Combinatorial analysis on spatial information statistics for the karst water environment in Guiyang,China

The karst groundwater system is extremely vulnerable and easily contaminated by human activities.To understand the spatial distribution of contaminants in the groundwater of karst urban areas and contributors to the contamination,this paper employs the spatial information statistics analysis theory and method to analyze the karst groundwater environment in Guiyang City.Based on the karst ground water quality data detected in 61 detection points of the research area in the last three years,we made Kriging evaluation isoline map with some ions in the karst groundwater,such as SO4 2-,Fe 3+,Mn 2+and F -,analyzed and evaluated the spatial distribution,extension and variation of four types of ions on the basis of this isoline map.The results of the analysis show that the anomaly areas of SO4 2-,Fe 3+,Mn 2+,Fand other ions are mainly located in Baba’ao,Mawangmiao and Sanqiao in northwestern Gui- yang City as well as in its downtown area by reasons of the original non-point source pollution and the contamination caused by human activities(industrial and domestic pollution).

Spatial Statistics and Age Structure of Leaf Cutting Ant Nests

In this study, we investigated the spatial aggregation of old and incipient nests of Atta sexdens rubropilosa by fitting Poisson and Negative binomial models to nest abundance data. Our aim is to analyse the distribution of ant nests in eucalypt regrowth, Cerrado and native forest fragment. We also investigated the correlation between nest abundance and climatic factors, as well as different nest ages. When comparing nests of different ages we observed an aggregated pattern for both old and incipient nests. On the other hand, analysing the distribution of nests separately, only taking into account the different areas and respective borders, old nests exhibited an aggregated pattern and incipient nests showed a random pattern, except for native forest with ants exhibiting only an aggregated pattern. The levels of aggregation changed in response to different areas and border gradients, with more external borders showing higher aggregation than more internal borders. Temperature was the variable showing the highest correlation with nest abundance and the correlation between nests of different ages was totally depending on the different areas.

Application of Integration of Spatial Statistical Analysis with GIS to Regional Economic Analysis

This paper summarizes a few spatial statistical analysis methods for to measuring spatial autocorrelation and spatial association, discusses the criteria for the identification of spatial association by the use of global Moran Coefficient, Local Moran and Local Geary. Furthermore, a user-friendly statistical module, combining spatial statistical analysis methods with GIS visual techniques, is developed in Arcview using Avenue. An example is also given to show the usefulness of this module in identifying and quantifying the underlying spatial association patterns between economic units.

Using Multivariate Statistical and Geostatistical Methods to Identify Spatial Variability of Trace Elements in Agricultural Soils in Dongguan City,Guangdong,China

Dongguan （东莞） City, located in the Pearl River Delta, South China, is famous for its rapid industrialization in the past 30 years. A total of 90 topsoil samples have been collected from agricultural fields, including vegetable and orchard soils in the city, and eight heavy metals （As, Cu, Cd, Cr, Hg, Ni, Pb, and Zn） and other items （pH values and organic matter） have been analyzed, to evaluate the influence of anthropic activities on the environmental quality of agricultural soils and to identify the spatial distribution of trace elements and possible sources of trace elements. The elements Hg, Pb, and Cd have accumulated remarkably here, incomparison with the soil background content of elements in Guangdong （广东） Province. Pollution is more serious in the western plain and the central region, which are heavily distributed with industries and rivers. Multivariate and geostatistical methods have been applied to differentiate the influences of natural processes and human activities on the pollution of heavy metals in topsoils in the study area. The results of cluster analysis （CA） and factor analysis （FA） show that Ni, Cr, Cu, Zn, and As are grouped in factor F1, Pb in F2, and Cd and Hg in F3, respectively. The spatial pattern of the three factors may be well demonstrated by geostatistical analysis. It is shown that the first factor could be considered as a natural source controlled by parent rocks. The second factor could be referred to as ＂industrial and traffic pollution sources＂. The source of the third factor is mainly controlled by long-term anthropic activities, as a consequence of agricultural activities, fossil fuel consumption, and atmospheric deposition.

