Analysis Of Spatial Correlations Research Articles

Multivariate Analysis of the Temporal and Spatial Correlations of the Global Human Rights Dataset

We propose an extension of a previously proposed method for lead-lag analysis of multivariate time series to include the analysis of spatial correlations. We applied the extended spatial and temporal method to CIRIGHTS, a large global human rights dataset, in order to determine the most influential and most influenced indicators of human rights, freedoms, and atrocities over time. We consider four target countries, each from a different continent. The previously proposed method used a weighted directed network with several lags of each variable as nodes and with edges weighted by transfer entropy. In this study, that method is extended to the spatial multivariate time series by investigating geographical correlations between each target country and its surrounding region to determine which variables best represent activity between the target country and the surrounding region. The nodes in this undirected network are variables in the target country and variables in the surrounding region, with edges weighted by mutual information. Eigenvector centrality is used to determine which variables best represent activity between the target country and the surrounding region. Key findings indicate that occupational and worker rights are the most influential and most influenced in the target countries over time and by region, and laws tend to influence future activity.

Modeling spatial patterns in a moving crowd of people using data-driven approach—A concept of Interplay Floor Field

On the basis of data-driven methodology, especially velocity correlations of pedestrians moving in a crowd, we have proposed a new model of pedestrian dynamics with an easy-adjustable space discretization. The model is based on Cellular Automata (CA) with an adaptive lattice and takes into account proxemics patterns among pedestrians. The proposed model uses agents located on the CA lattice, constructed using the concept of Floor Field (FF), namely, a set of gradient potential fields influencing the movement of agents. In the model, we have proposed three kinds of such fields: Static FF—responsible for navigation to agents’ Points of Interest (POIs), Wall FF—a repulsive influence with obstacles, and Interplay FF—to model agents’ volume and proxemics effects. The third field, which models mutual relations between pedestrians taking into account the rules of proxemics is an added value in the area of crowd modeling and simulation.During the creation of the proposed model, we have based on a set of experimental data provided by Jülich Supercomputing Centre and our previous analysis of spatial patterns and velocity correlations. Thus, the proposed model includes, among other things the most probable positions of the nearest neighbors in a crowd—including the shape of “the forbidden zone” around a pedestrian and the finding of rules explaining observed mutual spatial relations between neighbors in a crowd. In the described model the cell size is an adjustable parameter, with available values from 1 cm to 50 cm. It is possible with the usage of embodied agents which always occupy only one cell of Cellular Automata, actual pedestrian volume is modeled with the Interplay FF. An interesting property of the model is that changing the cell size does not involve changing the rules of the model, e.g. its transition function.

A spatial analysis of parliamentary elections in Sweden 1985\u20132018

Understanding where and why political change is happening in a country is a fundamental issue in political geography. While electoral choice is individual, it is influenced by various sociological, cultural, and geographical factors postulated to create ‘cultural fields’ influencing individual decision-making. Here, we test the cultural field hypothesis on Sweden, an important democracy of Europe long regarded as an example by other European countries, by studying the middle-long-term evolution of the spatial structure of political choice over the last three decades. In testing the cultural field hypothesis, an analysis of spatial correlations is combined with groupings of Swedish municipalities into larger communities reflecting the similarity of their voting profiles. We show that spatial correlations decay logarithmically, which is a sign of long-ranged interactions, and also demonstrate that Sweden can be divided into three or four large and stable politico-cultural communities. More precisely, a transition from three to four main politico-cultural communities is observed. The fourth community, which emerged in the early 2000s is of particular interest as it is characterized by a large vote-share for the Sweden Democrats, while almost all other parties underperform. Moreover, the Swedish electoral landscape seems to be increasingly fragmenting even when the voting profiles of the municipalities over the country are slowly converging.

