Non-spatial Models Research Articles

Testing the Solow Hypothesis for Fiscal Convergence: a Dynamic Spatial Analysis

The study of regional fiscal convergence is a recent extension of the neoclassical growth theory. Various studies have shown the existence of fiscal convergence across countries or states in federally governed countries. This paper tests the growth theory on income and fiscal variables differently in a centrally ruled country. Therefore, we estimate spatial and non-spatial panel models from 2004 to 2022 for Türkiye. A general-to-specific methodology is applied for selecting an appropriate model to determine spatial interactions of the variables by using the panel data at the level of 81 Turkish provinces. The Ordinary Least Squares (OLS) estimation results from the non-spatial model partially validate the growth theory as the study does not find evidence of absolute convergence for government expenditures. The results, however, confirm the conditional convergence for all variables. The Maximum Likelihood (ML) method is applied for the estimation of the dynamic and static spatial panel models to explain their spatial interactions. The ML findings are consistent with the OLS results. Moreover, unlike direct and total effects, it is not possible to define indirect effects explaining spatial spillover effects in the short and long terms.

Spatial externalities, R&D spillovers, and endogenous technological change

Forward-looking economic agents operating in a finite continuous geographic area choose how much to innovate at each point in time and space. Based on this assumption, the present study incorporates spatial interactions in endogenous growth models, addressing the criticism that such models are inconsistent with empirical evidence. More specifically, we introduce spatial production spillovers, knowledge diffusion across space, and the capability for spatial heterogeneity into a standard expanding variety growth model based on R&D. We study the properties of equilibrium and optimal allocations and argue that the characteristics are different from those of the non-spatial model, which alter the appropriate policy measures. Finally, we provide numerical examples demonstrating the importance of spatial dependent policy measures in achieving a balanced regional development.

Understanding the zero-emission vehicle market spatial diffusion and its determinants from 2019 to 2022 using spatial econometric models

Due to the rapid rise in the acceptance of zero-emission vehicles (ZEVs), much research has been conducted on the market evolution of alternative fuel technology. The research reported in this paper builds on prior studies by incorporating several new directions. Firstly, the spatial distribution of ZEV purchases in California is explored, then seven models (two nonspatial models and five spatially explicit models) are developed to predict regional ZEV sales. The predictions are based on fundamental and widely accessible characteristics such as infrastructure and demographic statistics. Next, a spatial econometric model known as the “spatial error model” is chosen to distribute ZEV sales from their original postal code (ZIP) level to the more often utilized US census tract level. This allows for carrying out a comprehensive analysis that includes studying the correlation of ZEV sales with local incentives and disadvantaged communities. Results reveal that ZEV sales in California increased significantly between 2019 and 2022, as spatial autocorrelation decreased. Vulnerable groups were less likely to have access to ZEVs, particularly during the initial stage of the ZEV market penetration in 2019. As expected, regions with considerable ZEV sales tended to also experience substantial purchase rebates and lower disadvantaged population, especially in 2019.

Maternal mortality in Brazil: an analysis of temporal trends and spatial clustering.

This article aims to analyze spatial and temporal patterns of maternal mortality in Brazil during the period 2010-2020 and identify related socioeconomic indicators. We conducted an ecological study of the maternal mortality ratio (MMR) in Brazil's municipalities using secondary data. Temporal analysis was performed using the joinpoint method. Bayesian statistics, spatial autocorrelation, the Getis Ord Gi* technique and the scan statistic were used to identify spatial clusters, and multiple non-spatial and spatial regression models were used to assess the association between factors and the MMR. There was an increase in the MMR in 2020 and an increase in deaths in the North and Southeast. Clusters were found in Amazonas, Tocantins, Piauí, Maranhão, Bahia and Mato Grosso do Sul. The following indicators were negatively associated with the MMR: cesarean section rate, Municipal Human Development Index, and per capita household income of people who are vulnerable to poverty. The MMR was stable up to 2019, followed by a sharp rise in 2020 coinciding with the onset of the Covid-19 pandemic in the country. It is essential that efforts to reduce maternal mortality in Brazil extend beyond the promotion of improvements in antenatal, childbirth and postpartum care to address the social determinants of the problem.

