A cluster-optimizing regression-based approach for precipitation spatial downscaling in mountainous terrain

Precipitation temporal and spatial variability often controls terrestrial hydrological processes and states. Common remote-sensing and modeling precipitation products have a spatial resolution that is often too coarse to reveal hydrologically important spatial variability. A statistical algorithm was developed for downscaling low-resolution spatial precipitation fields. This algorithm auto-searches precipitation spatial structures (rain-pixel clusters), and orographic effects on precipitation distribution without prior knowledge of atmospheric setting. It is composed of three components: rain-pixel clustering, multivariate regression, and random cascade. The only required input data for the downscaling algorithm are coarse-pixel precipitation map and a topographic map. The algorithm was demonstrated with 4 km × 4 km Next Generation Radar (NEXRAD) precipitation fields, and tested by downscaling NEXRAD-aggregated 16 km × 16 km precipitation fields to 4 km × 4 km pixel precipitation, which was then compared to the original NEXRAD data. The demonstration and testing were performed at both daily and hourly temporal resolutions for the northern New Mexico mountainous terrain and the central Texas Hill Country. The algorithm downscaled daily precipitation fields are in good agreement with the original 4 km × 4 km NEXRAD precipitation, as measured by precipitation spatial structures and the statistics between the downscaling and the original NEXRAD precipitation maps. For three daily precipitation events, downscaled precipitation map reproduces precipitation variance of the disaggregation field, and with Pearson correlation coefficients between the downscaled map and the NEXRAD map of 0.65, 0.71, and 0.80. The algorithm does not perform as well on downscaling hourly precipitation fields at the examined scale range (from 16 km to 4 km), which underestimates precipitation variance of the disaggregation field. For a scale range from 4 km to 1 km, the algorithm has potential to perform well at both daily and hourly precipitation fields, indicated from good regression performance.     

Local spatial regression models: a comparative analysis on soil contamination

Spatial data analysis focuses on both attribute and locational information. Local analyses deal with differences across space whereas global analyses deal with similarities across space. This paper addresses an experimental comparative study to analyse the spatial data by some weighted local regression models. Five local regression models have been developed and their estimation capacities have been evaluated. The experimental studies showed that integration of objective function based fuzzy clustering to geostatistics provides some accurate and general models structures. In particular, the estimation performance of the model established by combining the extended fuzzy clustering algorithm and standard regional dependence function is higher than that of the other regression models. Finally, it could be suggested that the hybrid regression models developed by combining soft computing and geostatistics could be used in spatial data analysis.     

Exploring the spatial and temporal relationships between mosquito population dynamics and dengue outbreaks based on climatic factors

Identifying the impact of climatic factors on mosquito population dynamics is of great importance for dengue outbreak control. The purpose of this study is to develop an approach to predict spatial/temporal mosquito reproduction and disease outbreaks. The prediction of a dengue outbreak is only possible if the temporal relationship between mosquito replication and the weather is known. At present, this is unclear and needs to be examined. Moreover, because the development of mosquito density is a dynamic process in the course of time, it should be observed as closely as possible, in this study in a 1-day timeframe. This paper makes a thorough study of the situation in southern Taiwan and analyzes a large amount of data from 1999 to 2004 related to dengue cases and larval density. We first use the method, k-means, to conduct data clustering and derive representative larvae replication patterns. Then, we propose mathematical models to approximate the development of larval density, describe the expansion of mosquito activity areas, and construct a surveillance system to raise alerts based on real-time input of weather data and larval indices. Analysis of historic data reveals some new information on the spatial and temporal relationships between larval density and dengue outbreaks. In Taiwan, if the weather becomes or remains warm and humid for 6?days after a bout of rain, there can be a sharp increase in the larval mosquito population. About 7?days after the Breteau index begins to rise, larval density reaches its climax; and, about 12?days after the climax of larval density, cases of dengue may be reported. The system is tested using subsequent data from 2005 to 2009 and shows satisfactory accuracy. Numerous data support these findings, and this new knowledge is thus validated and can be used to assist public health professionals to take effective dengue control measures.     