Statistical functions used for spatial modelling due to assessment of landslide distribution and landscape-interaction factors in Iran

Landslides influence the capacity for safe and sustainable development of mountainous environments.This study explores the spatial distribution of and the interactions between landslides that are mapped using global positioning system(GPS) and extensive field surveys in Mazandaran Province,Iran.Point-pattern assessment is undertaken using several univariate summary statistical functions,including pair correlation,spherical-contact distribution,nearest-neighbor analysis,and O-ring analysis,as well as bivariate summary statistics,and a markcorrelation function.The maximum entropy method was applied to prioritize the factors controlling the incidence of landslides and the landslides susceptibility map.The validation processes were considered for separated 30%data applying the ROC curves,fourfold plot,and Cohen’s kappa index.The results show that pair correlation and O-ring analyses satisfactorily predicted landslides at scales from 1 to 150 m.At smaller scales,from 150 to 400 m,landslides were randomly distributed.The nearest-neighbor distribution function show that the highest distance to the nearest landslide occurred in the 355 m.The spherical-contact distribution revealed that the patterns were random up to a spatial scale of 80 m.The bivariate correlation functions revealed that landslides were positively linked to several linear features(including faults,roads,and rivers) at all spatial scales.The mark-correlation function showed that aggregated fields of landslides were positively correlated with measures of land use,lithology,drainage density,plan curvature,and aspect,when the numbers of landslides in the groups were greater than the overall average aggregation.The results of analysis of factor importance have showed that elevation(topography map scale:1:25,000),distance to roads,and distance to rivers are the most important factors in the occurrence of landslides.The susceptibility model of landslides indicates an excellent accuracy,i.e.,the AUC value of landslides was 0.860.The susceptibility map of landslides analyzed has shown that 35% of the area is low susceptible to landslides.

Solution of Multiple-Point Statistics to Extracting Information from Remotely Sensed Imagery

Two phenomena of similar objects with different spectra and different objects with similar spectrum often result in the difficulty of separation and identification of all types of geographical objects only using spectral information. Therefore, there is a need to incorporate spatial structural and spatial association properties of the surfaces of objects into image processing to improve the accuracy of classification of remotely sensed imagery. In the current article, a new method is proposed on the basis of the principle of multiple-point statistics for combining spectral information and spatial information for image classification. The method was validated by applying to a case study on road extraction based on Landsat TM taken over the Chinese Yellow River delta on August 8, 1999. The classification results have shown that this new method provides overall better results than the traditional methods such as maximum likelihood classifier （MLC）.

A Novel Hybrid Method for Measuring the Spatial Autocorrelation of Vehicular Crashes: Combining Moran’s Index and Getis-Ord G_i<sup style='margin-left:-7px;'>*</sup>Statistic

Spatial autocorrelation is a measure of the correlation of an observation with other observations through space. Most statistical analyses are based on the assumption that the values of observations are independent of one another. Spatial autocorrelation violates this assumption, because observations at near-by locations are related to each other, and hence, the consideration of spatial autocorrelations has been gaining attention in crash data modeling in recent years, and research have shown that ignoring this factor may lead to a biased estimation of the modeling parameters. This paper examines two spatial autocorrelation indices: Moran’s Index;and Getis-Ord Gi* statistic to measure the spatial autocorrelation of vehicle crashes occurred in Boone County roads in the state of Missouri, USA for the years 2013-2015. Since each index can identify different clustering patterns of crashes, therefore this paper introduces a new hybrid method to identify the crash clustering patterns by combining both Moran’s Index and Gi*?statistic. Results show that the new method can effectively improve the number, extent, and type of crash clustering along roadways.

THE STATISTICAL DISTRIBUTION OF THE SPATIAL MAXIMA OF A WAVY SEA SURFACE

A statistical distribution of the maxima of a random function in two space variables is suggested to fit with stereo observations of the sea surface. The presented distribution is a complicated mix of Gaussian, Rayleighian and Maxwellian distributions and determined by three parameters of the directional spectrum, According to the changes of the three parameters it may approach the above three distributions respectively in special cases so that it has more probability of fitting stereo data better In addition, the fact that these parameters can be directly estimated from observed data is briefly in the paper.

