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Doppler lidar (DL) applications with a focus on turbulence measurements sometimes require measurement settings with a relatively small number of accumulated pulses per ray in order to achieve high sampling rates. Low pulse accumulation comes at the cost of the quality of DL radial velocity estimates and increases the probability of outliers, also referred to as "bad" estimates or noise. Careful filtering is therefore the first important step in a data processing chain that begins with radial velocity measurements as DL output variables and ends with turbulence variables as the target variable after applying an appropriate retrieval method. It is shown that commonly applied filtering techniques have weaknesses in distinguishing between "good" and "bad" estimates with the sensitivity needed for a turbulence retrieval. For that reason, new ways of noise filtering have been explored, taking into account that the DL background noise can differ from generally assumed white noise. It is shown that the introduction of a new coordinate frame for a graphical representation of DL radial velocities from conical scans offers a different perspective on the data when compared to the well-known velocity–azimuth display (VAD) and thus opens up new possibilities for data analysis and filtering. This new way of displaying DL radial velocities builds on the use of a phase-space perspective. Following the mathematical formalism used to explain a harmonic oscillator, the VAD's sinusoidal representation of the DL radial velocities is transformed into a circular arrangement. Using this kind of representation of DL measurements, bad estimates can be identified in two different ways: either in a direct way by singular point detection in subsets of radial velocity data grouped in circular rings or indirectly by localizing circular rings with mostly good radial velocity estimates by means of the autocorrelation function. The improved performance of the new filter techniques compared to conventional approaches is demonstrated through both a direct comparison of unfiltered with filtered datasets and a comparison of retrieved turbulence variables with independent measurements. [ABSTRACT FROM AUTHOR]

Identifying precursor information to rock failure is key to rock dynamic disaster warning. To solve the problem that a single in-situ monitoring method cannot accurately identify the precursors to rock failure, this paper combined a digital image correlation method, thermal infrared radiation and acoustic emission technology to conduct a multi-physical field joint monitoring experiment of granite under three-point bending. The information about strain, thermal infrared and acoustic emission of the rock brittle failure process could be obtained, and the variations in multi-physical field indices of the rock brittle failure process were analysed. Based on the theory of critical slowing-down, the study of the multi-physical field effects of critical slowing-down characteristics in the rock failure process, and further exploration of the precursor characteristics of the brittle failure of rock were undertaken. The results indicate that the strain, infrared, and acoustic emission parameters show sudden changes during brittle failure of granite. The lag step size has little effect on the variance of changes in multi-physical field parameters and exerts a similar influence on the changes in the autocorrelation coefficient curve. The degree of influence of the window length on the variance and autocorrelation coefficient curves of multi-physical field parameters is as follows: infrared field > strain field > acoustic emission field. Compared with the autocorrelation coefficient, the variance parameter is more universal and is better when the critical slowing-down theory is used to investigate the characteristics of multi-physical fields. The four parameters of the strain field variance, infrared field variance, the variance and autocorrelation coefficient for acoustic emission field increase sharply before fracture, representing precursor information to the brittle failure of granite. The four parameters can be used as disaster warning indicators in rock. Multi-mode joint monitoring and a comprehensive analysis of multi-physical information are beneficial to accurately determine the precursor points of rock failure disaster. [ABSTRACT FROM AUTHOR]

In the global challenge of Coronavirus disease 2019 (COVID-19) pandemic, accurate prediction of daily new cases is crucial for epidemic prevention and socioeconomic planning. In contrast to traditional local, one-dimensional time-series data-based infection models, the study introduces an innovative approach by formulating the shortterm prediction problem of new cases in a region as multidimensional, gridded time series for both input and prediction targets. A spatial-temporal depth prediction model for COVID-19 (ConvLSTM) is presented, and further ConvLSTM by integrating historical meteorological factors (Meteor-ConvLSTM) is refined, considering the influence of meteorological factors on the propagation of COVID-19. The correlation between 10 meteorological factors and the dynamic progression of COVID-19 was evaluated, employing spatial analysis techniques (spatial autocorrelation analysis, trend surface analysis, etc.) to describe the spatial and temporal characteristics of the epidemic. Leveraging the original ConvLSTM, an artificial neural network layer is introduced to learn how meteorological factors impact the infection spread, providing a 5-day forecast at a 0.01° × 0.01° pixel resolution. Simulation results using real dataset from the 3.15 outbreak in Shanghai demonstrate the efficacy of Meteor-ConvLSTM, with reduced RMSE of 0.110 and increased R2 of 0.125 (original ConvLSTM: RMSE = 0.702, R2 = 0.567; Meteor-ConvLSTM: RMSE = 0.592, R2 = 0.692), showcasing its utility for investigating the epidemiological characteristics, transmission dynamics, and epidemic development. [ABSTRACT FROM AUTHOR]

In a quantum many-body system, autocorrelation functions can determine linear responses nearby equilibrium and quantum dynamics far from equilibrium. In this letter, we bring out the connection between the operator complexity and the autocorrelation function. In particular, we focus on a particular kind of operator complexity called the Krylov complexity. We find that a set of Lanczos coefficients { b n } computed for determining the Krylov complexity can reveal the universal behaviors of autocorrelations, which are otherwise impossible. When the time axis is scaled by b 1 , different autocorrelation functions obey a universal function form at short time. We further propose a characteristic parameter deduced from { b n } that can largely determine the behavior of autocorrelations at the intermediate time. This parameter can also largely determine whether the autocorrelation function oscillates or monotonically decays in time. We present numerical evidences and physical intuitions to support these universal hypotheses of autocorrelations. We emphasize that these universal behaviors are held across different operators and different physical systems. [ABSTRACT FROM AUTHOR]

This work derives a theoretical value for the entropy of a Linear Additive Markov Process (LAMP), an expressive but simple model able to generate sequences with a given autocorrelation structure. Our research establishes that the theoretical entropy rate of a LAMP model is equivalent to the theoretical entropy rate of the underlying first-order Markov Chain. The LAMP model captures complex relationships and long-range dependencies in data with similar expressibility to a higher-order Markov process. While a higher-order Markov process has a polynomial parameter space, a LAMP model is characterised only by a probability distribution and the transition matrix of an underlying first-order Markov Chain. This surprising result can be explained by the information balance between the additional structure imposed by the next state distribution of the LAMP model, and the additional randomness of each new transition. Understanding the entropy of the LAMP model provides a tool to model complex dependencies in data while retaining useful theoretical results. To emphasise the practical applications, we use the LAMP model to estimate the entropy rate of the LastFM, BrightKite, Wikispeedia and Reuters-21578 datasets. We compare estimates calculated using frequency probability estimates, a first-order Markov model and the LAMP model, also considering two approaches to ensure the transition matrix is irreducible. In most cases the LAMP entropy rates are lower than those of the alternatives, suggesting that LAMP model is better at accommodating structural dependencies in the processes, achieving a more accurate estimate of the true entropy. [ABSTRACT FROM AUTHOR]

