Geographical Analysis最新文献_第9页

R Packages for Analyzing Spatial Data: A Comparative Case Study with Areal Data 分析空间数据的R包:与区域数据的比较案例研究

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2022-02-06 DOI: 10.1111/gean.12319

Roger Bivand

引用次数: 52

A deeper look at impacts in spatial Durbin model with sphet 用sphet对空间Durbin模型的影响进行更深入的研究

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2022-01-10 DOI: 10.1111/gean.12318

Gianfranco Piras, Paolo Postiglione

引用次数: 3

A Route Map for Successful Applications of Geographically Weighted Regression 地理加权回归成功应用的路线图

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2022-01-09 DOI: 10.1111/gean.12316

Alexis Comber, Christopher Brunsdon, Martin Charlton, Guanpeng Dong, Richard Harris, Binbin Lu, Yihe Lü, Daisuke Murakami, Tomoki Nakaya, Yunqiang Wang, Paul Harris

{"title":"A Route Map for Successful Applications of Geographically Weighted Regression","authors":"Alexis Comber, Christopher Brunsdon, Martin Charlton, Guanpeng Dong, Richard Harris, Binbin Lu, Yihe Lü, Daisuke Murakami, Tomoki Nakaya, Yunqiang Wang, Paul Harris","doi":"10.1111/gean.12316","DOIUrl":"https://doi.org/10.1111/gean.12316","url":null,"abstract":"Geographically Weighted Regression (GWR) is increasingly used in spatial analyses of social and environmental data. It allows spatial heterogeneities in processes and relationships to be investigated through a series of local regression models rather than a single global one. Standard GWR assumes that relationships between the response and predictor variables operate at the same spatial scale, which is frequently not the case. To address this, several GWR variants have been proposed. This paper describes a route map to decide whether to use a GWR model or not, and if so which of three core variants to apply: a standard GWR, a mixed GWR or a multiscale GWR (MS-GWR). The route map comprises 3 primary steps that should always be undertaken: (1) a basic linear regression, (2) a MS-GWR, and (3) investigations of the results of these in order to decide whether to use a GWR approach, and if so for determining the appropriate GWR variant. The paper also highlights the importance of investigating a number of secondary issues at global and local scales including collinearity, the influence of outliers, and dependent error terms. Code and data for the case study used to illustrate the route map are provided.","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"55 1","pages":"155-178"},"PeriodicalIF":3.6,"publicationDate":"2022-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gean.12316","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50142834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Recursive Estimation of the Spatial Error Model 空间误差模型的递归估计

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2022-01-03 DOI: 10.1111/gean.12317

Chiara Ghiringhelli, Gianfranco Piras, Giuseppe Arbia, Antonietta Mira

引用次数: 2

Spatial Causality: A Systematic Review on Spatial Causal Inference 空间因果关系：空间因果推理的系统综述

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2021-12-14 DOI: 10.1111/gean.12312

Kamal Akbari, Stephan Winter, Martin Tomko

{"title":"Spatial Causality: A Systematic Review on Spatial Causal Inference","authors":"Kamal Akbari, Stephan Winter, Martin Tomko","doi":"10.1111/gean.12312","DOIUrl":"10.1111/gean.12312","url":null,"abstract":"The growing interest in causal inference in recent years has led to new causal inference methodologies and their applications across disciplines and research domains. Yet, studies on spatial causal inference are still rare. Causal inference on spatial processes is faced with additional challenges, such as spatial dependency, spatial heterogeneity, and spatial effects. These challenges can lead to spurious results and subsequently, incorrect interpretations of the outcomes of causal analyses. Recognizing the growing importance of causal inference in the spatial domain, we conduct a systematic literature review on spatial causal inference based on a formal concept mapping. To identify how to assess and control for the adverse effects of spatial influences, we assess publications relevant to spatial causal inference based on criteria relating to application discipline, methods used, and techniques applied for managing issues related to spatial processes. We thus present a snapshot of state of the art in spatial causal inference and identify methodological gaps, weaknesses and challenges of current spatial inference studies, along with opportunities for future research.","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"55 1","pages":"56-89"},"PeriodicalIF":3.6,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49363047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

In memoriam: Martin Charlton 纪念马丁·查尔顿

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2021-12-03 DOI: 10.1111/gean.12309

