Search

Leptospirosis and enteric fever are prevalent tropical acute bacterial febrile illnesses in Kelantan, Malaysia, that exhibit overlapping features and shared transmission dynamics, yet their spatial relationship remains understudied. This study aimed to analyze their spatial distribution, investigating potential interactions and intersecting patterns. A total of 212 laboratory-confirmed cases of enteric fever and 1106 of leptospirosis between 2016 and 2022, were retrieved from the national e-Notifikasi registry. Point pattern analysis revealed clustering of both diseases in the northern region, but leptospirosis was predominant in the south, exhibiting higher spatial risk. Seven co-infection cases were identified in overlapping hotspot areas. Spatial dependence between the diseases was identified within 4 km distance on average, with varying patterns over time and regions. Recognizing spatial dependence has implications for accurate diagnosis, timely intervention, and tailored public health strategies. The findings underscore the need for multi-disease interventions to address shared risk factors and co-infections in similar geographical contexts. [ABSTRACT FROM AUTHOR]

Abstract.We study the estimation of nonlinear models with cross-sectional data using two-step generalized estimating equations within the quasi-maximum likelihood estimation framework. To improve efficiency, we propose a grouped estimator that accounts for potential spatial correlation in the underlying innovations of nonlinear models. Under mild weak dependence assumptions, we provide results on estimation consistency and asymptotic normality. Monte Carlo simulations demonstrate the efficiency gain of our approach compared to various estimation methods. Finally, we apply the proposed approach to examine the role of cultural distance in an extended gravity equation using international trade data from China. Compared to existing methods, our approach yields estimates with smaller standard errors and reinforces the hypothesis that both cultural and geographical distances significantly negatively influence international trade. [ABSTRACT FROM AUTHOR]

Making accurate and reliable predictions for monthly runoff in data scarce basins is still a major challenge. In this study, a new model, the CL-NDM, is developed by combining Convolutional Neural Network-Long Short-term Memory (CNN-LSTM) and a nonlinear dynamic model. The CL-NDM can overcome the deficiency of observed data by fusing spatial and temporal dependencies in runoff sequences at different stations. First, phase space reconstruction is used to enlarge the dimensions of the runoff sequences and reconstruct the attractors of the runoff sequences. Then, the CNN-LSTM is employed to construct the mapping between non-delay and delay attractors. Finally, the prediction set of the target variable is obtained by embedding multiple times. The CL-NDM is performed for monthly runoff prediction at eleven hydrological stations in the Weihe River, China. Compared with the CNN, LSTM and CNN-LSTM models, which require a large amount of training samples, the CL-NDM behaves much better, especially in situations with small training sample sizes. The maximum increase in R is 74%, and the maximum NSE is as large as 0.8. The maximum improvement in RMSE and MAPE is 53% and 88%, respectively. The CL-NDM has stronger ability to capture peak value while LSTM, CNN-LSTM and CNN models show obvious time lag in the prediction of peak point. The improved nonlinear dynamical model may provide a valuable method for runoff prediction in data-scarce regions. [ABSTRACT FROM AUTHOR]

This article proposes two new classes of nonparametric tests for the correct specification of linear spatial autoregressive models based on the "integrated conditional moment" approach. Our test statistics are constructed as continuous functionals of a residual marked empirical process as well as its projected version. We derive asymptotic properties of the test statistics under the null hypothesis, the alternative hypothesis, and a sequence of local alternatives. The proposed tests do not involve the selection of tuning parameters such as bandwidths and are able to detect a broad class of local alternatives converging to the null at the parametric rate n − 1 / 2 , with n being the sample size. We also propose a multiplier bootstrap procedure that is computationally simple to approximate the critical values. Monte Carlo simulations illustrate that our tests have a reasonable size and satisfactory power for different types of data-generating processes. Finally, an empirical analysis of growth convergence is carried out to demonstrate the usefulness of the tests. [ABSTRACT FROM AUTHOR]

AbstractExtreme precipitation events with large spatial extents may have more severe impacts than localized events as they can lead to widespread flooding. It is debated how climate change may affect the spatial extent of precipitation extremes, whose investigation often directly relies on simulations of precipitation from climate models. Here, we use a different strategy to investigate how future changes in spatial extents of precipitation extremes differ across climate zones and seasons in two river basins (Danube and Mississippi). We rely on observed precipitation extremes while exploiting a physics-based average-temperature covariate, enabling us to project future precipitation extents based on projected temperatures. We include the covariate into newly developed time-varying r-Pareto processes using suitably chosen spatial risk functionals r. This model captures temporal non-stationarity in the spatial dependence structure of precipitation extremes by linking it to the temperature covariate, derived from reanalysis data (ERA5-Land) for model calibration and from bias-corrected climate simulations (CMIP6) for projections. Our results show an increasing trend in the margins, with both significantly positive or negative trend coefficients depending on season and river (sub-)basin. During major rainy seasons, the significant trends indicate that future spatial extreme events will become relatively more intense and localized in several sub-basins. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work. [ABSTRACT FROM AUTHOR]

In this article, we introduce a new command, xtsfsp, for fitting spatial stochastic frontier models in Stata. Over the last decades, stochastic frontier models have seen important theoretical progress via the incorporation of various types of spatial components. Models that can account for spatial dependence and spillovers have been developed for efficiency and productivity analysis, drawing extensive attention from industry and academia. Because of the unavailability of the statistical packages, the empirical applications of the new stochastic frontier models appear to be lagging. The xtsfsp command provides a procedure for fitting spatial stochastic frontier models in the style of Orea and Álvarez (2019, Journal of Econometrics 213: 556-577) and Galli (2023, Spatial Economic Analysis 18: 239-258), enabling users to handle different sources of spatial dependence. In this article, we introduce spatial stochastic frontier models, describing the syntax and options of the new command and providing several examples to illustrate its usage. [ABSTRACT FROM AUTHOR]