Energy conversion in turbulent weakly collisional plasmas: Eulerian hybrid Vlasov-Maxwell simulations

Kinetic simulations based on the Eulerian Hybrid Vlasov-Maxwell (HVM) formalism permit the examination of plasma turbulence with a useful resolution of the proton velocity distribution function. The HVM model is employed here to study the balance of energy, focusing on channels of conversion that lead to proton kinetic effects, including growth of internal energy and temperature anisotropies. We show that this Eulerian simulation approach, which is almost noise-free, is able to provide an accurate energy balance for protons. The results demonstrate explicitly that the recovered temperature growth is directly related to the role of the pressure-strain interaction. Furthermore, analysis of local spatial correlations indicates that the pressure-strain interaction is qualitatively associated with strong-current, high-vorticity structures although other local terms—such as the heat flux—weaken the correlation. These numerical capabilities based on the Eulerian approach will enable a deeper study of transfer and conversion channels in weakly collisional Vlasov plasmas.

Low-Complexity Scalable Extension of the High-Efficiency Video Coding (SHVC) Encoding System

The scalable extension of the high-efficiency video coding (SHVC) system adopts a hierarchical quadtree-based coding unit (CU) that is suitable for various texture and motion properties of videos. Currently, the test model of SHVC identifies the optimal CU size by performing an exhaustive quadtree depth-level search, which achieves a high compression efficiency at a heavy cost in terms of the computational complexity. However, many interactive multimedia applications, such as remote monitoring and video surveillance, which are sensitive to time delays, have insufficient computational power for coding high-definition (HD) and ultra-high-definition (UHD) videos. Therefore, it is important, yet challenging, to optimize the SHVC coding procedure and accelerate video coding. In this article, we propose a fast CU quadtree depth-level decision algorithm for inter-frames on enhancement layers that is based on an analysis of inter-layer, spatial, and temporal correlations. When motion/texture properties of coding regions can be identified early, a fast algorithm can be designed for adapting CU depth-level decision procedures to video contents and avoiding unnecessary computations during CU depth-level traversal. The proposed algorithm determines the motion activity level at the treeblock size of the hierarchical quadtree by utilizing motion vectors from its corresponding blocks at the base layer. Based on the motion activity level, neighboring encoded CUs that have larger correlations are preferentially selected to predict the optimal depth level of the current treeblock. Finally, two parameters, namely, the motion activity level and the predicted CU depth level, are used to identify a subset of candidate CU depth levels and adaptively optimize CU depth-level decision processes. The experimental results demonstrate that the proposed scheme can run approximately three times faster than the most recent SHVC reference software, with a negligible loss of compression efficiency. The proposed scheme is efficient for all types of scalable video sequences under various coding conditions and outperforms state-of-the-art fast SHVC and HEVC algorithms. Our scheme is a suitable candidate for interactive HD/UHD video applications that are expected to operate in real-time and power-constrained scenarios.

Interatomic interaction effects on second-order momentum correlations and Hong-Ou-Mandel interference of double-well-trapped ultracold fermionic atoms

Identification and understanding of the evolution of interference patterns in two-particle momentum correlations as a function of the strength of interatomic interactions are important in explorations of the nature of quantum states of trapped particles. Together with the analysis of two-particle spatial correlations, they offer the prospect of uncovering fundamental symmetries and structure of correlated many-body states, as well as opening vistas into potential control and utilization of correlated quantum states as quantum information resources. With the use of the second-order density matrix constructed via exact diagonalization of the microscopic Hamiltonian, and an analytic Hubbard-type model, we explore here the systematic evolution of characteristic interference patterns in the two-body momentum and spatial correlation maps of two entangled ultracold fermionic atoms in a double well, for the entire attractive- and repulsive-interaction range. We uncover statistics-governed bunching and antibunching, as well as interaction-dependent interference patterns, in the ground and excited states, and interpret our results in light of the Hong-Ou-Mandel interference physics, widely exploited in photon indistinguishability testing and quantum information science.

Plenoptic Image Coding using Macropixel-based Intra Prediction.

