Spatial Statistics最新文献

Ground subsidence concerns the long-term development of mining areas, and if not addressed effectively, it could gradually evolve into a major issue limiting the future economic development and survival of mining firms and local populations. However, there is unpredictability and uncertainty in the analysis of ground subsidence in mining areas, which is a quantitative and qualitative problem coupled with multiple indicators. By creating a chain relationship between ground subsidence in mining areas, this research provides a spatiotemporal inference model that integrates remote sensing (RS), geographic information system (GIS), and probabilistic map theory. The model uses a dynamic Bayesian framework to integrate the ground subsidence hazard chain in mining areas, standardizes multi-source data using GIS, computes node probabilities, and applies the entropy weight approach to improve model parameters. The Pingshuo mining area in China served as the study area for the model, and the mean values of area under the curve (AUC) and Brier score (BS) of the inferred results were 0.85 and 0.18, respectively, demonstrating that the model had some accuracy and dependability. Further analysis was performed on the impact of weights on the outcomes and the sensitivity of the model to the input nodes. The findings indicated that the spatiotemporal distribution of the results inferred from the model essentially matched the actual circumstance and could offer data assistance for mine safety management. The matching of the subsidence areas was effectively improved by optimizing the model with weights. The accuracy would also grow as the number of input nodes increased. The model proposed in this study is not limited by data, and the structure can be adjusted with the change of disaster chains, which is applicable to the study of multiple uncertainty problems.

This study is motivated by the nonresponse problem in the official rice productivity survey conducted by Statistics Indonesia. Handling nonresponse is essential to support the vision as a quality statistical data provider for advanced Indonesia. This study aimed to improve the quality of official rice productivity data by imputing nonresponse data using the geo-additive mixed model with variable selection. Then we simulated three nonresponse data scenarios to determine whether the imputation technique is better than the listwise deletion. The results showed that the proposed imputation model was the best-imputed model for estimating rice productivity compared to the linear regression, SVM, and geo-additive mixed models without variable selection. The proposed model outperforms other models when the data conditions experience spatial autocorrelation and multicollinearity. The proposed model had two advantages. First, variable selection using the adaptive elastic net could overcome multicollinearity problems. Second, adding the mixed geo-additive function caused the model’s residuals to have no spatial autocorrelation. We showed by simulation using empirical data that the proposed imputation method reduces bias when the nonresponse data is not random. Our methodology presents a valuable alternative for improving the quality of official statistics.

Registration of multivariate functional data involves handling of both cross-component and cross-observation phase variations. Allowing for the two phase variations to be modelled as general diffeomorphic time warpings, in this work we focus on the hitherto unconsidered setting where phase variation of the component functions are spatially correlated. We propose an algorithm to optimize a metric-based objective function for registration with a novel penalty term that incorporates the spatial correlation between the component phase variations through a kriging prediction of an appropriate phase random field. The penalty term encourages the overall phase at a particular location to be similar to the spatially weighted average phase in its neighbourhood, and thus engenders a regularization that prevents over-alignment. Utility of the registration method, and its superior performance compared to methods that fail to account for the spatial correlation, is demonstrated through performance on simulated examples and two multivariate functional datasets pertaining to electroencephalogram signals and ozone concentration functions. The generality of the framework opens up the possibility for extension to settings involving different forms of correlation between the component functions and their phases.

Jin et al. (2020) proposed an efficient, distribution-free least squares estimation method that utilizes the eigendecomposition of a weight matrix in a dynamic space–time pooled panel data model. Their three-step approach is very powerful compared to the well-known instrumental variable techniques. Unfortunately, for short panels, their method can lead to biased estimates of the autoregressive time dependence parameter and the spatio-temporal diffusion parameter, even when using their bias-corrected estimator. We propose a bias correction method inspired from Bun and Carree (2005, 2006) of the Jin et al. (2020) procedure. We also extend their eigendecomposition-based least squares procedure to the random effects model, the fixed effects model, the Mundlak-type and Chamberlain-type correlated random effects models, the Hausman–Taylor model and the common correlated effects model. Extensive Monte Carlo experiments show the good finite sample properties of the proposed estimators. An application on the link between pollution and economic activities, using a dynamic space–time STIRPAT model with common correlated effects on a panel of 81 countries over 1991–2015, shows the relevance of this approach. It underlines the importance of human activities in the pollution growth while reforestation is one of the most important levers to reduce the CO${}_2$ emissions per capita.

