Geographical Analysis最新文献

Correction to “The Nonlinear Impact of Migrants on Regional Carbon Emissions in China: Evidence From Spatial Econometric Models” 对“移民对中国区域碳排放的非线性影响：来自空间计量模型的证据”的修正

IF 4.3 3区地球科学

Geographical Analysis Pub Date : 2026-04-10 DOI: 10.1111/gean.70037

引用次数: 0

Do Welfare Regimes Matter? A Comparative Analysis of Area-Level Inequalities in Life Expectancy in Germany and the United Kingdom, 2003–2021 福利制度重要吗？2003-2021年德国和英国地区预期寿命不平等的比较分析

IF 4.3 3区地球科学

Geographical Analysis Pub Date : 2026-04-03 DOI: 10.1111/gean.70040

Michael Mühlichen, Andreas Höhn, Nik Lomax, Petra Meier, Pavel Grigoriev

{"title":"Do Welfare Regimes Matter? A Comparative Analysis of Area-Level Inequalities in Life Expectancy in Germany and the United Kingdom, 2003–2021","authors":"Michael Mühlichen, Andreas Höhn, Nik Lomax, Petra Meier, Pavel Grigoriev","doi":"10.1111/gean.70040","DOIUrl":"10.1111/gean.70040","url":null,"abstract":"While area-level inequalities in life expectancy are well documented within individual countries, cross-national comparisons remain rare. We estimated and compared the magnitude of area-level life expectancy inequalities across districts in the UK and Germany, representing two distinct welfare regimes. We used mortality data from national statistical offices to estimate life expectancy for all districts between 2003 and 2021. Based on employment data, we assigned a deprivation decile to each district within both countries and calculated life expectancy for each decile. Using the slope index of inequality, we compared temporal trends in life expectancy inequalities across deciles in both countries. We found that, although life expectancy was similar in both countries, the UK consistently showed higher levels of area-level inequality than Germany. While district-level life expectancy inequalities increased in both countries over time, they rose more sharply in the UK, particularly during the first years of the Covid-19 pandemic. These findings are likely to reflect deeply rooted differences in governmental approaches to ensuring equitable living conditions across two distinct welfare regimes.","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"58 2","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gean.70040","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147686064","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

Spatial Proportionality Between Two Types of Points 两类点之间的空间比例

IF 4.3 3区地球科学

Geographical Analysis Pub Date : 2026-04-03 DOI: 10.1111/gean.70038

Yukio Sadahiro

引用次数: 0

When Proximity Falls Short: Inequalities in Commuting and Accessibility by Public Transport in Santiago, Chile 当距离不够近时：智利圣地亚哥公共交通的通勤和可达性不平等

IF 4.3 3区地球科学

Geographical Analysis Pub Date : 2026-03-26 DOI: 10.1111/gean.70039

Cesar Marin-Flores, Leo Ferres, Henrikki Tenkanen

{"title":"When Proximity Falls Short: Inequalities in Commuting and Accessibility by Public Transport in Santiago, Chile","authors":"Cesar Marin-Flores, Leo Ferres, Henrikki Tenkanen","doi":"10.1111/gean.70039","DOIUrl":"10.1111/gean.70039","url":null,"abstract":"Traditional measures of urban accessibility often rely on static models or survey data. However, location information from mobile networks enables large-scale, dynamic analyses of how people navigate cities. In this study, we employ eXtended Detail Records (XDRs) from mobile phone activity to analyze commuting patterns and accessibility inequalities in Santiago, Chile. We identify residential and work locations and model commuting routes by public transport and walking using the R5 multimodal routing engine. Spatial patterns are examined using bivariate local indicators of spatial association (LISA) alongside regression techniques to identify distinct commuting behaviors and their alignment with vulnerable population groups. Our results show that while average public transport commuting times do not differ significantly across socioeconomic groups, marked inequalities emerge when accessibility is considered. High-income neighborhoods consistently exhibit high accessibility, whereas low-income areas show substantially lower levels. Importantly, these disparities do not translate into longer commuting times for lower-income groups, indicating a weak relationship between proximity to opportunities and observed travel times. The analysis also reveals significant disparities across sociodemographic groups, particularly in relation to Indigenous populations and gender. The proposed approach is readily scalable and can support evaluations of changes in commuting patterns and the impacts of urban interventions.","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"58 2","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gean.70039","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147585091","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