Wetlands provide a wide range of ecosystem services; however, little is known about their perception value or use for improving urban planning and wetland management. This study explores the perception values towards the Los Batros Wetland in Chile, by inhabitants from different neighborhood typologies. A sample of 457 responses evaluated the wetland by applying the Kellert framework of 9 biophilic values using a Public Participation Geographic Information Systems (PPGIS) approach. A spatial autocorrelation analysis with hotspot revealed that the spatial distribution of biophilic values varies by neighborhood typology. Subsequent ANOVA and T-test suggest that such distribution is affected by perceived accessibility and visitation purposes, and is influenced by socio-demographic aspects that vary among neighborhood typology. Inhabitants of the garden city typology located next to the wetland area, whose residents have higher education and income levels and who have easy and moderate access to the wetland, agreed with a diverse type of biophilic values. In the garden city, the ecologistic-scientific value has the higher spatial concentration. In the condominium typology, with similar education and income levels and accessibility, but situated far from the wetland, there was less agreement with biophilic values, and these were more dispersed, i.e. biophilic values are less representative. In this case, the aesthetic value prevails over others. At the north area of the neighborhood unit, where residents had lower education and income levels along with moderate to difficult physical access to the wetland, but they developed horticultural activities alongside the wetland, inhabitants agree with various biophilic values, highlighting its symbolic value. Conversely, biophilic values of people in the favela were not in agreement nor disagreement, regardless they have similar physical access and distance to the wetland as residents in the condominium typology, but have the lowest income and education level. Findings suggest that effective wetlands management requires appreciation of the clusters of values assigned to wetland environments, which in this case relate to neighborhood typologies. These clusters should be considered when planning to restore, protect, and improve urban wetlands. [ABSTRACT FROM AUTHOR]

We propose a new time series model called Rational Gaussian Noise (rGn) to describe a pattern of long-range dependence. The rGn model is shown to be an extension of the traditional fractional Gaussian noise (fGn). Theoretical formulas such as autocorrelation function, and some properties of rGn are derived and compared to that of fGn. Transformed S&P500 daily excessive return data is used as a case study where parameters for both the rGn and fGn models are estimated. [ABSTRACT FROM AUTHOR]

Time series forecasting is an important application of many statistical methods. When it is appropriate to assume that the data may be projected towards the future based on the past history of the dataset, a preliminary examination is usually required to ensure that the data sequence is autocorrelated. This is a quite obvious assumption that has to be made and can be the object of a formal test of hypotheses. The most widely used test is the portmanteau test, i.e., a sum of the squared standardized autocorrelations up to an appropriate maximum lag (the truncation point). The choice of the truncation point is not obvious and may be data-driven exploiting supplementary information, e.g. the largest autocorrelation and the lag where such maximum is found. In this paper, we propose a portmanteau test with a truncation point equal to the lag of the largest (absolute value) estimated autocorrelation. Theoretical and simulation-based comparisons based on size and power are performed with competing portmanteau tests, and encouraging results are obtained. [ABSTRACT FROM AUTHOR]

Access to housing data in Ghana has been a challenge for researchers due to the lack of comprehensive data sources. However, the recent availability of big data sources has presented opportunities to bridge this data access gap. Using Greater Accra as a case, this study uses web scraping techniques to acquire publicly available housing data from two major E-commerce websites in Ghana and explores the Greater Accra Metropolitan Area's (GAMA) prevailing housing market. Spatial autocorrelation statistics show clustering of high median prices in known high-class neighborhoods. Median prices in high-class neighborhoods were three to five times higher than median prices in the entire metropolis, highlighting high housing costs in high-class neighborhoods. This research highlights the high housing cost in GAMA, making it impossible for the average resident to afford to buy a house. Hence, a more inclusive housing strategy is needed to provide affordable housing options for all. [ABSTRACT FROM AUTHOR]

Objective: This research aimed to investigate the relationship between gaze behaviour dynamics and operator performance. Background: Individuals differ in their approach when learning a new task often resulting in performance disparity. During training some individuals learn the structure and dynamics of the task and develop a systematic approach, whereas others may achieve the same result albeit with increased perceived workload, or indeed some may fail to achieve superior performance levels. Previous research has shown that comparing gaze of experts with novices can provide unique insights into cognitive functioning of superior performers. Methods: Twenty-five individuals participated in a computer-based simulation task. The concept of coefficient of variation (CoV) of task scores was used to compute the participants' consistency of performance. Based on CoV, the cohort was split into two performance categories. The temporal patterns in participants gaze data were transformed using autocorrelation, and recurrence quantification analysis (RQA) was employed to analyse and quantify the patterns. Results: A Mann–Whitney U analysis demonstrated significantly (p <.01) higher determinism, entropy and laminarity in the superior group compared to the moderate group. Pearson's correlation revealed a significant (p <.01) negative correlation between the consistency of task performance (CoV) and the RQA measures. Conclusion: The results demonstrated that eye gaze dynamics can be used as an objective measure of performance. Participants classified as superior performers consistently demonstrated a systematic gaze activity which were in line with the task structure. Application: The methods presented here are applicable to observe and evaluate operators' strategic distribution of gaze. Specifically, for tactical monitoring and decision making in task environments where spatial locations of elements-of-interest vary continuously. [ABSTRACT FROM AUTHOR]

In this article, we derive the asymptotic distributions of residual and normalized residual empirical autocovariances and autocorrelations of (parsimonious) periodic autoregressive moving‐average (PARMA) models under the assumption that the errors are uncorrelated but not necessarily independent. We then deduce the modified portmanteau statistics. We establish the asymptotic behavior of the proposed statistics. It is shown that the asymptotic distribution of the modified portmanteau tests is that of a weighted sum of independent chi‐squared random variables, which can be different from the usual chi‐squared approximation used under independent and identically distributed assumption on the noise. We also propose another test based on a self‐normalization approach to check the adequacy of PARMA models. A set of Monte Carlo experiments and an application to financial data are presented. [ABSTRACT FROM AUTHOR]

This paper looks at a novel type of seasonality labeled as a seasonal Generalized auto-regressive (GAR) model. The seasonal GAR models are found to be short-memory models, and expressions for the autocorrelation function and large sample results for the parameter estimates are established. Traditional Box-Jenkins seasonality models and Gegenbauer seasonality models are compared with the seasonal GAR model. Finally, the three methods are compared in the analysis of a specific process - the Mauna Loa CO2 data - showing that in this case, the seasonal GAR model provides forecasts with a lower mean squared error. [ABSTRACT FROM AUTHOR]

A new portmanteau test statistic is proposed for detecting nonlinearity in time series data. The new portmanteau statistic is calculated from the log of the determinant of a matrix comprised of the autocorrelations and cross-correlations of the residuals and squared residuals of a fitted time series. The asymptotic distribution of the proposed test statistic is derived as a linear combination of chi-square distributed random variables and can be approximated by a gamma distribution. A bootstrapping approach is shown to be robust when distributional assumptions are relaxed. The efficacy of the statistic is studied against linear and nonlinear dependency structures of some stationary time series models. It is shown that the new test can provide higher power than other tests in many situations. We demonstrate the advantages of the proposed test by investigating linear and nonlinear effects in an economic series and two environmental time series. [ABSTRACT FROM AUTHOR]

This paper deals with some probabilistic and statistical properties of a periodic generalized integer-valued autoregressive PINAR (1) model. The autocovariance structure is studied while establishing the closed-form of the periodic autocorrelation function. The Conditional Least Squares CLS-estimators of the underlying parameters are obtained. In addition, the asymptotic distribution of these estimators is provided, whether the innovation process mean is considered to be known or not, also for the nearly unstable and unstable integer-valued autoregressive PINAR(1) model. The performance of these estimators and their asymptotic distribution behavior are shown via a simulation study. [ABSTRACT FROM AUTHOR]

