Geographical Analysis最新文献_第5页

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

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":"https://doi.org/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":"147564926","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

Arriaga Meets Kitagawa. Life Expectancy Decomposition With Population Subgroups Arriaga遇见Kitagawa。人口亚群的预期寿命分解

IF 4.3 3区地球科学

Geographical Analysis Pub Date : 2026-02-25 DOI: 10.1111/gean.70033

Timothy Riffe, Rustam Tursun-zade, Sergi Trias Llimós

{"title":"Arriaga Meets Kitagawa. Life Expectancy Decomposition With Population Subgroups","authors":"Timothy Riffe, Rustam Tursun-zade, Sergi Trias Llimós","doi":"10.1111/gean.70033","DOIUrl":"10.1111/gean.70033","url":null,"abstract":"An Arriaga decomposition partitions differences in life expectancy into contributions from mortality rate differences in each age. A Kitagawa decomposition partitions a difference between two weighted means into effects from differences in structure and from differences in each element of the weighted value. Life expectancy differences between like-defined subpopulations can be decomposed using the Arriaga method, or a different suitable decomposition method. If combined (or total) life expectancy is treated as a weighted average of the subpopulations, then the results of subgroup-specific decompositions can substitute the rate component from a Kitagawa decomposition of combined life expectancy. This is valid as long as the relative weight of each subpopulation is part of the initial conditions of the combined lifetable, and group prevalence in later ages is determined only by mortality. The composition component of the same Kitagawa decomposition gives the effect of differing subgroup composition on total life expectancy differences. Notable properties of the method include: (i) it accommodates any number of subpopulations, (ii) it easily incorporates cause-of-death information, and (iii) composition is considered only in the initial conditions. We apply the method to Spanish cause- and education-specific data. This method can further disentangle the effects of mortality and composition differences, helping to explain or clarify paradoxes and contemporary or forthcoming life expectancy changes as partly driven by shifts in cohort composition. We give both R code and spreadsheet implementations of the method.","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"58 2","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gean.70033","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147568880","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