Housing price determinants in Ecuador: a spatial hedonic analysis

Purpose This paper aims to compare the efficiency of spatial and nonspatial hedonic price models in capturing housing submarkets dynamics for cities in developing countries. This study expects to contribute to a better understanding of the housing price determinants from both nonspatial and spatial perspectives. In addition, this paper fills a gap in the literature on the study of housing prices from a spatial perspective in Latin American cities. Design/methodology/approach This study uses a comparative analysis between an ordinary least squares regression and a geographical weighted regression, GWR. The study also assesses the performance of two distinct data sources: the city’s cadastral records and a real estate sales web portal. Findings The results suggest that compared to the traditional regression model, the spatial regression models are more effective at capturing housing market variations on a fine scale. Moreover, they reveal interesting findings on the spatial varying, sometimes contradictory effects of some housing attributes on housing prices in different areas of the city, suggesting the potential impact from segregation. Research limitations/implications The availability of data on housing prices and characteristics in Latin American cities is fragmented and complex. The level of detail, granularity and coverage is not consistent over time. For this reason, this study combines and compares data sets from official and unofficial sources in an effort to close this gap. Likewise, the socioeconomic variables that come from the census must be carefully analyzed, knowing the historical context in which they were constructed, what they represent and their interpretation. Practical implications This paper suggests that despite the improvement on the spatial models, the selection of a specific one should always be based on the diagnosis of it as it highly depends on the data used and the objectives of the study. Originality/value This study enriches the limited body of literature on spatial hedonic price models of housing in Latin American cities. It also shed light on the importance of spatial approaches to identify complex housing submarkets.

The Utility of Socioeconomic and Remoteness Indicators in Understanding the Geographical Variation in the Regional Prevalence of Early Childhood Vulnerability in Australia

The family lives of children and their early childhood development outcomes are attributable to the level of socio-economic disadvantage and relative isolation. This study aims to investigate how the disadvantage of the local area (i.e., socio-economic indexes for areas (SEIFA)) and the remoteness (i.e., accessibility/remoteness index of Australia (ARIA)) contribute to improved prevalence estimates of child development vulnerability in statistical areas level 3 (SA3) and 4 (SA4) across Australia. Data from the 2018 Australian Early Development Census (AEDC) has been used. The study included 308,953 children involved in the AEDC 2018 where one-in-ten of them were considered to be developmentally vulnerable, nationally. We developed models in a hierarchical Bayesian framework at the SA3 level using SEIFA and ARIA indices as covariates to account for spatial and unobserved heterogeneity. The performances of developed models are examined based on the consistency at SA3, SA4, and state level. The results reveal that SEIFA makes a significant contribution to explaining the spatial variation in childhood development vulnerability across small domains in Australia. Further, the inclusion of the ARIA score improves the model performance and provides better accuracy, particularly in remote and very remote regions. In these regions, the spatial model fails to distinguish the remoteness characteristics. The chosen non-spatial model accounting for heterogeneity at higher hierarchies performs best. The utilization of socio-economic disadvantage and geographic remoteness of the finer level domains helps to explain the geographic variation in child development vulnerability, particularly in sparsely populated remote regions in Australia.

Temporal pattern, spatial distribution, and factors associated with the incidence of HIV/AIDS among young people in BrazilPatrón temporal, distribución espacial y factores asociados a la incidencia de la infección por el VIH/sida en jóvenes en Brasil

To describe temporal and spatial patterns and identify the factors associated with the incidence of HIV/AIDS among young people in Brazil. Ecological study of young Brazilians aged 15-24 years with reported HIV/AIDS, from 2001 to 2021. The Joinpoint method was used for the temporal analysis. Spatial clusters were detected using Bayesian methods, spatial autocorrelation, Getis-Ord Gi*, and scan techniques. Four non-spatial and spatial regression models were used to identify factors associated with the result. All statistical analyses considered p < 0.05. In Brazil, the average incidence was 12.29 per 100 000 inhabitants, with an annual increase of 7.3% in the period 2007-2014 and a subsequent 3.4% decrease in 2014-2021. A high-high pattern and hotspots were observed, mainly in municipalities in the South, Southeast, Central-West, and North regions. The primary cluster was located in 572 municipalities in Rio Grande do Sul and Santa Catarina, with the highest relative risks in Manaus (Amazonas) and Rondonópolis (Mato Grosso). The illiteracy rate (β = -0.08), GINI Index (β = -3.74) and Family Health Strategy coverage (β = -0.70) were negatively associated with the result. In contrast, the Firjan Municipal Development Index (β = 2.37), Social Vulnerability Index (β = 6.30), percentage of Bolsa Família recipients (β = 0.04), and per capita income (β = 0.008) showed a positive association. There was an upward trend in the incidence of HIV/AIDS until 2014, followed by a decline until 2021. High-rate clusters were concentrated in municipalities in the North, South, Southeast and Central-West regions in particular. Indicators of socioeconomic vulnerability had positive or negative effects on the result, depending on the territory investigated.

