-
Graph convolutional networks for spatial interpolation of correlated data Spat. Stat. (IF 2.3) Pub Date : 2024-03-18 Marianne Abémgnigni Njifon, Dominic Schuhmacher
Several deep learning methods for spatial data have been developed that report good performance in a big data setting. These methods typically require the choice of an appropriate kernel and some tuning of hyperparameters, which are contributing reasons for poor performance on smaller data sets.
-
Regime-based precipitation modeling: A spatio-temporal approach Spat. Stat. (IF 2.3) Pub Date : 2024-03-05 Carolina Euán, Ying Sun, Brian J. Reich
In this paper, we propose a new regime-based model to describe spatio-temporal dynamics of precipitation data. Precipitation is one of the most essential factors for multiple human-related activities such as agriculture production. Therefore, a detailed and accurate understanding of the rain for a given region is needed. Motivated by the different formations of precipitation systems (convective, frontal
-
Mapping using an adaptive sampling design Spat. Stat. (IF 2.3) Pub Date : 2024-03-01 Mohammad Moradi, Jennifer Brown
Interpolation is commonly used in the construction of maps and images when there is limited information for some of the sites. The accuracy of interpolation methods depends, in part, on the location of the sample sites where more complete information has been gathered. An initial survey design where the sample sites are spaced so there is wide-spread coverage is desirable. However, when there is considerable
-
Geostatistical capture–recapture models Spat. Stat. (IF 2.3) Pub Date : 2024-02-06 Mevin B. Hooten, Michael R. Schwob, Devin S. Johnson, Jacob S. Ivan
Methods for population estimation and inference have evolved over the past decade to allow for the incorporation of spatial information when using capture–recapture study designs. Traditional approaches to specifying spatial capture–recapture (SCR) models often rely on an individual-based detection function that decays as a detection location is farther from an individual’s activity center. Traditional
-
Modeling left-censored skewed spatial processes: The case of arsenic drinking water contamination Spat. Stat. (IF 2.3) Pub Date : 2024-02-05 Qi Zhang, Alexandra M. Schmidt, Yogendra P. Chaubey
Commonly, observations from environmental processes are spatially structured and present skewed distributions. Recently, different models have been proposed to model spatial processes in their original scale. This work was motivated by modeling the levels of arsenic groundwater concentration in Comilla, a district of Bangladesh. Some of the observations are left censored. We propose spatial gamma models
-
Robust interaction detector: A case of road life expectancy analysis Spat. Stat. (IF 2.3) Pub Date : 2024-01-20 Zehua Zhang, Yongze Song, Lalinda Karunaratne, Peng Wu
Spatial stratified heterogeneity, revealing the disparity mechanisms across spatial strata, can be effectively quantified using the geographical detector (GD). GD requires reasonable spatial discretization strategies to investigate the spatial association between the target variable and numerical independent variables. In previous studies, the Robust Geographical Detector (RGD) optimized spatial strata
-
Deep graphical regression for jointly moderate and extreme Australian wildfires Spat. Stat. (IF 2.3) Pub Date : 2024-01-17 Daniela Cisneros, Jordan Richards, Ashok Dahal, Luigi Lombardo, Raphaël Huser
Recent wildfires in Australia have led to considerable economic loss and property destruction, and there is increasing concern that climate change may exacerbate their intensity, duration, and frequency. Hazard quantification for extreme wildfires is an important component of wildfire management, as it facilitates efficient resource distribution, adverse effect mitigation, and recovery efforts. However
-
Spatial classification in the presence of measurement error Spat. Stat. (IF 2.3) Pub Date : 2024-01-18 Yuhan Ma, Kyuhee Shin, GyuWon Lee, Joon Jin Song
In recent decades, spatial classification has received considerable attention in a wide array of disciplines. In practice, binary response variable is often subject to measurement error, misclassification. To account for the misclassified response in spatial classification, we proposed validation data-based adjustment methods that use interval validation data to rectify misclassified responses. Regression
-
Spatial Smoothing Using Graph Laplacian Penalized Filter Spat. Stat. (IF 2.3) Pub Date : 2024-01-17 H, i, r, o, s, h, i, , Y, a, m, a, d, a