The city comprises of a wide variety of heterogeneous territorial units (e.g. districts or neighbourhoods). In many – especially larger – cities, social capital assets (like community bonds) are mirrored at the level of neighbourhoods which form the home for many sociocultural communities or distinct socio‐economic classes. We postulate in this study that the big city is essentially an 'archipelago' made up of 'urban villages'. We analyse the residents' perceived attractiveness regarding their daily local neighbourhood by introducing the concept of 'village love' (or 'neighbourhood love'), inspired by the recent literature on 'city love' (comprising 'body', 'soul' and 'community' constituents of urban life). Based on an extensive and detailed multi‐annual database for all neighbourhoods in Rotterdam, the present paper seeks to identify the background factors shaping 'village love' in the city, with particular attention to the citizens' subjective appreciation for and access to a great variety of (physical and immaterial) urban amenities shaping the place‐based satisfaction of residents. The theoretical framing of our research resembles the basics of traditional central place theory here transmitted to the urban space in which local proximity to amenities plays a key role. A wide array of relevant amenities impacting on the place‐specific well‐being feelings ('village love') of residents in various neighbourhoods in the city of Rotterdam is distinguished using inter alia‐rich multi‐annual survey data. This approach is empirically tested and verified by means of LISA statistics and advanced spatial econometric dependence models ('urbanometrics'). The findings confirm the usefulness of a central place interpretation of 'urban village love' in the city. [ABSTRACT FROM AUTHOR]

Hüsler–Reiss vectors and Brown–Resnick fields are popular models in multivariate and spatial extreme-value theory, respectively, and are widely used in applications. We provide analytical formulas for the correlation between powers of the components of the bivariate Hüsler–Reiss vector, extend these to the case of the Brown–Resnick field, and thoroughly study the properties of the resulting dependence measure. The use of correlation is justified by spatial risk theory, while power transforms are insightful when taking correlation as dependence measure, and are moreover very suited damage functions for weather events such as wind extremes or floods. This makes our theoretical results worthwhile for, e.g., actuarial applications. We finally perform a case study involving insured losses from extreme wind speeds in Germany, and obtain valuable conclusions for the insurance industry. [ABSTRACT FROM AUTHOR]

Sitka spruce (Picea sitchensis (Bong.) Carr.), is a highly valuable commercial tree species in Ireland, UK and elsewhere in Europe but there is relatively little information available in relation to the performance of improved material. The primary objective of this study was to explore the genetic variation in competitive ability and determine how a spatial model improved the accuracy of genetic estimates over time for Sitka spruce. To achieve this, the study employed competition index (Hegyi's I) and spatial analysis on height and diameter data from genetic improvement trials in Ireland. A total of 54 half-sib families of Sitka spruce were included in the study. The trials had been established to identify superior genotypes among the plus trees which had been included in the improvement programme in Ireland. The study revealed that spatial heterogeneity significantly affects the genetic estimates for Sitka spruce. The results indicated that spatial analysis could provide valuable insights into the genetic trials of Sitka spruce and improve the accuracy of genetic evaluations. Heritability increased by 104% for diameter at breast height (DBH) using a spatial analysis approach, compared with a more traditional non-spatial model approach. It was found that thinning practices can affect the response of genetic performance to spatial heterogeneity. In addition, genetic variation in competitive ability was observed. The results highlight the importance of considering spatial dependence in the design but more importantly in the analysis of genetic trials and provides a framework for future research on the genetic improvement of Sitka spruce populations. [ABSTRACT FROM AUTHOR]

This paper analyzes the relationship between informal housing and labor informality at the intraurban level, considering these two phenomena' simultaneity and spatial dimensions. Our analysis focuses on the context of a city in a developing country, Medellín (Colombia), characterized by significant housing precariousness and low employment quality, where space seems to play an essential role in understanding this relationship. Using data from 176 analytical regions in Medellín for 2017, we estimate a series of spatial simultaneous equation models that consider the potential cross-equation correlations in the error terms. The results show that these two types of urban informality are highly persistent in space, with noticeable spatial clusters observed in the peripheral and marginalized areas of the city. Additionally, the estimated econometric models reveal that precarious working conditions are key to explaining the spatial choice of housing and its characteristics, and vice versa. These findings emphasize the need for place-based policies that specifically target disadvantaged areas and help improve residents' working and housing conditions to address urban informality. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new way of modeling spatial regimes using smooth transitions. We propose an autoregressive spatial lag model where a logistic function captures structural variation in the spatial lag parameter. In the regime-switching spatial lag model with smooth transitions, the effect of the spatial neighbors depends on the transition variable that governs the regime switch. An LM test for detecting nonlinearity is derived, and a simulation study, where the properties of the test are investigated, is conducted. The test shows good power in relatively small samples with moderate deviation from linearity. An empirical application is included, where data on median house prices from the Boston area is used to explore the spatial dependence between census tracts. A smooth shift between regimes governed by the variable property tax rate is found. The smooth spatial lag model performs slightly better than the regular spatial lag model in terms of model fit and capturing spatial dependence. [ABSTRACT FROM AUTHOR]

