Geographical Analysis最新文献

A Network-Constrained Spatiotemporal Scan Statistic for Identifying Geographic Flow Hotspots 基于网络约束的时空扫描统计量识别地理流量热点

IF 2.4 3区地球科学

Geographical Analysis Pub Date : 2026-08-25 DOI: 10.1111/gean.70056

Zhuoting Fu, Xiaorui Yan, Tao Pei, Ci Song, Jie Chen, Dayu Cheng, Xiaotong Wang, Yuqing Wang

{"title":"A Network-Constrained Spatiotemporal Scan Statistic for Identifying Geographic Flow Hotspots","authors":"Zhuoting Fu, Xiaorui Yan, Tao Pei, Ci Song, Jie Chen, Dayu Cheng, Xiaotong Wang, Yuqing Wang","doi":"10.1111/gean.70056","DOIUrl":"https://doi.org/10.1111/gean.70056","url":null,"abstract":"<div>\u0000 \u0000 <p>Geographic flows represent the movement of geographic objects between locations at different times. Flow hotspots reflect aggregated movements within specific spatiotemporal ranges. Accurately identifying these hotspots helps reveal their underlying causes and provides targeted insights. Among existing methods, scan statistics are widely used due to their flexible windows and unified significance testing. However, traditional Euclidean cylindrical windows are inadequate for road networks. Spatiotemporal scanning windows of network-constrained flows must account for network topology and origin–destination temporal precedence. To address this issue, this study proposes a novel spatiotemporal scanning window that integrates network topology and temporal constraints and develops the scan statistic under completely spatiotemporal randomness (CSTR) and global time permutation (GTP) null models. Constructed via the Cartesian product of origin and destination network-constrained prisms, the window adaptively extends along road networks to precisely capture flow distribution. Synthetic experiments demonstrate that our method outperforms baseline methods in identifying hotspots within complex scenarios. Case studies show that the CSTR model effectively identifies dominant macroscopic tidal travel patterns (such as commuting hotspots), whereas the GTP model successfully overcomes the interference from inhomogeneous backgrounds to accurately capture fine-grained flow hotspots during off-peak hours and between specific transit hubs.</p>\u0000 </div>","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"58 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848640","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: 0

A Spatial Clustering Approach of Economic Activities: An Application to the Agribusiness Sector in Brazil's Midwest Region 经济活动的空间聚类方法：在巴西中西部地区农业综合企业部门的应用

IF 2.4 3区地球科学

Geographical Analysis Pub Date : 2026-07-22 DOI: 10.1111/gean.70054

Gilvan Santos do Rosario Junior, Ticiana Grecco Zanon Moura, Mauro Ferrante

{"title":"A Spatial Clustering Approach of Economic Activities: An Application to the Agribusiness Sector in Brazil's Midwest Region","authors":"Gilvan Santos do Rosario Junior, Ticiana Grecco Zanon Moura, Mauro Ferrante","doi":"10.1111/gean.70054","DOIUrl":"https://doi.org/10.1111/gean.70054","url":null,"abstract":"<p>The spatial distribution of economic activities is central to regional economics. However, empirical tools for comparing industries based on the similarity of their spatial distributions remain limited. Traditional cluster analysis typically ignores geographical distance and connectivity, while spatial methods focus on clustering places rather than economic activities. This paper addresses this methodological gap by proposing a framework for clustering economic activities based on the similarity of their spatial distributions, explicitly incorporating geographical space. The Wasserstein distance is employed with a geographically informed cost matrix, that is, the Shimbel matrix, to measure the effort required to transform one activity's spatial distribution into another. This approach is compared with traditional non-spatial distance metrics through an illustrative example and then applied to 272 agribusiness activities across 466 municipalities in Brazil's Midwest region from 2013 to 2021. The results reveal four main clusters: one spread throughout the region and others concentrated in northern, southern, or eastern areas, consistent with regional endowments, historical trajectories, and agro-industrial linkages. The case study suggests that the approach offers a useful complementary tool for identifying co-distributed activities and spatially similar industrial patterns, with implications for regional development analysis, place-based policy design and future studies of economic linkages and shock exposure.</p>","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"58 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gean.70054","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615490","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