This paper considers a filter for smoothing spatial data. It can be used to smooth data on the vertices of arbitrary undirected graphs with arbitrary non-negative spatial weights. It consists of a quantity analogous to Geary’s , which is one of the most prominent measures of spatial autocorrelation. In addition, the quantity can be represented by a matrix called the graph Laplacian in spectral graph
-
A comparison of model validation approaches for echo state networks using climate model replicates Spat. Stat. (IF 2.3) Pub Date : 2024-01-17 Kellie McClernon, Katherine Goode, Daniel Ries
As global temperatures continue to rise, climate mitigation strategies such as stratospheric aerosol injections (SAI) are increasingly discussed, but the downstream effects of these strategies are not well understood. As such, there is interest in developing statistical methods to quantify the evolution of climate variable relationships during the time period surrounding an SAI. Feature importance
-
Variable selection via penalized quasi-maximum likelihood method for spatial autoregressive model with missing response Spat. Stat. (IF 2.3) Pub Date : 2024-01-08 Yuanfeng Wang, Yunquan Song
Spatial autoregressive model is widely concerned in the economic field, whereas when the data is missing, variable selection and parameter estimation of the model is quite challenging. Based on this, we discuss the variable selection in spatial autoregressive model with missing data. Under the condition that errors are independent and identically distributed, we have developed a penalized quasi-maximum
-
Impacts of spatial imputation on location-allocation problem solutions Spat. Stat. (IF 2.3) Pub Date : 2024-01-04 Dongeun Kim, Yongwan Chun, Daniel A. Griffith
Georeferenced data often contain missing values, and such missing values can considerably affect spatial modeling. A spatial location model can also suffer from this issue when there are missing values in its geographic distribution of weights. Although general imputation approaches have been developed, one distinguishing fact here is that spatial imputation generally performs better for georeferenced
-
A simplified spatial+ approach to mitigate spatial confounding in multivariate spatial areal models Spat. Stat. (IF 2.3) Pub Date : 2023-12-30 Arantxa Urdangarin, Tomás Goicoa, Thomas Kneib, María Dolores Ugarte
Spatial areal models encounter the well-known and challenging problem of spatial confounding. This issue makes it arduous to distinguish between the impacts of observed covariates and spatial random effects. Despite previous research and various proposed methods to tackle this problem, finding a definitive solution remains elusive. In this paper, we propose a simplified version of the spatial+ approach
-
A generalized additive model (GAM) approach to principal component analysis of geographic data Spat. Stat. (IF 2.3) Pub Date : 2023-12-29 Francisco de Asís López, Celestino Ordóñez, Javier Roca-Pardiñas
Geographically Weighted Principal Component Analysis (GWPCA) is an extension of classical PCA to deal with the spatial heterogeneity of geographical data. This heterogeneity results in a variance–covariance matrix that is not stationary but changes with the geographical location. Despite its usefulness, this method presents some unsolved issues, such as finding an appropriate bandwidth (size of the
-
Modeling lake conductivity in the contiguous United States using spatial indexing for big spatial data Spat. Stat. (IF 2.3) Pub Date : 2023-12-29 Michael Dumelle, Jay M. Ver Hoef, Amalia Handler, Ryan A. Hill, Matt Higham, Anthony R. Olsen
Conductivity is an important indicator of the health of aquatic ecosystems. We model large amounts of lake conductivity data collected as part of the United States Environmental Protection Agency’s National Lakes Assessment using spatial indexing, a flexible and efficient approach to fitting spatial statistical models to big data sets. Spatial indexing is capable of accommodating various spatial covariance
-
Dealing with location uncertainty for modeling network-constrained lattice data Spat. Stat. (IF 2.3) Pub Date : 2023-12-28 Álvaro Briz-Redón
The spatial analysis of traffic accidents has long been a useful tool for authorities to implement effective preventive measures. Initial studies were conducted at the areal level considering administrative or traffic-related units, but a more precise analysis at the street level is necessary for developing targeted interventions. In recent years, there has been a significant increase in studies conducted