Aims: Calls to prescribe safer supply hydromorphone (SSHM) as an alternative to the toxic drug supply increased during the COVID-19 pandemic but it is unknown whether prescribing behaviour was altered. We aimed to evaluate how the number of new SSHM dispensations changed during the pandemic in Ontario. Methods: We conducted a retrospective interrupted time-series analysis using provincial administrative databases. We counted new SSHM dispensations in successive 28-day periods from March 22, 2016 to August 30, 2021. We used segmented Poisson regression methods to test for both a change in level and trend of new dispensations before and after March 17, 2020, the date Ontario's pandemic-related emergency was declared. We adjusted the models to account for seasonality and assessed for over-dispersion and residual autocorrelation. We used counterfactual analysis methods to estimate the number of new dispensations attributable to the pandemic. Results: We identified 1489 new SSHM dispensations during the study period (434 [mean of 8 per 28-day period] before and 1055 [mean of 56 per 28-day period] during the pandemic). Median age of individuals initiating SSHM was 40 (interquartile interval 33–48) with 61.7% (N = 919) male sex. Before the pandemic, there was a small trend of increased prescribing (incidence rate ratio [IRR] per period 1.002; 95% confidence interval [95CI] 1.001–1.002; p<0.001), with a change in level (immediate increase) at the pandemic date (relative increase in IRR 1.674; 95CI 1.206–2.322; p = 0.002). The trend during the pandemic was not statistically significant (relative increase in IRR 1.000; 95CI 1.000–1.001; p = 0.251). We estimated 511 (95CI 327–695) new dispensations would not have occurred without the pandemic. Conclusion: The pandemic led to an abrupt increase in SSHM prescribing in Ontario, although the rate of increase was similar before and during the pandemic. The absolute number of individuals who accessed SSHM remained low throughout the pandemic. [ABSTRACT FROM AUTHOR]

Storing solar energy in the subsurface as heat is a promising way for energy storage and conversion, which has a great potential to address the temporal and spatial mismatch between energy demand and supply. Thermal energy storage in deep aquifers can convert intermittent solar energy into stable high temperature geothermal energy. In this study, a new solar energy storage and conversion system is proposed where solar energy is firstly converted into heat using parabolic troughs and then stored in deep aquifers by high temperature hot water circulation. The geostatistical modelling and hydro-thermo coupling simulations are adopted to investigate the feasibility and efficiency of solar energy storage in deep aquifers. Specifically, how rock permeability heterogeneity (in terms of autocorrelation length and global permeability heterogeneity) impacts the temporal and spatial evolution of temperature distribution and storage efficiency is examined. The simulation results indicate that increased horizontal autocorrelation length and global heterogeneity may accelerate thermal breakthrough, deteriorating storage efficiency. High permeability heterogeneity may also lead to high injection pressure. Deep aquifers with small horizontal autocorrelation lengths and low global heterogeneity tend to have higher storage efficiency. These findings may improve our understanding of solar energy storage mechanism in deep aquifers and guide field applications. [ABSTRACT FROM AUTHOR]

This paper aims to improve and extend the improved spatial Moran's I theory by analyzing multi-observation samples. By constructing an expanded spatial weight matrix, a vector definition of the improved spatial Moran's I is given. In order to improve the judgment basis of the improved spatial Moran's I, the range of the improved spatial Moran's I is derived using the non-negativity of variance. Since the improved Moran's I is only applicable to the analysis of a single variable with unknown distribution, a Moran's I matrix suitable for analyzing the spatial autocorrelation of multiple variables is proposed. The distribution of the elements of the Moran's I matrix is studied by Monte Carlo simulation. The simulation results show that only the elements on the non-main diagonal follow a normal distribution when the sample size is small. Any element follows a normal distribution when the sample size is large. Then it is proved that the Moran's I matrix follows a Wishart distribution when the spatial weight matrix is a positive definite matrix. Finally, several comprehensive evaluation indicators suitable for the theory of multivariate spatial autocorrelation are proposed based on the algebraic meaning of the Moran's I matrix. Spatial autocorrelation analysis is carried out in combination with multi-dimensional air pollution data. [ABSTRACT FROM AUTHOR]

Coronavirus (COVID-19) is a type of disease that spread widely during 2020. The transmission of this disease is connected by human activity and hygiene which also means that this disease might be related with population density. The goal of this study is to discover the correlation between COVID-19 transmission with population density. The research area used in this study is East Java Province during 2020. The data used for this study are COVID-19 confirmed cases and population density in 2020 East Java Province from Central Bureau of Statistics. Spatial Autocorrelation and Moran's I Methods used in this study discovered that there are some weak correlations (I=0.247) between COVID-19 cases and population density in East Java Province 2020. There are 9 areas with p value =0.05. These areas are Trenggalek Regency, Ngawi Regency, Madiun City with p=0.05 significance. Ponorogo Regency, Madiun Regency, Magetan Regency, Gresik Regency, Sidoarjo Regency and Surabaya City with p=0,001 significance. This discovery has some similarities with other studies. [ABSTRACT FROM AUTHOR]

Nonlinear models are commonly used for analyzing real-life data such as in medicine, engineering, and economics. To make efficient inferences about model parameter estimations and statistical results in nonlinear regression, assumptions related to error term are needed to be satisfied. Ordinary least squares and some modified least squares methods fail to give efficient parameter estimates when there are the problems of autocorrelation and outlier together in nonlinear regression. In this study, a novel hybrid robust tapering approach called as robust modified two-stage least squares is proposed to overcome the problems for obtaining more efficient parameter estimates in nonlinear regression. Two numerical examples and a comprehensive Monte–Carlo simulation study are given in order to examine the performance of robust modified two-stage least squares. [ABSTRACT FROM AUTHOR]

This paper presents a simplified joint synchronization scheme for the integer carrier frequency offset and physical cell identity using a primary synchronization signal (PSS) in 5G new radio (NR) communication systems. We demonstrate the efficiency of our proposed NR-PSS synchronization scheme by deriving its simplified implementation, which exploits the near-zero autocorrelation feature between cyclically shifted NR-PSS symbols. As a figure of merit, we compute the probability of detection failure of the proposed NR-PSS synchronization scheme and validate its accuracy via simulations. To illustrate the benefits and limitations of the proposed NR-PSS synchronization scheme, we compare it with the conventional NR-PSS synchronization scheme, considering factors such as detection performance and computation complexity. Numerical results indicate that, regardless of the channel environment, the proposed NR-PSS synchronization scheme achieves a significant reduction in arithmetic complexity while maintaining the same detection capability as existing NR-PSS synchronization schemes. [ABSTRACT FROM AUTHOR]

