For exposure estimation of air pollutants, data measurement errors and modeling uncertainty may lead to estimation bias and abnormal predictions and relationships between variables. This paper proposes a method of geospatial constrained optimization and deep learning to reliably simulate and predict spatiotemporal trends of air pollutants. In the proposed method, k-nearest neighbors (k-NN) was first

There has not been much research done to see if the Mantel method is particularly well suited for mesoscopic laddering. The homogeneous and heterogeneous choices in confined MICE spaces with a finite population were examined in this paper. Using a sample of 8,583 delegates taken from two large metropolitan facilities, we assessed the spatial impacts in sedentary co-presence (meetings/conferences) and

In this work we present a spatial approach to investigate mortality data referenced over a Lexis structure. We decompose the force of mortality into two interpretable components: a Markov random field, homogeneous with respect to age, period and cohort which explains the main pattern of mortality; and a secondary component of independent shocks, accounting for additional non structured patterns. Inference

Drug addiction can lead to many health-related problems and social concerns. Researchers are interested in the association between long-term drug usage and abnormal functional connectivity. Functional connectivity obtained from functional magnetic resonance imaging data promotes a variety of fundamental understandings in such association. Due to the complex correlation structure and large dimensionality

In this paper we propose a robust fuzzy clustering model, the STAR-based Fuzzy C-Medoids Clustering model with Noise Cluster, to define territorial partitions of the European regions (NUTS2) according to the workplaces mobility trends for places of work provided by Google with reference to the whole COVID-19 pandemic period. The clustering model takes into account both temporal and spatial information

The spatial epidemic dynamics of Covid-19 outbreak in Italy were modelled by means of an Object-Oriented Bayesian Network in order to explore the dependence relationships, in a static and a dynamic way, among the weekly incidence rate, the intensive care units occupancy rate and that of deaths. Following an autoregressive approach, both spatial and time components have been embedded in the model by

We propose an endemic-epidemic model: a negative binomial space–time autoregression, which can be employed to monitor the contagion dynamics of the COVID-19 pandemic, both in time and in space. The model is exemplified through an empirical analysis of the provinces of northern Italy, heavily affected by the pandemic and characterised by similar non-pharmaceutical policy interventions.

One of the basic assumptions in spatial statistic is second-order stationarity, which implies homogeneity and isotropy. However, when using a spatial random field framework to model socio-economical or epidemiological data – just to mention two examples – it is often unreasonable to believe that the relationship between variables could be modelled as a realization of a unique stationary process. In

This article presents a spatial–temporal generalized additive model for modeling geo-referenced COVID-19 mortality data in Toronto, Canada. A range of factors and spatial–temporal terms are incorporated into the model. The non-linear and interactive effects of the neighborhood-level factors, i.e., population density and average of income, are modeled as a two-dimensional spline smoother. The change

Spatial regression and geographically weighted regression models have been widely adopted to capture the effects of auxiliary information on a response variable of interest over a region. In contrast, relationships between response and auxiliary variables are expected to exhibit complex spatial patterns in many applications. This paper proposes a new approach for spatial regression, called spatially

In spatial statistics, the kriging predictor is the best linear predictor at unsampled locations, but not the optimal predictor for non-Gaussian processes. In this paper, we introduce a copula-based multiple indicator kriging model for the analysis of non-Gaussian spatial data by thresholding the spatial observations at a given set of quantile values. The proposed copula model allows for flexible marginal

Recent advancements in remote sensing technology and the increasing size of satellite constellations allow for massive geophysical information to be gathered daily on a global scale by numerous platforms of different fidelity. The auto-regressive co-kriging model provides a suitable framework for the analysis of such data sets as it is able to account for cross-dependencies among different fidelity

Binary regression models are commonly used in disciplines such as epidemiology and ecology to determine how spatial covariates influence individuals. In many studies, binary data are shared in a spatially aggregated form to protect privacy. For example, rather than reporting the location and result for each individual that was tested for a disease, researchers may report that a disease was detected

We propose a root-Gaussian Cox process for the analysis of data that is aggregated in space and where the boundaries of geographic regions change over time. A generalized EMS algorithm exploits sparse structure resulting in significant computational gains. Inference for a spatial intensity surface is conducted using a non-central chi-square distribution. A simulation study over the state of Kentucky

Data from the American Community Survey (ACS) provides a wealth of information useful for learning more about social determinants of health and their spatial distributions within a defined region. Although available data includes quantified indicators of uncertainty in aggregated location-specific estimates for a range of variables, this uncertainty is often ignored, the consequences of which may include