The plenoptic image in a super high resolution is composed of a number of macropixels recording both spatial and angular light radiance. Based on the analysis of spatial correlations of macropixel structure, this paper proposes a macropixel-based intra prediction method for plenoptic image coding. After applying an invertible image reshaping method to the plenoptic image, the macropixel structures are aligned with the coding unit grids of a block-based video coding standard. The reshaped and regularized image is compressed by the video encoder comprising the proposed macropixel-based intra prediction, which includes three modes: multi-block weighted prediction mode (MWP), co-located single-block prediction mode (CSP), and boundary matching based prediction mode (BMP). In the MWP mode and BMP mode, the predictions are generated by minimizing spatial Euclidean distance and boundary error among the reference samples, respectively, which can fully exploit spatial correlations among the pixels beneath the neighboring microlens. The proposed approach outperforms HEVC by an average of 47.0% bitrate reduction. Compared with other state-of-the-art methods, like pseudo-video based on tiling and arrangement method (PVTA), intra block copy (IBC) mode, and locally linear embedding (LLE) based prediction, it can also achieve 45.0%, 27.7% and 22.7% bitrate savings on average, respectively.

Seismicity During the Initial Stages of the Guy‐Greenbrier, Arkansas, Earthquake Sequence

AbstractWe analyze the background seismicity, initiation, and earliest stages of the Guy‐Greenbrier, Arkansas, earthquake sequence, which was potentially induced by wastewater injection starting in July 2010, during the 3 month time period 1 June to 1 September 2010. High‐resolution observations of low‐magnitude seismicity, and the high‐quality Arkansas public well database, facilitate detailed analysis of spatial and temporal correlations between earthquakes, wastewater injection, and hydraulic fracturing. We detected 14,604 earthquakes, with magnitudes −1.5≤ML≤2.9, using two sensitive, waveform similarity‐based event detection methods in parallel: Fingerprint And Similarity Thresholding, and template matching. We located the 1,740 largest earthquakes that form 16 spatially compact clusters, using P and S phases from 3 stations with the double‐difference relocation algorithm and an improved velocity model constrained by the location of quarry blasts. We enhanced the temporal resolution of these event clusters by assigning smaller unlocated events to a cluster based on waveform similarity. Most clustered earthquakes during this time were both spatially and temporally correlated with hydraulic fracturing stimulation at several production wells. For one cluster, microseismicity was correlated with individual stages of stimulation. Many other wells had no detectable nearby seismicity during stimulation. We found a smaller number of events located on the Guy‐Greenbrier Fault that were likely induced by wastewater injection. The concurrent presence of seismicity induced by hydraulic fracturing and wastewater injection presents a challenge for attribution and seismic hazard characterization, but the combination of precision seismology and high‐quality well information allows us to disentangle the effects of these two processes.

The P3 cognitive ERP has at least some sensory modality-specific generators: Evidence from high-resolution EEG.

The P3 can arguably be named the most intensely studied ERP. In spite of more than 40 years of research, fundamental questions regarding the nature of its neural generators remain unresolved. While most studies conclude that the P3 is a true classical "endogenous" potential, and that its surface potential distribution remains constant across sensory modalities, these results are largely based on low-density EEG recordings, without the use of high-resolution methods such as the spherical spline Laplacian (SSL). Seventeen healthy participants performed a three-stimulus oddball task in visual and auditory modality while their EEG was recorded using a 128-channel system. Comparison of amplitude-normalized SSL estimated P3 brain-surface potentials, and analysis of spatial and temporal correlations revealed significant differences between visual and auditory evoked P3 topographies from target and distractor stimuli (but not target minus frequent or distractor minus frequent comparisons). Based on these results, we postulate the likely existence of at least some sensory modality-specific neuronal generators of the P3.

Numerical analysis of long-range spatial correlations in surface growth.