How Software Development Conceals and Constrains Critical Analytic Decisions: Tracing the Encoded Choices Behind a Failure to Reproduce a Spatial Scan Statistic 软件开发如何隐藏和限制关键的分析决策：追踪空间扫描统计数据重现失败背后的编码选择

IF 2.4 3区地球科学

Geographical Analysis Pub Date : 2026-07-21 DOI: 10.1111/gean.70053

Emily Zhou, Joseph Holler, Peter Kedron

{"title":"How Software Development Conceals and Constrains Critical Analytic Decisions: Tracing the Encoded Choices Behind a Failure to Reproduce a Spatial Scan Statistic","authors":"Emily Zhou, Joseph Holler, Peter Kedron","doi":"10.1111/gean.70053","DOIUrl":"https://doi.org/10.1111/gean.70053","url":null,"abstract":"<div>\u0000 \u0000 <p>Researchers increasingly call for transparent reporting of analytic decisions as foundations for reproducible research and collective knowledge production. However, these calls for openness often overlook a critical dimension: software environments encode implicit computational decisions that are not always visible to researchers, even when the same statistical methods are applied. Analyses may be technically reproducible, while the epistemological function of reproduction to evaluate and validate prior conclusions is undermined by the opacity of software design. Moreover, unexamined software behavior can compromise the conceptual rigor of research by shaping analytic choices unbeknownst to researchers. While attempting to reproduce a study correlating spatial distributions of COVID-19 and people with disabilities, we investigated how software-encoded decisions influenced intermediate analytic outputs, propagated into downstream inference, and ultimately shaped the research design. Through systematic comparison across software, we demonstrate that (1) software-encoded decisions structure both intermediate results and final analytic conclusions; (2) researchers must remain attentive to the decisions and materials required to execute an analysis, understand what software is doing on their behalf, and consider the numerical and conceptual ramifications of those decisions; and (3) cross-software sensitivity analysis is particularly important when analytic workflows span multiple platforms or when outputs from one system are used as inputs to another.</p>\u0000 </div>","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"58 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148534303","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: 0

Analytical Derivations of the Neighborhood Effect Averaging Problem (NEAP) and Its Two Variants: From Representative Samples to Unbiased Health Effect Sizes and Critical Insights Into Health Geography 邻域效应平均问题（NEAP）及其两个变体的解析推导：从代表性样本到无偏健康效应大小和对健康地理学的重要见解

IF 2.4 3区地球科学

Geographical Analysis Pub Date : 2026-07-07 DOI: 10.1111/gean.70052

Yang Liu, Mei-Po Kwan

{"title":"Analytical Derivations of the Neighborhood Effect Averaging Problem (NEAP) and Its Two Variants: From Representative Samples to Unbiased Health Effect Sizes and Critical Insights Into Health Geography","authors":"Yang Liu, Mei-Po Kwan","doi":"10.1111/gean.70052","DOIUrl":"https://doi.org/10.1111/gean.70052","url":null,"abstract":"<p>Accurate environmental exposure measurements that maximally mitigate contextual errors are necessary for reliable modeling of human health outcomes. Recent advances in environmental exposure measurements highlight a new manifestation of contextual errors called the neighborhood effect averaging problem (NEAP). This study conceptualizes the NEAP in terms of geostatistical sampling and provides rigorous derivations. We aimed to articulate that the NEAP can be an inevitable methodological issue for the population's environmental exposure distribution when ignoring people's daily mobility in exposure measurements. We also illustrated that using spatially clustered and unrepresentative samples of the population may lead to the counter manifestation of this methodological issue called the neighborhood effect polarization problem (NEPP). Finally, we demonstrated how mitigating the NEAP may mitigate the underestimation of health effect sizes of environmental exposures, while the NEPP may inversely lead to the double underestimation of the health effect sizes. This study provides the theoretical foundation and a baseline analytical framework for the recent debate on the NEAP and may also provide essential insights into a range of studies in health geography, environmental epidemiology, and public health.</p>","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"58 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gean.70052","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148462931","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