-
A flexible likelihood-based neural network extension of the classic spatio-temporal model Spat. Stat. (IF 2.3) Pub Date : 2023-12-19 Malte Jahn
The inclusion of the geographic information into regression models is becoming increasingly popular due to the increased availability of corresponding geo-referenced data. In this paper, a novel framework for combining spatio-temporal regression techniques and artificial neural network (ANN) regression models is presented. The key idea is to use the universal approximation property of the ANN function
-
Estimation for single-index spatial autoregressive model with covariate measurement errors Spat. Stat. (IF 2.3) Pub Date : 2023-12-21 Ke Wang, Dehui Wang
This paper explores the estimators of parameters for a spatial data single-index model which has measurement errors of covariates in the nonparametric part. The related estimations are considered to combine a local-linear smoother based simulation-extrapolation (SIMEX) algorithm, the estimation equation and the estimation method for profile maximum likelihood. Under regular conditions, asymptotic properties
-
Robust second-order stationary spatial blind source separation using generalized sign matrices Spat. Stat. (IF 2.3) Pub Date : 2023-12-16 Mika Sipilä, Christoph Muehlmann, Klaus Nordhausen, Sara Taskinen
Consider a spatial blind source separation model in which the observed multivariate spatial data are assumed to be a linear mixture of latent stationary spatially uncorrelated random fields. The objective is to recover an unknown mixing procedure as well as the latent random fields. Recently, spatial blind source separation methods that are based on the simultaneous diagonalization of two or more scatter
-
Using spatial ordinal patterns for non-parametric testing of spatial dependence Spat. Stat. (IF 2.3) Pub Date : 2023-12-14 Christian H. Weiß, Hee-Young Kim
We analyze data occurring in a regular two-dimensional grid for spatial dependence based on spatial ordinal patterns (SOPs). After having derived the asymptotic distribution of the SOP frequencies under the null hypothesis of spatial independence, we use the concept of the type of SOPs to define the statistics to test for spatial dependence. The proposed tests are not only implemented for real-valued
-
Copula-Based Data-Driven Multiple-Point Simulation Method Spat. Stat. (IF 2.3) Pub Date : 2023-12-10 Babak Sohrabian, Abdullah Erhan Tercan
Multiple-point simulation is a commonly used method in modeling complex curvilinear structures. The method is based on the application of training images that are open to manipulation. The present study introduces a new data-driven multiple-point simulation method that derives multiple point statistics directly from sparse data using copulas and applies them in simulation of complex mineral deposits
-
Correlation-based hierarchical clustering of time series with spatial constraints Spat. Stat. (IF 2.3) Pub Date : 2023-11-30 Alessia Benevento, Fabrizio Durante
Correlation-based hierarchical clustering methods for time series typically are based on a suitable dissimilarity matrix derived from pairwise measures of association. Here, this dissimilarity is modified in order to take into account the presence of spatial constraints. This modification exploits the geometric structure of the space of correlation matrices, i.e. their Riemannian manifold. Specifically
-
A criterion and incremental design construction for simultaneous kriging predictions Spat. Stat. (IF 2.3) Pub Date : 2023-11-29 Helmut Waldl, Werner G. Müller, Paula Camelia Trandafir
In this paper, we further investigate the problem of selecting a set of design points for universal kriging, which is a widely used technique for spatial data analysis. Our goal is to select the design points in order to make simultaneous predictions of the random variable of interest at a finite number of unsampled locations with maximum precision. Specifically, we consider as response a correlated
-
Computationally efficient localised spatial smoothing of disease rates using anisotropic basis functions and penalised regression fitting Spat. Stat. (IF 2.3) Pub Date : 2023-11-29 Duncan Lee
The spatial variation in population-level disease rates can be estimated from aggregated disease data relating to N areal units using Bayesian hierarchical models. Spatial autocorrelation in these data is captured by random effects that are assigned a Conditional autoregressive (CAR) prior, which assumes that neighbouring areal units exhibit similar disease rates. This approach ignores boundaries in