When Proximity Falls Short: Inequalities in Commuting and Accessibility by Public Transport in Santiago, Chile 当距离不够近时：智利圣地亚哥公共交通的通勤和可达性不平等

IF 4.3 3区地球科学

Geographical Analysis Pub Date : 2026-03-26 DOI: 10.1111/gean.70039

Cesar Marin-Flores, Leo Ferres, Henrikki Tenkanen

{"title":"When Proximity Falls Short: Inequalities in Commuting and Accessibility by Public Transport in Santiago, Chile","authors":"Cesar Marin-Flores, Leo Ferres, Henrikki Tenkanen","doi":"10.1111/gean.70039","DOIUrl":"https://doi.org/10.1111/gean.70039","url":null,"abstract":"Traditional measures of urban accessibility often rely on static models or survey data. However, location information from mobile networks enables large-scale, dynamic analyses of how people navigate cities. In this study, we employ eXtended Detail Records (XDRs) from mobile phone activity to analyze commuting patterns and accessibility inequalities in Santiago, Chile. We identify residential and work locations and model commuting routes by public transport and walking using the R5 multimodal routing engine. Spatial patterns are examined using bivariate local indicators of spatial association (LISA) alongside regression techniques to identify distinct commuting behaviors and their alignment with vulnerable population groups. Our results show that while average public transport commuting times do not differ significantly across socioeconomic groups, marked inequalities emerge when accessibility is considered. High-income neighborhoods consistently exhibit high accessibility, whereas low-income areas show substantially lower levels. Importantly, these disparities do not translate into longer commuting times for lower-income groups, indicating a weak relationship between proximity to opportunities and observed travel times. The analysis also reveals significant disparities across sociodemographic groups, particularly in relation to Indigenous populations and gender. The proposed approach is readily scalable and can support evaluations of changes in commuting patterns and the impacts of urban interventions.","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"58 2","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gean.70039","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147585092","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

Coarse-to-Fine Spatial Modeling: A Scalable, Machine-Learning-Compatible Framework 从粗到精的空间建模：一个可扩展的、与机器学习兼容的框架

IF 4.3 3区地球科学

Geographical Analysis Pub Date : 2026-03-19 DOI: 10.1111/gean.70034

Daisuke Murakami, Alexis Comber, Takahiro Yoshida, Narumasa Tsutsumida, Chris Brunsdon, Tomoki Nakaya

{"title":"Coarse-to-Fine Spatial Modeling: A Scalable, Machine-Learning-Compatible Framework","authors":"Daisuke Murakami, Alexis Comber, Takahiro Yoshida, Narumasa Tsutsumida, Chris Brunsdon, Tomoki Nakaya","doi":"10.1111/gean.70034","DOIUrl":"https://doi.org/10.1111/gean.70034","url":null,"abstract":"This study proposes coarse-to-fine spatial modeling (CFSM) as a scalable and machine learning-compatible alternative to conventional spatial process models. Unlike conventional covariance-based spatial models, CFSM represents spatial processes using a multiscale ensemble of local models. To ensure stable model training, larger-scale patterns that are easier to learn are modeled first, followed by smaller-scale patterns, with training terminated once the validation score stops improving. The training procedure, which is based on holdout validation, can be easily integrated with other machine learning algorithms, including random forests and neural networks. CFSM training is computationally efficient because it avoids explicit matrix inversion, which is a major computational bottleneck in conventional spatial Gaussian processes. Comparative Monte Carlo experiments demonstrated that the CFSM, as well as its integration with random forests, achieved superior predictive performance compared to existing models. Finally, we applied the proposed methods to an analysis of residential land prices in the Tokyo metropolitan area, Japan. The CFSM is implemented in an R package spCF (\u0000https://cran.r-project.org/web/packages/spCF/).","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"58 2","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gean.70034","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147567026","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

Coarse-to-Fine Spatial Modeling: A Scalable, Machine-Learning-Compatible Framework 从粗到精的空间建模：一个可扩展的、与机器学习兼容的框架