A spectral approach to estimation and smoothing of continuous spatial processes

This paper is concerned with developing computational methods and approximations for maximum likelihood estimation and minimum mean square error smoothing of irregularly observed two-dimensional stationary spatial processes. The approximations are based on various Fourier expansions of the covariance function of the spatial process, expressed in terms of the inverse discrete Fourier transform of the spectral density function of the underlying spatial process. We assume that the underlying spatial process is governed by elliptic stochastic partial differential equations (SPDE's) driven by a Gaussian white noise process. SPDE's have often been used to model the underlying physical phenomenon and the elliptic SPDE's are generally associated with steady-state problems.A central problem in estimation of underlying model parameters is to identify the covariance function of the process. The cumbersome exact analytical calculation of the covariance function by inverting the spectral density function of the process, has commonly been used in the literature. The present work develops various Fourier approximations for the covariance function of the underlying process which are in easily computable form and allow easy application of Newton-type algorithms for maximum likelihood estimation of the model parameters. This work also develops an iterative search algorithm which combines the Gauss-Newton algorithm and a type of generalized expectation-maximization (EM) algorithm, namely expectation-conditional maximization (ECM) algorithm, for maximum likelihood estimation of the parameters.We analyze the accuracy of the covariance function approximations for the spatial autoregressive-moving average (ARMA) models analyzed in Vecchia (1988) and illustrate the performance of our iterative search algorithm in obtaining the maximum likelihood estimation of the model parameters on simulated and actual data.     

An information-fusion method to identify pattern of spatial heterogeneity for improving the accuracy of estimation

While spatial autocorrelation is used in spatial sampling survey to improve the precision of the feature’s estimate of a certain population at area units, spatial heterogeneity as the stratification frame in survey also often have a considerable effect upon the precision. Under the context of increasingly enriched spatiotemporal data, this paper suggests an information-fusion method to identify pattern of spatial heterogeneity, which can be used as an informative stratification for improving the estimation accuracy. Data mining is major analysis components in our method: multivariate statistics, association analysis, decision tree and rough set are used in data filter, identification of contributing factors, and examination of relationship; classification and clustering are used to identify pattern of spatial heterogeneity using the auxiliary variables relevant to the goal and thus to stratify the samples. These methods are illustrated and examined in the case study of the cultivable land survey in Shandong Province in China. Different from many stratification schemes which just uses the goal variable to stratify which is too simplified, information from multiple sources can be fused to identify pattern of spatial heterogeneity, thus stratifying samples at geographical units as an informative polygon map, and thereby to increase the precision of estimates in sampling survey, as demonstrated in our case research.     

Goodness-of-fit tests for the spatial spectral density

Detection and modeling the spatial correlation is an important issue in spatial data analysis. We extend in this work two different goodness-of-fit testing techniques for the spatial spectral density. The first approach is based on a smoothed version of the ratio between the periodogram and a parametric estimator of the spectral density. The second one is a generalized likelihood ratio test statistic, based on the log-periodogram representation as the response variable in a regression model. As a particular case, we provide tests for independence. Asymptotic normal distribution of both statistics is obtained, under the null hypothesis. For the application in practice, a resampling procedure for calibrating these tests is also given. The performance of the method is checked by a simulation study. Application to real data is also provided.     

Spatial mode estimation for functional random fields with application to bioturbation problem

This work provides a useful tool to study the effects of bioturbation on the distribution of oxygen within sediments. We propose here heterogeneity measurements based on functional spatial mode. To obtain the mode, one usually needs to estimate the spatial probability density. The approach considered here consists in looking each observation as a curve that represents the history of the oxygen concentration at a fixed pixel.     

Ground motion spatial correlation fitting methods and estimation uncertainty

Ground shaking intensity varies spatially in earthquakes, and many studies have estimated correlations of intensity from past earthquake data. This paper presents a framework for quantifying uncertainty in the estimation of correlations and true variability in correlations from earthquake to earthquake. A procedure for evaluating estimation uncertainty is proposed and used to evaluate several methods that have been used in past studies to estimate correlations. The results indicate that a weighted least squares algorithm is most effective in estimating spatial correlation models and that earthquakes with at least 100 recordings are needed to produce informative earthquake-specific estimates of spatial correlations. The proposed procedure is also used to distinguish between estimation uncertainty and the true variability in model parameters that exist in a given data set. The estimation uncertainty is seen to vary between well-recorded and poorly recorded earthquakes, whereas the true variability is more stable.     

Non-linear optimal multivariate spatial design using spatial vine copulas

A multivariate spatial sampling design that uses spatial vine copulas is presented that aims to simultaneously reduce the prediction uncertainty of multiple variables by selecting additional sampling locations based on the multivariate relationship between variables, the spatial configuration of existing locations and the values of the observations at those locations. Novel aspects of the methodology include the development of optimal designs that use spatial vine copulas to estimate prediction uncertainty and, additionally, use transformation methods for dimension reduction to model multivariate spatial dependence. Spatial vine copulas capture non-linear spatial dependence within variables, whilst a chained transformation that uses non-linear principal component analysis captures the non-linear multivariate dependence between variables. The proposed design methodology is applied to two environmental case studies. Performance of the proposed methodology is evaluated through partial redesigns of the original spatial designs. The first application is a soil contamination example that demonstrates the ability of the proposed methodology to address spatial non-linearity in the data. The second application is a forest biomass study that highlights the strength of the methodology in incorporating non-linear multivariate dependence into the design.     