-
Locally adaptive spatial quantile smoothing: Application to monitoring crime density in Tokyo Spat. Stat. (IF 2.3) Pub Date : 2023-11-18 Takahiro Onizuka, Shintaro Hashimoto, Shonosuke Sugasawa
Spatial trend estimation under potential heterogeneity is an important problem to extract spatial characteristics and hazards such as criminal activity. By focusing on quantiles, which provide substantial information on distributions compared with commonly used summary statistics such as means, it is often useful to estimate not only the average trend but also the high (low) risk trend additionally
-
An object-oriented approach to the analysis of spatial complex data over stream-network domains Spat. Stat. (IF 2.3) Pub Date : 2023-11-13 Chiara Barbi, Alessandra Menafoglio, Piercesare Secchi
We address the problem of spatial prediction for Hilbert data, when their spatial domain of observation is a river network. The reticular nature of the domain requires to use geostatistical methods based on the concept of Stream Distance, which captures the spatial connectivity of the points in the river induced by the network branching. Within the framework of Object Oriented Spatial Statistics (O2S2)
-
A spatial model with vaccinations for COVID-19 in South Africa Spat. Stat. (IF 2.3) Pub Date : 2023-11-09 Claudia Dresselhaus, Inger Fabris-Rotelli, Raeesa Manjoo-Docrat, Warren Brettenny, Jenny Holloway, Nada Abdelatif, Renate Thiede, Pravesh Debba, Nontembeko Dudeni-Tlhone
Since the emergence of the novel COVID-19 virus pandemic in December 2019, numerous mathematical models were published to assess the transmission dynamics of the disease, predict its future course, and evaluate the impact of different control measures. The simplest models make the basic assumptions that individuals are perfectly and evenly mixed and have the same social structures. Such assumptions
-
General spatial model meets adaptive shrinkage generalized moment estimation: Simultaneous model and moment selection Spat. Stat. (IF 2.3) Pub Date : 2023-11-07 Yunquan Song, Yaqi Liu, Xiaodi Zhang, Yuanfeng Wang
Spatial data are widely used in various scenarios of life and are highly valued, and their analysis and research have achieved remarkable results. Spatial data have spatial effects and do not satisfy the assumption of independence; thus, the traditional econometric analysis methods cannot be directly used in spatial models, and the spatial autocorrelation and spatial heterogeneity of spatial data make
-
Data-driven modeling of wildfire spread with stochastic cellular automata and latent spatio-temporal dynamics Spat. Stat. (IF 2.3) Pub Date : 2023-11-10 Nicholas Grieshop, Christopher K. Wikle
We propose a Bayesian stochastic cellular automata modeling approach to model the spread of wildfires with uncertainty quantification. The model considers a dynamic neighborhood structure that allows neighbor states to inform transition probabilities in a multistate categorical model. Additional spatial information is captured by the use of a temporally evolving latent spatio-temporal dynamic process
-
Geographically Weighted Zero-Inflated Negative Binomial Regression: A general case for count data Spat. Stat. (IF 2.3) Pub Date : 2023-11-04 Alan Ricardo da Silva, Marcos Douglas Rodrigues de Sousa
Poisson and Negative Binomial Regression Models are often used to describe the relationship between a count dependent variable and a set of independent variables. However, these models fail to analyze data with an excess of zeros, being Zero-Inflated Poisson (ZIP) and Zero-Inflated Negative Binomial (ZINB) models the most appropriate to fit this kind of data. To Incorporate the spatial dimension into
-
Review of Sujit Sahu’s “Bayesian modeling of spatio-temporal data with R” Spat. Stat. (IF 2.3) Pub Date : 2023-10-31 Patrick E. Brown
Abstract not available
-
A more accurate estimation with kernel machine for nonparametric spatial lag models Spat. Stat. (IF 2.3) Pub Date : 2023-10-12 Yu Shu, Jinwen Liang, Yaohua Rong, Zhenzhen Fu, Yi Yang
Ignoring potential spatial autocorrelation in georeferenced data may cause biased estimators. Furthermore, existing studies assume insufficiently flexible structure of spatial lag model for some practical applications, which makes it difficult to portray the complex relationship between responses and covariates. Thus, we propose a novel garrotized kernel machine estimation method for the nonparametric
