Home > Research > Publications & Outputs > Grid-enabling Geographically Weighted Regression

Research

Links

Text available via DOI:

View graph of relations

Grid-enabling Geographically Weighted Regression: A Case Study of Participation in Higher Education in England

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

Standard

Grid-enabling Geographically Weighted Regression: A Case Study of Participation in Higher Education in England. / Grose, Daniel; Harris, Richard; Brunsdon, Chris et al.
In: Transactions in GIS, Vol. 14, No. 1, 2010, p. 43-61.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Grose, D, Harris, R, Brunsdon, C, Longley, P & Singleton, A 2010, 'Grid-enabling Geographically Weighted Regression: A Case Study of Participation in Higher Education in England', Transactions in GIS, vol. 14, no. 1, pp. 43-61. https://doi.org/10.1111/j.1467-9671.2009.01181.x

APA

Grose, D., Harris, R., Brunsdon, C., Longley, P., & Singleton, A. (2010). Grid-enabling Geographically Weighted Regression: A Case Study of Participation in Higher Education in England. Transactions in GIS, 14(1), 43-61. https://doi.org/10.1111/j.1467-9671.2009.01181.x

Vancouver

Grose D, Harris R, Brunsdon C, Longley P, Singleton A. Grid-enabling Geographically Weighted Regression: A Case Study of Participation in Higher Education in England. Transactions in GIS. 2010;14(1):43-61. doi: 10.1111/j.1467-9671.2009.01181.x

Author

Grose, Daniel ; Harris, Richard ; Brunsdon, Chris et al. / Grid-enabling Geographically Weighted Regression : A Case Study of Participation in Higher Education in England. In: Transactions in GIS. 2010 ; Vol. 14, No. 1. pp. 43-61.

Bibtex

@article{9004a49663a4400abeafc27dbca803d7,
title = "Grid-enabling Geographically Weighted Regression: A Case Study of Participation in Higher Education in England",
abstract = "Geographically Weighted Regression (GWR) is a method of spatial statistic alanalysis used to explore geographical differences in the effect of one or more predictor variables upon a response variable. However, as a form of local analysis,it does not scale well to (especially) large data sets because of the repeated processes of fitting and then comparing multiple regression surfaces. A solution is to make use of developing grid infrastructures, such as that provided by the National Grid Service (NGS) in the UK, treating GWR as an “embarrassing parallel” problem and building on existing software platforms to provide a bridge between an open source implementation of GWR (in R) and the grid system. To demonstrate the approach, we apply it to a case study of participation in Higher Education, using GWR to detect spatial variation in social, cultural and demographic indicators of participation.",
author = "Daniel Grose and Richard Harris and Chris Brunsdon and Paul Longley and Alex Singleton",
year = "2010",
doi = "10.1111/j.1467-9671.2009.01181.x",
language = "English",
volume = "14",
pages = "43--61",
journal = "Transactions in GIS",
issn = "1361-1682",
publisher = "Wiley-Blackwell",
number = "1",

}

RIS

TY - JOUR

T1 - Grid-enabling Geographically Weighted Regression

T2 - A Case Study of Participation in Higher Education in England

AU - Grose, Daniel

AU - Harris, Richard

AU - Brunsdon, Chris

AU - Longley, Paul

AU - Singleton, Alex

PY - 2010

Y1 - 2010

N2 - Geographically Weighted Regression (GWR) is a method of spatial statistic alanalysis used to explore geographical differences in the effect of one or more predictor variables upon a response variable. However, as a form of local analysis,it does not scale well to (especially) large data sets because of the repeated processes of fitting and then comparing multiple regression surfaces. A solution is to make use of developing grid infrastructures, such as that provided by the National Grid Service (NGS) in the UK, treating GWR as an “embarrassing parallel” problem and building on existing software platforms to provide a bridge between an open source implementation of GWR (in R) and the grid system. To demonstrate the approach, we apply it to a case study of participation in Higher Education, using GWR to detect spatial variation in social, cultural and demographic indicators of participation.

AB - Geographically Weighted Regression (GWR) is a method of spatial statistic alanalysis used to explore geographical differences in the effect of one or more predictor variables upon a response variable. However, as a form of local analysis,it does not scale well to (especially) large data sets because of the repeated processes of fitting and then comparing multiple regression surfaces. A solution is to make use of developing grid infrastructures, such as that provided by the National Grid Service (NGS) in the UK, treating GWR as an “embarrassing parallel” problem and building on existing software platforms to provide a bridge between an open source implementation of GWR (in R) and the grid system. To demonstrate the approach, we apply it to a case study of participation in Higher Education, using GWR to detect spatial variation in social, cultural and demographic indicators of participation.

U2 - 10.1111/j.1467-9671.2009.01181.x

DO - 10.1111/j.1467-9671.2009.01181.x

M3 - Journal article

VL - 14

SP - 43

EP - 61

JO - Transactions in GIS

JF - Transactions in GIS

SN - 1361-1682

IS - 1

ER -