Economic growth and pollutant emissions in China: a spatial econometric analysis

This paper studies the emissions of SO₂ and COD in China using fine-scale, countylevel data. Using a widely used spatial autocorrelation index, Moran’s I statistics, we first estimate the spatial autocorrelations of SO₂ and COD emissions. Distinct patterns of spatial concentration are identified. To investigate the driving forces of emissions, we then use spatial econometric models, including a spatial error model (SEM) and a spatial lag model (SLM), to evaluate the effects of variables that reflect level of economic development, population density, and industrial structure. Our results show that these explanatory variables are highly correlated with the level of SO₂ and COD emissions, though their impacts on SO₂ and COD vary. Compared to ordinary least square regression, the advantages of SLM and SEM are demonstrated as they effectively reveal the existence and significance of spatial dependence. The SEM, in particular, is chosen over the SLM as the role of spatial correlation is stronger in the error model than in the lag model. Based on the research results, we present some preliminary policy recommendations, especially for those high–high cluster regions that face significant environmental degradation and challenge.     

中国地震科学实验场地震时空演变规律研究

通过分析某一区域地震事件的时空演化过程可以了解该区域地震的演化特征,为评估该地区地震的危险性提供依据。基于中国地震科学实验场2000年至2019年3.0级以上的地震事件数据,利用加权平均中心、标准差椭圆和全局空间自相关等空间统计学方法探索该地区地震事件的时空演变规律。结果表明:（1）汶川地震之后该地区地震的发生频次总体呈现出下降趋势,地震的活动性逐渐减弱。（2）地震加权平均中心呈现出"折返"型的移动规律,在东北-西南方向上来回震荡。（3）地震事件的空间分布呈现"东北-西南"格局走向,与映秀-北川断裂带的方向基本一致。（4）该地区地震事件的空间分布模式以聚集模式为主,且正处于上升阶段,但上升速度较为缓慢。     

高密度空间采样地震数据特点分析

中国陆相沉积盆地的地质结构复杂,储层岩性的多变,需要有高精度的勘探方法。高密度空间采样是提高地震勘探精度的一项新技术。本文简要说明了点激发和点接收技术,分析高密度空间采样的野外工作方法,介绍了Gijs j．o．Vermeer提出的对称采样原理,从波场连续性的角度探讨了高密度空间采样技术。重点分析高密度空间采样数据的特点, 即:高密集的初至波有利于近地表结构的调查,可提高静校正的精度;小偏移距、小点距接收增加了浅层的有效覆盖次数,提高浅层反射的成像精度; 对规则噪声可实现无假频采样,室内的噪声分析和噪声压制的精度随着提高;高密度采集使波场连续性增强,使得各种数学变换的精度提高,有利于不同波场的分离。最后指出高密度空间采样地震勘探技术的难点在于室内数据处理,针对海量数据的分析和处理方法还需要进行深入细致的研究工作。     

Representing spatial dependence and spatial discontinuity in ecological epidemiology: a scale mixture approach

Variation in disease risk underlying observed disease counts is increasingly a focus for Bayesian spatial modelling, including applications in spatial data mining. Bayesian analysis of spatial data, whether for disease or other types of event, often employs a conditionally autoregressive prior, which can express spatial dependence commonly present in underlying risks or rates. Such conditionally autoregressive priors typically assume a normal density and uniform local smoothing for underlying risks. However, normality assumptions may be affected or distorted by heteroscedasticity or spatial outliers. It is also desirable that spatial disease models represent variation that is not attributable to spatial dependence. A spatial prior representing spatial heteroscedasticity within a model accommodating both spatial and non-spatial variation is therefore proposed. Illustrative applications are to human TB incidence. A simulation example is based on mainland US states, while a real data application considers TB incidence in 326 English local authorities.     