Previous research shows that large, densely populated urban areas have higher rates of child victimization that have persisted over time. However, few investigations have inquired about the processes that produce and sustain hot and cold spots of child victimization. As a result, the mechanisms that produce the observed spatial clustering of child victimization, and hence "why" harms against children tend to cluster in space, remains unknown. Does the likelihood of being a victim of violence in one location depend on a similar event happening in a nearby location within a specified timeframe? Rather, are child victims of violence more likely to reside in suboptimal neighborhood conditions? This paper aims to present an analytical and theoretical framework for distinguishing between these locational (point) processes to determine whether the empirical spatial patterns undergirding child victimization are more reflective of the "spread" via contagion (i.e., dependency) or whether they are produced by neighborhood structural inequality resulting from spatial heterogeneity. To detect spatial dependence, we applied the inhomogeneous K -function to Los Angeles Medical Examiner data on child homicide victim locations while controlling for regional differences in victimization events (i.e., heterogeneity). Our analysis found strong evidence of spatial clustering in child victimization at small spatial scales but inhibition at larger scales. We further found limited support for the spatiotemporal clustering of child victimization indicative of a contagion effect. Overall, our results support the role of neighborhood structural vulnerability in the underlying mechanisms producing patterns of child victimization across Los Angeles County. We conclude by discussing the policy implications for understanding this spatial patterning in geographical context and for developing effective and targeted preventive interventions. [ABSTRACT FROM AUTHOR]

Multiplicative errors in addition to spatially referenced observations often arise in geodetic applications, particularly with light detection and ranging (LiDAR) measurements. However, regression involving multiplicative errors remains relatively unexplored in such applications. In this regard, we present a penalized modified least squares estimator to handle the complexities of a multiplicative error structure while identifying significant variables in spatially dependent observations. The proposed estimator can be also applied to classical additive error spatial regression. By establishing asymptotic properties of the proposed estimator under increasing domain asymptotics with stochastic sampling design, we provide a rigorous foundation for its effectiveness. A comprehensive simulation study confirms the superior performance of our proposed estimator in accurately estimating and selecting parameters, outperforming existing approaches. To demonstrate its real-world applicability, we employ our proposed method, along with other alternative techniques, to estimate a rotational landslide surface using LiDAR measurements. The results highlight the efficacy and potential of our approach in tackling complex spatial regression problems involving multiplicative errors. [ABSTRACT FROM AUTHOR]

AbstractCount data with complex features arise in many disciplines, including ecology, agriculture, criminology, medicine, and public health. Zero inflation, spatial dependence, and non-equidispersion are common features in count data. There are currently two classes of models that allow for these features—the mode-parameterized Conway–Maxwell–Poisson (COMP) distribution and the generalized Poisson model. However both require the use of either constraints on the parameter space or a parameterization that leads to challenges in interpretability. We propose spatial mean-parameterized COMP models that retain the flexibility of these models while resolving the above issues. We use a Bayesian spatial filtering approach in order to efficiently handle high-dimensional spatial data and we use reversible-jump MCMC to automatically choose the basis vectors for spatial filtering. The COMP distribution poses two additional computational challenges—an intractable normalizing function in the likelihood and no closed-form expression for the mean. We propose a fast computational approach that addresses these challenges by, respectively, introducing an efficient auxiliary variable algorithm and pre-computing key approximations for fast likelihood evaluation. We illustrate the application of our methodology to simulated and real datasets, including Texas HPV-cancer data and US vaccine refusal data. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Using a novel subnational-level dataset of Thailand, the results show a strong spatial association between poverty and educational attainment in Thailand. Provinces with more educated populations are more likely to have lower poverty incidence. In particular, the findings of this study suggest that attainment of tertiary education can significantly affect poverty reduction, while the negligible effects of primary and secondary education could be due to the disparities in education quality. Therefore, the 9-year compulsory education provided by the Thai government might no longer be enough to reduce poverty, and tertiary education might play a more critical role in poverty alleviation. [ABSTRACT FROM AUTHOR]

Predicting the effects of warming temperatures on the abundance and distribution of organisms under future climate scenarios often requires extrapolating species–environment correlations to climatic conditions not currently experienced by a species, which can result in unrealistic predictions. For poikilotherms, incorporating species' thermal physiology to inform extrapolations under novel thermal conditions can result in more realistic predictions. Furthermore, models that incorporate species and spatial dependencies may improve predictions by capturing correlations present in ecological data that are not accounted for by predictor variables. Here, we present a joint species, spatially dependent physiologically guided abundance (jsPGA) model for predicting multispecies responses to climate warming. The jsPGA model uses a basis function approach to capture both species and spatial dependencies. We apply the jsPGA model to predict the response of eight fish species to projected climate warming in thousands of lakes in Minnesota, USA. By the end of the century, the cold‐adapted species was predicted to have high probabilities of extirpation across its current range—with 10% of lakes currently inhabited by this species having an extirpation probability >0.90. The remaining species had varying levels of predicted changes in abundance, reflecting differences in their thermal physiology. Though the model did not identify many strong species dependencies, the variation in estimated spatial dependence across species suggested that accounting for both dependencies was important for predicting the abundance of these fishes. The jsPGA model provides a new tool for predicting changes in the abundance, distribution, and extirpation probability of poikilotherms under novel thermal conditions. [ABSTRACT FROM AUTHOR]

The study re-examines convergence among Indian states by estimating their total factor productivity (TFP) for the period 1990/91–2018/19. The novelty of the study is the decomposition of TFP change into efficiency change and technological change using the Malmquist index in a spatial dependence framework to test convergence. Findings reveal that, on average, productivity increased at an aggregate level for the entire period on account of the impressive growth of technological innovation. However, technological growth convergence could not lead to productivity growth convergence in the second sub-period because of no convergence in efficiency growth. Alongside, while we witness significant spatial spillovers in both technology and efficiency, convergence was observed only with respect to technology growth in the second sub-period. The congruence of technological convergence and significant spatial spillovers indicates that while states may be converging in technology, they do not do so independently but display movements similar to their regional neighbours. [ABSTRACT FROM AUTHOR]