Analytical Derivations of the Neighborhood Effect Averaging Problem (NEAP) and Its Two Variants: From Representative Samples to Unbiased Health Effect Sizes and Critical Insights Into Health Geography 邻域效应平均问题（NEAP）及其两个变体的解析推导：从代表性样本到无偏健康效应大小和对健康地理学的重要见解

IF 2.4 3区地球科学

Geographical Analysis Pub Date : 2026-07-07 DOI: 10.1111/gean.70052

Yang Liu, Mei-Po Kwan

{"title":"Analytical Derivations of the Neighborhood Effect Averaging Problem (NEAP) and Its Two Variants: From Representative Samples to Unbiased Health Effect Sizes and Critical Insights Into Health Geography","authors":"Yang Liu, Mei-Po Kwan","doi":"10.1111/gean.70052","DOIUrl":"https://doi.org/10.1111/gean.70052","url":null,"abstract":"<p>Accurate environmental exposure measurements that maximally mitigate contextual errors are necessary for reliable modeling of human health outcomes. Recent advances in environmental exposure measurements highlight a new manifestation of contextual errors called the neighborhood effect averaging problem (NEAP). This study conceptualizes the NEAP in terms of geostatistical sampling and provides rigorous derivations. We aimed to articulate that the NEAP can be an inevitable methodological issue for the population's environmental exposure distribution when ignoring people's daily mobility in exposure measurements. We also illustrated that using spatially clustered and unrepresentative samples of the population may lead to the counter manifestation of this methodological issue called the neighborhood effect polarization problem (NEPP). Finally, we demonstrated how mitigating the NEAP may mitigate the underestimation of health effect sizes of environmental exposures, while the NEPP may inversely lead to the double underestimation of the health effect sizes. This study provides the theoretical foundation and a baseline analytical framework for the recent debate on the NEAP and may also provide essential insights into a range of studies in health geography, environmental epidemiology, and public health.</p>","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"58 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gean.70052","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148462894","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

Advancing Spatial Health Inequalities Research: Innovations in Data, Methods, and Theory—Special Issue Introduction 推进空间健康不平等研究：数据、方法和理论的创新——特刊导论

IF 2.4 3区地球科学

Geographical Analysis Pub Date : 2026-06-30 DOI: 10.1111/gean.70051

Andreas Höhn, Alison Heppenstall, Nik Lomax

{"title":"Advancing Spatial Health Inequalities Research: Innovations in Data, Methods, and Theory—Special Issue Introduction","authors":"Andreas Höhn, Alison Heppenstall, Nik Lomax","doi":"10.1111/gean.70051","DOIUrl":"https://doi.org/10.1111/gean.70051","url":null,"abstract":"<div>\u0000 \u0000 <p>Inequalities in population health outcomes, often captured between or within areas, remain a widespread feature of societies around the world. Many of these inequalities are deeply rooted in structural mechanisms, stemming from long-standing disparities in the distribution of and access to key resources impacting health. Despite extensive research and policy efforts to reduce these spatial health inequalities, many have proven persistent—and some have further widened within recent years. This special issue seeks to deepen our understanding of spatial health inequalities by showcasing a range of innovations in an increasingly interdisciplinary field. All included contributions are novel and often extend well-established approaches within their respective core disciplines either analytically or conceptually. Despite a high degree of innovation, causality remains a central challenge. Most contributions highlight these persistent difficulties in causal inference explicitly, calling for integrative frameworks that combine quantitative, qualitative, and participatory methods. Collectively, this special issue demonstrates how interdisciplinary collaboration can deepen our understanding of spatial health inequalities and their determinants. At the same time, findings highlight clearly that a real-world narrowing of spatial health inequalities will very likely also depend on the effective translation of evidence into actionable policy recommendations and political will.</p>\u0000 </div>","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"58 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gean.70051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148387037","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