This paper examines the random variation of the residual radius along the length of both naturally corroded and artificially corroded rebars. The rebars are washed to remove rust, and their residual area is measured by the water volume method for every 20-mm long segment of each rebar. Tests are carried out to determine the stationarity or non-stationarity of the stochastic field used to model the residual radius of the specimens. Then, the autocorrelation function, power spectrum and marginal probability distribution function of each specimen are estimated. It is found that the residual radius field of both naturally and artificially corroded rebars with mean corrosion levels below 20% is stationary, while the situation can change as the mean corrosion level increases and concrete cover spalling occurs. The residual radius field along the rebar length of stationary specimens is found to be white noise for both naturally and artificially corroded rebars. The marginal probability distribution function of the residual radius is determined to be skewed and can be fitted with a Beta distribution for naturally corroded rebars and with a Weibull distribution for artificially corroded rebars. This is the first paper studying and comparing both naturally and artificially corroded rebars. [ABSTRACT FROM AUTHOR]

Inference procedures for spatial autocorrelation statistics assume that the underlying configurations of spatial units are fixed. However, sometimes this assumption can be disadvantageous, for example, when analyzing social media posts or moving objects. This article examines for the case of point geometries how a change from fixed to random spatial indexes affects inferences about global Moran's I, a popular spatial autocorrelation measure. Homogeneous and inhomogeneous Matérn and Thomas cluster processes are studied and for each of these processes, 10,000 random point patterns are simulated for investigating three aspects that are key in an inferential context: the null distributions of I when the underlying geometries are varied; the effect of the latter on critical values used to reject null hypotheses; and how the presence of point processes affects the statistical power of Moran's I. The results show that point processes affect all three characteristics. Inferences about spatial structure in relevant application contexts may therefore be different from conventional inferences when this additional source of randomness is taken into account. [ABSTRACT FROM AUTHOR]

Spatial autocorrelation analysis is essential for understanding the distribution patterns of spatial flow data. Existing methods focus mainly on the origins and destinations of flow units and the relationships between them. These methods measure the autocorrelation of gravity or the positional and directional autocorrelations of flow units that are treated as objects. However, the intrinsic complexity of actual flow data necessitates the consideration of not only gravity, positional, and directional autocorrelations but also the autocorrelations of the variables of interest. This study proposes a global spatial autocorrelation method to measure the variables of interest of flow data. This method mainly consists of three steps. First, the proximity constraints of the origin and destination of a flow unit are defined to ensure similarity of flow units in terms of direction, distance, and position. This undertaking aims to determine the neighborhood of flow units and generate their adjacent matrices. Second, a spatial autocorrelation measurement model for flow data is constructed on the basis of the adjacent matrix generated. Artificial data sets are also employed to test the validity of the model. Finally, the proposed method is applied to the flow data analysis of population migration in central and eastern China to prove the practical application value of the model. The proposed method is universal and can be generalized to the global spatial autocorrelation analysis of any type of flow data. [ABSTRACT FROM AUTHOR]

Along with broad applications of the linear Doppler effect, the rotational Doppler effect (RDE) of a structured light source carrying orbital angular momentum (OAM) has attracted significant attention for applications ranging from optical sensors to Doppler cooling. However, the high‐purity structured source's low energy efficiency and unknown optimal OAM parameters have significantly degraded the RDE's performance in previous work. Here, instead of utilizing an optical vortex source, the Doppler features are analyzed in scattered twisted photons carrying OAM with a common light source, and it is demonstrated that the RDE induced by a typical rotator can be extracted using a spiral phase spatial filter (SPSF) at the receiver. This model reveals that a rotating rough surface scatters abundant twisted photons carrying varied OAM values, and the OAM spectrum distribution is modulated by its angular coherence of spatial signature. Furthermore, common surfaces with different autocorrelation structures on received signals using the SPSF method are analyzed theoretically and experimentally. Such a scheme facilitates rotator detection with a generalized nonvortex simple source and dozen–fold improved efficiency with robustness against noncoaxial problems. These demonstrations open a path for studying and applying scattered twisted photons during light detection. [ABSTRACT FROM AUTHOR]

Many applications in science and engineering involve data defined at specific geospatial locations, which are often modeled as random fields. The modeling of a proper correlation function is essential for the probabilistic calibration of the random fields, but traditional methods were developed with the assumption to have observations with evenly spaced data. Available methods dealing with irregularly spaced data generally require either interpolation or computationally expensive solutions. Instead, we propose a simple approach based on least square regression to estimate the autocorrelation function. We first tested our methodology on an artificially produced dataset to assess the performance of our method. The accuracy of the method and its robustness to the level of noise in the data indicate that it is suitable for use in realistic problems. In addition, the methodology was used on a major application, the modeling of animal species connected with zoonotic diseases. Understanding the population dynamics of reservoirs of zoonotic diseases, such as bats, is a crucial first step to predict and prevent potential spillover of deadly viruses like Ebola. Due to the limited data on bats across Africa, their density and migrations can only be studied with probabilistic numerical models based on samples of the ecological bare carrying capacity (K0$$ {K}_0 $$). For this purpose, the bare carrying capacity was modeled as a random field and its statistics calibrated with the available data. The bare carrying capacity of bats was found to be denser in central Africa. This is because climatic and environmental conditions are more suitable for the survival of bats. The proposed methodology for random field calibration was shown to be a promising approach, which can cope with large gaps in data and with complex applications involving large geographical areas and high resolution. [ABSTRACT FROM AUTHOR]

The factors controlling yearly fluctuations in honey production are poorly understood. To evaluate yearly fluctuations in honey production, the coefficient of variation (CV) and the autocorrelation analysis were used to determine the magnitude and periodicity of the fluctuations for seven major nectar-source plants (basswood, black locust, Amur cork tree, white clover, buckwheat, Kamchatka thistle, and horse chestnut) using 15-years of data obtained in Hokkaido, Japan. Regression analysis was conducted to evaluate the relationship between annual honey production and the corresponding weather conditions during the flowering month. The periodic synchronous flowering and fruiting observed in tree species is called masting or masting behavior. A masting model, based on the resource budget, was applied to tree species if periodicity was detected by an autocorrelation analysis. The CV differed markedly among species. Black locust and the Amur cork tree had the smallest and largest CVs, respectively, whereas basswood was periodic. A negative correlation was observed between monthly precipitation and honey production in black locust, white clover, thistle and the Amur cork tree. This result implies that rain inhibits honey bee foraging. Production of buckwheat honey, however, tended to increase with increasing mean temperature. It might be difficult to detect the effect of rain on the honey production using buckwheat presumably because of the long flowering period. Annual honey production in basswood could be described by the resource budget model, but not in horse chestnut. Some horse chestnut trees produce flowers even in nonmast year implying that it was difficult to detect the periodicity in this species. According to beekeepers, honey production in this last decade in Hokkaido has been disrupted by climate change and the transitioning of the industrial structure. [ABSTRACT FROM AUTHOR]