Due to its better results over the traditional co-simulation methods, joint simulation of variables through orthogonal factors has gained popularity. In this approach, practitioners transform variables into orthogonal factors, simulate them independently, and back-transform the results into the initial space. It is unlikely to generate spatially uncorrelated factors at more than two lags. Therefore

As with the advancement of geographical information systems, non-Gaussian spatial data sets are getting larger and more diverse. This study develops a general framework for fast and flexible non-Gaussian regression, especially for spatial/spatiotemporal modeling. The developed model, termed the compositionally-warped additive mixed model (CAMM), combines an additive mixed model (AMM) and the compositionally-warped

Due to the well-known computational showstopper of the exact Maximum Likelihood Estimation (MLE) for large geospatial observations, a variety of approximation methods have been proposed in the literature, which usually require tuning certain inputs. For example, the recently developed Tile Low-Rank approximation (TLR) method involves many tuning parameters, including numerical accuracy. To properly

The aim of the work is to identify a clustering structure for the 20 Italian regions according to the main variables related to COVID-19 pandemic. Data are observed over time, spanning from the last week of February 2020 to the first week of February 2021. Dealing with geographical units observed at several time occasions, the proposed fuzzy clustering model embedded both space and time information

To properly characterize a spatio-temporal random process, it is necessary to understand the process’ dependence structure. It is common to describe this dependence using a single random error having a complicated covariance. Instead of using the single random error approach, we describe spatio-temporal random processes using linear mixed models having several random errors; each random error describes

In this study, we develop factor analysis to explore areal data collected in space and time. The main goal is to incorporate the framework with nonlinear interactions to handle a spatio-temporal random effect in the structure of a mixed generalized linear regression. The spatial dependence between regions is established through the CAR model specified for each column of the loadings matrix. Temporal

The overwhelming spatio-temporal nature of the spread of the ongoing Covid-19 pandemic demands urgent attention of data analysts and model developers. Modelling results obtained from analytical tool development are essential to understand the ongoing pandemic dynamics with a view to helping the public and policy makers. The pandemic has generated data on a huge number of interesting statistics such

The present study attempts to detect anomalies and evaluate statistical climatological trends of recorded total columnar ozone (RTCO) over the Indian sites. The 30–54 years of RTCO data recorded by the Dobson Spectrophotometer obtained from the India Meteorological Department (IMD) is used. TCO Anomalies are detected using predicted TCO (PTCO) from a Long Short-Term Memory (LSTM) based neural network

The Matérn family of isotropic covariance functions has been central to the theoretical development and application of statistical models for geospatial data. For global data defined over the whole sphere representing planet Earth, the natural distance between any two locations is the great circle distance. In this setting, the Matérn family of covariance functions has a restriction on the smoothness

It is increasingly understood that the assumption of stationarity is unrealistic for many spatial processes. In this article, we combine dimension expansion with a spectral method to model big non-stationary spatial fields in a computationally efficient manner. Specifically, we use Mejía and Rodríguez-Iturbe’s (1974) spectral simulation approach to simulate a spatial process with a covariogram at locations

Probability (PDF) and cumulative distribution function (CDF) are used extensively to calculate probability features in engineering, natural sciences, hydrology, meteorology and many other fields. In this study, a different type of Burr distribution is suggested, which provides an alternative probability calculation. The type is fitted to the Weibull empirical equation results on the vertical axis versus

This paper presents a new algorithm for statistical inference and analysis of spatial patterns assumed to be realisations of Gibbs point processes. This approach has a general character and it contributes to the existing methods based on Approximate Bayesian Computation, by providing control properties of the proposed solution. Results on simulated data and real data are presented. The real data application

Several model comparison techniques exist to select the best fitting model from a set of candidate models. This study explores the performance of model comparison tools that are commonly used in Bayesian spatial disease mapping and that are available among several Bayesian software packages: the deviance information criterion (DIC), the Watanabe–Akaike information criterion (WAIC) and the log marginal

The economic literature has largely ignored the existence of global common factors and local spatial dependence in the assessment of the real effects of U.S. banking deregulation. Motivated by consistency concerns, this study uses spatial econometric models with common factors to analyze the impact of U.S. banking deregulation on county-level economic growth during the 1970–2000 period. We estimate

The preferential sampling of locations chosen to observe a spatio-temporal process has been identified as a major problem across multiple fields. Predictions of the process can be severely biased when standard statistical methodologies are applied to preferentially sampled data without adjustment. Detecting preferential sampling is currently a technically demanding task. As a result, the problem is