Advancing Spatial Health Inequalities Research: Innovations in Data, Methods, and Theory—Special Issue Introduction 推进空间健康不平等研究：数据、方法和理论的创新——特刊导论

IF 2.4 3区地球科学

Geographical Analysis Pub Date : 2026-06-30 DOI: 10.1111/gean.70051

Andreas Höhn, Alison Heppenstall, Nik Lomax

{"title":"Advancing Spatial Health Inequalities Research: Innovations in Data, Methods, and Theory—Special Issue Introduction","authors":"Andreas Höhn, Alison Heppenstall, Nik Lomax","doi":"10.1111/gean.70051","DOIUrl":"https://doi.org/10.1111/gean.70051","url":null,"abstract":"<div>\u0000 \u0000 <p>Inequalities in population health outcomes, often captured between or within areas, remain a widespread feature of societies around the world. Many of these inequalities are deeply rooted in structural mechanisms, stemming from long-standing disparities in the distribution of and access to key resources impacting health. Despite extensive research and policy efforts to reduce these spatial health inequalities, many have proven persistent—and some have further widened within recent years. This special issue seeks to deepen our understanding of spatial health inequalities by showcasing a range of innovations in an increasingly interdisciplinary field. All included contributions are novel and often extend well-established approaches within their respective core disciplines either analytically or conceptually. Despite a high degree of innovation, causality remains a central challenge. Most contributions highlight these persistent difficulties in causal inference explicitly, calling for integrative frameworks that combine quantitative, qualitative, and participatory methods. Collectively, this special issue demonstrates how interdisciplinary collaboration can deepen our understanding of spatial health inequalities and their determinants. At the same time, findings highlight clearly that a real-world narrowing of spatial health inequalities will very likely also depend on the effective translation of evidence into actionable policy recommendations and political will.</p>\u0000 </div>","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"58 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gean.70051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148387038","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

Bayesian Spatial Framework for Quantifying Uncertainty in Labor Market Delineation 量化劳动力市场不确定性的贝叶斯空间框架

IF 2.4 3区地球科学

Geographical Analysis Pub Date : 2026-06-26 DOI: 10.1111/gean.70050

Jamintha Samarakoon, Helen Thompson, Gentry White

{"title":"Bayesian Spatial Framework for Quantifying Uncertainty in Labor Market Delineation","authors":"Jamintha Samarakoon, Helen Thompson, Gentry White","doi":"10.1111/gean.70050","DOIUrl":"https://doi.org/10.1111/gean.70050","url":null,"abstract":"<p>Labor market delineation typically relies on deterministic regionalization algorithms that treat commuting flows as fixed inputs and produce a single optimal partition. These approaches obscure uncertainty in boundary placement and cannot distinguish stable labor market cores from transitional regions where affiliation is ambiguous. We develop a Bayesian framework for quantifying uncertainty in labor market boundaries by integrating hierarchical Poisson spatial modeling of commuting flows with cohesion-based regionalization. Commuting intensities are modeled using origin- and destination-specific socioeconomic covariates, distance, and conditional autoregressive (CAR) priors for spatially structured random effects. Posterior predictive commuting matrices are propagated through the Adaptive Simulated Annealing algorithm for Autonomous Labor Market Delineation, generating a distribution of regionalizations rather than a single partition. We introduce three complementary uncertainty measures: edge-level boundary probabilities, region-level membership stability, and local boundary pressure. Applied to Queensland, Australia commuting data (520 regions), the framework identifies approximately 10% of regions exhibit transitional behavior, with instability concentrated in peri-metropolitan growth corridors. Labor market boundaries emerge as probabilistic spatial equilibria characterized by stable cores and geographically localized transitional frontiers. The framework provides a rigorous basis for evaluating boundary robustness, directing expert review boundary delineation, and identifying areas of structural instability for regional policy and infrastructure planning.</p>","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"58 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gean.70050","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148324909","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

