binbin lu
binbin lu
Lecturer at School of Remoting sensing and information engineering, Wuhan University
Verified email at whu.edu.cn - Homepage
TitleCited byYear
GWmodel: an R package for exploring spatial heterogeneity using geographically weighted models
I Gollini, B Lu, M Charlton, C Brunsdon, P Harris
Journal of Statistical Software 63 (17), 1-50, 2015
1972015
Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data
B Lu, M Charlton, P Harris, AS Fotheringham
International Journal of Geographical Information Science 28 (4), 660-681, 2014
962014
The GWmodel R package: further topics for exploring spatial heterogeneity using geographically weighted models
B Lu, P Harris, M Charlton, C Brunsdon
Geo-spatial Information Science 17 (2), 85-101, 2014
942014
Geographically weighted regression using a non-Euclidean distance metric with a study on London house price data
B Lu, M Charlton, AS Fotheringhama
Procedia Environmental Sciences 7, 92-97, 2011
582011
Geographically weighted regression with parameter-specific distance metrics
B Lu, C Brunsdon, M Charlton, P Harris
International Journal of Geographical Information Science 31 (5), 982-998, 2017
232017
The Minkowski approach for choosing the distance metric in geographically weighted regression
B Lu, M Charlton, C Brunsdon, P Harris
International Journal of Geographical Information Science 30 (2), 351-368, 2016
202016
Geographically weighted regression‐based determinants of malaria incidences in northern China
Y Ge, Y Song, J Wang, W Liu, Z Ren, J Peng, B Lu
Transactions in GIS 21 (5), 934-953, 2017
192017
Calibrating a geographically weighted regression model with parameter-specific distance metrics
B Lu, P Harris, M Charlton, C Brunsdon
Procedia Environmental Sciences 26, 109-114, 2015
142015
The importance of scale in spatially varying coefficient modeling
D Murakami, B Lu, P Harris, C Brunsdon, M Charlton, T Nakaya, ...
Annals of the American Association of Geographers 109 (1), 50-70, 2019
122019
Improvements to the calibration of a geographically weighted regression with parameter-specific distance metrics and bandwidths
B Lu, W Yang, Y Ge, P Harris
Computers, Environment and Urban Systems 71, 41-57, 2018
122018
Introducing bootstrap methods to investigate coefficient non-stationarity in spatial regression models
P Harris, C Brunsdon, B Lu, T Nakaya, M Charlton
Spatial Statistics 21, 241-261, 2017
112017
GWmodel: an R package for exploring spatial heterogeneity
B Lu, P Harris, I Gollini, M Charlton, C Brunsdon
GISRUK 2013, 3-5, 2013
52013
Converting a spatial network to a graph in R
B Lu, M Charlton
The R User Conference, useR! 2011 August 16-18 2011 University of Warwick …, 2011
52011
Distance metric choice can both reduce and induce collinearity in geographically weighted regression
A Comber, K Chi, MQ Huy, Q Nguyen, B Lu, HH Phe, P Harris
Environment and Planning B: Urban Analytics and City Science, 2399808318784017, 2018
42018
Geographically weighted regression using a non-Euclidean distance metric with simulation data
B Lu, M Charlton, P Harris
2012 First International Conference on Agro-Geoinformatics (Agro …, 2012
42012
Shp2graph: Tools to convert a spatial network into an igraph graph in r
B Lu, H Sun, P Harris, M Xu, M Charlton
ISPRS International Journal of Geo-Information 7 (8), 2018
32018
A response to ‘A comment on geographically weighted regression with parameter-specific distance metrics’
B Lu, C Brunsdon, M Charlton, P Harris
International Journal of Geographical Information Science 33 (7), 1300-1312, 2019
22019
Package ‘GWmodel’
B Lu, P Harris, M Charlton, C Brunsdon, T Nakaya, I Gollini
National University of Ireland Maynooth, 2015
22015
Introducing the GWmodel R and python packages for modelling spatial heterogeneity
B Lu, P Harris, I Gollini, M Charlton, C Brunsdon
Proceedings of the 12th International Conference on GeoComputation, 2013
22013
Scalable GWR: A linear-time algorithm for large-scale geographically weighted regression with polynomial kernels
D Murakami, N Tsutsumida, T Yoshida, T Nakaya, B Lu
arXiv preprint arXiv:1905.00266, 2019
12019
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