GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models

被引:0
|
作者
Gollini, Isabella [1 ]
Lu, Binbin [2 ]
Charlton, Martin [3 ]
Brunsdon, Christopher [3 ]
Harris, Paul
机构
[1] Univ Bristol, Dept Civil Engn, Bristol BS8 1TR, Avon, England
[2] Wuhan Univ, Wuhan, Peoples R China
[3] NUI Maynooth, Maynooth, Kildare, Ireland
来源
JOURNAL OF STATISTICAL SOFTWARE | 2015年 / 63卷 / 17期
基金
爱尔兰科学基金会;
关键词
geographically weighted regression; geographically weighted principal components analysis; spatial prediction; robust; R package; VARYING COEFFICIENT MODELS; REGRESSION; SELECTION; PRICES; TESTS;
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Spatial statistics is a growing discipline providing important analytical techniques in a wide range of disciplines in the natural and social sciences. In the R package GWmodel, we present techniques from a particular branch of spatial statistics, termed geographically weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localized calibration provides a better description. The approach uses a moving window weighting technique, where localized models are found at target locations. Outputs are mapped to provide a useful exploratory tool into the nature of the data spatial heterogeneity. Currently, GWmodel includes functions for: GW summary statistics, GW principal components analysis, GW regression, and GW discriminant analysis; some of which are provided in basic and robust forms.
引用
收藏
页码:1 / 50
页数:50
相关论文
共 50 条
  • [31] Spatial heterogeneity of urban illegal parking behavior: A geographically weighted Poisson regression approach
    Zhou, Xizhen
    Ding, Xueqi
    Yan, Jie
    Ji, Yanjie
    JOURNAL OF TRANSPORT GEOGRAPHY, 2023, 110
  • [33] Simulating the Spatial Heterogeneity of Housing Prices in Wuhan, China, by Regionally Geographically Weighted Regression
    Wang, Zengzheng
    Zhao, Yangyang
    Zhang, Fuhao
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2022, 11 (02)
  • [34] Modelling urban spatial structure using Geographically Weighted Regression
    Noresah, M. S.
    Ruslan, R.
    18TH WORLD IMACS CONGRESS AND MODSIM09 INTERNATIONAL CONGRESS ON MODELLING AND SIMULATION: INTERFACING MODELLING AND SIMULATION WITH MATHEMATICAL AND COMPUTATIONAL SCIENCES, 2009, : 1950 - 1956
  • [35] Weighted Distance-Based Models for Ranking Data Using the R Package rankdist
    Qian, Zhaozhi
    Yu, Philip L. H.
    JOURNAL OF STATISTICAL SOFTWARE, 2019, 90 (05):
  • [36] Erratum to: The Use of Geographically Weighted Regression for Spatial Prediction: An Evaluation of Models Using Simulated Data Sets
    P. Harris
    A. S. Fotheringham
    R. Crespo
    M. Charlton
    Mathematical Geosciences, 2011, 43 : 399 - 399
  • [37] Pspatreg: R Package for Semiparametric Spatial Autoregressive Models
    Minguez, Roman
    Basile, Roberto
    Durban, Maria
    MATHEMATICS, 2024, 12 (22)
  • [38] Geographically weighted machine learning for modeling spatial heterogeneity in traffic crash frequency and determinants in US
    Wang, Shuli
    Gao, Kun
    Zhang, Lanfang
    Yu, Bo
    Easa, Said M.
    ACCIDENT ANALYSIS AND PREVENTION, 2024, 199
  • [39] Harnessing Spatial Heterogeneity in Composite Indicators through the Ordered Geographically Weighted Averaging (OGWA) Operator
    Fusco, Elisa
    Liborio, Matheus Pereira
    Rabiei-Dastjerdi, Hamidreza
    Vidoli, Francesco
    Brunsdon, Chris
    Ekel, Petr Iakovlevitch
    GEOGRAPHICAL ANALYSIS, 2024, 56 (03) : 530 - 553
  • [40] Identifying the spatial heterogeneity of housing financialization in China: Insights from a multiscale geographically weighted regression
    Wang, Yang
    Yue, Xiaoli
    Wang, Min
    Huang, Gengzhi
    HELIYON, 2024, 10 (06)