Differential gene expression analysis of spatial transcriptomic experiments using spatial mixed models

Spatial transcriptomics (ST) assays represent a revolution in how the architecture of tissues is studied by allowing for the exploration of cells in their spatial context. A common element in the analysis is delineating tissue domains or “niches” followed by detecting differentially expressed genes to infer the biological identity of the tissue domains or cell types. However, many studies approach differential expression analysis by using statistical approaches often applied in the analysis of non-spatial scRNA data (e.g., two-sample t-tests, Wilcoxon’s rank sum test), hence neglecting the spatial dependency observed in ST data. In this study, we show that applying linear mixed models with spatial correlation structures using spatial random effects effectively accounts for the spatial autocorrelation and reduces inflation of type-I error rate observed in non-spatial based differential expression testing. We also show that spatial linear models with an exponential correlation structure provide a better fit to the ST data as compared to non-spatial models, particularly for spatially resolved technologies that quantify expression at finer scales (i.e., single-cell resolution).

Crystallization-Inspired Design and Modeling of Self-Assembly Lattice-Formation Swarm Robotics.

Self-assembly formation is a key research topic for realizing practical applications in swarm robotics. Due to its inherent complexity, designing high-performance self-assembly formation strategies and proposing corresponding macroscopic models remain formidable challenges and present an open research frontier. Taking inspiration from crystallization, this paper introduces a distributed self-assembly formation strategy by defining free, moving, growing, and solid states for robots. Robots in these states can spontaneously organize into user-specified two-dimensional shape formations with lattice structures through local interactions and communications. To address the challenges posed by complex spatial structures in modeling a macroscopic model, this work introduces the structural features estimation method. Subsequently, a corresponding non-spatial macroscopic model is developed to predict and analyze the self-assembly behavior, employing the proposed estimation method and a stock and flow diagram. Real-robot experiments and simulations validate the flexibility, scalability, and high efficiency of the proposed self-assembly formation strategy. Moreover, extensive experimental and simulation results demonstrate the model's accuracy in predicting the self-assembly process under different conditions. Model-based analysis indicates that the proposed self-assembly formation strategy can fully utilize the performance of individual robots and exhibits strong self-stability.

Does 2D and 3D built environment matter for the spreading of COVID-19 at early stages in Wuhan, China: A spatial and multi-scale analysis

Evidence for the impacts of urban physical attributes on the spread of COVID-19 remains weak due to few investigations into the modifiable areal unit problem (MAUP) and spatial autocorrelation, as well as the lack of fine-grained COVID-19 case data. This study presents a spatial and multi-scale approach to examine the relationships between two-dimensional (2D) and three-dimensional (3D) urban parameters and accumulative confirmed infection cases. The spatial distributions of COVID-19 infections are mapped using anonymized neighborhood-level report data with nearly 50,000 confirmed cases at the early stages of the epidemic in Wuhan, China, and the 2D and 3D built environment factors at multiple spatial scales are characterized. Next, geographical detector model is used to examine the MAUP in the spread of COVID-19 and effects of urban parameters, and spatial regression models are then used to address spatial autocorrelation in the results at the identified appropriate spatial scales. Results reveal that the influences of most urban parameters are sensitive to the scale and areal unit used, and 3D urban parameters, such as building height, have a greater influence than 2D parameters. The estimates from both spatial and non-spatial models suggest the presence of spatial dependence and unobserved autocorrelated factors, and the overestimation of the effects of most urban parameters.

Spatial modeling connecting childhood atopic dermatitis prevalence with household exposure to pollutants