-
Which parameterization of the Matérn covariance function? Spat. Stat. (IF 2.3) Pub Date : 2023-10-12 Kesen Wang, Sameh Abdulah, Ying Sun, Marc G. Genton
The Matérn family of covariance functions is currently the most popularly used model in spatial statistics, geostatistics, and machine learning to specify the correlation between two geographical locations based on spatial distance. Compared to existing covariance functions, the Matérn family has more flexibility in data fitting because it allows the control of the field smoothness through a dedicated
-
Spatio-temporal mapping of stunting and wasting in Nigerian children: A bivariate mixture modeling Spat. Stat. (IF 2.3) Pub Date : 2023-10-06 Ezra Gayawan, Osafu Augustine Egbon
Studies have shown that stunting and wasting indicators are strongly correlated among children, with the potential of concurrently affecting their physical and cognitive development. However, the identification of subpopulations of children with varying risks of stunting and wasting could be valuable for targeted intervention. This work proposed a bivariate spatio-temporal mixture model within a Bayesian
-
A spatiotemporal inference model for hazard chains based on weighted dynamic Bayesian networks for ground subsidence in mining areas Spat. Stat. (IF 2.3) Pub Date : 2023-09-29 Yahong Liu, Jin Zhang
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
-
Generalised hyperbolic state space models with application to spatio-temporal heat wave prediction Spat. Stat. (IF 2.3) Pub Date : 2023-09-16 Daisuke Murakami, Gareth W. Peters, François Septier, Tomoko Matsui
As global warming progresses, it is increasingly important to monitor and analyse spatio-temporal patterns of heat waves and other extreme climate-related events that impact urban areas. In this work, we present a novel dynamic spatio-temporal model by combining a state space model (SSM) and a generalised hyperbolic distribution to flexibly describe a spatial–temporal profile of the tail behaviour
-
Semiparametric regression for spatial data via deep learning Spat. Stat. (IF 2.3) Pub Date : 2023-09-09 Kexuan Li, Jun Zhu, Anthony R. Ives, Volker C. Radeloff, Fangfang Wang
In this work, we propose a deep learning-based method to perform semiparametric regression analysis for spatially dependent data. To be specific, we use a sparsely connected deep neural network with rectified linear unit (ReLU) activation function to estimate the unknown regression function that describes the relationship between response and covariates in the presence of spatial dependence. Under
-
Spatial linear discriminant analysis approaches for remote-sensing classification Spat. Stat. (IF 2.3) Pub Date : 2023-09-03 Thomas Suesse, Alexander Brenning, Veronika Grupp
Linear Discriminant Analysis (LDA) is a popular and simple classification tool that often outperforms more sophisticated modern machine learning techniques in remote sensing. We introduce a novel LDA method that uses spatial autocorrelation of all pixels of an object to be classified but also of other objects of the training set that are spatially close to improve classification performance. To simplify
-
A parametric specification test for linear spatial autoregressive models Spat. Stat. (IF 2.3) Pub Date : 2023-09-01 Yangbing Tang, Jiang Du, Zhongzhan Zhang
We propose a new test for the specification of linear spatial autoregressive models where the spatial weights matrix is prespecified. Our test is built on the difference of two estimates of the spatial parameter where the two estimates are obtained by the parametric and nonparametric GMM estimation methods, respectively. Under mild assumptions, we derive the limiting null distribution and show consistency
-
Spatio-temporal DeepKriging for interpolation and probabilistic forecasting Spat. Stat. (IF 2.3) Pub Date : 2023-08-25 Pratik Nag, Ying Sun, Brian J. Reich
Gaussian processes (GP) and Kriging are widely used in traditional spatio-temporal modelling and prediction. These techniques typically presuppose that the data are observed from a stationary GP with a parametric covariance structure. However, processes in real-world applications often exhibit non-Gaussianity and nonstationarity. Moreover, likelihood-based inference for GPs is computationally expensive
-
Information criteria for matrix exponential spatial specifications Spat. Stat. (IF 2.3) Pub Date : 2023-08-25 Osman Doğan, Ye Yang, Süleyman Taşpınar