Self-exciting models are statistical models of count data where the probability of an event occurring is influenced by the history of the process. In particular, self-exciting spatio-temporal models allow for spatial dependence as well as temporal self-excitation. For large spatial or temporal regions, however, the model leads to an intractable likelihood. An increasingly common method for dealing with large spatio-temporal models is by using Laplace approximations (LA). This method is convenient as it can easily be applied and is quickly implemented. However, as we will demonstrate in this manuscript, when applied to self-exciting Poisson spatial–temporal models, Laplace Approximations result in a significant bias in estimating some parameters. Due to this bias, we propose using up to sixth-order corrections to the LA for fitting these models. We will demonstrate how to do this in a Bayesian setting for self-exciting spatio-temporal models. We will further show there is a limited parameter space where the extended LA method still has bias. In these uncommon instances we will demonstrate how a more computationally intensive fully Bayesian approach using the Stan software program is possible in those rare instances. The performance of the extended LA method is illustrated with both simulation and real-world data.

We propose a novel class of dynamic factor models for spatiotemporal areal data. This novel class of models assumes that the spatiotemporal process may be represented by some few latent factors that evolve through time according to dynamic linear models. As the dimension of the vector of latent factors is typically much smaller than the number of subregions, our proposed class of models may achieve substantial dimension reduction. At each time point, the vector of observations is linearly related to the vector of latent factors through a matrix of factor loadings. Each column of this matrix may be seen as a vectorized map of factor loadings relating one latent factor to the vector of observations. Thus, to account for spatial dependence, we assume that each column of the matrix of factor loadings follows an intrinsic conditional autoregressive (ICAR) process. Hence, we call our class of models the Dynamic ICAR Spatiotemporal Factor Models (DIFM). We develop a Gibbs sampler for exploration of the posterior distribution. In addition, we develop model selection through a Laplace-Metropolis estimator of the predictive density. We present two case studies. The first case study, which is for simulated data, demonstrates that our DIFMs are identifiable and that our proposed inferential procedure works well at recovering the underlying data generating process. Finally, the second case study demonstrates the utility and flexibility of our DIFM framework with an application to the drug overdose epidemic in the United States from 2015 to 2021.

Log-Gaussian Cox processes (LGCPs) are a popular and flexible tool for modelling point pattern data. While maximum likelihood estimation of the parameters of such a model is attractive in principle, the likelihood function is not available in closed form. Various Monte Carlo approximations have been proposed, but these have seen very limited use in the literature and are often dismissed as impractical. This article provides a comprehensive study of the computational properties of Monte Carlo maximum likelihood estimation (MCMLE) for LGCPs. We compare various importance sampling algorithms for MCMLE, and also consider their performance against other methods of inference (such as minimum contrast) in numerical studies. We find that the best MCMLE algorithm is a practical proposition for parameter estimation given modern computing power, but the performance of this methodology is rather sensitive to the choice of reference parameters defining the importance sampling distribution.

Spatial clustering detection has a variety of applications in diverse fields, including identifying infectious disease outbreaks, pinpointing crime hotspots, and identifying clusters of neurons in brain imaging applications. Ripley’s K-function is a popular method for detecting clustering (or dispersion) in point process data at specific distances. Ripley’s K-function measures the expected number of points within a given distance of any observed point. Clustering can be assessed by comparing the observed value of Ripley’s K-function to the expected value under complete spatial randomness. While performing spatial clustering analysis on point process data is common, applications to areal data commonly arise and need to be accurately assessed. Inspired by Ripley’s K-function, we develop the positive area proportion function (PAPF) and use it to develop a hypothesis testing procedure for the detection of spatial clustering and dispersion at specific distances in areal data. We compare the performance of the proposed PAPF hypothesis test to that of the global Moran’s I statistic, the Getis–Ord general G statistic, and the spatial scan statistic with extensive simulation studies. We then evaluate the real-world performance of our method by using it to detect spatial clustering in land parcels containing conservation easements and US counties with high pediatric overweight/obesity rates.