Chris Brunsdon

About Chris Brunsdon

Chris Brunsdon, With an exceptional h-index of 58 and a recent h-index of 40 (since 2020), a distinguished researcher at Maynooth University, specializes in the field of Geocomputation, Spatial statistics, Visualisation.

His recent articles reflect a diverse array of research interests and contributions to the field:

GWmodelS: a standalone software to train geographically weighted models

sfislands: An R Package for Accommodating Islands and Disjoint Zones in Areal Spatial Modelling

A Multilevel Spatial Model to Investigate Voting Behaviour in the 2019 UK General Election

Multiscale spatially varying coefficient modelling using a Geographical Gaussian Process GAM

GWmodelS: A software for geographically weighted models

Multiscale Spatially and Temporally Varying Coefficient Modelling Using a Geographic and Temporal Gaussian Process GAM (GTGP-GAM)(Short Paper)

Smarter Than Your Average Model-Bayesian Model Averaging as a Spatial Analysis Tool (Short Paper)

3115–PACESS: PRACTICAL AI-BASED CELL EXTRACTION AND SPATIAL STATISTICS FOR LARGE 3D BIOLOGICAL IMAGES

Chris Brunsdon Information

University

Position

___

Citations(all)

28865

Citations(since 2020)

12269

Cited By

21871

hIndex(all)

58

hIndex(since 2020)

40

i10Index(all)

139

i10Index(since 2020)

93

Email

University Profile Page

Google Scholar

Chris Brunsdon Skills & Research Interests

Geocomputation

Spatial statistics

Visualisation

Top articles of Chris Brunsdon

GWmodelS: a standalone software to train geographically weighted models

Geo-spatial Information Science

2024/5/2

sfislands: An R Package for Accommodating Islands and Disjoint Zones in Areal Spatial Modelling

arXiv preprint arXiv:2404.09863

2024/4/15

Chris Brunsdon
Chris Brunsdon

H-Index: 41

A Multilevel Spatial Model to Investigate Voting Behaviour in the 2019 UK General Election

Applied Spatial Analysis and Policy

2024/1/11

Chris Brunsdon
Chris Brunsdon

H-Index: 41

Multiscale spatially varying coefficient modelling using a Geographical Gaussian Process GAM

International Journal of Geographical Information Science

2024/1/2

GWmodelS: A software for geographically weighted models

SoftwareX

2023/2/1

Multiscale Spatially and Temporally Varying Coefficient Modelling Using a Geographic and Temporal Gaussian Process GAM (GTGP-GAM)(Short Paper)

2023

Smarter Than Your Average Model-Bayesian Model Averaging as a Spatial Analysis Tool (Short Paper)

2023

3115–PACESS: PRACTICAL AI-BASED CELL EXTRACTION AND SPATIAL STATISTICS FOR LARGE 3D BIOLOGICAL IMAGES

Experimental Hematology

2023/1/1

Harnessing Spatial Heterogeneity in Composite Indicators through the Ordered Geographically Weighted Averaging (OGWA) Operator

Geographical Analysis

2023/12/22

Francesco Vidoli
Francesco Vidoli

H-Index: 11

Chris Brunsdon
Chris Brunsdon

H-Index: 41

Real-time suicide surveillance: comparison of international surveillance systems and recommended best practice

Archives of suicide research

2023/10/2

Package ‘pycno’

2023/9/28

Chris Brunsdon
Chris Brunsdon

H-Index: 41

A practical extraction and spatial statistical pipeline for large 3D bioimages

bioRxiv

2022/12/29

A Rejoinder to the Commentaries on “A Route Map for Successful Applications of Geographically Weighted Regression” by Comber et al.(2022)

Geographical Analysis

2022/11/5

High-performance solutions of geographically weighted regression in R

Geo-Spatial Information Science

2022/10/2

Measuring the exposure of Black, Asian and other ethnic groups to Covid-infected neighbourhoods in English towns and cities

Applied Spatial Analysis and Policy

2022/9

Richard Harris
Richard Harris

H-Index: 13

Chris Brunsdon
Chris Brunsdon

H-Index: 41

The development and validation of a dashboard prototype for real-time suicide mortality data

Frontiers in digital health

2022/8/20

gwverse: A Template for a New Generic Geographically Weighted R Package

Geographical Analysis

2022/7

Spatially varying coefficient regression with GAM gaussian process splines: GAM (e)-on

AGILE: GIScience Series

2022/6/10

Quantitative methods to detect suicide and self-harm clusters: a systematic review

2022/4/27

See List of Professors in Chris Brunsdon University(Maynooth University)

Co-Authors

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