Automatic modal parameter identification of high arch dams: feasibility verification

Modal parameters, including fundamental frequencies, damping ratios, and mode shapes, could be used to evaluate the health condition of structures. Automatic modal parameter identification, which plays an essential role in realtime structural health monitoring, has become a popular topic in recent years. In this study, an automatic modal parameter identification procedure for high arch dams is proposed. The proposed procedure is implemented by combining the density-based spatial clustering of applications with noise (DBSCAN) algorithm and the stochastic subspace identification (SSI). The 210-m-high Dagangshan Dam is investigated as an example to verify the feasibility of the procedure. The results show that the DBSCAN algorithm is robust enough to interpret the stabilization diagram from SSI and may avoid outline modes. This leads to the proposed procedure obtaining a better performance than the partitioned clustering and hierarchical clustering algorithms. In addition, the errors of the identified frequencies of the arch dam are within 4%, and the identified mode shapes are in agreement with those obtained from the finite element model, which implies that the proposed procedure is accurate enough to use in modal parameter identification. The procedure is feasible for online modal parameter identification and modal tracking of arch dams.

     

The limit properties of spatial coherence of seismic ground motion

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Spatio-temporal epidemic type aftershock sequence model for Tangshan aftershock sequence

Shallow earthquakes usually show obvious spatio-temporal clustering patterns. In this study, several spatio-temporal point process models are applied to investigate the clustering characteristics of the well-known Tangshan sequence based on classical empirical laws and a few assumptions. The relative fit of competing models is compared by Akaike Information Criterion. The spatial clustering pattern is well characterized by the model which gives the best fit to the data. A simulated aftershock sequence is generated by thinning algorithm and compared with the real seismicity.     

空间域位场延拓新方法研究

在空间域进行位场延拓,需要数值求解第一类Fredholm积分方程,由于所得方程组系数矩阵不是稀疏矩阵,求解该方程组需要的计算机内存大,计算量大,导致延拓算法在一般计算机上难以实现,阻碍了对空间域位场延拓方法的研究.在分析系数矩阵结构特征的基础上,本文证明了方程组系数矩阵是对称的分块Toeplitz型矩阵.利用系数矩阵的对称性和分块Toeplitz型矩阵与向量相乘的快速算法,解决了系数矩阵的存储和计算问题,使得空间域位场延拓成为可能,为研究新的位场延拓方法和分析延拓误差提供了一条新的途径.利用模型数据和实测资料,对空间域位场向上延拓、空间域积分迭代法向下延拓进行了检验,结果证实了空间域位场延拓的可行性和正确性.     

Application of digital image processing to the determination of spatial distribution of earthquakes

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Estimation of radar-rainfall error spatial correlation

The study presents a theoretical framework for estimating the radar-rainfall error spatial correlation (ESC) using data from relatively dense rain gauge networks. The error is defined as the difference between the radar estimate and the corresponding true areal rainfall. The method is analogous to the error variance separation that corrects the error variance of a radar-rainfall product for gauge representativeness errors. The study demonstrates the necessity to consider the area–point uncertainties while estimating the spatial correlation structure in the radar-rainfall errors. To validate the method, the authors conduct a Monte Carlo simulation experiment with synthetic fields with known error spatial correlation structure. These tests reveal that the proposed method, which accounts for the area–point distortions in the estimation of radar-rainfall ESC, performs very effectively. The authors then apply the method to estimate the ESC of the National Weather Service’s standard hourly radar-rainfall products, known as digital precipitation arrays (DPA). Data from the Oklahoma Micronet rain gauge network (with the grid step of about 5 km) are used as the ground reference for the DPAs. This application shows that the radar-rainfall errors are spatially correlated with a correlation distance of about 20 km. The results also demonstrate that the spatial correlations of radar–gauge differences are considerably underestimated, especially at small distances, as the area–point uncertainties are ignored.     

A methodology for quantifying the value of spatial information for dynamic Earth problems

We develop a methodology for assessing the value of information (VOI) from spatial data for groundwater decisions. Two sources of uncertainty are the focus of this VOI methodology: the spatial heterogeneity (how it influences the hydrogeologic response of interest) and the reliability of geophysical data (how they provide information about the spatial heterogeneity). An existing groundwater situation motivates and in turn determines the scope of this research. The objectives of this work are to (1) represent the uncertainty of the dynamic hydrogeologic response due to spatial heterogeneity, (2) provide a quantitative measure for how well a particular information reveals this heterogeneity (the uncertainty of the information) and (3) use both of these to propose a VOI workflow for spatial decisions and spatial data. The uncertainty of the hydraulic response is calculated using many Earth models that are consistent with the a priori geologic information. The information uncertainty is achieved quantitatively through Monte Carlo integration and geostatistical simulation. Two VOI results are calculated which demonstrate that a higher VOI occurs when the geophysical attribute (the data) better discriminates between geological indicators. Although geophysical data can only indirectly measure static properties that may influence the dynamic response, this transferable methodology provides a framework to estimate the value of spatial data given a particular decision scenario.