Clipped multiscale spatial processes for multivariate plant cover data

Batch process performance monitoring has been achieved primarily using process measurements with the extracted information being associated with the physical parameters of the process. With increasing attention now being paid to the application of on‐line real‐time process analytics through spectrometry, together with the FDA Process Analytical Technologies (PAT) initiative, the use of spectroscopic information for enhanced monitoring of reactions is gaining impetus. The harmonious integration of process data and spectroscopic data then becomes a major challenge. By integrating the process and spectroscopic measurements for multivariate statistical data modelling and analysis, it is conjectured that improved process understanding and fault diagnosis can be achieved. An investigation into combining process and spectral data using multiblock and multiresolution analysis is proposed and the results from the analysis of experimental data from two industrial application studies are presented to demonstrate the improvements achievable in terms of process performance monitoring and fault diagnosis.

Delays in delivering spatial information to support the decisions about maintenance of bridges introduce stoppages into the bridge management workflow. Previous studies show that spatial data collection and analysis are major components of these inspection programs. However, the time consuming spatial data processing cause days or weeks of delays in delivering the needed spatial information. Federal agencies, in many cases, either wait for the tedious spatial data processing, or use partial data processing results containing high uncertainties. Using the processing of 3D laser scanning point clouds as an example, this paper examines the technical feasibility and scientific challenges of quantifying the data processing time and the quality (e.g., accuracy) of derived information (e.g., minimum under-clearance of a bridge) useful for bridge management. Different decisions need different geometric attributes and relationships with various accuracy requirements. The optimal data processing strategy varies with the needed information quality and time limits. It is difficult to manually analyze the time-quality trade-offs of spatial data processing due to its ad-hoc nature and large number of possible parameter settings in data processing workflows. The authors proposed a computational framework that automatically records data processing histories of engineers along with the engineering needs. Analyzing those histories lead to insights into the trade-offs between required computational complexity/time and quality of delivered spatial information. This paper presents time-quality analysis results from two bridge inspection cases.

Most plant species are spatially aggregated. Local demographic and ecological processes (e.g. vegetative growth and limited seed dispersal) result in a clustered spatial pattern within an environmentally homogenous area. Spatial aggregation should be considered when modelling plant abundance data. Commonly, plant abundance is quantified by measuring cover within multiple areal plots or along multiple lines randomly placed within a study area. A common practice for analysing plant cover is to use statistical methods that rely on the normal distribution for quantifying uncertainty. This is problematic because plant cover data tend to be left‐skewed (J‐shaped), right skewed (L‐shaped) or U‐shaped and, therefore, commonly violate classic statistical assumptions, such as normality. We outline statistical analyses that explicitly account for spatial aggregation by assuming that plant cover is beta‐distributed. The beta distribution is a flexible choice because within the open unit interval it can take on a wide range of shapes (L, J, U, or a bell‐shaped). We discuss and introduce extensions to the beta distribution that address common analysis issues encountered in plant cover datasets, such as (a) the treatment of zero and one cover values, (b) hierarchical data structures and (c) observations errors. For heuristic purposes, we focus on single species analyses, but we demonstrate how the outlined methods can be generalized to more species. The assumption that plant cover is beta‐distributed allows us to estimate the degree of spatial aggregation, and the ecological significance of this new knowledge is discussed. We provide a summary of available software for analyses (emphasizing standard R packages) and include worked examples and a simulation study comparing analysis options as supplemental information. Synthesis. Previously, the state of the statistical software made it practically difficult for empirical plant ecologists to analyze their cover data correctly, but new theory and R‐packages have been developed, and this difficulty no longer exists. We recommend that empirical plant ecologists embrace the new statistical possibilities for exploring the exciting ecological features in spatial variation of plant cover.

