GW Bibliography

Bibliography on GWR and related models:

Assunção RM (2003) Space varying coefficient models for small area data. Environmetrics 14:453-473

Assunção RM, Potter JE, Cavenaghi SM (2002) A Bayesian space varying parameter model applied to estimating fertility schedules. Statistics in Medicine 21:2057-2075

Atkinson PM (2001) Geographical information science: Geocomputation and nonstationarity. Progress in Physical Geography 25:111-122

Atkinson PM, German SE, Sear DA, Clark MJ (2003) Exploring the relations between riverbank erosion and geomorphological controls using geographically weighted logistic regression. Geographical Analysis 35:58-82

Austin M (2007) Species distribution models and ecological theory: A critical assessment and some possible new approaches. Ecological Modelling 200:1-19

Bitter C, Mulligan GF, Dall’erba S (2007) Incorporating spatial variation in housing attribute prices: a comparison of geographically weighted regression and the spatial expansion method. Journal of Geographical Systems 9:7-27

Bivand R, Yu D (2014) spgwr R package URL: http://cran.r-project.org/web/packages/spgwr/index.html

Brunsdon C, Aitkin M, Fotheringham AS, Charlton ME (1999) A comparison of random coefficient modelling and geographically weighted regression for spatially non-stationary regression problems. Geographical and Environmental Modelling 3(1):47-62

Brunsdon C, Fotheringham AS, Charlton ME (1996) Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity. Geographical Analysis 28(4):281-298

Brunsdon C, Fotheringham AS, Charlton ME (1998) Geographically weighted regression – modelling spatial non-stationarity. Journal of the Royal Statistical Society, Series D-The Statistician 47(3):431-443

Brunsdon C, Fotheringham AS, Charlton ME (1998) Spatial nonstationarity and autoregressive models. Environment and Planning A 30(6):957-993

Brunsdon C, Fotheringham AS, Charlton ME (1999) Some notes on parametric signficance tests for geographically weighted regression. Journal of Regional Science 39(3):497-524

Brunsdon C, Fotheringham AS, Charlton ME (2002) Geographically weighted summary statistics – a framework for localised exploratory data analysis. Computers, Environment and Urban Systems 26:501-524

Brunsdon C, Fotheringham AS, Charlton ME (2002) Geographically Weighted Local Statistics Applied to Binary Data. Lecture Notes in Computer Science 2478:38-50

Brunsdon C, Fotheringham AS, Charlton ME (2007) Geographically Weighted Discriminant Analysis. Geographical Analysis 39:376-996

Chen Y, Deng W, Yang T, Mathews S (2012) Geographically weighted quantile regression (GWQR): An application to U.S. Mortality data. Geographical Analysis 44:134-150

Cleveland WS (1979) Robust Locally Weighted Regression and Smoothing Scatterplots. Journal of the American Statistical Association, 74 (368):829–836

Congdon P (2003) Modelling spatially varying impacts of socioeconomic predictors on mortality outcomes. Journal of Geographical Systems 5:161-184

Congdon P (2007) Bayesian modelling strategies for spatially varying regression coefficients: A multivariate perspective for multiple outcomes. Computational Statistics and Data Analysis 51:2586-2601

Demšar U, Fotheringham AS, Charlton M (2008) Exploring the Spatio-temporal Dynamics of Geographical Processes with Geographically Weighted Regression and Geovisual Analytics. Information Visualisation 7:181-197

Demšar U, Fotheringham AS, Charlton M (2008) Combining Geovisual Analytics with Spatial Statistics: the Example of Geographically Weighted Regression. The Cartographic Journal 45:182-192

Dykes J, Brunsdon C (2007) Geographically weighted visualization – interactive graphics for scale-varying exploratory analysis. IEEE Transactions on Visualization and Computer Graphics, 13:1161–1168

Farber S, Páez A (2007) A systematic investigation of cross-validation in GWR model estimation: empirical analysis and Monte Carlo simulations. Journal of Geographical Systems 9:371-396

Finlay AO (2011) Comparing spatially-varying coefficient models for analysis of ecological data with non-stationary and anisotropic residual dependence. Methods in Ecology and Evolution 2:143-154

Foley P, Demšar U (2013) Using geovisual analytics to compare the performance of geographically weighted discriminant analysis versus its global counterpart, linear discriminant analysis. International Journal of Geographical Information Science 27(4): 633-661

Foody GM (2003) Geographical weighting as a further refinement to regression modelling: An example focused on the NDVI–rainfall relationship. Remote Sensing of Environment 88:283–293

Fotheringham AS (1997) Trends in Quantitative Methods I: Stressing the Local. Progress in Human Geography 21:88-96

Fotheringham AS, Brunsdon C, Charlton ME (1998) Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environment and Planning A 30(11):1905-1927

Fotheringham AS, Brunsdon C, Charlton ME (2000) Quantitative Geography. London: Sage

Fotheringham AS, Brunsdon C, Charlton ME (2002) Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Chichester: Wiley.