Christopher Brunsdon, Alexis Comber, Paul Harris, Jan Rigby, Andrew Large

引用次数: 0

GeoDa, From the Desktop to an Ecosystem for Exploring Spatial Data GeoDa，从桌面到探索空间数据的生态系统

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2021-11-19 DOI: 10.1111/gean.12311

Luc Anselin, Xun Li, Julia Koschinsky

引用次数: 15

Reproducibility of Research During COVID-19: Examining the Case of Population Density and the Basic Reproductive Rate from the Perspective of Spatial Analysis COVID-19期间研究的可重复性:基于空间分析视角的人口密度与基本繁殖率案例检验

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2021-11-18 DOI: 10.1111/gean.12307

Antonio Paez

引用次数: 14

Reproducible Science Is Vital for a Stronger Evidence Base During the COVID-19 Pandemic 在COVID-19大流行期间，可再生科学对于建立更强有力的证据基础至关重要

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2021-11-16 DOI: 10.1111/gean.12314

Karla Therese L. Sy, Laura F. White, Brooke E. Nichols

{"title":"Reproducible Science Is Vital for a Stronger Evidence Base During the COVID-19 Pandemic","authors":"Karla Therese L. Sy, Laura F. White, Brooke E. Nichols","doi":"10.1111/gean.12314","DOIUrl":"10.1111/gean.12314","url":null,"abstract":"Reproducible research becomes even more imperative as we build the evidence base on SARS-CoV-2 epidemiology, diagnosis, prevention, and treatment. In his study, Paez assessed the reproducibility of COVID-19 research during the pandemic, using a case study of population density. He found that most articles that assess the relationship of population density and COVID-19 outcomes do not publicly share data and code, except for a few, including our paper, which he stated “illustrates the importance of good reproducibility practices”. Paez recreated our analysis using our code and data from the perspective of spatial analysis, and his new model came to a different conclusion. The disparity between our and Paez’s findings, as well as other existing literature on the topic, give greater impetus to the need for further research. As there has been near exponential growth of COVID-19 research across a wide range of scientific disciplines, reproducible science is a vital component to produce reliable, rigorous, and robust evidence on COVID-19, which will be essential to inform clinical practice and policy in order to effectively eliminate the pandemic.","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"55 1","pages":"203-206"},"PeriodicalIF":3.6,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8652901/pdf/GEAN-9999-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39719204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Balancing Spatial and Non-Spatial Variation in Varying Coefficient Modeling: A Remedy for Spurious Correlation 在变系数模型中平衡空间和非空间差异:对虚假相关的补救

IF 3.6 3区地球科学

Geographical Analysis Pub Date : 2021-11-14 DOI: 10.1111/gean.12310

Daisuke Murakami, Daniel A. Griffith

{"title":"Balancing Spatial and Non-Spatial Variation in Varying Coefficient Modeling: A Remedy for Spurious Correlation","authors":"Daisuke Murakami, Daniel A. Griffith","doi":"10.1111/gean.12310","DOIUrl":"10.1111/gean.12310","url":null,"abstract":"This study discusses the importance of balancing spatial and non-spatial variation in spatial regression modeling. Unlike spatially varying coefficients (SVC) modeling, which is popular in spatial statistics, non-spatially varying coefficients (NVC) modeling has largely been unexplored in spatial fields. Nevertheless, as we will explain, consideration of non-spatial variation is needed not only to improve model accuracy but also to reduce spurious correlation among varying coefficients, which is a major problem in SVC modeling. We consider a Moran eigenvector approach modeling spatially and non-spatially varying coefficients (S&NVC). A Monte Carlo simulation experiment comparing our S&NVC model with existing SVC models suggests both modeling accuracy and computational efficiency for our approach. Beyond that, somewhat surprisingly, our approach identifies true and spurious correlations among coefficients nearly perfectly, even when usual SVC models suffer from severe spurious correlations. It implies that S&NVC model should be used even when the analysis purpose is modeling SVCs. Finally, our S&NVC model is employed to analyze a residential land price data set. Its results suggest existence of both spatial and non-spatial variation in regression coefficients in practice. The S&NVC model is now implemented in the R package spmoran.","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"55 1","pages":"31-55"},"PeriodicalIF":3.6,"publicationDate":"2021-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gean.12310","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44618791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13