To analyze long-range spatial correlations in surface growth, we study numerically a class of generalized Kardar-Parisi-Zhang equation with a fractional Laplacian and driven by long-range spatially correlated noise, and investigate interplay of the fractional Laplacian and correlated noise. We find that the growth system with long-range correlation exhibits nontrivial scaling properties, such as strong dependence on the noise correlation and weak dependence on the fractional order. The growth instability is also discussed in various parameter regimes.

Panel‐based stratified cluster sampling and analysis for photovoltaic outdoor measurements

We study a stratified multisite cluster‐sampling panel time series approach in order to analyse and evaluate the quality and reliability of produced items, motivated by the problem to sample and analyse multisite outdoor measurements from photovoltaic systems. The specific stratified sampling in spatial clusters reduces sampling costs and allows for heterogeneity as well as for the analysis of spatial correlations due to defects and damages that tend to occur in clusters. The analysis is based on weighted least squares using data‐dependent weights. We show that this does not affect consistency and asymptotic normality of the least squares estimator under the proposed sampling design under general conditions. The estimation of the relevant variance–covariance matrices is discussed in detail for various models including nested designs and random effects. The strata corresponding to damages or manufacturers are modelled via a quality feature by means of a threshold approach. The analysis of outdoor electroluminescence images shows that spatial correlations and local clusters may arise in such photovoltaic data. Further, relevant statistics such as the mean pixel intensity cannot be assumed to follow a Gaussian law. We investigate the proposed inferential tools in detail by simulations in order to assess the influence of spatial cluster correlations and serial correlations on the test's size and power. ©2016 The Authors. Applied Stochastic Models in Business and Industry published by John Wiley & Sons, Ltd.

Extraction of reduced-order process-structure linkages from phase-field simulations

Phase-field simulations have achieved notable success in capturing characteristic details of microstructure evolution in directional solidification of ternary eutectic alloys. In spite of the impressive advances in high performance computations, phase-field simulations for most practical problems in materials design are resource intensive because of the need to incorporate multiple physical fields over large-scale three-dimensional domains. There is, therefore, a need to learn and capture the underlying materials knowledge embedded in the results produced by such expensive simulations, and facilitating an easy transferability to new problems of interest. This paper demonstrates the viability of extracting the salient process-structure linkages from phase-field simulations, while casting them in forms amenable for a rapid and efficient exploration of a relatively large process space. The presented framework is based on low dimensional representation of material structure obtained through principal component analysis (PCA) of 2-point spatial correlations.

Analysis of field-scale spatial correlations and variations of soil nutrients using geostatistics.

Spatial correlations and soil nutrient variations are important for soil nutrient management. They help to reduce the negative impacts of agricultural nonpoint source pollution. Based on the sampled available nitrogen (AN), available phosphorus (AP), and available potassium (AK), soil nutrient data from 2010, the spatial correlation, was analyzed, and the probabilities of the nutrient's abundance or deficiency were discussed. This paper presents a statistical approach to spatial analysis, the spatial correlation analysis (SCA), which was originally developed for describing heterogeneity in the presence of correlated variation and based on ordinary kriging (OK) results. Indicator kriging (IK) was used to assess the susceptibility of excess of soil nutrients based on crop needs. The kriged results showed there was a distinct spatial variability in the concentration of all three soil nutrients. High concentrations of these three soil nutrients were found near Anzhou. As the distance from the center of town increased, the concentration of the soil nutrients gradually decreased. Spatially, the relationship between AN and AP was negative, and the relationship between AP and AK was not clear. The IK results showed that there were few areas with a risk of AN and AP overabundance. However, almost the entire study region was at risk of AK overabundance. Based on the soil nutrient distribution results, it is clear that the spatial variability of the soil nutrients differed throughout the study region. This spatial soil nutrient variability might be caused by different fertilizer types and different fertilizing practices.