IF 4.3 3区地球科学

Geographical Analysis Pub Date : 2026-03-19 DOI: 10.1111/gean.70034

Daisuke Murakami, Alexis Comber, Takahiro Yoshida, Narumasa Tsutsumida, Chris Brunsdon, Tomoki Nakaya

{"title":"Coarse-to-Fine Spatial Modeling: A Scalable, Machine-Learning-Compatible Framework","authors":"Daisuke Murakami, Alexis Comber, Takahiro Yoshida, Narumasa Tsutsumida, Chris Brunsdon, Tomoki Nakaya","doi":"10.1111/gean.70034","DOIUrl":"https://doi.org/10.1111/gean.70034","url":null,"abstract":"This study proposes coarse-to-fine spatial modeling (CFSM) as a scalable and machine learning-compatible alternative to conventional spatial process models. Unlike conventional covariance-based spatial models, CFSM represents spatial processes using a multiscale ensemble of local models. To ensure stable model training, larger-scale patterns that are easier to learn are modeled first, followed by smaller-scale patterns, with training terminated once the validation score stops improving. The training procedure, which is based on holdout validation, can be easily integrated with other machine learning algorithms, including random forests and neural networks. CFSM training is computationally efficient because it avoids explicit matrix inversion, which is a major computational bottleneck in conventional spatial Gaussian processes. Comparative Monte Carlo experiments demonstrated that the CFSM, as well as its integration with random forests, achieved superior predictive performance compared to existing models. Finally, we applied the proposed methods to an analysis of residential land prices in the Tokyo metropolitan area, Japan. The CFSM is implemented in an R package spCF (\u0000https://cran.r-project.org/web/packages/spCF/).","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"58 2","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gean.70034","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147567027","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

Massive Retail Location Choice as a Human-Flow-Covering Problem 人流覆盖问题下的大规模零售区位选择

IF 4.3 3区地球科学

Geographical Analysis Pub Date : 2026-03-12 DOI: 10.1111/gean.70036

Hezhishi Jiang, Yihang Li, Qing Lu, Yu Liu, Liyan Xu, Hongmou Zhang

引用次数: 0

Massive Retail Location Choice as a Human-Flow-Covering Problem 人流覆盖问题下的大规模零售区位选择

IF 4.3 3区地球科学

Geographical Analysis Pub Date : 2026-03-12 DOI: 10.1111/gean.70036

Hezhishi Jiang, Yihang Li, Qing Lu, Yu Liu, Liyan Xu, Hongmou Zhang

引用次数: 0

Focal-Feature Regression Kriging 焦点特征回归

IF 4.3 3区地球科学

Geographical Analysis Pub Date : 2026-03-11 DOI: 10.1111/gean.70035

Peng Luo, Yilong Wu, Yongze Song

{"title":"Focal-Feature Regression Kriging","authors":"Peng Luo, Yilong Wu, Yongze Song","doi":"10.1111/gean.70035","DOIUrl":"10.1111/gean.70035","url":null,"abstract":"Spatial interpolation is a crucial task in geography. As perhaps the most widely used interpolation methods, geostatistical models-such as Ordinary Kriging (OK)-assume spatial stationarity, which makes it difficult to capture the nonstationary characteristics of geographic variables. A common solution is trend surface modeling (e.g., Regression Kriging, RK), which relies on external explanatory variables to model the trend and then applies geostatistical interpolation to the residuals. However, this approach requires high-quality and readily available explanatory variables, which are often lacking in many spatial interpolation scenarios-such as estimating heavy metal concentrations underground. This study proposes a Focal Feature Regression Kriging (FFRK) method, which automatically extracts geospatial features to construct a regression-based trend surface without requiring external explanatory variables. We conducted experiments on the spatial prediction of three heavy metals in a mining area in Australia. In comparison with 17 classical interpolation methods, the results indicate that FFRK, which relies solely on extracted geospatial features, consistently outperforms both conventional Kriging techniques and machine learning models that depend on explanatory variables. This approach effectively addresses spatial nonstationarity while reducing the cost of acquiring explanatory variables, improving both prediction accuracy and generalization ability.","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"58 2","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gean.70035","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564913","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