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

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

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

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

Although ambulance call data typically come in the form of spatio-temporal point patterns, point process-based modelling approaches presented in the literature are scarce. In this paper, we study a unique set of Swedish spatio-temporal ambulance call data, which consist of the spatial (GPS) locations of the calls (within the four northernmost regions of Sweden) and the associated days of occurrence

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

In recent years, in many application fields, extracting information from data in the form of functions is of most interest rather than investigating traditional multivariate vectors. Often these functions have complex spatial dependences that need to be accounted for using appropriate statistical analysis. Spatial Functional Statistics presents a fruitful analytics framework for this analysis. The

We consider a random marked tessellation in which the marks are crystal lattice orientations. A natural task is to construct a statistical test to decide whether the orientations are independently assigned to the cells of the underlying tessellation. The distribution of a given orientation is either identical for all cells or may depend on the corresponding cell. For both cases, non-parametric tests

This is the editorial letter for the Special Issue dedicated to the conference Spatial Statistics 2019 Towards Spatial Data Science held in Sitges (Spain) from July 10 to 13, 2019. This fifth international conference on Spatial Statistics was run under the theme Towards Spatial Data Science with the aim to honour the emerging field of Data Science with a focus on spatial and spatio-temporal methods

A motivating example for this paper is a large human location information system that collects two types of information on mobile device locations: 1) large amounts of low-accuracy cell tower triangulation (CTT) calculated location data and 2) small amounts of high-accuracy assisted global positioning system (AGPS) pinpointed location data. Integrating the CTT and AGPS data and extracting more complete

In environmental sciences, it is very common to observe spatio-temporal multiple data concerning several correlated variables which are measured in time over a monitored spatial domain. In multivariate Geostatistics, the evaluation of their behavior is often based on the knowledge of the spatio-temporal multivariate covariance structure. Since this last is often unknown it has to be estimated and modeled

Technological developments and open data policies have made large, global environmental datasets accessible to everyone. For analyzing such datasets, including spatiotemporal correlations using traditional models based on Gaussian processes does not scale with data volume and requires strong assumptions about stationarity, separability, and distance measures of covariance functions that are often unrealistic

The joint influence of different facilities plays an important role in understanding situational profiles of crime incidents. While spatial conjunctive analysis of case configurations (CACC) is widely used to explore situational profiles of crime, complex spatial interactions (e.g., spatial autocorrelation of crime incidents, spatial interactions among multiple facilities) have not been fully considered

Monitoring of variables like temperature, precipitation, and air quality is performed to determine their current situation, exhibit the presence of trends and occurrence of outliers. These variables are measured at specific locations and to obtain a full estimation map, we need to predict values at unknown locations. This study focuses on making a minimum air temperature map using copula interpolation

Information Theory provides a fundamental basis for analysis, and for a variety of subsequent methodological approaches, in relation to uncertainty quantification. The transversal character of concepts and derived results justifies its omnipresence in scientific research, in almost every area of knowledge, particularly in Physics, Communications, Geosciences, Life Sciences, etc. Information-theoretic

Objective: Road traffic crashes (RTCs) are a major global public health problem and cause substantial burden on national economy and healthcare. There is little systematic understanding of the geography of RTCs and the spatial correlations of RTCs in the Middle-East region, particularly in Oman where RTCs are the leading cause of disability-adjusted life years lost. The overarching goal of this paper

In some applications, clustered survival data are arranged spatio-temporally such as geographical regions over multiple time periods. Incorporating spatio-temporal variation in these data not only can improve the accuracy and efficiency of parameter estimation, but it also investigates spatial pattern of survivorship over the study period for identifying high-risk areas. Competing risks in survival

Identifying poverty determinants in a region is crucial for taking effective poverty reduction measures. This paper utilizes two variable importance analysis methods to identify the relative importance of different geographic factors to explain the spatial distribution of poverty: the Lindeman, Merenda, and Gold (LMG) method used in multiple linear regression (MLR) and variable importance used in random