Analysis of spatial correlations in a model two-dimensional liquid through eigenvalues and eigenvectors of atomic-level stress matrices

Considerations of local atomic-level stresses associated with each atom represent a particular approach to address structures of disordered materials at the atomic level. We studied structural correlations in a two-dimensional model liquid using molecular dynamics simulations in the following way. We diagonalized the atomic-level stress tensor of every atom and investigated correlations between the eigenvalues and orientations of the eigenvectors of different atoms as a function of distance between them. It is demonstrated that the suggested approach can be used to characterize structural correlations in disordered materials. In particular, we found that changes in the stress correlation functions on decrease of temperature are the most pronounced for the pairs of atoms with separation distance that corresponds to the first minimum in the pair density function. We also show that the angular dependencies of the stress correlation functions previously reported by Wu etal. [Phys. Rev. E 91, 032301 (2015)10.1103/PhysRevE.91.032301] do not represent the anisotropic Eshelby's stress fields, as it is suggested, but originate in the rotational properties of the stress tensors.

Cokriging for cross-attribute fusion in sensor networks

Optimisation of the number of required measurement points and their location is an important research topic in sensor networks. Finding the optimal positions increases spatial coverage and reduces deployment costs. This paper presents an approach for the case that two attributes have to be measured with a different number of available sensors. The proposed cokriging method performs cross-attribute fusion in sensor networks by being based on the analysis of multi-variable spatial correlations. To the best of our knowledge, this scientific work is the first one considering kriging and cokriging interpolations as IF methods. The single-variable ordinary kriging and bi-variable methods were applied to experimental data. The combination of humidity and temperature data in a refrigerated container is used as exemplary case, humidity measurements are considered to be the expensive attribute to measure. The average estimation error for intermediate points was estimated as a function of the number of humidity sensors. When variability is high, data fusion using the bi-variable method produced results as accurate as the single-variable one, without the necessity of deploying a large number of humidity measuring points, by complementing the estimation with temperature measurements.

Spatial coherence of weakly interacting one-dimensional nonequilibrium bosonic quantum fluids

We present a theoretical analysis of spatial correlations in a one-dimensional driven-dissipative nonequilibrium condensate. Starting from a stochastic generalized Gross-Pitaevskii equation, we derive a noisy Kuramoto-Sivashinsky equation for the phase dynamics. For sufficiently strong interactions, the coherence decays exponentially in close analogy to the equilibrium Bose gas. When interactions are small on a scale set by the nonequilibrium condition, we find through numerical simulations a crossover between a Gaussian and exponential decay with peculiar scaling of the coherence length on the fluid density and noise strength.

Molecular dynamics simulation of water/BHDC cationic reverse micelles. structural characterization, dynamical properties, and influence of solvent on intermicellar interactions.

We report results obtained from molecular dynamics (MD) experiments of benzylhexadecyldimethylammonium chloride (BHDC) cationic reverse micelles (RMs). In particular we analyzed equilibrium and dynamical characteristics of water/BHDC RMs in pure benzene, at two different water/BHDC ratios (W0 = 5 and W0 = 10). The RMs appear as elliptical aggregates with eccentricities close to ∼0.9. Analysis of the different spatial correlations reveals three different spatial domains in the RMs: a water inner pool, the surfactant interface, and the external solvent. The calculated accessible surface areas for the aqueous inner cores suggest a strong penetration of solvent molecules within the micellar interface domains. Comparison between the density profiles of both RMs shows an increment of the broadness in the distributions of all species at the interface, along with an increasing overlap between the tail segments of the surfactant and benzene molecules as one considers larger micelles. For the dynamical side, the rotational characteristic time scale for the confined water was found to be 1 order of magnitude larger than that of the bulk water. A similar effect was also observed for hydrogen bond dynamics. Both retardation effects diminish with the size of the aggregate. To the estimate the influence of the external solvent on the intermicellar interactions, free energy profiles for the coalescence process between RMs of similar size in pure benzene and in a n-heptane/benzene mixture were also investigated. The results indicate that the association process is facilitated by the presence of n-heptane in the external nonpolar phase. Comparison with previous theoretical and experimental results is also carried out.