Bayesian Spatial Framework for Quantifying Uncertainty in Labor Market Delineation 量化劳动力市场不确定性的贝叶斯空间框架

IF 2.4 3区地球科学

Geographical Analysis Pub Date : 2026-06-26 DOI: 10.1111/gean.70050

Jamintha Samarakoon, Helen Thompson, Gentry White

{"title":"Bayesian Spatial Framework for Quantifying Uncertainty in Labor Market Delineation","authors":"Jamintha Samarakoon, Helen Thompson, Gentry White","doi":"10.1111/gean.70050","DOIUrl":"https://doi.org/10.1111/gean.70050","url":null,"abstract":"<p>Labor market delineation typically relies on deterministic regionalization algorithms that treat commuting flows as fixed inputs and produce a single optimal partition. These approaches obscure uncertainty in boundary placement and cannot distinguish stable labor market cores from transitional regions where affiliation is ambiguous. We develop a Bayesian framework for quantifying uncertainty in labor market boundaries by integrating hierarchical Poisson spatial modeling of commuting flows with cohesion-based regionalization. Commuting intensities are modeled using origin- and destination-specific socioeconomic covariates, distance, and conditional autoregressive (CAR) priors for spatially structured random effects. Posterior predictive commuting matrices are propagated through the Adaptive Simulated Annealing algorithm for Autonomous Labor Market Delineation, generating a distribution of regionalizations rather than a single partition. We introduce three complementary uncertainty measures: edge-level boundary probabilities, region-level membership stability, and local boundary pressure. Applied to Queensland, Australia commuting data (520 regions), the framework identifies approximately 10% of regions exhibit transitional behavior, with instability concentrated in peri-metropolitan growth corridors. Labor market boundaries emerge as probabilistic spatial equilibria characterized by stable cores and geographically localized transitional frontiers. The framework provides a rigorous basis for evaluating boundary robustness, directing expert review boundary delineation, and identifying areas of structural instability for regional policy and infrastructure planning.</p>","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"58 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gean.70050","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148324869","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

Simulating Area-Level Population Outcomes: Should We Use Multilevel Regression and Poststratification Over Spatial Microsimulation? 模拟区域水平的人口结果：我们是否应该使用多水平回归和后分层而不是空间微观模拟？

IF 2.4 3区地球科学

Geographical Analysis Pub Date : 2026-06-25 DOI: 10.1111/gean.70049

Roger Beecham, Stephen Clark, Jose Pina-Sánchez

{"title":"Simulating Area-Level Population Outcomes: Should We Use Multilevel Regression and Poststratification Over Spatial Microsimulation?","authors":"Roger Beecham, Stephen Clark, Jose Pina-Sánchez","doi":"10.1111/gean.70049","DOIUrl":"https://doi.org/10.1111/gean.70049","url":null,"abstract":"<p>Estimating unknown outcomes at small-area population level is a routine task in spatial analysis. We demonstrate how multilevel regression and poststratification (MRP), widely used in political polling, overcomes some deficiencies in spatial microsimulation (SPM), the <i>de facto</i> approach in quantitative geography. Using individual-level data from the Health Survey for England and population-level data from the 2021 UK Census, we evaluate MRP and SPM at estimating two known health outcomes that occur with high and low frequency in the population. With few SPM constraints, covariates in MRP, there are only slight differences in estimation between the two approaches. With more constraints, extreme errors in the SPM estimates begin to accumulate, and these are particularly pronounced for the low-frequency outcome. Additionally, where uncertainty ranges from MRP posteriors begin to widen we find they map to absolute errors, providing a useful validity check when the true population distribution is unknown. This is the first direct comparison of MRP and SPM for small-area estimation. Alongside metrics for evaluating estimates, we highlight the value of non-compositional area-level variables that may constrain outcomes or capture varying processes over spatial units, and of a principled approach to model specification and uncertainty quantification—both central to MRP practice.</p>","PeriodicalId":12533,"journal":{"name":"Geographical Analysis","volume":"58 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gean.70049","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148324428","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