Fotheringham AS, Charlton ME, Brunsdon C (1996) The Geography of Parameter Space: An Investigation into Spatial Non-Stationarity. International Journal of Geographical Information Systems 10:605-627

Fotheringham AS, Charlton ME, Brunsdon C (1997) Two Techniques for Exploring Non-stationarity in Geographical Data. Geographical Systems 4:59-82

Gamerman D, Moreira ARB, Rue H (2003) Space-varying regression models: specifications and simulation: Computational Statistics and Data Analysis 42:513-533

Gao X, Asami Y, Chung C (2006) An empirical evaluation of spatial regression models. Computers & Geosciences 32:1040-1051

Gelfand AE, Kim HJ, Sirmans CJ, Banerjee S (2003) Spatial modelling with spatially varying coefficient processes. Journal of American Statistical Association 98:387-396

Gelfand AE, Banerjee S, Gamerman D (2005) Spatial process modelling for univariate and multivariate dynamic spatial data. Environmetrics 16:465-479

Gelfand AE, Banerjee S, Sirmans CF, Tu Y, Ong SE (2007) Multilevel modelling using spatial processes: application to the Singapore housing market. Computational Statistics and Data Analysis 51:3567-3597

Gollini I, Lu B, Charlton M, Brunsdon C, Harris P (2014) GWmodel: an R Package for exploring Spatial Heterogeneity using Geographically Weighted Models. Accepted for publication in Journal of Statistical Software

Gollini I, Lu B, Charlton M, Brunsdon C, Harris P (2013) GWmodel: an R Package for exploring Spatial Heterogeneity using Geographically Weighted Models. http://arxiv.org/abs/1306.0413

Goovaerts P (2005) Analysis and detection of health disparities using geostatistics and a space-time information system. Proceedings of GIS planet 2005

Griffith DA (2008) Spatial-filtering-based contributions to a critique of geographically weighed regression (GWR). Environment and Planning A 40:2751-2769

Guo D (2010) Local entropy map: a nonparametric approach to detecting spatially varying multivariate relationships. International Journal of Geographical Information Science 24(9):1367-1389

Haas TC (1996) Multivariate spatial prediction in the presence of non-linear trend and covariance nonstationarity. Environmetrics 7:145-165

Harris P, Brunsdon C (2010) Exploring spatial variation and spatial relationships in a freshwater acidification critical load data set for Great Britain using geographically weighted summary statistics. Computers & Geosciences 36:54-70

Harris P, Charlton M, Fotheringham AS (2010) Moving window kriging with geographically weighted variograms. Stochastic Environmental Research and Risk Assessment 24:1193-1209

Harris P, Fotheringham AS, Juggins S (2010) Robust geographically weighed regression: a technique for quantifying spatial relationships between freshwater acidification critical loads and catchment attributes. Annals of the Association of American Geographers 100(2): 286-306

Harris P, Fotheringham AS, Crespo R, Charlton M (2010) The use of geographically weighted regression for spatial prediction: an evaluation of models using simulated data sets. Mathematical Geosciences 42:657-680

Harris P, Brunsdon C, Fotheringham AS (2011) Links, comparisons and extensions of the geographically weighted regression model when used as a spatial predictor. Stochastic Environmental Research and Risk Assessment 25:123-138

Harris P, Brunsdon C, Charlton M (2011) Geographically weighted principal components analysis. International Journal of Geographical Information Science 25:1717-1736

Harris P, Juggins S (2011) Estimating freshwater critical load exceedance data for Great Britain using space-varying relationship models. Mathematical Geosciences 43:265-292

Harris P, Brunsdon C, Charlton M (2013) The comap as a diagnostic tool for nonstationary kriging models. International Journal of Geographical Information Science 27(3):511-541