Doppler lidar (DL) applications with a focus on turbulence measurements sometimes require measurement settings with a relatively small number of accumulated pulses per ray in order to achieve high sampling rates. Low pulse accumulation comes at the cost of the accuracy of radial velocity estimates and increases the probability of outliers, also referred to as "bad" estimates. Careful noise filtering is therefore the first important step in the data processing chain from raw data to the retrieved turbulence variable. It is shown that commonly applied filtering techniques have weaknesses in distinguishing between "good" and "bad" estimates with the sensitivity needed for a turbulence retrieval. For that reason, new ways of noise filtering have been explored, taking into account that the DL background noise can differ from generally assumed white noise. It is shown that the introduction of a new coordinate frame for a graphical representation of DL radial velocities from conical scans offers a different perspective onto the data when compared to the well-known velocity-azimuth-display (VAD) and thus opens up new possibilities for data analysis and filtering. This new way of displaying DL radial velocities builds on the use of a phase-space perspective. Following the mathematical formalism used to explain a harmonic oscillator, the VAD's sinusoidal representation of the DL radial velocities is transformed into a circular arrangement. Using this kind of representation of DL measurements, "bad" estimates can be identified in two different ways. Either in a direct way, by singular point detection in subsets of radial velocity data grouped in circular rings, or indirectly, by localizing circular rings with mostly "good" radial velocity estimates by means of the autocorrelation function. The improved performance of the new filter techniques compared to conventional approaches is demonstrated, both through a direct comparison of unfiltered with filtered data sets, and through a comparison of retrieved turbulence variables with independent measurements. [ABSTRACT FROM AUTHOR]

With the wide application of direct sequence spread spectrum (DSSS) signals, the comprehensive performance of DSSS communication systems has been continuously improved, making the electronic reconnaissance link in communication countermeasures more difficult. Electronic reconnaissance technology, as the fundamental means of modern electronic warfare, mainly includes signal detection, recognition, and parameter estimation. At present, research on DSSS detection algorithms is mostly based on the correlation characteristics of DSSS signals, and autocorrelation algorithm is the most mature and widely used method in practical engineering. With the continuous development of deep learning, deep-learning-based methods have gradually been introduced to replace traditional algorithms in the field of signal processing. This paper proposes a spread spectrum signal detection method based on convolutional neural network (CNN). Through experimental analysis, the detection performance of the CNN model proposed in this paper on DSSS signals in various situations has been compared and analyzed with traditional autocorrelation detection methods for different signal-to-noise ratios. The experiments verified the estimation performance of the model in this paper under different signal-to-noise ratios, different spreading code lengths, different spreading code types, and different modulation methods and compared it with the autocorrelation detection algorithm. It was found that the detection performance of the model in this paper was higher than that of the autocorrelation detection method, and the overall performance was improved by 4 dB. [ABSTRACT FROM AUTHOR]

The presence of global spatial autocorrelation usually leads to the spurious identification of spatial hotspots and hinders the identification of local hotspots. Despite the use of statistical methods to address global spatial autocorrelation in spatial hotspot detection, accurately modeling global spatial autocorrelation structure without the stationarity assumption of spatial processes is difficult. To overcome this challenge, we fitted the global spatial autocorrelation structure from a geometric perspective and identified the optimal global spatial autocorrelation structure by analyzing the variances in spatial data. Hotspots were detected from the residuals obtained by removing the global spatial autocorrelation structure from the original dataset. We upgraded a weighted moving average method based on binomial coefficients (Yang Chizhong filtering) to fit the global spatial autocorrelation structure for field-like geographic phenomena. A variance decay indicator, based on the variance in the original and filtered data, was used to identify the optimal global spatial autocorrelation structure. Yang Chizhong filtering does not require a spatial stationarity assumption and can preserve local autocorrelation structures in the residuals as much as possible. Experimental results showed that hotspot detection methods combined with Yang Chizhong filtering can effectively reduce type-I and -II errors in the results and discover implicit and valuable urban hotspots. [ABSTRACT FROM AUTHOR]

Random impulsive noise is a special kind of noise, which has strong impact features and random disturbances with large amplitude, short duration, and long intervals. This type of noise often displays nonGaussianity, while common background noise obeys Gaussian distribution. Hence, random impulsive noise greatly differs from common background noise, which renders many commonly used approaches in bearing fault diagnosis inapplicable. In this work, we explore the challenge of bearing fault detection in the presence of random impulsive noise. To deal with this issue, an improved adaptive multipoint optimal minimum entropy deconvolution (IAMOMED) is introduced. In this IAMOMED, an envelope autocorrelation function is used to automatically estimate the cyclic impulse period instead of setting an approximate period range. Moreover, the target vector in the original MOMED is rearranged to enhance its practical applicability. Finally, particle swarm optimization is employed to determine the optimal filter length for selection purposes. According to these improvements, IAMOMED is more suitable for detecting bearing fault features in the case of random impulsive noise when compared to the original MOMED. The contrast experiments demonstrate that the proposed IAMOMED technique is capable of effectively identifying fault characteristics from the vibration signal with strong random impulsive noise and, in addition, it can accurately diagnose the fault types. Thus, the proposed method provides an alternative fault detection tool for rotating machinery in the presence of random impulsive noise. [ABSTRACT FROM AUTHOR]

The nonlinearity and additive nonlinearity of a function are defined as the Hamming distances, respectively, to the set of all affine mappings and to the set of all mappings having nontrivial additive translators. On the basis of the revealed relation between the nonlinearities and the Fourier coefficients of the characters of a function, convenient formulas for nonlinearity evaluation for practically important classes of functions over an arbitrary finite field are found. In the case of a field of even characteristic, similar results were obtained for the additive nonlinearity in terms of the autocorrelation coefficients. The formulas obtained made it possible to present specific classes of functions with maximal possible and high nonlinearity and additive nonlinearity. [ABSTRACT FROM AUTHOR]

Zika virus (ZIKV) infection became a global public health concern, causing an epidemic in Latin America from 2015 to 2016, when a sudden increase in cases of microcephaly and other congenital anomalies was observed. In 2016, the Centers for Disease Control and Prevention and the World Health Organization defined congenital Zika-associated syndrome (CZS) as a set of congenital anomalies seen in children born to mothers with a history of gestational Zika fever, who have microcephaly as the most prevalent clinical sign. In order to describe the magnitude of CZS in Brazil, this study estimated the burden of disease due to CZS in Brazil using the disability-adjusted life years (DALY) indicator and other frequency measures, such as incidence and mortality rate, during the years 2015–2020. The association of these indicators with socioeconomic variables was also evaluated using Spearman's correlation coefficient. Choropleth maps were used to evaluate the spatial distribution of the indicators evaluated and the spatial autocorrelation was verified by the Bivariate Moran Local Index. From 2015 to 2020, 3,591 cases of CZS were confirmed in Brazil, with an incidence of 44.03 cases per 1000 live births, and a specific mortality of 12.35 deaths per 1000 live births. A global loss of 30,027.44 DALYs was estimated from 2015 to 2020. The Northeast region had the highest values for all health indicators assessed. Spatial correlation and autocorrelation analyses showed significant associations between health and socioeconomic indicators, such as per capita income, Gini index, illiteracy rate and basic sanitation. The study allowed us to have access to all reported cases of CZS, showing us the possible situation of the disease in Brazil; therefore, we believe that our results can help in the understanding of future studies. [ABSTRACT FROM AUTHOR]

