Geographical General Regression Neural Network (GGRNN) tool for geographically weighted regression analysis

Irfan, Muhammad ORCID: https://orcid.org/0000-0001-7991-1154, Koj, Aleksandra ORCID: https://orcid.org/0000-0002-5294-2492, Thomas, Hywel ORCID: https://orcid.org/0000-0002-3951-0409 and Sedighi, Majid 2016. Geographical General Regression Neural Network (GGRNN) tool for geographically weighted regression analysis. Presented at: The Eighth International Conference on Advanced Geographic Information Systems, Applications, and Services, Venice, Italy, April 24- April 28 2016. GEOProcessing 2016, The Eighth International Conference on Advanced Geographic Information Systems, Applications, and Services. Geoprocessing2016. pp. 154-159.

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Abstract

This paper presents a new geographically weighted regression analysis tool, based upon a modified version of a General Regression Neural Network (GRNN). The new Geographic General Regression Neural Network (GGRNN) tool allows for local variations in the regression analysis. The algorithm of the GRNN has been extended to allow for both globally independent variables and local variables, restricted to a given spatial kernel. This mimics the results of Geographically Weighted Regression (GWR) analysis in a given geographical space. The GGRNN tool allows the user to load geographic data from the Shapefile into the underlying neural networks data structure. The spatial kernel can be either a fixed radius or adaptive, by using a given number of neighboring regions. The Holdout Method has been used to compare the fitness of a given model. An application of the tool has been presented using the benchmark working-age deaths in the Tokyo metropolitan area, Japan. Standardized residual maps produced by the GGRNN tool have been compared with those produced by the GWR4 tool for validation. The tool has been developed in the .Net C# programming language using the DotSpatial open source library. The tool is valuable because it allows the user to investigate the influence of spatially non-stationary processes in the regression analysis. The tool can also be used for prediction or interpolation purposes for a range of environmental, socioeconomic and public health applications.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Engineering
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
ISBN:	9781612084695
Date of First Compliant Deposit:	9 June 2016
Date of Acceptance:	24 April 2016
Last Modified:	06 Jan 2024 02:06
URI:	https://orca.cardiff.ac.uk/id/eprint/91693

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