Spatial Statistics最新文献

英文中文

Computationally efficient spatio-temporal disease mapping for big data

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-04-23 DOI: 10.1016/j.spasta.2025.100901

Duncan Lee

Disease mapping models estimate the spatio-temporal variation in population-level disease risks or rates across a set of

K

areal units for

N

time periods, aiming to identify temporal trends and spatial hotspots. Highly parameterised Bayesian hierarchical models with over

K N

random effects are commonly used to estimate this spatio-temporal variation, which are assigned autoregressive and conditional autoregressive prior distributions. These models work well when there are tens of thousands of data points, but are likely to be computationally burdensome when this rises to hundreds of thousands or above. This paper proposes a computationally efficient alternative, which can fit a range of spatio-temporal disease trends almost as well as existing highly parameterised models but only takes around 5% to 40% of the time to implement. It achieves this by modelling the average spatial and temporal trends in the data with autoregressive type random effects, which are augmented by an observation-driven process using functions of earlier data as additional covariates in the model. The efficacy of this methodology is tested by simulation, before being applied to the motivating study that estimates the spatio-temporal trends in asthma, cancer, coronary heart and chronic obstructive pulmonary disease prevalences for

K = 32, 751

small areas over

N = 13

years in England.

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引用次数: 0

A spatial autoregressive graphical model

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-04-15 DOI: 10.1016/j.spasta.2025.100893

Sjoerd Hermes , Joost van Heerwaarden , Pariya Behrouzi

Within the statistical literature, a significant gap exists in methods capable of modelling asymmetric multivariate spatial effects that elucidate the relationships underlying complex spatial phenomena. For such a phenomenon, observations at any location are expected to arise from a combination of within- and between-location effects, where the latter exhibit asymmetry. This asymmetry is represented by heterogeneous spatial effects between locations pertaining to two different categories, that is, a feature inherent to each location in the data, such that based on the feature label, asymmetric spatial relations are postulated between neighbouring locations with different labels. Our novel approach synergises the principles of multivariate spatial autoregressive models and the Gaussian graphical model. This synergy enables us to effectively address the gap by accommodating asymmetric spatial relations, overcoming the usual constraints in spatial analyses. However, the resulting flexibility comes at a cost: the spatial effects are not identifiable without either prior knowledge of the underlying phenomenon or additional parameter restrictions. Using a Bayesian-estimation framework, the model performance is assessed in a simulation study. We apply the model on intercropping data, where spatial effects between different crops are unlikely to be symmetric, in order to illustrate the usage of the proposed methodology. An R package containing the proposed methodology can be found on https://CRAN.R-project.org/package=SAGM.

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引用次数: 0

Isotropy testing in spatial point patterns: nonparametric versus parametric replication under misspecification

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-04-05 DOI: 10.1016/j.spasta.2025.100898

Jakub J. Pypkowski, Adam M. Sykulski, James S. Martin

Several hypothesis testing methods have been proposed to validate the assumption of isotropy in spatial point patterns. A majority of these methods are characterised by an unknown distribution of the test statistic under the null hypothesis of isotropy. Parametric approaches to approximating the distribution involve simulation of patterns from a user-specified isotropic model. Alternatively, nonparametric replicates of the test statistic under isotropy can be used to waive the need for specifying a model. In this paper, we first present a general framework which allows for the integration of a selected nonparametric replication method into isotropy testing. We then conduct a large simulation study comprising application-like scenarios to assess the performance of tests with different parametric and nonparametric replication methods. In particular, we explore distortions in test size and power caused by model misspecification, and demonstrate the advantages of nonparametric replication in such scenarios.