Emerging satellite radar and lidar platforms are being developed to produce gridded aboveground biomass (AGB) predictions that are poised to expand our understanding of global carbon stocks and changes. However, the spatial resolution of AGB map products from these platforms is often larger than the available field plot data underpinning model calibration and validation efforts. Intermediate-resolution/extent remotely sensed data, like airborne lidar, can serve as a bridge between small plots and map resolution, but methods are needed to estimate and propagate uncertainties with multiple layers of data. Here, we introduce a workflow to estimate the pixel-level mean and variance in AGB maps by propagating uncertainty from a lidar-based model using small plots, taking into account prediction uncertainty, residual uncertainty, and residual spatial autocorrelation. We apply this workflow to estimate AGB uncertainty at a 100 m map resolution (1 ha pixels) using 0.04 ha field plots from 11 sites across four ecoregions. We compare uncertainty estimates using site-specific models, ecoregion-specific models, and a general model using all sites. The estimated AGB uncertainty for 1 ha pixels increased with mean AGB, reaching 7.8–33.3 Mg ha−1 for site-specific models (one standard deviation), 11.1–28.2 Mg ha−1 for ecoregion-specific models, and 21.1–22.1 Mg ha−1 for the general model for pixels in the AGB range of 80–100 Mg ha−1. Only 3 of 11 site-specific models had a total uncertainty of <15 Mg ha−1 in this biomass range, suitable for the calibration or validation of AGB map products. Using two additional sites with larger field plots, we show that lidar-based models calibrated with larger field plots can substantially reduce 1 ha pixel AGB uncertainty for the same range from 18.2 Mg ha−1 using 0.04 ha plots to 10.9 Mg ha−1 using 0.25 ha plots and 10.1 Mg ha−1 using 1 ha plots. We conclude that the estimated AGB uncertainty from models estimated from small field plots may be unacceptably large, and we recommend coordinated efforts to measure larger field plots as reference data for the calibration or validation of satellite-based map products at landscape scales (≥0.25 ha). [ABSTRACT FROM AUTHOR]

The ever-growing popularity of citizen science, as well as recent technological and digital developments, have allowed the collection of data on species' distributions at an extraordinary rate. In order to take advantage of these data, information of varying quantity and quality needs to be integrated. Point process models have been proposed as an elegant way to achieve this for estimates of species distributions. These models can be fitted efficiently using Bayesian methods based on integrated nested Laplace approximations (INLA) with stochastic partial differential equations (SPDEs). This approach uses an efficient way to model spatial autocorrelation using a Gaussian random field and a triangular mesh over the spatial domain. The mesh is constructed by user-defined variables, so effectively represents a free parameter in the model. However, there is a lack of understanding about how to set these mesh parameters, and their effect on model performance. Here, we assess how mesh parameters affect predictions and model fit to estimate the distribution of the serotine bat, Eptesicus serotinus, in Great Britain. A Bayesian INLA model was fitted using five meshes of varying densities to a dataset comprising both structured observations from a national monitoring programme and opportunistic records. We demonstrate that mesh density impacted spatial predictions with a general loss of accuracy with increasing mesh coarseness. However, we also show that the finest mesh was unable to overcome spatial biases in the data. In addition, the magnitude of the covariate effects differed markedly between meshes. This confirms that mesh parameterisation is an important and delicate process with implications for model inference. We discuss how species distribution modellers might adapt their use of INLA in the light of these findings. [ABSTRACT FROM AUTHOR]

One of the error conditions that are required to be met is the absence of autocorrelation problems. Autocorrelation is a correlation of errors between observations, the existence of an error correlation between observations will cause deviations from the actual statistical value. One of the statistical methods for overcoming autocorrelation is the Prais Winsten method. This study aims to explain the Prais Winsten method in overcoming the problem of autocorrelation on factors that affect the life expectancy of East Java Province in 2018. This research is a secondary data analysis, data obtained from the East Java Province's health profile in 2018 with the dependent variable, namely life expectancy, and independent variables, namely prevalence of diarrhea, clean and healthy living behavior, and mean years of school. The results of this research indicated the finding of autocorrelation problems in the factors that affected the life expectancy of East Java Province in 2018. Improvements with the Prais Winsten method showed that the Durbin Watson value was at the critical point limit, Mean Square Error and coefficient of determination (R2) value was decreasing. This research concludes that the Prais Winsten method can overcome autocorrelation. [ABSTRACT FROM AUTHOR]

Due to their perfect periodic autocorrelation property, Zadoff–Chu sequences are often used as stimulus signals in the measurement of radio channel responses. In this letter, the cross‐correlation of a linear shift‐invariant system's response to a Zadoff–Chu sequence is derived for the case that a multiplicative perturbation such as phase noise is present at its output. The perturbation signal is shown to incur a spurious discrete Fourier transform, thus degrading the correlation's peak to sidelobe ratio. The implications for the performance limits of channel measurement systems are discussed. [ABSTRACT FROM AUTHOR]

Background: Leprosy remains a significant public health problem of high importance. This investigation aims to analyze the spatial distribution of the leprosy epidemiological risk in the municipalities of Minas Gerais. Methods: This ecological study was conducted with new leprosy cases diagnosed from 2004 to 2019 in the municipalities of the state of Minas Gerais. Based on the epidemiological indicators, a composite indicator called the leprosy epidemiological risk index was estimated, classifying municipalities as high, medium, low and very low risk. For the spatial analysis, the global and local spatial autocorrelation statistics were used to identify the spatial distribution of the leprosy epidemiological risk in the periods 2004–2011 and 2012–2019 and classified as High/High, Low/Low, High/Low and Low /High. Results: Although leprosy is declining in the state of Minas Gerais, the Global Moran Index confirmed the spatial dependence between municipalities for the two analyzed periods, characterizing the formation of clusters. When performing the local spatial autocorrelation, it was found that the macroregions with the highest number of municipalities with high indices, surrounded by other municipalities with high indices (high-high), were Northwest, East, South East, North, and Northeast. The low risk macroregions were Southeast, Center, South-Center and South. Conclusion: Leprosy has a heterogeneous spatial pattern and remains concentrated in historically endemic areas of the state. It underscores the importance of intensifying actions to combat leprosy in these municipalities and macroregions. Promote improved access to health services and combat stigma and prejudice to eliminate leprosy as a public health problem. Author summary: Leprosy remains an important public health problem. More than 200,000 cases are still diagnosed yearly, with Brazil in second place regarding the highest number of cases. Studies involving spatial analysis have been used to assist in planning, implementing, monitoring and evaluating health actions to prioritize areas with higher transmission, therefore, presents a higher risk of illness. Thus, this study aimed to analyze the spatial distribution of the epidemiological risk of leprosy in the municipalities of Minas Gerais. The epidemiological risk was estimated from leprosy indicators related to the magnitude of the endemic disease and the strength of morbidity and transmission of the disease. We identified the formation of areas with higher leprosy epidemiological risk in the state, highlighting the macroregions Northwest, Northeast, East, and South East, which were found to have a higher proportion of municipalities at high epidemiological risk throughout the study period. Moreover, the North macroregion gained epidemiological importance in the last analyzed period. The low risk macroregions were Southeast, Central, Center-South and South. Our results provide potential ways to guide the organization of timely epidemiological surveillance practices and reinforce the importance of intensifying actions to combat leprosy in these municipalities. [ABSTRACT FROM AUTHOR]