The prevalence and severity of food insecurity vary across locations due to spatial diversity in natural resources, physical and non-physical conditions, and environmental factors. Thus, it is becoming increasingly important to place food insecurity in a spatial context so as to identify the geographic clusters (hotspots and coldspots) of food insecurity. This will ensure effective targeting of policies that prominently feature food security concerns. This paper therefore attempts to analyze the spatial pattern of household food insecurity across 109 Senatorial Districts (SDs)using data obtained mainly from 2018/2019 Nigeria Living Standard Survey (NLSS). The study identified 28 SDs in the hotspots of food insecurity and 19 SDs in the Coldspots, each with an average food security index of 0.87 and 2.12, respectively. The hotspot SDs are predominantly in the North Central and North East regions of the country while the coldspots are mainly in the South West and South-South regions. The study also provides evidence of spatial dependence between neighbouring senatorial districts, indicating that areas with high (low) food insecurity are more likely to be bounded by areas with high (low) food insecurity. Findings from the spatial regression model highlights age of the household head, household size, amount of rainfall, percentage of households with access to or ownership of arable land, asset ownership, and SD area as major determinants of food security in hotspots. In coldspots, household size, access to or ownership of arable land, and amount of rainfall are identified as key determinants. Given the spatial clustering of food insecurity in Nigeria, it is crucial for the government and humanitarian community to consider territorial dimensions in their commitments to food-based safety net initiatives. [ABSTRACT FROM AUTHOR]

The Belt and Road Initiative (BRI) tremendously impacts the Sustainable Development Goals (SDGs) of the countries involved. However, the BRI's impacts and impact pathways on the synergies and trade‐offs in these countries' SDGs remain unclear. Using global data from 2005 to 2021, we find that the BRI has dramatically promoted low‐income countries' SDGs and the synergies between high‐income countries' SDGs. Continuing to prioritize economic growth as the main focus of the BRI may not be the optimal path for achieving mutual development that benefits all of humanity. Although there is environmental pressure involved with the BRI, this factor does not force involved countries to make trade‐offs between improving SDGs and environmental resources. Furthermore, the BRI affects involved countries' SDGs through two key channels: fostering industries, innovation, and infrastructure (SDG 9) by enhancing people's well‐being (SDG 3) and promoting zero hunger (SDG 2) through sustainable cities (SDG 11). [ABSTRACT FROM AUTHOR]

Infectious disease outbreaks present ongoing and substantial challenges to health systems at local, national, and global levels, testing their preparedness, response capabilities, and resilience. This study aimed to identify and analyze critical health system-level factors that influence infection outbreaks, focusing on the experience of the COVID-19 pandemic in Korea. Conducted as a secondary data analysis, this study utilized national datasets from Korea. Given the inherent spatial dependencies in the spread of infectious diseases, we employed a spatial lag model to analyze data. While city-specific characteristics did not emerge as significant factors, health system variables, particularly the number of community health centers and health budgets, showed significant influence on the course of the COVID-19 outbreak, along with spatial autocorrelation coefficients. Our findings underscore the importance of enhancing public healthcare infrastructure, considering regional specificities, and promoting collaboration among local governments to bolster preparedness for future outbreaks. These insights are crucial for policymakers and healthcare professionals in formulating effective strategies to prevent, manage, and mitigate the impact of infectious disease outbreaks. [ABSTRACT FROM AUTHOR]

Machine learning methods have seen increased application to geospatial environmental problems, such as precipitation nowcasting, haze forecasting, and crop yield prediction. However, many of the machine learning methods applied to mosquito population and disease forecasting do not inherently take into account the underlying spatial structure of the given data. In our work, we apply a spatially aware graph neural network model consisting of GraphSAGE layers to forecast the presence of West Nile virus in Illinois, to aid mosquito surveillance and abatement efforts within the state. More generally, we show that graph neural networks applied to irregularly sampled geospatial data can exceed the performance of a range of baseline methods including logistic regression, XGBoost, and fully‐connected neural networks. Plain Language Summary: Many machine learning methods have been applied to geospatial data, that is data that has a spatial dimension and corresponds to particular regions on Earth. However, many of these methods do not account for the fact that data recorded at nearby locations will be correlated. We apply a deep learning method called a graph neural network to overcome this issue. Our application to West Nile virus forecasting in Illinois could aid mosquito surveillance and abatement efforts within the state. This shows that graph neural networks could be a good option for other geospatial data, since they outperform a range of baseline methods in our particular problem. Key Points: Many machine learning methods applied to environmental problems do not account for spatial dependenceWe apply a spatially aware graph neural network model to forecast West Nile virusGraph neural networks applied to irregularly sampled geospatial data can outperform a range of baseline methods [ABSTRACT FROM AUTHOR]

Information about some poverty indicators is important not only for the large administrative level but also for lower administrative level. This information can be obtained from many surveys. Unfortunately, many surveys are usually designed to satisfy accuracy for large populations. As a result, it is often encountered that the sample size from some sub-populations which can be obtained from a survey is too small to produce a reliable direct estimator. The sub-population which the selected sample from it is not large enough to produce a reliable direct estimator is also called a small area. In this paper, we propose the spatial empirical best predictor (SEBP) for some poverty indicators in some small areas. The SEBP is derived under a unit-level spatial lognormal mixed model which incorporates spatial dependence into the covariance structure. The mean square prediction error (MSPE) of the SEBP is estimated by the parametric bootstrap method. A simulation study was conducted to evaluate the performance of the SEBP compared to the direct estimates as well as the empirical best predictor (EBP). Further, the SEBP was also applied to obtain the estimates of some poverty indicators for some sub-districts in Bogor, Indonesia. The results showed that there is a substantial reduction in MSPE of the SEBP over the direct estimates and the EBP for almost all sub-districts. [ABSTRACT FROM AUTHOR]