Aim Myanmar, an Indo‐Burmese biodiversity hotspot, lacks baseline data on species occurrence and distribution. This hinders biodiversity monitoring and optimisation of conservation and development plans. We aim to document baseline mammal occupancy, interactions with environmental factors and scale‐dependent responses. Location Hkakaborazi National Park, Htamanthi Wildlife Sanctuary, Alaungdaw Kathapa National Park, Rakhine Yoma Elephant Range, Say Taung and Myinmoletkhat Key Biodiversity Areas distributed across Myanmar. Methods Camera trap data throughout Myanmar were used to analyse species occupancy. We conducted a multiscale hierarchical spatial modelling process, using local and pooled data across Myanmar. We also optimised spatial scale across five scales and six predictors, using univariate occupancy models. We then selected scale‐optimised variables for multivariate modelling, repeating this process for each species across local, regional and national datasets. Results The study identified 47 terrestrial species and observed strong scale‐dependent nonstationarity in occupancy estimates. Relationships with environmental variables differed among species and were highly scale dependent. Importantly, occupancy estimates produced by pooling data across sites were greatly different from any of the estimates for the individual sites, suggesting that high heterogeneity in occurrence and abundance across sites among species requires local or nested occupancy estimates to account for spatial heterogeneity and variation. Main Conclusions Future conservation efforts should focus on Northern Myanmar if range‐restricted and rare species are to be protected, while focus should still be given to common species which serve as potential indicators of overall community structure. The nonstationarity of occupancy results from different datasets underscores the potential for misleading interpretations from aggregated data in nonstationary ecological systems. Metareplicated analyses of local, geographically and ecologically proximal regional datasets provide an important view of spatial variation in occupancy patterns guiding conservation design and improving understanding of the drivers of biodiversity patterns and change across large regions, such as Southeast Asia.

BACKGROUND: In many works, to study intra- and inter-network connections, a method for constructing networks is used — ROI-analysis (region of interest analysis). The conflicting results obtained when assessing brain connectivity using ROI-analysis can be explained by methodological differences associated with the statistical processing of fMRI data. In this regard, it is relevant to conduct a study with a comparative assessment of various statistical methods of ROI-analysis in processing resting state fMRI data.
 AIM: to assess the functional connectivity of the main resting state networks of the brain using ROI-analysis using various statistical approaches.
 MATERIALS AND METHODS: We analyzed data from 15 resting-state fMRI studies of the brain of patients without neurological and mental pathology. fMRI scanning was performed on a Phillips Ingenia 1.5 T scanner using a gradient echo-planar imaging (EPI-BOLD) sequence. ROI-analysis was used to build networks. Statistical data processing was performed using methods: functional network connectivity, randomization/permutation spatial pairwise clustering statistics, and threshold-free cluster enhancement.
 RESULTS: The number of connections between the structures of brain networks recorded using the method of functional network connectivity is 280, spatial pairwise clustering — 186, threshold-free cluster enhancement — 182. An interesting fact is that negative connections were identified only when using parametric statistics.
 CONCLUSION: A comparative assessment of methods for statistical processing of fMRI data during ROI-analysis was carried out. The functional network connectivity method based on multivariate parametric statistics turned out to be more informative than randomization/permutation spatial pairwise clustering statistics and the method based on threshold-free cluster enhancement. Despite the growing popularity in recent years of resting-state fMRI in the study of functional activity and connectivity of the brain, there are no standardized algorithms for constructing networks of the brain.

SUMMARY The solution of the remote reference method, a frequently used technique in magnetotelluric (MT) data processing, can be viewed as a product of the two-input–multiple-output relationship between the local electromagnetic field and the reference field at a remote station. By applying a robust estimator to the two-input–multiple-output system, one can suppress the influence of outliers in the local magnetic field as well as those in the local electric field based on regression residuals. Therefore, this study develops a new robust remote reference estimator with the aid of robust multivariate linear regression. By applying the robust multivariate regression S-estimator to the multiple-output system, the present work derives a set of equations for robust estimates of the transfer function, noise variances, and the scale of the Mahalanobis distance simultaneously. The noise variances are necessary for the multivariate analysis to normalize the residuals of dependent variables. The Mahalanobis distance, a distance measure for multivariate data, is a commonly used indicator of outliers in multivariate statistics. By updating those robust estimates iteratively, the new robust remote reference estimator seeks the transfer function that minimizes the robust scale estimate of the Mahalanobis distance. The developed estimator can avoid bias in the MT transfer function even if there are significant noises in the reference magnetic field and handle outlying data more robustly than previously proposed robust remote reference estimators. The authors applied the developed method to a synthetic data set and real-world data. The test results demonstrate that the developed method downweights outliers in the local electric and magnetic fields and gives an unbiased transfer function.

The second principle of thermodynamics introduces a very important concept: the entropy of a macrosystem made of a very great number of microsystems. One of the aims of this work is to point out how, in data analysis, entropy and information, that Maxwell’s demon should have to allow a macroscopic reversible process, are closely related. The measure of the information content of a data set allows the analyst to permanently keep under control the variation of information through the successive steps of data processing. On the other hand, multivariate statistics, and among them the factorial analysis of correspondence, can extract the independent and orthogonal sources of information contained in the experimental data. Through several examples dealing with very different experimental situations (stroboscopic observation of a mechanical stress, time-dependent spectroscopy, quantitative elemental mapping by X-ray radiography) we illustrate the usefulness of data analysis before and during data processing.