Modelling the small-area spatio-temporal dynamics of the Covid-19 pandemic is of major public health importance, because it allows health agencies to better understand how and why the virus spreads. However, in Scotland during the first wave of the pandemic testing capacity was severely limited, meaning that large numbers of infected people were not formally diagnosed as having the virus. As a result

The problem of estimating the spatio-functional quantile regression for a given spatial mixing structure (Xi,Yi)∈F×R, when i∈ZN, N≥1 and F is a separable Hilbert space, is investigated. We construct and compare four estimators of the regression quantile. The proposed estimators cover the two main aspects of the statistical analysis, namely the parametric and nonparametric approaches. Precisely, using

Detailed spatial representation of socioeconomic data has the potential to improve the reliability and quality of spatial assessment. In this paper, we propose a novel method to spatialize the transportation industry output of Hunan Province in China by disaggregating the administrative-unit level to the grid-cell one based on the classification and regression tree and the spatial copula model. The

This paper presents a case study carried out in multiple cities of the Valencian Community (Spain) to determine the effect of sociodemographic and road characteristics on traffic accident risk. The analyzes are performed at the road segment level, considering the linear network representing the road structure of each city as a spatial lattice. The number of accidents observed in each road segment from

We propose a model based on discrete latent variables, which are spatially associated and time specific, for the analysis of incident cases of SARS-CoV-2 infections. We assume that for each area the sequence of latent variables across time follows a Markov chain with initial and transition probabilities that also depend on latent variables in neighboring areas. The model is estimated by a Markov chain

We address the problem of characterizing spatially variable Natural Background Levels (NBLs) of concentrations of chemical species of environmental concern in a large-scale groundwater body. Assessment of NBLs is critical to identify significant trends of (possibly hazardous) chemical concentrations in aquifer systems, the latter being typically associated with spatially heterogeneous hydrogeochemical

The statistical modeling of multivariate count data observed on a space–time lattice has generally focused on using a hierarchical modeling approach where space–time correlation structure is placed on a continuous, latent, process. The count distribution is then assumed to be conditionally independent given the latent process. However, in many real-world applications, especially in the modeling of

Spatial direction vector data can be represented cylindrically by linear magnitudes and circular angles. We analyze such data by using a hierarchical Markov random field model with latent discrete classes and conditionally independent cylindrical data given the classes. The structure of a Potts model segments the spatial domain, and each class defines a cylindrical density that represents a specific

Intensity estimation is a common problem in statistical analysis of spatial point pattern data. This paper proposes a nonparametric Bayesian method for estimating the spatial point process intensity based on mixture of finite mixture (MFM) model. MFM approach leads to a consistent and simultaneous estimate of the intensity surface of spatial point pattern and the clustering information (number of clusters

Several methods have been proposed in the spatial statistics literature to analyse big data sets in continuous domains. However, new methods for analysing high-dimensional areal data are still scarce. Here, we propose a scalable Bayesian modelling approach for smoothing mortality (or incidence) risks in high-dimensional data, that is, when the number of small areas is very large. The method is implemented

Because most natural phenomena exhibit dependence at multiple scales like locations of earthquakes or forest fire occurrences, spatio-temporal single-scale point process models are unrealistic in many applications. This motivates us to construct generalizations of classical Gibbs models. In this paper, we extend the Geyer saturation point process model to the spatio-temporal multi-scale framework.

Modeling the spatial correlation structure of coregionalized data is a frequent task in numerous fields of the natural sciences. Even in the isotropic case, experimental covariances may exhibit complex features, such as a maximum cross-correlation attained at non-collocated locations (dimple or hole effect). Current construction principles for multivariate covariance models on Euclidean spaces do not

A common problem in geostatistics is to interpolate a variable at unsampled locations using available data. Kriging has been the conventional method of solving this problem by providing the weighted average of samples, which is determined by minimizing the estimation variance. Kriging variance is a function of the samples’ spatial configuration and the variable’s spatial dependence structure. The latter

There is currently a gap in theory for point patterns that lie on the surface of objects, with researchers focusing on patterns that lie in a Euclidean space, typically planar and spatial data. Methodology for planar and spatial data thus relies on Euclidean geometry and is therefore inappropriate for analysis of point patterns observed in non-Euclidean spaces. Recently, there has been extensions to