Analysis and Characterization of Probability Distribution and Small-Scale Spatial Variability of Rainfall in Singapore Using a Dense Gauge Network

AbstractHourly rainfall measurements from a network of 49 rain gauges on the tropical island of Singapore are analyzed to characterize variability of rainfall for temporal and spatial scales ranging from 1 to 24 h and from 1 to 45 km, respectively. First, the probability distributions of rain rates are characterized using the method ofLmoments. The analysis showed that the Pearson type-3 (PE3) distribution best fitted the rain rates for all time scales of concern. The parameters of the PE3 distribution are found to be related to the time scale through simple power laws. Second, the spatial structure of rainfall is characterized using spatial correlations. The decay of correlations with intergauge distance is parameterized using a powered-exponential function. In general, thee-folding correlation distance (distance at which the correlation drops to 1/e) varied from 10 km at hourly scales to 33 km at daily scales. The study also examined diurnal, seasonal, and anisotropic patterns in the spatial correlation structure of rainfall. The rainfall patterns are smoothest in December and January and are most variable in February, April, and October. Diurnal analysis of spatial correlations showed that the rainfall patterns are smoothest in the early hours between 0100 and 0600 local time and are most variable during the afternoon between 1500 and 1900 local time. The results also showed complex anisotropic patterns in spatial correlations, with considerable dependence of rainfall orientation on spatial scale and the time of the year.

Analysis of spatial correlations between patterns of DNA damage response and DNA replication in nuclei of cells subjected to replication stress or oxidative damage

Sites of DNA replication (EdU incorporation) and DNA damage signaling (γH2AX) induced by camptothecin (Cpt) or hydrogen peroxide (H2O2) form characteristic patterns of foci in cell nuclei. The overlap between these patterns is a function of the number of DNA double strand breaks (DSBs) formed in replication sites. The goal of this study was to optimize a method of quantitative assessment of a degree of correlation between these two patterns. Such a correlation can be used to estimate a probability of inducing damage in sections of replicating DNA. The damage and replication foci are imaged in 3D with confocal microscopy and their respective positions within nuclei are determined with adaptive image segmentation. Using correlation functions spatial proximity of the resultant point patterns is quantified over the range of distances in cells in early-, mid- and late S-phase. As the numbers (and nuclear densities) of γH2AX and replication foci differ significantly in the subsequent substages of S phase, the detected association values were corrected for the expected random overlap between both classes of foci. Thus, the probability of their nonrandom association was estimated. Moreover, self association (clustering) of DNA replication sites in different stages of S-phase of the cell cycle was detected and accounted for. While the analysis revealed a strong correlation between the γH2AX foci and the sites of DNA replication in cells treated with Cpt, only a low correlation was apparent in cells exposed to H2O2. © 2013 International Society for Advancement of Cytometry.

Lagrangian predictability in the DWH region from HF radar observations and model output

Predictability of surface drifter trajectories in the Deep Water Horizon oil spill region is used as a criterion for optimizing the parameters of the 2d variational (2dVar) interpolation of high-frequency radar (HFR) data, and assessing the accuracy of the surface currents' simulations by regional models. It is shown that penalizing the magnitude and enforcing smoothness of the divergence field significantly increases the Lagrangian predictability of the 2dVar output at the forecast times of 3–9 days while preserving it at the shorter forecast times. Applying preliminary gap-filling technique based on the analysis of spatial correlations of the radial velocities adds an extra 1–2% to the 2dVar forecast skill. Comparison of the forecast skills provided by the 2dVar interpolation of the HFR data and the assimilative solutions of the Navy Coastal Ocean Model demonstrates 25–30% better skill of the 2dVar product, indicating potential benefits of assimilating HFR data into regional models.