This paper empirically tests housing market efficiency in the spatial dimension by using the spatial autoregressive conditional heteroskedastic (ARCH) and spatial quantile regression models. The tests were conducted in terms of both housing returns and squared returns (volatility). The sale price data used is from Cook County residential MLS for the years 2010–2016. The main findings are that housing returns are not spatially correlated but squared returns are spatially correlated, and the spatial dependence of squared returns seems to be stronger for higher squared return quantiles. [ABSTRACT FROM AUTHOR]

This paper is concerned with spatial single-index autoregressive model (SSIM), where the spatial lag effect enters the model linearly and the relationship between variables is a nonparametric function of a linear combination of multivariate regressors. It addresses challenges related to the curse of dimensionality and interactions among non-independent variables in spatial data. The local Walsh-average regression has proven to be a robust and efficient method for handling single-index models. We extend this approach to the spatial domain, propose a regularized local Walsh-average (RLWA) estimation strategy where the nonparametric component is established by a local Walsh-average approach and the estimation of the parametric part by Walsh-average method. Under specific assumptions, we establish the asymptotic properties of both parametric and nonparametric partial estimators. Additionally, we propose a robust shrinkage method termed regularized local Walsh-average (RLWA) that can construct robust parametric variable selection and robust nonparametric component estimation simultaneously. Theoretical analysis reveals RLWA works beautifully, including consistency in variable selection and oracle property in estimation. We propose a parameter selection process based on a robust BIC-type approach with an oracle property. The effectiveness of the proposed estimation procedure is evaluated through three Monte Carlo simulations and real data applications, demonstrating its performance in finite samples. [ABSTRACT FROM AUTHOR]

Abstract The objective of this study was to evaluate the classification of productive capacity in eucalyptus stands using regression kriging. The study was carried out in 62 stands with clonal plantations of Eucalyptus sp. Data were collected at 24, 36, 48, 60, 72 and 84 months in 170 sampling units of 400 m². The variables diameter at 1.30 m above the ground (DBH), total height (Ht) were measured and, subsequently, the average height of the dominant trees (Hd) was obtained. The classification of productive capacity was carried out using the algebraic difference method, with the models: Schumacher, Chapman and Richards and Bailey and Clutter. The site index was interpolated by applying ordinary kriging and regression kriging. The results indicated that the site index showed spatial dependence in all adjustments. It is concluded that regression models for estimating the site index can be used in combination with regression kriging techniques.

Background: Out-of-hospital cardiac arrest (OHCA) incidence and survival often vary within regions according to patient-related and contextual factors. This study aims to establish the overall spatial dependence of incidence, bystander cardiopulmonary resuscitation (BCPR) and 48-h survival of OHCA with their associated demographic and socioeconomic characteristics in a Swiss region. Methods: We conducted a retrospective study using data of all OHCAs recorded between 2007 and 2019 in the canton of Vaud and, more specifically, in the Lausanne area. Provision of BCPR and 48-h survival were analysed using Getis-Ord Gi statistics and OHCA incidence by local Moran’s I with empirical Bayes standardised rates. Demographic and socioeconomic characteristics were compared between incidence clusters generated by local Moran’s I method. Results: Significant spatial variations of OHCA incidence, BCPR and 48-h mortality were observed. Although BCPR was statistically more likely in rural areas, 48-h survival was improved in a few main cities. At the cantonal level, postcode areas with a higher incidence of OHCAs were less densely inhabited with lower salary levels, more Swiss citizens, and an older population. At city level, small area variations were detected within urban neighbourhoods. The more affected hectares with more OHCAs were less inhabited, with a better median salary, more Swiss citizens, and off-centre. Conclusions: Spatial variations associated with demographic and socioeconomic factors were observed for OHCA incidence and survival, with sparsely populated areas particularly at risk. These data suggest an unmet need for targeted prevention interventions and structural modifications of the existing prehospital system at the cantonal level.

In this paper, we analyze the effect of natural hazards on migration in the United States (US) and the importance of spatial dependence in such assessments. We use two measures of migration: migration rates and flows. The model for migration flows is estimated using the gravity model, whereas out- and in-migration rates are analyzed using the spatial Durbin model. Our results indicate there is a major and significant impact of economic damage caused by natural hazards on out-migration rates and outward migration flows. In the spatial Durbin model and in the gravity model, a $1,000 dollar damage per capita is associated with an increase in out-migration of 16.0% and 9.1%, respectively. However, when spatial dependence is not accounted for, the effect of natural hazards on migration is substantially overestimated: the coefficients are 1.5–2 times larger when spatial dependence is not considered. [ABSTRACT FROM AUTHOR]

Habitat models often rely on data with incomplete or patchy survey coverage, making it necessary to project observed habitat relationships into unsampled areas. Model transferability is often assumed, rarely tested, and may result in uninformed management recommendations if not validated. In the eastern Gulf of Maine, data from the federal trawl survey has historically been used to identify suitable habitat for juvenile Atlantic cod (Gadus morhua), despite mainly sampling in offshore waters. This study evaluated the spatial transferability of a habitat suitability index model by projecting offshore habitat relationships inshore and comparing them to models built using two inshore surveys. Due to rare warm-water exposure in offshore waters, the federal survey model was unable to identify suitable habitat in the warmer, shallower areas that the inshore surveys identified, suggesting that offshore models are not transferable inshore for juvenile cod in the eastern Gulf of Maine. The results of this study have implications for fisheries worldwide and suggest that incorrectly assuming model transferability could pose a significant hurdle for effective conservation and management. [ABSTRACT FROM AUTHOR]