Harris P, Brunsdon C, Charlton M, Juggins S, Clarke A (2014) Multivariate spatial outlier detection using robust geographically weighted methods. Mathematical Geosciences 46(1):1-31

Harris P, Clarke A, Juggins S, Brunsdon C, Charlton M (2014) Enhancements to a geographically weighted principal components analysis in the context of an application to an environmental data set. Accepted for publication in Geographical Analysis

Harris P, Clarke A, Juggins S, Brunsdon C, Charlton M (2014) Geographically weighted methods and their use in network re-designs for environmental monitoring. Stochastic Environmental Research and Risk Assessment DOI:10.1007/s00477-014-0851-1

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

Harris R, G Dong, Zhang W (2013) Using Contextualized Geographically Weighted Regression to Model the Spatial Heterogeneity of Land Prices in Beijing, China. Transactions in GIS 17(6):901-919

Hastie T, Tibshirani (1986) Generalized Additive Models. Statistical Science 1(3):297-310

Hastie T, Loader C (1993) Local Regression: Automatic Kernel Carpentry. Statistical Science 8(2):120-129

Hastie T, Tibshirani (1993) Varying-Coefficient Models. Journal of the Royal Statistical Society, Series B 55(4):757-796

Holt JB, Lo CP (2008) The geography of mortality in the Atlanta metropolitan area. Computers, Environment and Urban Systems 32:149-164

Huang B, Wu B, Barry M (2010) Geographically and temporally weighted regression for modelling spatiotemporal variation in house prices. International Journal of Geographical Information Science 24(3):383-401

Leung Y, Mei C-L, Zhang W-X (2000) Statistical tests for spatial nonstationarity based on the geographically weighted regression model. Environment and Planning A 32:9-32

Leung Y, Mei C-L, Zhang W-X (2000) Testing for spatial autocorrelation among the residuals of geographically weighted regression. Environment and Planning A 32:871-890

Lloyd CD (2010) Nonstationary models for exploring and mapping monthly precipitation in the United Kingdom. International Journal of Climatology 30:390-405

Lloyd CD (2010) Analysing population characteristics using geographically weighted principal components analysis: a case study of Northern Ireland in 2001. Computers, Environment and Urban Systems 34:389-399

Lloyd CD (2010) Exploring population spatial concentrations in Northern Ireland by community background and other characteristics: an application of geographically weighted spatial statistics. International Journal of Geographical Information Science 24(8):1193-1221

Lloyd CD (2011) Local models for spatial analysis (second edition). CRC Press

Lloyd CD, Shuttleworth I (2005) Analysing commuting using local regression techniques: scale, sensitivity, and geographical patterning. Environment and Planning A 37:81-103

Longley P. Tobón C (2004) Spatial Dependence and Heterogeneity in Patterns of Hardship: An Intra-Urban Analysis. Annals of the Association of American Geographers 94(3):503-519

Lu B, Harris P, Charlton M, Brunsdon C (2013) The GWmodel R Package: Further Topics for Exploring Spatial Heterogeneity using Geographically Weighted Models. http://arxiv-web3.library.cornell.edu/abs/1312.2753

Lu B, Charlton M, Harris P, Fotheringham AS (2014) Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data. International Journal of Geographical Information Science DOI:10.1080/13658816.2013.865739

Machuca-Mory DF, Deutsch CV (2013) Non-stationary Geostatistical Modeling Based on Distance Weighted Statistics and Distributions. Mathematical Geosciences 45(1):31-48

McMillen DP, McDonald JF (1997) A nonparametric analysis of employment density in a polycentric city. Journal of Regional Science 37(4):591-612

McMillen DP (2014) McSpatial R package, URL: http://cran.r-project.org/web/packages/McSpatial/index.html

Mei L-M, He S-Y, Fang K-T (2004) A note on the mixed geographically weighted regression model. Journal of regional science 44(1):143-157

Mei L-M, Wang N, Zhang W-X (2006) Testing the importance of the explanatory variables in a mixed geographically weighted regression model. Environment and Planning A 38:587-598

Mennis J (2006) Mapping the results of Geographically weighted regression. The Cartographic Journal 43(2):171-179

Mur J, Lopez F, Angulo A (2008) Symptoms of Instability in Models of Spatial Dependence. Geographical Analysis 40(2):189-211

Nakaya T (2001) Local spatial interaction modelling based on the geographically weighted regression approach. GeoJournal 53:347-358