BackgroundAtopic dermatitis (AD) is a chronic, inflammatory disease characterized by dry, pruritic skin. In the U.S., the prevalence of AD has increased over three-fold since the 1970s. We previously reported a geographic association between isocyanate-containing air pollution and AD as well as mechanistic data demonstrating that isocyanates induce skin dysbiosis and activate the host itch receptor TRPA1. However, non-spatial models are susceptible to spatial confounding and may overlook other meaningful associations.MethodsWe added spatial analysis to our prior model, contrasting pollution data with clinical visits. In addition, we conducted a retrospective case-control survey of childhood exposure to BTEX-related products. Finally, we assessed implicated compounds, in pure form and as part of synthetic fabric, for their effect on the growth and metabolism of skin commensal bacteria.ResultsSpatial analysis implicate benzene, toluene, ethylbenzene, and, most significantly, xylene (BTEX) compounds. Survey odds ratios for AD were significant for xylene-derived polyester bed sheets (OR = 9.5; CI 2.2–40.1) and diisocyanate-containing wallpaper adhesive (OR = 6.5; CI 1.5–27.8). Staphylococcus aureus lives longer on synthetic textiles compared to natural textiles. Meanwhile, synthetic fabric exposure shifts the lipid metabolism of health-associated commensals (Roseomonas mucosa and S. epidermidis) away from therapeutic pathways.ConclusionsWe propose that BTEX chemicals in their raw forms and in synthetic products represent a unifying hypothesis for environmentally induced AD flares through their ability to create dysbiosis in the skin microbiota and directly activate TRPA1. Unequal distribution of these pollutants may also influence racial disparities in AD rates.

Greenness, air pollution, and temperature exposure effects in predicting premature mortality and morbidity: A small-area study using spatial random forest model

BackgroundAlthough studies have provided negative impacts of air pollution, heat or cold exposure on mortality and morbidity, and positive effects of increased greenness on reducing them, a few studies have focused on exploring combined and synergetic effects of these exposures in predicting these health outcomes, and most had ignored the spatial autocorrelation in analyzing their health effects. This study aims to investigate the health effects of air pollution, greenness, and temperature exposure on premature mortality and morbidity within a spatial machine-learning modeling framework. MethodsYears of potential life lost reflecting premature mortality and comparative illness and disability ratio reflecting chronic morbidity from 1673 small areas covering Greater Manchester for the year 2008–2013 obtained. Average annual levels of NO2 concentration, normalized difference vegetation index (NDVI) representing greenness, and annual average air temperature were utilized to assess exposure in each area. These exposures were linked to health outcomes using non-spatial and spatial random forest (RF) models while accounting for spatial autocorrelation. ResultsSpatial-RF models provided the best predictive accuracy when accounted for spatial autocorrelation. Among the exposures considered, air pollution emerged as the most influential in predicting mortality and morbidity, followed by NDVI and temperature exposure. Nonlinear exposure-response relations were observed, and interactions between exposures illustrated specific ranges or sweet and sour spots of exposure thresholds where combined effects either exacerbate or moderate health conditions. ConclusionAir pollution exposure had a greater negative impact on health compared to greenness and temperature exposure. Combined exposure effects may indicate the highest influence of premature mortality and morbidity burden.

Abstract 7394: Delineating spatial expression signatures of lung adenocarcinoma subtypes from spatial single-cell transcriptomics using graph neural networks

Abstract Lung Adenocarcinoma (LUAD) subtypes including complex acinar (CA), micropapillary, and solid regions are characterized by distinct morphological and biological features that bear significant implications for prognosis and treatment strategies. While histology-based subtype prediction has provided a structural understanding, it overlooks crucial biological insights related to molecular mechanisms and the influences of tumor microenvironments (TMEs). Recent advances in spatially resolved single-cell profiling enable precise measurements of gene expression at the cellular and sub-cellular levels within intact tissues. We hypothesize that spatial gene expression (SGE) patterns can effectively identify LUAD subtypes and characterize the influence of TMEs. Thus, to address the limitations of histology-based subtyping of LUAD, we developed an AI-driven approach that leverages SMI samples to predict LUAD subtypes and elucidate their underlying molecular landscape and associated TMEs. For this study, we leverage the NanoString CosMx Spatial Molecular Imaging (SMI) NSCLC FFPE datasets comprising of 8 LUAD tissue samples, annotated by pathologists at UPMC for subtype identification. The dataset consists of approximately 98k cells per sample with 18 cell types and 960 genes per cell. We encoded the spatial organization of cells as a graph with nodes representing cells with expression features and edges based on k-nearest neighbors of 30 cells. We considered the prediction of micropapillary, CA, and solid for each tumor cell and developed a Graph Convolutional Network (GCN) with semi-supervised training to account for the unlabeled nodes. A subgraph splitting strategy with cross-validation was developed to train on localized spatial regions and ensure balanced label proportions in each subgraph. Our GCN achieved 92.63% test accuracy, surpassing non-spatial deep learning models (77.45%). Notably, explanation analysis using the GNNExplainer algorithm of our trained model identified high-attribution cells in CA and solid TME, revealing unique differentially expressed genes (DEGs) - JUNB in CA, and VIM, GPX1, PSAP, IFI27 in solid tumors, compared to low-attribution cells within the same subtype. Further analysis revealed an enrichment of EMT and interferon alpha/gamma, suggesting a more inflamed phenotype than low-attribution cells. Differential expression analysis of high-attribution CA vs solid cells identified significant upregulation of NDRG1 in CA and COL1A1 in solid. Functional enrichment analysis of these DEGs revealed PI3k-Akt in CA and MAPK signaling pathway in solid, suggesting an aggressive solid TME and CA with high survival and metastasis. We further identified a distinct neutrophil-enriched TME associated with CA and revealed cell-cell communication through MIF interactions with the CD74/CXCR4 and CD44/CD74 axes. Citation Format: Arun Das, Md Musaddaqul Hasib, Zhentao Liu, Wen Meng, Yu-Chiao Chiu, Gabriel Sica, Shou-Jiang Gao. Delineating spatial expression signatures of lung adenocarcinoma subtypes from spatial single-cell transcriptomics using graph neural networks [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7394.