This article studies the evolution of per-capita income disparities and spatial dependence across 77 provinces of Thailand over the period 1995–2017. Results show that – on average – regional income disparities are decreasing over time and initially poor provinces are catching up with rich provinces, indicating the presence of sigma and beta convergence. However, when we study the evolution of disparities – beyond the average – we reject the hypothesis that all provinces would eventually converge to a common long-run equilibrium. The evolution of these disparities suggests the existence of three local equilibria or convergence clubs. Further analyses based on global and local indicators of spatial association reveal significant spatial dependence in the evolution of disparities and the location of the convergence clubs. This article concludes arguing that an excessive focus on global or average assessments can be incomplete, and that spatial dependence has played a significant role in the formation of local convergence clubs. Furthermore, as different clubs may need different policy treatments, there is no one-size-fits-all territorial policy for reducing regional disparities in Thailand. [ABSTRACT FROM AUTHOR]

A large part of market theory literature focuses on the cost pass-through behaviour of firms, as it is an essential instrument in the analytical toolkit of competition authorities when carrying out market structure-related examinations. Although spatial relations are well documented in many markets, cost passthrough analysis frameworks do not seem to incorporate these issues. Applying an error correction-based estimation strategy extended by a spatial econometric framework, the present paper provides evidence that spatial dependence appears significantly in the cost pass-through behaviour of firms. This suggests that, on the one hand, the detected symmetric cost pass-through may cover the properties of the competition. On the other hand, competition authorities should consider spatial dependencies when preparing court-case decisions to avoid verdicts arising from presumably spurious analysis results. [ABSTRACT FROM AUTHOR]

Macrophytes are fundamental photosynthetic organisms for functioning freshwater ecosystems, identified as potential bioindicators of mercury (Hg) in the environment. We quantified the concentrations of total Hg (THg) in water and macrophytes (Paspalum repens Berg.) from 17 lakes on the Araguaia River floodplain, aiming to compare the bioconcentration factor (BCF) in the aerial tissues and roots; evaluate the translocation factor (TF) between plant tissues; and assess the influence of environmental factors and land use on THg concentrations in water and macrophytes. The BCF was significantly higher in roots (1.29 ± 0.32) than in aerial tissues (0.41 ± 0.34), with low TF between plant tissues (0.14 ± 0.06). The highest concentrations of THg in water were determined in lakes with higher land use intensity and a pH close to neutral, indicating the transport of particulate-bound Hg and the immobilization in the water column. In contrast, wetlands were priority areas for the bioconcentration of THg in macrophytes, associated with sulfate, dissolved oxygen, and oxidation–reduction potential in the water. Thus, although P. repens is not a suitable bioindicator of Hg mobilization by anthropogenic land use in our study area, our results suggest the potential of macrophytes as bioindicators of sites that are favorable to Hg methylation. [ABSTRACT FROM AUTHOR]

Copyright of Revista Brasileira de Engenharia Agricola e Ambiental - Agriambi is the property of Revista Brasileira de Engenharia Agricola e Ambiental and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper provides two specification tests for the system of spatial autoregressive model of order m. We derive the theoretical limit distributions and show in a detailed Monte Carlo simulation study that the tests result in reasonable sized testing procedures with large power. In the empirical application, we analyze Euro Stoxx 50 returns in two different time spans, looking for insights how well models with different specifications of the spatial weighting matrices (local, country, industry and country-industry specific dependencies including interaction effects) fit to the data. The analyzes also demonstrate the ability of the tests to detect inaccurate Value-at-Risk forecasts. [ABSTRACT FROM AUTHOR]

Urbanization has significantly altered the carbon cycle of the terrestrial environment, particularly in relation to net primary productivity (NPP). Gaining a more comprehensive comprehension of how NPP is affected by urbanization is crucial for obtaining fresh perspectives on sustainable urban landscape design and decision making. While there is a significant body of research examining the geographical and temporal patterns of NPP supply capacity, there are only a few studies that have investigated the spatial relationships between NPP and urbanization, particularly at the grid scale. This research investigated the temporal and geographical features and patterns of NPP and their impact mechanisms. In order to estimate NPP and the level of urbanization in the Yangtze River Delta Urban Agglomeration (YRDUA), we used a combination of different models and datasets. To evaluate the geographical correlations and dependence between NPP and urbanization, we utilized local bivariate autocorrelation methods and spatial regression models to describe and visualize these relationships. The findings revealed that there was a consistent negative relationship between NPP and urbanization on a global scale from 1990 to 2020. However, when examining the local scale, the geographical correlations could be classified into four distinct categories: areas with both low NPP and low urbanization, areas with high NPP and high urbanization, areas with low NPP and high urbanization, and areas with high NPP and low urbanization. Our analysis showed that spatial regression models are more suitable for quantifying the spatial relationship between NPP and urbanization due to their ability to include the impacts of spatial Moran's I techniques. Due to the growing urbanization, the highest NPP value was recorded in 2005, followed by 2000, 2020, and 2010. Conversely, the smallest association was observed in 2015. Examining the geographical connection between NPP and urbanization offers theoretical and practical insights for urban planning that prioritizes human needs and promotes sustainable development. It also aids in the development of reasonable methods for organizing ecological functional systems. [ABSTRACT FROM AUTHOR]