引用次数: 0

Nonparametric approaches for direct approximation of the spatial quantiles

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-04-05 DOI: 10.1016/j.spasta.2025.100896

Pilar García-Soidán , Tomás R. Cotos-Yáñez

The estimation of the spatial quantiles provides information on the thresholds of a spatial variable. This methodology is particularly appealing for its application to data of pollutants, so as to assess their level of risk. A spatial quantile can be approximated through different mechanisms, proposed in the statistics literature, although these approaches suffer from several drawbacks, regarding their lack of optimality or the fact of not leading to direct approximations. Thus, the current work introduces alternative procedures, which try to overcome the aforementioned issues by employing order statistics, similarly as done for independent data. With this aim, the available observations are appropriately transformed to yield a sample of the process at each target site, so that the data obtained are then ordered and used to derive the spatial quantile at the corresponding location. The new methodology can be directly applied to data from processes that are either stationary or that deviate from this condition for a non-constant trend and, additionally, it can be even extended to heteroscedastic data. Simulation studies under different scenarios have been accomplished, whose results show the adequate performance of the proposed estimators. A further step of this research is the application of the new approaches to data of nitrogen dioxide concentrations, to exemplify the potential of the quantile estimates to check the thresholds of a pollutant at a specific moment, as well as their evolution over time.

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引用次数: 0

Similarity and geographically weighted regression considering spatial scales of features space

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-03-29 DOI: 10.1016/j.spasta.2025.100897

Shifeng Yu , Xiaoyu Hu , Yehua Sheng , Chenmeng Zhao

Unlike a geographically weighted regression (GWR), a similarity and geographically weighted regression (SGWR) calculates weights using attribute and geographic similarities, thereby effectively improving the accuracy of the model. Nevertheless, SGWR does not set an attribute similarity bandwidth. This leads to its inability to measure the scale of variation of spatial processes in feature space. In addition, owing to the solving method used, SGWR can get stuck in local optima. To address these issues, this study proposed an improved similarity and geographically weighted regression model (SGWR-GD) that adds bandwidth to the attribute similarity kernel function. This parameter gives SGWR-GD the ability to measure the scale of change of spatial processes in the attribute dimension and thus enhances the flexibility of modelling. When solving the model, SGWR-GD first calculated the gradient of the model's Modified Akaike Information Criterion (AICc) with respect to the two bandwidths and the impact ratio. Subsequently, the optimal global solution of the model was obtained based on a gradient descent algorithm with box constraints. SGWR-GD and SGWR were applied to five different datasets and the accuracies of their fitting results were compared. SGWR-GD significantly improved the accuracy of the model compared to SGWR. In addition, the SGWR-GD stably determined the global optimal solution for each parameter. Simultaneously, the distribution of local residuals was also more stable.

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引用次数: 0

Magnitude-weighted goodness-of-fit scores for earthquake forecasting

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-03-28 DOI: 10.1016/j.spasta.2025.100895

Frederic Schoenberg

Current methods for evaluating earthquake forecasts, such as the

N

-test,

L

-test, or log-likelihood score, typically do not disproportionately reward a model for more accurately forecasting the largest events, or disproportionately punish a model for less accurately forecasting the largest events. However, since the largest earthquakes are by far the most destructive and therefore of most interest to practitioners, in many circumstances, a weighted likelihood score may be more useful. Here, we propose various weighted measures, weighting each earthquake by some function of its magnitude, such as potency-weighted log-likelihood, and consider their properties. The proposed methods are applied to a catalog of earthquakes in the Western United States.

引用次数: 0

Fast computation of the statistical significance test for spatio-temporal receptive field estimates obtained using spike-triggered averaging of binary pseudo-random sequences

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-03-27 DOI: 10.1016/j.spasta.2025.100899

Murat Okatan

Background

Spatio-temporal receptive fields (STRFs) of visual neurons are often estimated using spike-triggered averaging (STA) with binary pseudo-random stimulus sequences. An exact analytical test—called the STA-BPRS test—has been developed to determine the statistical significance of each pixel in the STRF estimate. However, computing this test can take minutes to days, or even longer, for certain neurons.

New method

Here, the STA-BPRS test is accelerated by approximating the null distribution of STRF pixel estimates with a Normal distribution. This methodological refinement significantly reduces computation time, making large-scale data analysis feasible.

Results

The approximate test is systematically validated on real mouse retinal ganglion cell data and synthetic spike train data, demonstrating that it yields identical significance thresholds to the exact test. For neurons where, exact computation would be prohibitively long (e.g., hundreds of years), the approximate test completes in seconds or minutes.