Data processing using neural networks

The article explores the current trends, challenges, and prospects of automated mapping in land management, focusing on the increasing demand for high-precision, up-to-date cadastral information. Traditional cartographic methods, based on manual data processing and analog technologies, are labor-intensive and time-consuming, often leading to inconsistencies and inaccuracies. Automated mapping is an important direction in the development of modern geographic information systems and technologies in land management. The introduction of automated methods allows to significantly increase the productivity, accuracy and reliability of cartographic work, as well as to ensure the prompt updating of cartographic information. The study emphasizes the advantages of automated mapping through the integration of modern geoinformation technologies, including remote sensing (RS), global navigation satellite systems (GNSS), geographic information systems (GIS), and unmanned aerial vehicles (UAVs). These technologies significantly enhance the efficiency, accuracy, and reliability of spatial data collection, processing, and visualization. The article reviews recent scientific studies on the application of automated methods for processing remote sensing data, including aerial and satellite imagery, in the creation and updating of cartographic materials. Special attention is given to the use of UAVs for high-precision orthophoto mapping and 3D terrain modeling. The research also highlights the importance of ensuring data compatibility and consistency between various sources, such as cadastral registers, GPS measurements, and geoinformation resources. Furthermore, the study addresses the regulatory and legal aspects of automated mapping in land management, analyzing key legislative acts and government regulations in Ukraine that define the standards for cadastral mapping and land use planning. It discusses the necessity of implementing automated data validation and quality control procedures to improve the credibility of cartographic information. The article concludes by identifying key benefits of automated mapping, including increased accuracy, reduced costs, faster data processing, and improved decision-making in land management. It provides recommendations for the integration of advanced geoinformation technologies in land administration, ensuring the timely and reliable update of cadastral data, optimizing land resource management, and supporting sustainable land use planning.

Hazard prediction and risk assessment over regions exposed to natural and technological processes are complex tasks that require exposure to quantization of its uncertainty related to the prediction of future events through statistical methods, spatial data analysis, case studies and process evolution interpretation in conditions of uncertainty.All too often decision makers, DMs, similarly to judges in environmental legal practice, do not have technical training to enable them to communicate/understand the associated uncertainty from technical specialists.In particular communication is a challenge with those who can provide prediction maps and associated statistics to support decisions on disaster prevention, avoidance or mitigation.An interactive short course was prepared to overcome such obstacles to responsible land use planning and proactive measure taking, for example, by asking a set of questions.A first phase in the training follows steps that are to facilitate the comprehension of a spatial database on landslide hazard, of its data processing, and of the interpretation of the analysis results.Integral parts of a second phase are the theory of predictive methods, the strategy in prediction map generation and visualization, including validation via blind tests and the representation of the associated spatial and prediction uncertainties.A successive third phase of the training brings in environmental and socioeconomic spatial indicators to assign vulnerabilities and values to exposed elements in the spatial database.Scenarios for hazard development in the future are then provided.They allow to estimate the uncertainty associated with the probabilities of hazardous occurrences and to resolve the risk equation for different settings.The DM training course includes interactive and iterative

The main topic of this study is a human health risk assessment of a defined exposure scenario in the floodplain soils of the headwater areas of the central European watershed, with the aim of exploring both multivariate and regional data structures. Flood-prone areas are recognized worldwide to be susceptible to contamination and its redistribution. Contributions of various classes of toxic compounds (organochlorine pesticides (OCPs), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs)) to human health risks were assessed in a screening risk assessment. However, due to the relative nature of our data and a high PAH dominancy over the data ensemble, reliance solely on the standard statistical processing of raw data might lead to incomplete insight into the structure of the multivariate data. Explanatory analysis of the data structure using the compositional approach was found to be beneficial to elucidating human health risk profiles and provided robust evidence that a contrast between agricultural and airborne industrial pollution controlled the whole human toxicological variation of persistent organic pollutants (POPs) in floodplain soils. These results were effectively quantified with the subcomposition of benzo(a)pyrene, DDT, and alpha-hexachlorocyclohexane (aHCH), allowing for an interpretation of structural differences in regional pollution patterns, which conferred different extents and compositions of human health risks in floodplain soils.