This paper uses a Markov-switching approach to examine why there is house price cycle comovement across some U.S. metropolitan areas (MSAs) but not others, and which MSAs cluster together for each of these reasons. Past studies have attributed common housing downturns in different regions as possible explanations for comovement. We explore other channels, and find some clusters based on common industry concentration (such as information technology), house price elasticity, as well as a cluster of MSAs that are desirable for retirees (in the sun belt). We find seven clusters of MSAs, where each cluster experiences idiosyncratic house price downturns, plus one distinct national house price cycle. Notably, only the housing downturn associated with the Great Recession spread across all the MSAs in our sample; all other house price downturns remained contained to a single cluster. We also identify MSA economic and geographic characteristics that correlate with housing price cluster membership, which implies comovement due to mobility of residents. In addition, while prior research has found housing and business cycles to be related closely at the national level, we find very different house price comovement and employment comovement across clusters and across MSAs. [ABSTRACT FROM AUTHOR]

This article proposes a uniform functional inference method for nonparametric regressions in a panel-data setting that features general unknown forms of spatio-temporal dependence. The method requires a long time span, but does not impose any restriction on the size of the cross section or the strength of spatial correlation. The uniform inference is justified via a new growing-dimensional Gaussian coupling theory for spatio-temporally dependent panels. We apply the method in two empirical settings. One concerns the nonparametric relationship between asset price volatility and trading volume as depicted by the mixture of distribution hypothesis. The other pertains to testing the rationality of survey-based forecasts, in which we document nonparametric evidence for information rigidity among professional forecasters, offering new support for sticky-information and noisy-information models in macroeconomics. [ABSTRACT FROM AUTHOR]

In this article, we consider a matrix exponential unbalanced panel data model that allows for (i) spillover effects using matrix exponential terms, (ii) unobserved heterogeneity across entities and time, and (iii) potential heteroscedasticity in the error terms across entities and time. We adopt a likelihood based direct estimation approach in which we jointly estimate the common parameters and fixed effects. To ensure that our estimator has the standard large sample properties, we show how the score functions should be suitably adjusted under both homoscedasticity and heteroscedasticity. We define our suggested estimator as the root of the adjusted score functions, and therefore our approach can be called the M-estimation approach. For inference, we suggest an analytical bias correction approach involving the sample counterpart and plug-in methods to consistently estimate the variance-covariance matrix of the suggested M-estimator. Through an extensive Monte Carlo study, we show that the suggested M-estimator has good finite sample properties. In an empirical application, we use our model to investigate the third country effects on the U.S. outward foreign direct investment (FDI) stock at the industry level. [ABSTRACT FROM AUTHOR]

Conventional higher-order spatial autoregressive models assume that all regression coefficients are constant, which ignores dynamic feature that may exist in spatial data. In this paper, we introduce a semiparametric dynamic higher-order spatial autoregressive model by allowing regression coefficients in classical higher-order spatial autoregressive models to smoothly vary with a continuous explanatory variable, which enables us to explore dynamic feature in spatial data. We develop a sieve two-stage least squares method for the proposed model and derive asymptotic properties of resulting estimators. Furthermore, we develop two testing methods to check appropriateness of certain linear constraint condition on the spatial lag parameters and stationarity of the regression relationship, respectively. Simulation studies show that the proposed estimation and testing methods perform quite well in finite samples. The Boston house price data are finally analyzed to demonstrate the proposed model and its estimation and testing methods. [ABSTRACT FROM AUTHOR]

Forest ecotones show a great floristic diversity, but anthropogenic disturbances may imply in their vegetation structure reduce. This study aimed to evaluate the understory diversity of an urban and nonurban forests in Maranhão state. We tested the following hypotheses: 1) the understory of the non-urban forest has greater richness and diversity; 2) the spatial distribution of diversity in the non-urban understory has weak spatial dependence. The study was carried out in the São Luís (urban area) and São José de Ribamar (nonurban area) cities. Thirty plots (25m2) were allocated in each area and the all tree individuals up to 2 m in height were measured. Density, frequency, importance value, cover value, Shannon index (H'), and Pielou index (J') were calculated. The Mann–Whitney test, non-metric multidimensional scaling, and Hutcheson t-test were used to analyze if there were any stastical difference between the richness, density, and diversity of both areas. We used the ordinary kriging in order to visualize the variation in richness and diversity of the vegetation. There was a difference in the floristic composition and density of each species in the two areas; the richness and diversity were significantly greater in the nonurban area. Weak spatial dependence was found in the urban area, suggesting de-characterization the fragment. The presence of urbanization in the ecotonal forest fragment has reduced the richness, diversity and spatial dependence of the understory. The diversity is high even with the changes caused by urbanization, due to the ecotonal nature of the fragments sampled. [ABSTRACT FROM AUTHOR]

Applying a spatialized data envelopment analysis, this paper estimates and analyzes the efficiency of European Union NUTS‐2 regions during the period 2000–2014. The space‐dependent efficiency scores estimated with the proposed model show a bimodal distribution that is not detected by the aspatial approach. The results confirm the crucial role of location of production units, offering important new insights on both the causes of regional disparities in labor productivity and the observed polarization of the European distribution of per capita income. The findings further suggest significant differences for the two subgroups (spatial clusters) of regional economies found in the correlation analysis between the efficiency scores and the components of the EU Regional Competitiveness Index. Consequently, policies suitable for one group of regions may not be suitable or appropriate for the other. [ABSTRACT FROM AUTHOR]