In this study, we suggest using information criteria for nested and non-nested model selection problems for the matrix exponential spatial specifications (MESS) under both homoskedasticity and heteroskedasticity. To this end, we consider the deviance information criterion, the Akaike information criterion and the Bayesian information criterion in a Bayesian setting. In the heteroskedastic case, we
-
Deep learning and spatial statistics Spat. Stat. (IF 2.3) Pub Date : 2023-08-23 Christopher K. Wikle, Jorge Mateu, Andrew Zammit-Mangion
Abstract not available
-
A zero-dose vulnerability index for equity assessment and spatial prioritization in low- and middle-income countries Spat. Stat. (IF 2.3) Pub Date : 2023-08-21 C.E. Utazi, H.M.T. Chan, I. Olowe, A. Wigley, N. Tejedor-Garavito, A. Cunningham, M. Bondarenko, J. Lorin, D. Boyda, D. Hogan, A.J. Tatem
Many low- and middle-income countries (LMICs) continue to experience substantial inequities in vaccination coverage despite recent efforts to reach missed communities and reduce zero-dose prevalence. Geographic inequities in vaccination coverage are often characterized by a multiplicity of risk factors which should be operationalized through data integration to inform more effective and equitable vaccination
-
A spatial panel autoregressive model specification with inverse quantile separation distances of locations Spat. Stat. (IF 2.3) Pub Date : 2023-08-19 Bedanie G. Bulty, Butte Gotu, Gemechis Djira
This paper proposes an alternative spatial weight that efficiently captures a spatial dependence. In the past, researchers often used sparse or inverse distance spatial weights. A dense spatial weight is defined by partitioning the separation distances between locations based on quantile values over large spatial scales, where each partition forms conjoint neighborhood sectors and the weights of the
-
A Poisson cokriging method for bivariate count data Spat. Stat. (IF 2.3) Pub Date : 2023-08-12 David Payares-Garcia, Frank Osei, Jorge Mateu, Alfred Stein
Bivariate spatially correlated count data appear naturally in several domains such as ecology, economy and epidemiology. Current methods for analysing such data lack simplicity, interpretability and computational awareness. This paper introduces Poisson cokriging, a bivariate geostatistical technique to model and predict spatially correlated count variables. Our method exploits classical geostatistical
-
Distribution free prediction for geographically weighted functional regression models Spat. Stat. (IF 2.3) Pub Date : 2023-08-09 Andrea Diana, Elvira Romano, Antonio Irpino
Recently, there has been significant interest in distribution-free prediction within the fields of machine learning and statistics. Distribution-free prediction involves techniques that aim to make predictions or create prediction intervals without relying on explicit assumptions about the underlying distribution of the data. In this study, we introduce an inductive conformal prediction strategy specifically
-
Effects of geographically stratified random sampling initial solutions on solving a continuous surface p-median location problem using the ALTERN heuristic Spat. Stat. (IF 2.3) Pub Date : 2023-08-07 Changho Lee, Daniel A. Griffith, Yongwan Chun, Hyun Kim
In the fields of location theory and spatial optimization, heuristic algorithms have been developed to overcome the NP-hard nature of solutions to their problems, which results in an exponential increase in computation time. These algorithms aim to generate good initial solutions, narrow the solution space, and guide the search process to optimality. Geographically stratified random sampling (GSRS)
-
Fuzzy clustering of spatial interval-valued data Spat. Stat. (IF 2.3) Pub Date : 2023-08-02 Pierpaolo D’Urso, Livia De Giovanni, Lorenzo Federico, Vincenzina Vitale
In this paper, two fuzzy clustering methods for spatial interval-valued data are proposed, i.e. the fuzzy C-Medoids clustering of spatial interval-valued data with and without entropy regularization. Both methods are based on the Partitioning Around Medoids (PAM) algorithm, inheriting the great advantage of obtaining non-fictitious representative units for each cluster. In both methods, the units are
-
Nonparametric spatial autoregressive model using deep neural networks Spat. Stat. (IF 2.3) Pub Date : 2023-07-31 Shuyue Xiao, Yunquan Song, Zhijian Wang
With the rapid development of social networks, spatial autoregressive models with covariates are increasingly used in practice. We introduce spatial effects into the artificial neural network model and propose a new method for spatial data prediction. Our method is based on artificial neural network, combined with the idea of nonparametric spatial autoregressive model. The spatial lag term is a input