In contrast to classical econometric approaches which are based on prespecified isotropic weighting schemes, we suggest that the spatial weighting matrix in the presence of directional dependencies should be estimated. We identify this direction based on different candidate neighbourhood sets. In this paper, we consider two different types of processes – namely spatial autoregressive and spatial autoregressive

Geolocation uncertainty is common in epidemiological studies that depend on addresses to determine exposure. We developed a spatial construct and statistical framework by which to characterize geolocation uncertainty, develop analysis methods, and compare those methods. Exposure is represented by a three-dimensional step function over the partitioned spatial surface and a person’s geolocation boundary

Recent extreme value theory literature has seen significant emphasis on the modelling of spatial extremes, with comparatively little consideration of spatio-temporal extensions. This neglects an important feature of extreme events: their evolution over time. Many existing models for the spatial case are limited by the number of locations they can handle; this impedes extension to space–time settings

Understanding spatial inhomogeneity and clustering in point patterns arises in many contexts, ranging from disease outbreak monitoring to analyzing radiologically-based emphysema in biomedical images. This can often involve classifying individual points as being part of a feature/cluster or as being part of a background noise process. Existing methods for this task can struggle when there are differences

This paper explores the spatial distribution of crime incidences in Nigeria and evaluates the association between the geographical variations and the socio-demographic determinants of crimes. The analyses are based on 2017 reported crime Statistics obtained from the Nigeria‘s National Bureau of Statistics. This paper analysed the spatial patterns of four types of crimes (armed robbery, theft, rape

In this paper, we consider a spatial linear model under the multivariate Student’s t-distribution with finite second moment. This distribution, which contains the normal distribution, offers a more flexible framework for modelling spatial data. We use a reparameterized version of the multivariate Student’s t-distribution, so that the scale matrix corresponds to the covariance matrix of the spatial

In regression models for spatial data, it is often assumed that the marginal effects of covariates on the response are constant over space. In practice, this assumption might often be questionable. In this article, we show how a Gaussian process-based spatially varying coefficient (SVC) model can be estimated using maximum likelihood estimation (MLE). In addition, we present an approach that scales

Model-assisted estimation of forest wood volume is approached exploiting the wall-to-wall information available from satellite data and partial information achieved from airborne laser scanning (ALS) covering a portion of the survey area. If the portion covered by ALS is selected by a probabilistic sampling scheme, two-phase estimators are considered in which the two sources of information are exploited

Many countries have enforced social distancing to stop the spread of COVID-19. Within countries, although the measures taken by governments are similar, the incidence rate varies among areas (e.g., counties, cities). One potential explanation is that people in some areas are more vulnerable to the coronavirus disease because of their worsened health conditions caused by long-term exposure to poor air

We propose a novel class of multiscale spatiotemporal models for multivariate Gaussian data. First, we decompose the multivariate data and the underlying latent process with a novel multivariate multiscale decomposition. This decomposition results in multiscale coefficient matrices with elements that are multiscale approximations of spatial directional derivatives. We then assume that the associated

Stochastic weather generators (SWGs) are digital twins of complex weather processes and widely used in agriculture and urban design. Due to improved measuring instruments, an accurate SWG for high-frequency precipitation is now possible. However, high-frequency precipitation data are more zero-inflated, skewed, and heavy-tailed than common (hourly or daily) precipitation data. Therefore, classical

In the last few decades, sampling theory has been given a substantial boost by the growing availability of geo-referenced finite populations. Unfortunately, geo-referentiation is often incomplete or affected by locational errors for a portion of the units. Spatial sampling methods produce efficient estimates but suffer from consequences of flaws in geo-referentiation. This paper proposes a mixed sampling

In spatial regression analysis, a suitable specification of the mean regression model is crucial for unbiased analysis. Also, to enhance statistical efficiency of the mean regression analysis, we need to suitably account for the underlying spatial correlation structure. In this paper, we focus on selection of an appropriate mean model in spatial regression analysis under a general anisotropic nested

Dendritic spines, which are small protrusions on the dendrites of a neuron, are of interest in neuroscience as they are related to cognitive processes such as learning and memory. We analyse the distribution of spine locations on six different dendrite trees from mouse neurons using point process theory for linear networks. Besides some possible small-scale repulsion, we find that two of the spine

Complex valued random fields, a natural generalization of real valued random fields, represent a powerful tool for modeling phenomena which evolve in time, spatial vectorial data in two dimensions and spatio-temporal vectorial data (i.e., a wind field). However, only a few efforts have been proposed in the literature to address some relevant aspects of spatial and spatio-temporal geostatistical analysis