Identifying childhood malaria hotspots and risk factors in a Nigerian city using geostatistical modelling approach

Malaria ranks high among prevalent and ravaging infectious diseases in sub-Saharan Africa (SSA). The negative impacts, disease burden, and risk are higher among children and pregnant women as part of the most vulnerable groups to malaria in Nigeria. However, the burden of malaria is not even in space and time. This study explores the spatial variability of malaria prevalence among children under five years (U5) in medium-sized rapidly growing city of Akure, Nigeria using model-based geostatistical modeling (MBG) technique to predict U5 malaria burden at a 100 × 100 m grid, while the parameter estimation was done using Monte Carlo maximum likelihood method. The non-spatial logistic regression model shows that U5 malaria prevalence is significantly influenced by the usage of insecticide-treated nets—ITNs, window protection, and water source. Furthermore, the MBG model shows predicted U5 malaria prevalence in Akure is greater than 35% at certain locations while we were able to ascertain places with U5 prevalence > 10% (i.e. hotspots) using exceedance probability modelling which is a vital tool for policy development. The map provides place-based evidence on the spatial variation of U5 malaria in Akure, and direction on where intensified interventions are crucial for the reduction of U5 malaria burden and improvement of urban health in Akure, Nigeria.

Structural identifiability analysis of linear reaction–advection–diffusion processes in mathematical biology

Effective application of mathematical models to interpret biological data and make accurate predictions often requires that model parameters are identifiable. Approaches to assess the so-called structural identifiability of models are well established for ordinary differential equation models, yet there are no commonly adopted approaches that can be applied to assess the structural identifiability of the partial differential equation (PDE) models that are requisite to capture spatial features inherent to many phenomena. The differential algebra approach to structural identifiability has recently been demonstrated to be applicable to several specific PDE models. In this brief article, we present general methodology for performing structural identifiability analysis on partially observed reaction–advection–diffusion PDE models that are linear in the unobserved quantities. We show that the differential algebra approach can always, in theory, be applied to such models. Moreover, despite the perceived complexity introduced by the addition of advection and diffusion terms, consideration of spatial analogues of non-spatial models cannot exacerbate structural identifiability. We conclude by discussing future possibilities and the computational cost of performing structural identifiability analysis on more general PDE models.

Mastering geographically weighted regression: key considerations for building a robust model.

Geographically weighted regression (GWR) takes a prominent role in spatial regression analysis, providing a nuanced perspective on the intricate interplay of variables within geographical landscapes (Brunsdon et al., 1998). However, it is essential to have a strong rationale for employing GWR, either as an addition to, or a complementary analysis alongside, non-spatial (global) regression models (Kiani, Mamiya et al., 2023). Moreover, the proper selection of bandwidth, weighting function or kernel types, and variable choices constitute the most critical configurations in GWR analysis (Wheeler, 2021). [...].

The role of natural recovery category in malaria dynamics under saturated treatment.

