weighted Regression:
Urbanization in the Twin Cities Metropolitan Area (TCMA)
Abstract: In this paper, we present a hybrid approach, robust ponent
geographically weighted regression (RPCGWR), in examining the land change 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 urban land use
via the proxy of impervious surfaces. We then integrated two different methods, Robust
ponent Analysis (RPCA) and Geographically Weighted Regression (GWR) to
create an innovative approach to model urban land use. The RPCGWR results show significant
spatial heterogeneity in the relationships between proportion of impervious surface and the
explanatory factors in TCMA. We link this heterogeneity to the ‘sprawling’ nature of land
change that has moved outward from the core Twin Cities through to their suburbs and exurbs.
Keywords: Land use, robust ponent analysis, geographically weighted regression,
TCMA.
1
1 Introduction
We have long altered the land by clearing forests, farming, and building settlements. This
land change has serious social and environmental impacts, many of which are increasingly
evident in urban areas that now host the majority of the world’s population. In the United States,
urbanization is driven primarily by suburbanization, or decentralized, low-density residential
land use, and exurbanzaiton, creation of far-flung suburbs. While suburbanization offers