-
Dynamic ICAR Spatiotemporal Factor Models Spat. Stat. (IF 2.3) Pub Date : 2023-06-28 Hwasoo Shin, Marco A.R. Ferreira
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
-
Extended Laplace approximation for self-exciting spatio-temporal models of count data Spat. Stat. (IF 2.3) Pub Date : 2023-06-23 Nicholas J. Clark, Philip M. Dixon
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
-
Spatially penalized registration of multivariate functional data Spat. Stat. (IF 2.3) Pub Date : 2023-06-22 Xiaohan Guo, Sebastian Kurtek, Karthik Bharath
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
-
Feasibility of Monte-Carlo maximum likelihood for fitting spatial log-Gaussian Cox processes Spat. Stat. (IF 2.3) Pub Date : 2023-06-09 Bethany J. Macdonald, Tilman M. Davies, Martin L. Hazelton
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
-
Dynamic space–time panel data models: An eigendecomposition-based bias-corrected least squares procedure Spat. Stat. (IF 2.3) Pub Date : 2023-06-02 Georges Bresson, Anoop Chaturvedi
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
-
A hypothesis test for detecting distance-specific clustering and dispersion in areal data Spat. Stat. (IF 2.3) Pub Date : 2023-05-19 Stella Self, Anna Overby, Anja Zgodic, David White, Alexander McLain, Caitlin Dyckman
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
-
Data fusion of distance sampling and capture-recapture data Spat. Stat. (IF 2.3) Pub Date : 2023-05-11 Narmadha M. Mohankumar, Trevor J. Hefley, Katy M. Silber, W. Alice Boyle
Species distribution models (SDMs) are increasingly used in ecology, biogeography, and wildlife management to learn about the species–habitat relationships and abundance across space and time. Distance sampling (DS) and capture-recapture (CR) are two widely collected data types to learn about species–habitat relationships and abundance; still, they are seldomly used in SDMs due to the lack of spatial
-
A deep learning synthetic likelihood approximation of a non-stationary spatial model for extreme streamflow forecasting Spat. Stat. (IF 2.3) Pub Date : 2023-05-08 Reetam Majumder, Brian J. Reich
Extreme streamflow is a key indicator of flood risk, and quantifying the changes in its distribution under non-stationary climate conditions is key to mitigating the impact of flooding events. We propose a non-stationary process mixture model (NPMM) for annual streamflow maxima over the central US (CUS) which uses downscaled climate model precipitation projections to forecast extremal streamflow. Spatial
-
A unified geographically weighted regression model Spat. Stat. (IF 2.3) Pub Date : 2023-05-05 Ying Wu, Zhipeng Tang, Shifeng Xiong
Spatial heterogeneity and spatial dependence are two cornerstones of spatial data research. It becomes more and more important to simultaneously deal with them in analyzing today’s complex spatial datasets. Along this direction, we introduce a new class of geographically weighted regression models, called unified geographically weighted regression (UGWR) models, to generalize existing geographically
-
A Bayesian spatial–temporal model for predicting passengers occupancy at Beijing Metro Spat. Stat. (IF 2.3) Pub Date : 2023-05-02 Stefano Cabras, Sun He
The growing population density in cities requires urban transportation to meet the travel needs of citizens fast and accurately. Therefore, the correct prediction of daily passenger flow in urban subway transportation is of great practical importance for rationalizing the traffic arrangement and safely responding to unexpected passenger flow. This work builds a Bayesian spatial–temporal model for predicting
-
Bayesian Physics Informed Neural Networks for data assimilation and spatio-temporal modelling of wildfires Spat. Stat. (IF 2.3) Pub Date : 2023-04-23 Joel Janek Dabrowski, Daniel Edward Pagendam, James Hilton, Conrad Sanderson, Daniel MacKinlay, Carolyn Huston, Andrew Bolt, Petra Kuhnert
We apply the Physics Informed Neural Network (PINN) to the problem of wildfire fire-front modelling. We use the PINN to solve the level-set equation, which is a partial differential equation that models a fire-front through the zero-level-set of a level-set function. The result is a PINN that simulates a fire-front as it propagates through the spatio-temporal domain. We show that popular optimisation