In the process of malaria transmission, natural recovery individuals are slightly infectious compared with infected individuals. Our concern is whether the infectivity of natural recovery category can be ignored in areas with limited medical resources, so as to reveal the epidemic pattern of malaria with simpler analysis. To achieve this, we incorporate saturated treatment into two-compartment and three-compartment models, and the infectivity of natural recovery category is only reflected in the latter. The non-spatial two-compartment model can admit backward bifurcation. Its spatial version does not possess rich dynamics. Besides, the non-spatial three-compartment model can undergo backward bifurcation, Hopf bifurcation and Bogdanov-Takens bifurcation. For spatial three-compartment model, due to the complexity of characteristic equation, we apply Shengjin's Distinguishing Means to realize stability analysis. Further, the model exhibits Turing instability, Hopf bifurcation and Turing-Hopf bifurcation. This makes the model may admit bistability or even tristability when its basic reproduction number less than one. Biologically, malaria may present a variety of epidemic trends, such as elimination or inhomogeneous distribution in space and periodic fluctuation in time of infectious populations. Notably, parameter regions are given to illustrate substitution effect of two-compartment model for three-compartment model in both scenarios without or with spatial movement. Finally, spatial three-compartment model is used to present malaria transmission in Burundi. The application of efficiency index enables us to determine the most effective method to control the number of cases in different scenarios.

A Bayesian spatially explicit estimation of daily egg production: application to anchovy in the Bay of Biscay

Biomass estimates of fish resources by the daily egg production method (DEPM) are sensitive to the high variability of the daily egg production ( P0) and egg mortality (Z) in space. This work presents a Bayesian approach to estimate these parameters. A prior distribution of Z based on literature serves to overcome the biologically implausible Z estimates that can result from frequentist approaches. In addition to the classical estimation of a single P0 over the spawning area, the Bayesian framework allows also the modelling of egg densities in space, by including either spatial random effects, smoothing functions, or kriging like models, providing insights into the spatial variability of P0. The Bayesian approach was applied to the Bay of Biscay anchovy DEPM surveys. Results showed that this Bayesian approximation solved the implausible Z problem resulting in tighter credible intervals of both P0 and Z. Overall, spatial models outperformed the non-spatial model in terms of goodness of fit and resulted in slightly different total production estimates across models for each year, with a moderate decrease on uncertainty estimates.

수치해석 접근법에 따른 지하수 공간분포 예측

This study applied the MODFLOW model using finite difference method and the NNRK model combining neural network and residual kriging for spatial groundwater level prediction in Jeju Island, South Korea. For the MODFLOW model, the differences between observed and predicted groundwater levels were 0.014-4.327 m and the relative errors were 0.50-50.82 %. For the NNRK model, the differences between observed and predicted groundwater levels were 0.022-6.185 m and the relative errors were 0.66-49.35 %. Based on MSRE, IDW model, which is a non-spatial statistical model, showed better spatial prediction results than SK and OK models, whereas RK and NNRK models outperformed the IDW model. The MODFLOW model showed relatively better results than the NNRK model in dense areas considering recharge and geological structure although the MODFLOW and NNRK models produced similar results. Furthermore, the MODFLOW model was found to effectively reflect groundwater flow by strata and temporal groundwater head fluctuations due to pumping compared to the NNRK model.

The geography of arthritis-attributable pain outcomes: a county-level spatial analysis.

Research on the geographic distribution of pain and arthritis outcomes, especially at the county level, is limited. This is a high-priority topic, however, given the heterogeneity of subnational and substate regions and the importance of county-level governments in shaping population health. Our study provides the most fine-grained picture to date of the geography of pain in the United States. Combining 2011 Behavioral Risk Factor Surveillance System data with county-level data from the Census and other sources, we examined arthritis and arthritis-attributable joint pain, severe joint pain, and activity limitations in US counties. We used small area estimation to estimate county-level prevalences and spatial analyses to visualize and model these outcomes. Models considering spatial structures show superiority over nonspatial models. Counties with higher prevalences of arthritis and arthritis-related outcomes are mostly clustered in the Deep South and Appalachia, while severe consequences of arthritis are particularly common in counties in the Southwest, Pacific Northwest, Georgia, Florida, and Maine. Net of arthritis, county-level percentages of racial/ethnic minority groups are negatively associated with joint pain prevalence, but positively associated with severe joint pain prevalence. Severe joint pain is also more common in counties with more female individuals, separated or divorced residents, more high school noncompleters, fewer chiropractors, and higher opioid prescribing rates. Activity limitations are more common in counties with higher percentages of uninsured people. Our findings show that different spatial processes shape the distribution of different arthritis-related pain outcomes, which may inform local policies and programs to reduce the risk of arthritis and its consequences.