Various methods for directional analysis of spatial point patterns have been introduced in the literature. In this paper, we formulate a unifying framework for methods based on integral transforms of the second order product density. Examples include directional versions of Ripley’s K-function, wavelet transforms, and spectral analysis. Furthermore, we propose an additional method based on the projection

This paper introduces an approach for detecting differences in the first-order structures of spatial point patterns. The proposed approach leverages the kernel mean embedding in a novel way by introducing its approximate version tailored to spatial point processes. While the original embedding is infinite-dimensional and implicit, our approximate embedding is finite-dimensional and comes with explicit

In lattice type of spatial data analysis, the choice of spatial weighting matrices is a main component of any spatial autocorrelation measures and spatial autoregressive models because the choice assumes priori structures of spatial dependency. Typically, weighting matrices are constructed based on the concept of spatial proximity or geographical distance-based functions. However, depending on the

Images are particular and well–known instances of spatial big data. Typically spatial data are scale specific and in this paper, we propose mechanisms to effectively address issues of scale in the analysis of images. We focus on spatial data extracted from images using the Discrete Pulse Transform (DPT). The DPT extracts discrete pulses from images at multiple scales that are recognisable as connected

This paper proposes a new class of covariance functions for bivariate random fields on spheres, having the same properties as the bivariate Matérn model proposed in Euclidean spaces. The new class depends on the geodesic distance on a sphere; it allows for indexing differentiability (in the mean square sense) and fractal dimensions of the components of any bivariate Gaussian random field having such

Under the realization that Geographically Weighted Regression (GWR) is a data-borrowing technique, this paper derives expressions for the amount of bias introduced to local parameter estimates by borrowing data from locations where the processes might be different from those at the regression location. This is done for both GWR and Multiscale GWR (MGWR). We demonstrate the accuracy of our expressions

Let x={x(1),…,x(n)} be a space filling-design of n points defined in 0,1d. In computer experiments, an important property seek for x is a nice coverage of 0,1d. This property could be desirable as well as for any projection of x onto 0,1ι for ι

In this paper, we develop a method for estimating and clustering two-dimensional spectral density functions (2D-SDFs) for spatial data from multiple subregions. We use a common set of adaptive basis functions to explain the similarities among the 2D-SDFs in a low-dimensional space and estimate the basis coefficients by maximizing the Whittle likelihood with two penalties. We apply these penalties to

This paper proposes a Bayesian method for spatial regression using eigenvector spatial filtering (ESF) and Piironen and Vehtari (2017)’s regularized horseshoe (RHS) prior. ESF models are most often estimated using variable selection procedures such as stepwise selection, but in the absence of a Bayesian model averaging procedure variable selection methods cannot properly account for parameter uncertainty

A non-stationary Gaussian random field model is developed based on a combination of the stochastic partial differential equation (SPDE) approach and the classical deformation method. With the deformation method, a stationary field is defined on a domain which is deformed so that the field becomes non-stationary. We show that if the stationary field is a Matérn field defined as a solution to a fractional

In Geostatistics, vector data with two components, such as measures for wind field, electromagnetic field and ocean currents can be appropriately modeled by recalling the theory of complex-valued random fields. This is especially suitable for describing phenomena whose variables are expressed in the same unit of measurement and refer to homogeneous quantities. In this paper, a first approach useful

Citizen Scientists together with an increasing access to technology provide large datasets that can be used to study e.g. ecology and biodiversity. Unknown and varying sampling effort is a major issue when making inference based on citizen science data. In this paper we propose a modeling approach for accounting for variation in sampling effort due to accessibility. The paper is based on an illustrative

The object of this work is to predict the seismic rate in Chile by using two Deep Neural Network (DNN) architectures, Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). For this, we propose a methodology based on a three-module approach: a pre-processing module, a spatial and temporal estimation module, and a prediction module. The first module considers the Epidemic-Type Aftershock