Limited resources are available to timely inspect and maintain the aging civil infrastructure across the United States. Reality capturing technologies, such as laser scanning, is replacing visual inspection and manual surveying for improved data qualities and reduced resource requirements, while bringing challenges of timely processing terabytes of spatial data. Even using state-of-art 3D reverse engineering environments, inspectors need to manually select data processing algorithms, compose and configure data processing workflows, and verify the correctness of these workflows. Such manual design and execution of spatial data processing workflows are tedious, and result in sub-optimal workflows that do not fully utilize time and resources for producing accurate and detailed spatial information needed by domain applications. This paper proposes a computational framework that will assist in the infrastructure inspection process through streamlined spatial data processing workflow generation, execution, and optimization. Based on previous studies on spatial information query, spatial data processing, and building information modeling (BIM), the authors are exploring the feasibility of automatically generating and optimizing spatial data processing workflows based on formalized representations of these workflows.

Effective connectivity (EC) refers to directional or causal influences between interacting neuronal populations or brain regions and can be estimated from functional magnetic resonance imaging (fMRI) data via dynamic causal modeling (DCM). In contrast to functional connectivity, the impact of data processing varieties on DCM estimates of task‐evoked EC has hardly ever been addressed. We therefore investigated how task‐evoked EC is affected by choices made for data processing. In particular, we considered the impact of global signal regression (GSR), block/event‐related design of the general linear model (GLM) used for the first‐level task‐evoked fMRI analysis, type of activation contrast, and significance thresholding approach. Using DCM, we estimated individual and group‐averaged task‐evoked EC within a brain network related to spatial conflict processing for all the parameters considered and compared the differences in task‐evoked EC between any two data processing conditions via between‐group parametric empirical Bayes (PEB) analysis and Bayesian data comparison (BDC). We observed strongly varying patterns of the group‐averaged EC depending on the data processing choices. In particular, task‐evoked EC and parameter certainty were strongly impacted by GLM design and type of activation contrast as revealed by PEB and BDC, respectively, whereas they were little affected by GSR and the type of significance thresholding. The event‐related GLM design appears to be more sensitive to task‐evoked modulations of EC, but provides model parameters with lower certainty than the block‐based design, while the latter is more sensitive to the type of activation contrast than is the event‐related design. Our results demonstrate that applying different reasonable data processing choices can substantially alter task‐evoked EC as estimated by DCM. Such choices should be made with care and, whenever possible, varied across parallel analyses to evaluate their impact and identify potential convergence for robust outcomes.

Geovisual analytics can enable a user to explore multivariate spatio-temporal health datasets and to understand spatial distribution of diseases especially in relation to external factors that may influence outbreaks. External data are presently distributed using geo web services. Web services are used in health mainly to present results leading to a supplier-driven service model limiting the exploration of health data. In this paper, we illustrate server-side approach of designing a geovisual analytics environment that allows user-driven geovisual analytics. The server-side combines a data query, processing technique, and styling methodology to rapidly visually summarize properties of a dataset. We illustrate this functionality on a typical workflow used by a health researcher and demonstrate analytical functionality in cases where a consistent classification and styling scheme is needed across dynamically aggregated multivariate spatio-temporal datasets. Since the framework builds on the existing Open Geospatial Consortium web mapping standards, it integrates the existing geo web services as well as stand-alone health data repositories into an infrastructure that allows combination and interactive exploration of these heterogeneous datasets in a visual environment.

In a continuous GPS deformation monitoring scheme, any action taken generally relies on a description of the state of the process or events as given by GPS measurements. Timely and correct interpretation of the GPS data is essential to improved quality control, safer system operations, and reduction in the number of false alarms. Unfortunately, GPS data are contaminated by both random and systematic errors of unknown sources. Instrument failure, poor or uncalibrated instrumentation, receiver noise and multipath, all can contribute to data problems. Without proper pretreatment, the necessary interpretation is difficult, if not impossible. Data contaminated by outliers must be eliminated and noise levels reduced. In many cases, critical information occurs over a short duration, and hence is difficult to detect. Wavelets can be used to pre-process data in order to better locate and identify significant events. Combining this type of data pre-processing with multivariate statistics can generate useful insights into the problems of deformation monitoring, data analysis and data interpretation.Multivariate techniques can be used to identify process variability and to develop models for online monitoring and control. By comparing new observations with a reference model that describes normal variability, simple control charts can be constructed to detect data inconsistencies and processing problems. This paper extends these concepts by demonstrating that correlated output of multiple sensors such as GPS receivers, accelerometers, anemometers or temperature sensors can be significantly improved with pre-filtering of the time series signals using a median filter, and a time-scale decomposition using a multi-resolution wavelet function. After the data are filtered and decomposed, the multivariate statistical method of Principal Component Analysis is used to develop a deformation monitoring model.KeywordsFIR median hybrid filterwavelet transformationprincipal component analysisGPS deformation studies