Multivariate time series anomaly detection has made significant progress and has been studied in many fields. One of the difficulties in time-series data analysis is the complex nonlinear dependencies between multiple time steps and multiple variables. Therefore, detecting anomalies in these data is challenging. Although many studies used classical attention mechanisms to model the temporal patterns of data, few have combined multiple attention mechanisms and analyzed the data's temporal characteristics and feature correlations. Therefore, we propose an autocorrelation and attention mechanism-based anomaly detection (ACAM-AD) framework that combines an autocorrelation model based on the Autoformer model, which is superior to the self-attention mechanism, a multi-head graph attention network, and a dot-product attention mechanism to model the complex dependencies of data considering temporal and feature dimensions. The autoregressive model is parallelized with the neural network, and a sparse autocorrelation mechanism and sparse graph attention network are used to reduce model complexity. Experiments on public datasets show that the model is effective and performs better than the baseline model. [ABSTRACT FROM AUTHOR]

The determination of errors in the runoff probability distribution parameters when estimating them on a heterogeneous sample is considered. For the case that allows using composite curves (or a mixture of distributions), some expressions for errors of the estimates of the mean and the variation coefficient are proposed. It has been found that accounting of the sample heterogeneity based on a probabilistic model of the time series, which allows its examination in the form of two stationary sections without accounting the averaged autocorrelation, magnifies the error of the mean value estimate. It is shown that the overestimation of the first autocorrelation coefficient assessed on the entire series without taking into account inhomogeneity should be considered. [ABSTRACT FROM AUTHOR]

Despite abundant literature studies on the economic impact of foreign direct investment (FDI) on developing countries, few studies have investigated the impact on spatial urbanization. This study explored the spatiotemporal distribution of FDI inflows and its impact on urban land expansion in Vietnam, a transition country in Southeast Asia. While previous research was limited to specific case studies due to data limitations, this study used panel data from provincial-level regions nationwide on FDI inflows and urban land change between 2000 and 2018. The study applied panel and spatial panel models to examine the direct and indirect impact of FDI on urban land growth in the host and neighboring provinces. FDI inflows in Vietnam were most prominent in the manufacturing and real estate sectors and spatially concentrated in the metropolitan regions and coastal areas. The estimated model revealed that an increase in FDI inflows positively affected urban land expansion in the host province and that regions had spatial autocorrelation and were spatially grouped. On the other hand, it negatively affected urban land expansion in neighboring provinces. The results showed that the positive effect of FDI was significant in provinces with high-rank FDI inflows compared to surrounding areas. However, in some provinces with relatively small inflows of FDI, the negative indirect effect is greater than the direct effect, and urban land expansion may decrease due to FDI into neighboring provinces. This result implies that FDI inflows have influenced deepening regional disparities in urban growth. It needs an institutional response for regional management of the economic and environmental spillover effects of FDI inflows. [ABSTRACT FROM AUTHOR]

Apart from quick detection of abnormal changes in a multivariate process, it is also critical to accurately identify the factors of abnormal changes after an out-of-control signal in multivariate statistical process control. Some diagnostic methods for fault identification were proposed by scholars on which one of the basic assumptions is that the process data are independent. The independent assumption is reasonable for many applications. However, with the development of industrial automation, the process data usually meet the phenomenon of autocorrelation. As it is well known, the autocorrelation affects detection ability of control chart. It is a natural question that is whether the autocorrelation affects the diagnostic performance of the diagnostic procedures. In the article, we propose the method adopted the analogy residual sequences and compare it with the performance of existing diagnostic methods for different shift sizes and various autocorrelation and cross-correlation structures. To limit the complexity, our discussion employs a first-order vector autoregressive process and focuses mainly on bivariate data in this article. The results of the simulation show the performances of diagnostic procedures were affected by autocorrelation and the impacts become greater as autocorrelation increases. On the other hand, the proposed method in the article can be free from the impacts of autocorrelation. Finally, a real example is also presented to demonstrate the implementation of the proposed method. [ABSTRACT FROM AUTHOR]

Based on the theoretical knowledge of technological innovation, this paper designs a sensor data value incubation model and a sensor data collection model to collect the original data on green innovation efficiency. In order to explore the spatial and temporal differentiation characteristics of China's provincial green innovation efficiency and the spatial spillover effect of China's provincial green innovation efficiency, the entropy weight TOPSIS model is adopted to measure the green innovation efficiency of China's 30 provinces (cities) from 2005 to 2021 and analyze its temporal and spatial evolution characteristics. This paper uses an exploratory spatial data analysis method to prove the agglomeration phenomenon of China's provincial green innovation efficiency in the ground space. Finally, a spatial econometric model is introduced to study the impact of government R&D investment, green supervision, and green innovation efficiency on spatial spillover effects. The study found the following three conclusions: First, the efficiency of green innovation in China's provinces has been fluctuating in stages, and the overall trend has been increasing year by year. The overall efficiency of green innovation in China's provinces is low, and the overall development is uneven and uncoordinated. Second, from the results of the spatial autocorrelation test, there is a clear and positive spatial correlation between China's provincial green innovation efficiency and agglomeration in the geospatial space. Third, government R&D inputs and different types of green regulations have a significant impact on green innovation efficiency and have significant spatial spillover effects. Along from the eastern region to the central region, then to the northeast and western regions, the degree of effect on green innovation efficiency and the intensity of spatial spillover effects have gradually weakened. It is believed that the government should reduce R&D investment in the eastern region and increase green sewage charges. Instead, the government should raise R&D investment in the central, northeast, and western regions and offer enterprises green innovation subsidies. [ABSTRACT FROM AUTHOR]

Background: Millions of dollars have been spent in fighting malaria in Namibia. However, malaria remains a major public health concern in Namibia, mostly in Kavango West and East, Ohangwena and Zambezi region. The primary goal of this study was to fit a spatio-temporal model that profiles spatial variation in malaria risk areas and investigate possible associations between disease risk and environmental factors at the constituency level in highly risk northern regions of Namibia. Methods: Malaria data, climatic data, and population data were merged and Global spatial autocorrelation statistics (Moran's I) was used to detect the spatial autocorrelation of malaria cases while malaria occurrence clusters were identified using local Moran statistics. A hierarchical Bayesian CAR model (Besag, York and Mollie's model "BYM") known to be the best model for modelling the spatial and temporal effects was then fitted to examine climatic factors that might explain spatial/temporal variation of malaria infection in Namibia. Results: Average rainfall received on an annual basis and maximum temperature were found to have a significant spatial and temporal variation on malaria infection. Every mm increase in annual rainfall in a specific constituency in each year increases annual mean malaria cases by 0.6%, same to average maximum temperature. The posterior means of the time main effect (year t) showed a visible slightly increase in global trend from 2018 to 2020. Conclusion: The study discovered that the spatial temporal model with both random and fixed effects best fit the model, which demonstrated a strong spatial and temporal heterogeneity distribution of malaria cases (spatial pattern) with high risk in most of the Kavango West and East outskirt constituencies, posterior relative risk (RR: 1.57 to 1.78). [ABSTRACT FROM AUTHOR]