A large number of approaches for modelling spatially dependent functional variables often assume that the functional regression coefficients are constant over the region of interest. However, in many occasions it is far more realistic that functional coefficients vary at a local level. The present paper proposes a calibrated heteroskedastic geographically weighted regression model (H-GWR) in the functional

Nonparametric resampling methods such as Direct Sampling are powerful tools to simulate new datasets preserving important data features such as spatial patterns from observed datasets while using only minimal assumptions. However, such methods cannot generate extreme events beyond the observed range of data values. We here propose using tools from extreme value theory for stochastic processes to extrapolate

For a given region, we have a dataset composed of car theft locations along with a linked dataset of recovery locations which, due to partial recovery, is a relatively small subset of the set of theft locations. For an investigator seeking to understand the behavior of car thefts and recoveries in the region, several questions are addressed. Viewing the set of theft locations as a point pattern, can

This study investigates the propagation power and effects of the coronavirus disease 2019 (COVID-19) in light of published data. We examine the factors affecting COVID-19 together with the spatial effects, and use spatial panel data models to determine the relationship among the variables including their spatial effects. Using spatial panel models, we analyse the relationship between confirmed cases

Issues on sampling procedure definition led numerous study results to be biased and object of controversy. Choosing relevant sampling design and number of samples is a difficult task when wanted to set up or optimize a survey. The survey design choice is very important to avoid bias and increase the survey cost-efficiency. It can have a strong effect on the sample size needed to achieve some targeted

Building clusters for pattern recognition and analysis of geographical areas can be a useful way to provide relevant information for economic and social decisions. In this paper, we introduce a novel spatial clustering technique, called Bootstrap ClustGeo (BCG), which is a hierarchical approach, based on bootstrap techniques with spatial constraints. We evaluate the performance of the proposed approach

This paper analyzes the spatiotemporal pattern of gunfire reports collected by the collaborative mobile app Fogo Cruzado in the Rio de Janeiro metropolitan area (Brazil). We apply nonparametric first and second-order inference tools to characterize gunfire behavior, and test whether gunfire patterns meet the assumptions of crime prediction models, such as kernel hotspot maps or self-exciting point

Workers around the world spend nearly a quarter of their time at work Occupational health is gaining great importance due to the profound impact on people long term health. The health status of the primary sector workforce is a great unknown for medical geography where health maps and spatial patterns have not been able to explain years of changing disease rates. This article proposes a new approach

A common task in the analysis of point patterns is to estimate the intensity of the underlying process, i.e., the expected number of points per unit area or volume at each position over space. In biological studies, a specific feature of point patterns and processes is their confinement within bounded domains of Rd because of the organization of biological systems into various compartments (tissues

We review recent research on statistical methods for analysing spatial patterns of points on a network of lines, such as road accident locations along a road network. Due to geometrical complexities, the analysis of such data is extremely challenging, and we describe several common methodological errors. The intrinsic lack of homogeneity in a network militates against the traditional methods of spatial

Testing for a covariate effect in a parametric point process model is usually done through the Wald test, which relies on an asymptotic null distribution of the test statistic. We propose a Monte Carlo version of the test that also allows local investigation of the covariate effect in the globally fitted model. Two different test statistics are suggested for this purpose: the first, a spatial statistic

We develop a new stochastic spatio-temporal cointegration (SSTC) framework to model spatial and temporal dependence dynamics in high-dimensional non-stationary spatio-temporal data of geological hazards. Such data, often collected from real-time remote sensing, are common in complex geophysical systems such as landslides, earthquakes and volcano eruptions. Our framework employs cointegrated vector

Spatial scan statistic has been widely employed in spatial disease surveillance and spatial cluster detection. However, the over-dispersion and excess of zeros are often presented in real-world data, causing not only the violation of likelihood assumption for the Poisson model, but also excessive Type I error or false alarms. In this paper, we propose the Bell scan and the zero-inflated Bell scan statistics