Comparison with existing methods

Few approaches address pixel-by-pixel significance in STA-based STRF estimates. While subspace methods like spike-triggered covariance exist for STRF estimation, they typically do not provide direct voxel-wise or pixel-wise p-values. The proposed method specifically accelerates an exact distribution-based test.

Conclusions and impact

The proposed Normal approximation drastically reduces computation time, enabling high-throughput analysis of STRF mapping from spike data. This advancement may foster broader adoption of precise statistical tests of STRFs in large-scale, high-density electrophysiological recordings. Moreover, fast detection of significant STRF features could facilitate closed-loop experiments where stimuli dynamically adapt to changing STRF structures.

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引用次数: 0

Integrating multi-source geospatial information using Bayesian maximum entropy: A case study on design ground snow load prediction

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-03-26 DOI: 10.1016/j.spasta.2025.100894

Kinspride Duah, Yan Sun, Brennan Bean

Environmental data are often imprecise due to various limitations and uncertainties in the measuring process. As a result, they often consist of a combination of both precise and imprecise information, referred to as hard and soft data, respectively. Often in practice, soft data are characterized as intervals as a simple form to properly preserve the underlying imprecision. Bayesian maximum entropy (BME) is a generalized spatial interpolation method that processes both hard and soft data simultaneously to effectively account for both spatial uncertainty and measurement imprecision. This paper presents a rigorous evaluation to compare the performances of BME and kriging through both simulation and a case study of reliability-targeted design ground snow load (RTDSL) prediction in Utah. The dataset contains a mixture of hard and soft-interval observations, and kriging uses the soft-interval data by extracting the midpoints in addition to the hard data. The cross-validated results show that BME outperforms kriging on multiple error metrics. Specifically for hard data locations where precise observations are known, BME yields a mean error (ME) of 0.0334, a mean absolute error (MAE) of 0.2309, and a root mean squared error (RMSE) of 0.2833, whereas kriging produces a ME of 0.1960, MAE of 0.2793, and RMSE of 0.3698. These results highlight the superior prediction accuracy of BME, particularly in the presence of soft data and/or non-Gaussian hard data.

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引用次数: 0

Functional summary statistics and testing for independence in multi-type point processes on the surface of three dimensional convex shapes

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-03-13 DOI: 10.1016/j.spasta.2025.100891

S. Ward, E.A.K. Cohen, N.M. Adams

The fundamental functional summary statistics used for studying spatial point patterns are developed for marked homogeneous and inhomogeneous point processes on the surface of a sphere. These are extended to point processes on the surface of three dimensional convex shapes given the bijective mapping from the shape to the sphere is known. These functional summary statistics are used to test for independence between the marginals of multi-type spatial point processes with methods for sampling the null distribution developed and discussed. This is illustrated on both simulated data and the RNGC galaxy point pattern, revealing attractive dependencies between different galaxy types.

引用次数: 0

Bayesian adaptive Lasso estimation for partially linear hierarchical spatial autoregressive model

IF 2.1 2区数学 Q3 GEOSCIENCES, MULTIDISCIPLINARY

Spatial Statistics

Pub Date : 2025-03-12 DOI: 10.1016/j.spasta.2025.100892

Miao Long, Zhimeng Sun

This paper presents a Bayesian adaptive Lasso estimation approach for partially linear hierarchical spatial autoregressive models. Despite advancements in spatial modeling, two key gaps remain: the lack of non-linear components in hierarchical spatial autoregressive models to capture complex spatial relationships, and the insufficient application of dimensionality reduction techniques to address high-dimensionality and overfitting. This paper addresses these issues by combining partially linear models with spatial autoregressive structures and incorporating dimensionality reduction techniques to enhance model efficiency and mitigate overfitting. The hierarchical structure facilitates multi-level modeling, accommodating complex data relationships. The Bayesian adaptive Lasso technique ensures effective variable selection and regularization, improving model interpretability and performance. Simulations and real data applications demonstrate the proposed method’s excellent performance. This work offers valuable insights for researchers and practitioners in dealing with spatially correlated data in various fields.

引用次数: 0

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