Nakaya T, Fotheringham AS, Brunsdon C, Charlton M (2005) Geographically Weighted Poisson Regression for Disease Association Mapping, Statistics in Medicine 24:2695-2717

Nakaya T, Nakase K, Osaka K (2005) Spatio-temporal modelling of the HIV epidemic in Japan based on the national HIV/AIDS surveillance. Journal of Geographical Systems 7:313-336

Osborne PE, Foody GM, Suarez-Seone S (2007) Non-stationarity and local approaches to modelling the distributions of wildlife. Diversity and Distributions 13:313-323

Páez A (2004) Anisotropic variance functions in geographically weighted regression models. Geographical Analysis 36:299-314

Páez A (2006) Exploring contextual variations in land use and transport analysis using a probit model with geographical weights. Journal of Transport Geography 14:167-176

Páez A, Farber S, Wheeler D (2011) A simulation-based study of geographically weighted regression as a method for investigating spatially varying relationships, Environment and Planning A 43(12):2992-3010

Páez A, Long F, Farber S (2008) Moving window approaches for hedonic price estimation: an empirical comparison of modelling techniques. Urban Studies 45:1565-1581

Páez A, Uchida T, Miyamoto K (2002) A general framework for estimation and inference of geographically weighted regression models: 1. Location-specific kernel bandwidths and a test for locational heterogeneity. Environment and Planning A 34:733-754

Páez A, Uchida T, Miyamoto K (2002) A general framework for estimation and inference of geographically weighted regression models: 2. Spatial association and model specification tests. Environment and Planning A 34:883-904

Salas C, Ene L, Gregoire TG, Næsset E, Gobakken T (2010) Modelling tree diameter from airborne laser scanning derived variables: A comparison of spatial statistical models. Remote Sensing of Environment 114:1277-1285

Salvati N, Tzavidis N, Pratesi M, Chambers R (2012) Small area estimation via M-quantile geographically weighted regression. Test 21(1):1-28

Silva Paez M, Gamerman D, De Oliveira V (2005) Interpolation of a spatio-temporal model with spatially varying coefficients: application to PM10 concentrations in Rio de Janeiro. Environmental and Ecological Statistics 12:169-193

Waller LA, Zhu L, Gotway CA, Gorman DM, Grunewald PJ (2007) Quantifying geographic variations in associations between alcohol distribution and violence: a comparison of geographically weighted regression and spatially varying coefficient models. Stochastic Environmental Research and Risk Assessment 21:573-588

Wang N, Mei C, Yan X (2008) Local linear estimation of spatially varying coefficient models: an improvement on the geographically weighted regression technique. Environment and Planning A 40:986-1005

Wheeler D (2007) Diagnostic tools and a remedial method for collinearity in geographically weighted regression. Environment and Planning A 39:2464-2481

Wheeler D (2009) Simultaneous coefficient penalization and model selection in geographically weighted regression: the geographically weighted lasso. Environment and Planning A 41:722-742

Wheeler D. (2010) Visualizing and diagnosing coefficients from geographically weighted regression. In Geospatial Analysis and Modeling of Urban Structure and Dynamics, Eds. B.Jiang and X. Yao, Springer

Wheeler D. (2013) Geographically weighted regression. In Handbook of Regional Science, Eds. M. Fischer and P. Nijkamp, Springer

Wheeler D, Calder CA (2007) An assessment of coefficient accuracy in linear regression models with spatially varying coefficients. Journal of Geographical Systems 9:145-166

Wheeler D, Tiefelsdorf M (2005) Multicollinearity and correlation among local regression coefficients in geographically weighted regression. Journal of Geographical Systems 7:161-187

Wheeler D, Waller L (2009) Comparing spatially varying coefficient models: a case study examining violent crime rates and their relationships to alcohol outlets and illegal drug arrests. Journal of Geographical Systems 11:1-22

Wheeler D (2014) gwrr R package, URL: http://cran.r-project.org/web/packages/gwrr/index.html

Young LJ, Gotway CA (2007) Linking spatial data from different sources: the effects of change of support. Stochastic Environmental Research and Risk Assessment 21:589-600

Zhang L, Gove JH, Heath LS (2005) Spatial residual analysis of six modelling techniques. Ecological modelling 186:154-177

Zhang H, Mei C (2011) Local least absolute deviation estimation of spatially varying coefficient models: robust geographically weighted regression approaches. International Journal of Geographical Information Science 25:1467-1489

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