Urban green space takes a dominant role in alleviating the urban heat island (UHI) effect. Most investigations into the effects of cooling factors from urban green spaces on the UHI have evaluated the correlation between each factor and land surface temperature (LST) separately, and the contribution weights of various typical cooling factors in mitigating the thermal environment have rarely been analyzed. For this research, three periods of Landsat 8 data captured between 2014 and 2018 of Xuzhou during the summer and autumn seasons were selected along with corresponding meteorological and flux measurements. The mono-window method was employed to retrieve LST. Based on the characteristics of the vegetation and spatial features of the green space, eight factors related to green space were selected and computed, consisting of three indices that measure vegetation and five metrics that evaluate landscape patterns: vegetation density (VD), evapotranspiration (ET), green space shading degree (GSSD), patch area ratio (PLAND), largest patch index (LPI), patch natural connectivity (COHESION), patch aggregation (AI), and patch mean shape index distribution (SHPAE_MN). Linear regression and bivariate spatial autocorrelation analyses between each green space factor and LST showed that there were significant negative linear and spatial correlations between all factors and LST, which proved that the eight factors were all cooling factors. In addition, LST was strongly correlated with all factors (|r| > 0.5) except for SHPAE_MN, which was moderately correlated (0.3 < |r| < 0.5). Based on this, two principal components were extracted by applying principal component analysis with all standardized green space factors as the original variables. To determine the contribution weight of each green space factor in mitigating the urban heat island (UHI) effect, we multiplied the influence coefficient matrix of the initial variables with the standardized multiple linear regression coefficients between the two principal component variables and LST. The final results indicated that the vegetation indices of green space contribute more to the alleviation of the UHI than its landscape pattern metrics, and the contribution weights are ranked as VD ≥ ET > GSSD > PLAND ≈ LPI > COHESION > AI > SHAPE_MN. Our study suggests that increasing vegetation density is preferred in urban planning to mitigate urban thermal environment, and increasing broadleaf forests with high evapotranspiration and shade levels in urban greening is also an effective way to reduce ambient temperature. For urban green space planning, a priority is to multiply the regional green space proportion or the area of largest patches. Second, improving the connectivity or aggregation among patches of green space can enhance their ability to cool the surrounding environment. Altering the green space spatial shape is likely the least significant factor to consider. [ABSTRACT FROM AUTHOR]

The average S-wave velocity (VS) in the upper 30 m (V S 30) is a proxy for seismic wave amplification. Microtremor array exploration is one of the available methods for site characterization, but the recorded data require complicated processing that can lead to different estimations of V S 30 depending on the analyst and processing software. We propose a method of estimating V S 30 by using derivatives obtained in the early stages of microtremor array data processing. Statistical analysis with 2376 virtually generated subsurface VS structure models revealed that the frequencies at which the spatial-autocorrelation (SPAC) coefficients and centreless circular array (CCA) coefficients take specific values strongly correlate with V S 30, which we used to develop formulas for estimating V S 30. Numerical validations using actual VS profiles at 616 sites in Japan showed that the proposed method could estimate V S 30 with a root-mean-square deviation (RMSD) of 57–80 m/s with SPAC coefficients and 56m/s with CCA coefficients. Our proposed methods were applicable for 98–100 per cent of the VS profiles when we limited our estimation to sites with V S 30 < 760 m/s. The results indicated that SPAC coefficients from arrays with radii of 8–20 m can be used for V S 30 estimation and are less affected by incoherent noise. In contrast, CCA coefficients are much more sensitive to incoherent noise, which resulted in the overestimation of V S 30. The estimated V S 30 values from the recorded microtremor array data were in good agreement with the reference values from the actual VS profiles. The proposed method allows for robust and efficient V S 30 estimation without relying on the analyst's skills or software. [ABSTRACT FROM AUTHOR]

Spatial regression models have recently received a lot of attention in a variety of fields to address the spatial autocorrelation effect. One important class of spatial models is the Conditional Autoregressive (CA). Theses models have been widely used to analyze spatial data in various areas, as geography, epidemiology, disease surveillance, civilian planning, mapping of poorness signals and others. In this article, we propose the Liu-type pretest, shrinkage and positive shrinkages estimators for the large-scale effect parameter vector of the CA regression model. The set of the proposed estimators are evaluated analytically via their asymptotic bias, quadratic bias, the asymptotic quadratic risks, and numerically via their relative mean squared errors. Our results demonstrate that the proposed estimators are more efficient than Liu-type estimator. To conclude this paper, we apply the proposed estimators to the Boston housing prices data, and applied a bootstrapping technique to evaluate the estimators based on their mean squared prediction error. [ABSTRACT FROM AUTHOR]

Passive seismic methods in highly populated urban areas have gained much attention from the geophysics and civil engineering communities. Linear arrays are usually deployed for passive surface wave investigations because of their high convenience, and passive surface wave imaging methods commonly used for linear arrays can be grouped as non-interferometric methods (e.g. passive multichannel analysis of surface wave, refraction microtremor) and interferometric methods (e.g. multichannel analysis of passive surface waves and spatial autocorrelation). It is well known that the seismic interferometry method is able to retrieve Green's function between inter-station pairs based on passive seismic data and that is how interferometric methods work. Although non-interferometric methods are also popular and effective in near-surface seismic imaging, particularly in the geotechnical industry, there is no theoretical proof to clarify the accuracy and/or the bias of these methods. In this study, we use numerical derivations and simulations to demonstrate the underlying physics for both non-interferometric and interferometric methods, under two common noise source environments including a homogeneous source distribution and a dominant in-line source distribution. We also prove the strength of interferometric methods for accurate dispersion imaging over the non-interferometric methods, and provide a way to estimate the biases in non-interferometric measurements. Finally, we present comprehensive comparisons between different passive surface wave methods with three typical field examples considering various observation systems. [ABSTRACT FROM AUTHOR]

The current study investigates the effect of within‐profile and between‐profile autocorrelations on the performance of four monitoring methods of simple linear profiles in Phase II. To this end, a general correlation model between error terms is considered such that the correlation structure of within‐profiles errors and the error terms between consecutive profiles follow an autoregressive (AR) times series model of order one. Extensive simulations have been done to assess the effect of both autocorrelation types and the profile size on the estimations of the model parameters as well as the performance of control charts. The performance of the monitoring schemes is investigated and compared with respect to the average run length (ARL) metric. The simulation study results show that the autocorrelation within and between profiles has a negative impact on the monitoring technique's performance. It reduces the control chart's ability to detect process shifts compared to no or weak autocorrelation cases. Moreover, the performance of all the methods is improved by increasing the profile size. An illustrative example is also provided to demonstrate the use of the proposed methods for monitoring the stability of profiles in the chemical industry. [ABSTRACT FROM AUTHOR]

In this article, we propose a two-stage LASSO estimation approach for the estimation of a full spatial weight matrix of spatiotemporal autoregressive models. In addition, we allow for an unknown number of structural breaks in the local means of each spatial location. These locally varying mean levels, however, can easily be mistaken as spatial dependence and vice versa. Thus, the proposed approach jointly estimates the spatial dependence, all structural breaks, and the local mean levels. For selection of the penalty parameter, we propose a completely new selection criterion based on the distance between the empirical spatial autocorrelation and the spatial dependence estimated in the model. Through simulation studies, we will show the finite-sample performance of the estimators and provide practical guidance as to when the approach could be applied. Finally, the method will be illustrated by an empirical example of intra-city monthly real-estate prices in Berlin between 1995 and 2014. The spatial units will be defined by the respective postal codes. The new approach allows us to estimate local mean levels and quantify the deviation of the observed prices from these levels due to spatial spillover effects. In doing so, the entire spatial dependence structure is estimated on a data-driven basis. for this article are available online. [ABSTRACT FROM AUTHOR]