Understanding population segregation from Landsat ETM+ imagery: A geographically weighted regression approach

Danlin Yu, Changshan Wu

Research output: Contribution to journalArticle

41 Citations (Scopus)

Abstract

This study attempts to understand population segregation issues in Milwaukee County, Wisconsin utilizing remote sensing and regression technologies. Population segregation was measured with a local segregation index Di based on the theory of the index of dissimilarity. Remote sensing information was extracted from a Landsat ETM+ image through spectral mixture analysis, unsupervised classification, and texture analysis. Global ordinary least squares (OLS regression and geographically weighted regression (GWR) analyses were applied to explore the relationships between population segregation and remote sensing variables. Results indicate that remote sensing information has the potential to increase our understanding of socio-cultural issues such as population segregation.

Original languageEnglish
Pages (from-to)187-206
Number of pages20
JournalGIScience and Remote Sensing
Volume41
Issue number3
DOIs
StatePublished - 1 Jan 2004

Fingerprint

Landsat
imagery
remote sensing
unsupervised classification
texture
analysis
index

Cite this

@article{8261f57031de4d9f99e20f54de818c39,
title = "Understanding population segregation from Landsat ETM+ imagery: A geographically weighted regression approach",
abstract = "This study attempts to understand population segregation issues in Milwaukee County, Wisconsin utilizing remote sensing and regression technologies. Population segregation was measured with a local segregation index Di based on the theory of the index of dissimilarity. Remote sensing information was extracted from a Landsat ETM+ image through spectral mixture analysis, unsupervised classification, and texture analysis. Global ordinary least squares (OLS regression and geographically weighted regression (GWR) analyses were applied to explore the relationships between population segregation and remote sensing variables. Results indicate that remote sensing information has the potential to increase our understanding of socio-cultural issues such as population segregation.",
author = "Danlin Yu and Changshan Wu",
year = "2004",
month = "1",
day = "1",
doi = "10.2747/1548-1603.41.3.187",
language = "English",
volume = "41",
pages = "187--206",
journal = "GIScience and Remote Sensing",
issn = "1548-1603",
publisher = "Taylor and Francis Ltd.",
number = "3",

}

Understanding population segregation from Landsat ETM+ imagery : A geographically weighted regression approach. / Yu, Danlin; Wu, Changshan.

In: GIScience and Remote Sensing, Vol. 41, No. 3, 01.01.2004, p. 187-206.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Understanding population segregation from Landsat ETM+ imagery

T2 - A geographically weighted regression approach

AU - Yu, Danlin

AU - Wu, Changshan

PY - 2004/1/1

Y1 - 2004/1/1

N2 - This study attempts to understand population segregation issues in Milwaukee County, Wisconsin utilizing remote sensing and regression technologies. Population segregation was measured with a local segregation index Di based on the theory of the index of dissimilarity. Remote sensing information was extracted from a Landsat ETM+ image through spectral mixture analysis, unsupervised classification, and texture analysis. Global ordinary least squares (OLS regression and geographically weighted regression (GWR) analyses were applied to explore the relationships between population segregation and remote sensing variables. Results indicate that remote sensing information has the potential to increase our understanding of socio-cultural issues such as population segregation.

AB - This study attempts to understand population segregation issues in Milwaukee County, Wisconsin utilizing remote sensing and regression technologies. Population segregation was measured with a local segregation index Di based on the theory of the index of dissimilarity. Remote sensing information was extracted from a Landsat ETM+ image through spectral mixture analysis, unsupervised classification, and texture analysis. Global ordinary least squares (OLS regression and geographically weighted regression (GWR) analyses were applied to explore the relationships between population segregation and remote sensing variables. Results indicate that remote sensing information has the potential to increase our understanding of socio-cultural issues such as population segregation.

UR - http://www.scopus.com/inward/record.url?scp=8744247168&partnerID=8YFLogxK

U2 - 10.2747/1548-1603.41.3.187

DO - 10.2747/1548-1603.41.3.187

M3 - Article

AN - SCOPUS:8744247168

VL - 41

SP - 187

EP - 206

JO - GIScience and Remote Sensing

JF - GIScience and Remote Sensing

SN - 1548-1603

IS - 3

ER -