Due to climate change and human activity, wildfires are expected to become more frequent and extreme worldwide, causing economic and ecological disasters. The deployment of preventive measures and operational forecasts can be aided by stochastic modeling that helps to understand and quantify the mechanisms governing the occurrence intensity. We here develop a point process framework for wildfire ignition

Diagnosis is often based on the exceedance or not of continuous health indicators of a predefined cut-off value, so as to classify patients into positives and negatives for the disease under investigation. In this paper, we investigate the effects of dichotomization of spatially-referenced continuous outcome variables on geostatistical inference. Although this issue has been extensively studied in

Mixed models are commonly used to analyze spatial data which frequently occur in practice such as in health sciences and life studies. It is customary to incorporate spatial random effects into the model to account for spatial variation of the data. In particular, Poisson mixed models are used to analyze spatial count data. It is often assumed that the observations in each area, conditional on the

We consider the problem of non-parametric testing of independence of two components of a stationary bivariate spatial process. In particular, we revisit the random shift approach that has become the standard method for testing the independent superposition hypothesis in spatial statistics, and it is widely used in a plethora of practical applications. However, this method has a problem of liberality

Species distribution models (SDMs) are important statistical tools for ecologists to understand and predict species range. However, standard SDMs do not explicitly incorporate dynamic processes like dispersal. This limitation may lead to bias in inference about species distribution. Here, we adopt the theory of ecological diffusion that has recently been introduced in statistical ecology to incorporate

The scope of spatial statistics problems arising in ecology is substantial. They concern both plant and animal behavior. Here, we discuss only plants. The range of issues is still very large and so we confine ourselves to four challenges, each within the context of model development. These are: species distribution models, both individual and joint; preferential sampling and bias in presence/absence

In the age of big data, imaging techniques such as imaging mass spectrometry (IMS) stand out due to the combination of data size and spatial referencing. However, the data analytic tools readily accessible to investigators often ignore the spatial information or provide results with vague interpretations. We focus on imaging techniques like IMS that collect data along a regular grid and develop methods

We present a flexible hierarchical Bayesian model and develop a comprehensive Bayesian decision theoretic framework for point process theory. We closely investigate the commonly used point process model for independent events, the Poisson process, using a finite mixture of exponential family components to model the intensity function. We employ a Bayesian hierarchical framework for parameter estimation

Geographic Information Systems (GIS) and related technologies have generated substantial interest among statisticians with regard to scalable methodologies for analyzing large spatial datasets. A variety of scalable spatial process models have been proposed that can be easily embedded within a hierarchical modeling framework to carry out Bayesian inference. While the focus of statistical research has

We clear land for agricultural purposes, we draw water from streams and aquifers, and we build houses in coastal regions for their ready access to the sea. Our need for food, water, and shelter is basic, but so is variability in our natural environment. Understanding this is key to the long-term sustainability of the anthropocene. At any location in the environment, long-term temporal averages may

When data for observations are missing, scientists often remove those observations from an analysis, or replace them with imputations, with few other options available to these analysts. Confining an analysis to those observations with complete data can waste resources expended for, especially, those observations with near-complete data. Selectively retaining variables with complete data for observations

Child mortality has remained persistently high in most sub-Saharan African countries. Majority of the effort in analyzing the determinants, or covariables did not consider the duration of exposure to mortality risks. In addition, covariates are usually linked to the mean of the response variable, thereby neglecting the possible association with other higher moments. In this paper, we account for the

We focus on the discussion of modeling processes that are observed at fixed locations of a region (geostatistics). A standard approach is to assume that the process of interest follows a Gaussian Process with some mean and (valid) covariance functions. It is common to model the covariance function as the product between a variance parameter, and a correlation function which is a function of the Euclidean

We propose an interacting cluster model for the spatial distribution of epidermal nerve fibers (ENF). The model consists of a spatial process of parent points modeling the base points of nerve fiber bundles. To each base point there is an offspring point process of fibers end points associated. The parent process is, possibly, inhibited by fiber endings that belong to different base bundles. The fibers