PM2.5 particles with an aerodynamic diameter of less than 2.5 μm are receiving increasing attention in China. Understanding how complex factors affect PM2.5 particles is crucial for the prevention of air pollution. This study investigated the influence of meteorological factors and land use on the dynamics of PM2.5 concentrations in four urban agglomerations of China at different scales from 2010 to 2020, using the Durbin spatial domain model (SDM) at five different grid scales. The results showed that the average annual PM2.5 concentration in four core urban agglomerations in China generally had a downward trend, and the meteorological factors and land use types were closely related to the PM2.5 concentration. The impact of temperature on PM2.5 changed significantly with an increase in grid scale, while other factors did not lead to obvious changes. The direct and spillover effects of different factors on PM2.5 in inland and coastal urban agglomerations were not entirely consistent. The influence of wind speed on coastal urban clusters (the Pearl River urban agglomeration (PRD) and Yangtze River urban agglomeration (YRD)) was not significant among the meteorological factors, but it had a significant impact on inland urban clusters (the Beijing–Tianjin–Hebei urban agglomeration (BTH) and Chengdu–Chongqing urban agglomeration (CC)). The direct effect of land use type factors showed an obvious U-shaped change with an increase in the research scale in the YRD, and the direct effect of land use type factors was almost twice as large as the spillover effect. Among land use type factors, human factors (impermeable surfaces) were found to have a greater impact in inland urban agglomerations, while natural factors (forests) had a greater impact in coastal urban agglomerations. Therefore, targeted policies to alleviate PM2.5 should be formulated in inland and coastal urban agglomerations, combined with local climate measures such as artificial precipitation, and urban land planning should be carried out under the consideration of known impacts. [ABSTRACT FROM AUTHOR]

To forecast the tourism demand across a set of tourist attractions with spatial dependence, a new model is proposed, which has three stages: tourist attraction selection, base predictor generation, and base predictor combination. In stage 1, a method for selecting associated attractions based on multi-dimensional scaling is used to determine the strength of the spatial dependence between each pair of attractions. In stage 2, a hybrid base predictor based on LSTM networks and Autoregressive model is developed, where the LSTM networks are used to capture the spatial dependence among attractions, and the Autoregressive model is used capture the scale of tourist volume at each attraction. In stage 3, a strategy for combining these base predictors is proposed; it can alleviate the overfitting problem of LSTM and improve the stability of forecasts. Finally, the superiority of the model is verified through the data on tourist volumes at 77 attractions in Beijing. [ABSTRACT FROM AUTHOR]

Regions are embedded in complex webs of interactions that influence, among other things, their economic resilience. However, a general lack of attention is given to the spatial dependence underlying regions' economic resilience. This paper advocates investigating such spatial interactions and, in doing so, provides a road map to guide researchers through the specification search. The road map is theoretically underpinned by the argument that regions' resilience is influenced by local spillovers. The several articles in the resilience literature that incorporate spatial dependence are evaluated, and their shortcomings discussed. The paper provides an empirical illustration of the road map. [ABSTRACT FROM AUTHOR]

This paper studies the impact of macroeconomic factors on co-movement in the foreign exchange rate markets. Data of foreign exchange rates from 24 merging markets is used to this end along with a dynamic spatial Durbin model as to examine spatial dependencies among markets. Our empirical findings show no evidence that cultural ties exert a role in spreading macroeconomic shocks in the exchange rate of a country to the exchange rates of other countries. Moreover, we show that economic closeness through foreign direct investment (FDI) and international bilateral trade is the most prominent channel in spreading macroeconomic shocks and spatial effects in emerging markets through the foreign exchange rates. In addition, geographical proximity reinforces the interdependence relationship of emerging markets. Our findings show that the co-movement of foreign exchange rate markets across the selected emerging markets is positively influenced by their gross domestic product (GDP) and interest rate differential and negatively affected by the terms of trade and remittance. In addition, we reveal that terms of trade, the inflation differential, and remittance are the most prominent fundamental factors affecting foreign exchange rate movements. [ABSTRACT FROM AUTHOR]

This study utilized a max-stable process (MSP) model with a dependence structure defined via a non-Euclidean distance metric, with the goal of modelling extreme flood data on a river network. The dataset was composed of mean daily discharge observations from 22 United States Geological Survey streamflow gaging stations for river basins in Missouri and Arkansas. The analysis included the application of the elastic-net penalty to automatically build spatially varying trend surfaces to model the marginal distributions. The dependence model accounted for the river distance between hydrologically connected gaging sites and the hydrologic distance, defined as the Euclidean distance between the centers of site’s associated drainage areas, for all stations. Modelling the marginal distributions and spatial dependence among the extremes are two key components for spatially modelling extremes. Among the 16 covariates evaluated for marginal fitting, 7 were selected to spatially model the generalized extreme value (GEV) location parameter (for each gaging station’s contributing drainage basin, its outlet elevation, centroid x coordinate, centroid elevation, area, average basin width, elevation range, and median land surface slope). The three covariates selected for the GEV scale parameter included the area, average basin width, and median land surface slope. The GEV shape parameter was assumed to be constant throughout the entire study area. Comparisons of estimates obtained from the spatial covariate model with their corresponding “at-site” estimates resulted in computed values of 0.95, 0.95, 0.94 and 0.85, 0.84, 0.90 for the coefficient of determination, Nash–Sutcliffe efficiency, and Kling–Gupta efficiency for the GEV location and scale parameters, respectively. Brown–Resnick MSP models were fit to independent multivariate events extracted from a set of common discharge data, transformed to unit Fréchet margins while considering different permutations of the non-Euclidean dependence model. Each of the fitted model’s log-likelihood values indicated improved fits when using hydrologic distance rather than Euclidean distance. They also demonstrated that accounting for flow-connected dependence and anisotropy further improved model fit. In this study, the results from both parts were illustrative; however, further research with larger datasets and more heterogeneous systems is recommended.

This paper focuses on the causal relationship between territorial levels of income deprivation and depression. It seeks to shed light on the importance of spatial dependence in said relationship.