Martin Charlton

Economics and Econometrics · 2001–2014

A researcher in the Science corpus: 10 papers; trusts 33 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Economics and Econometrics — 38 citations into it; #4,156 of 23,481 by trust here, #4,166 by citations
• Statistics and Probability — 19 citations into it
• Environmental Engineering — 17 citations into it; #4,202 of 5,479 by trust here, #4,892 by citations
• Global and Planetary Change — 9 citations into it; #6,853 of 10,225 by trust here, #7,711 by citations
• Artificial Intelligence — 7 citations into it; #29,392 of 35,345 by trust here, #32,362 by citations
• Transportation — 7 citations into it; #1,606 of 2,062 by trust here, #1,495 by citations
• Civil and Structural Engineering — 5 citations into it

Whom Martin Charlton trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. David C. Wheeler
2. Chris Brunsdon
3. Edzer J. Pebesma
4. William S. Cleveland
5. Christopher D. Lloyd
6. Adrian W. Bowman
7. Mark D. Partridge
8. Paul Harris
9. S. Fotheringham
10. Montserrat Fuentes

Who trusts Martin Charlton overall

1. Binbin Lu
2. Isabella Gollini
3. Francky Fouedjio
4. Chris Brunsdon
5. Paul Harris
6. Maryam Kordi
7. Philip Kostov
8. A. Stewart Fotheringham
9. Hamid Reza Pourghasemi
10. Kai Cao

Papers from Martin Charlton's perspective

Ordered by how strongly the researchers Martin Charlton trusts endorse each paper. Same corpus, a different order for every researcher.

1. Simultaneous Coefficient Penalization and Model Selection in Geographically Weighted Regression: The Geographically Weighted Lasso
2. Diagnostic Tools and a Remedial Method for Collinearity in Geographically Weighted Regression
3. Geostatistics for Natural Resources Evaluation
4. Links, comparisons and extensions of the geographically weighted regression model when used as a spatial predictor
5. Multicollinearity and correlation among local regression coefficients in geographically weighted regression (Journal of Geographical Systems)
6. Generalized Linear Models
7. Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity
8. A General Framework for Estimation and Inference of Geographically Weighted Regression Models: 1. Location-Specific Kernel Bandwidths and a Test for Locational Heterogeneity
9. Multivariable geostatistics in S: the gstat package (Computational Geosciences)
10. Can Geographically Weighted Regressions Improve Regional Analysis and Policy Making?

Their papers

• Geographically weighted Poisson regression for disease association mapping (2005) — 545 citations
• GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models (2013) — 582 citations
• Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity (2010) — 3,163 citations
• Moving window kriging with geographically weighted variograms (2010) — 62 citations
• Geographically Weighted Regression Using a Non-Euclidean Distance Metric with a Study on London House Price Data (2011) — 98 citations
• Spatial Variations in School Performance: A Local Analysis Using Geographically Weighted Regression (2001) — 248 citations
• Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data (2014) — 375 citations
• Living with Collinearity in Local Regression Models (2012) — 47 citations
• Semiparametric geographically weighted generalisedlinear modelling in GWR 4.0 (2009) — 123 citations
• Geographically weighted summary statistics — aframework for localised exploratory data analysis (2002) — 263 citations