Spatial downscaling of climate variables using three statistical methods in Central Iran

Spatial downscaling methods are widely used for the production of bioclimatic variables(e.g. temperature and precipitation) in studies related to species ecological niche and drainage basin management and planning. This study applied three different statistical methods, i.e. the moving window regression(MWR), nonparametric multiplicative regression(NPMR), and generalized linear model(GLM), to downscale the annual mean temperature(Bio1) and annual precipitation(Bio12) in central Iran from coarse scale(1 km × 1 km) to fine scale(250 m ×250 m). Elevation, aspect, distance from sea and normalized difference vegetation index(NDVI) were used as covariates to create downscaled bioclimatic variables. Model assessment was performed by comparing model outcomes with observational data from weather stations. Coefficients of determination(R2), bias, and root-mean-square error(RMSE) were used to evaluate models and covariates. The elevation could effectively justify the changes in bioclimatic factors related to temperature and precipitation. Allthree models could downscale the mean annual temperature data with similar R2, RMSE, and bias values. The MWR had the best performance and highest accuracy in downscaling annual precipitation(R2=0.70; RMSE=123.44). In general, the two nonparametric models, i.e. MWR and NPMR, can be reliably used for the downscaling of bioclimatic variables which have wide applications in species distribution modeling.

Statistical Significance of Geographic Heterogeneity Measures in Spatial Epidemiologic Studies

Assessing geographic variations in health events is one of the major tasks in spatial epidemiologic studies. Geographic variation in a health event can be estimated using the neighborhood-level variance that is derived from a generalized mixed linear model or a Bayesian spatial hierarchical model. Two novel heterogeneity measures, including median odds ratio and interquartile odds ratio, have been developed to quantify the magnitude of geographic variations and facilitate the data interpretation. However, the statistical significance of geographic heterogeneity measures was inaccurately estimated in previous epidemiologic studies that reported two-sided 95% confidence intervals based on standard error of the variance or 95% credible intervals with a range from 2.5th to 97.5th percentiles of the Bayesian posterior distribution. Given the mathematical algorithms of heterogeneity measures, the statistical significance of geographic variation should be evaluated using a one-tailed P value. Therefore, previous studies using two-tailed 95% confidence intervals based on a standard error of the variance may have underestimated the geographic variation in events of their interest and those using 95% Bayesian credible intervals may need to re-evaluate the geographic variation of their study outcomes.

Spatial Statistical Analysis and Comprehensive Evaluation of High-Tech Industry Development

After 30 years of economic development, the high-tech industry has played an important role in China’s national economy. The development of high-level technological industry plays a leading role in guiding the transformation of China’s economy from “investment-driven” to “technology-driven”. The high-tech industry represents the future industrial development direction and plays a positive role in promoting the transformation of traditional industries. The rapid development of high-tech industry is the key to social progress. In this paper, the traditional analytical model of statistics is combined with principal component analysis and spatial analysis, and R language is used to express the analytical results intuitively on the map. Finally, a comprehensive evaluation is established.

Generation of irregular particle packing with prescribed statistical distribution, spatial arrangement, and volume fraction

A method for packing irregular particles with a prescribed volume fraction is proposed.Furthermore,the generated granular material adheres to the prescribed statistical distribution and satisfies the desired complex spatial arrangement.First,the irregular geometries of the realistic particles were obtained from the original particle images.Second,the Minkowski sum was used to check the overlap between irregular particles and place an irregular particle in contact with other particles.Third,the optimised advance front method(OAFM)generated irregular particle packing with the prescribed statistical dis-tribution and volume fraction based on the Minkowski sum.Moreover,the signed distance function was introduced to pack the particles in accordance with the desired spatial arrangement.Finally,seven biaxial tests were performed using the UDEC software,which demonstrated the accuracy and potential usefulness of the proposed method.It can model granular material efficiently and reflect the meso-structural characteristics of complex granular materials.This method has a wide range of applications where discrete modelling of granular media is necessary.

Far-zone behaviors of scattering-induced statistical properties of partially polarized spatially and spectrally partially coherent electromagnetic pulsed beam

In this study, we explore the far-zero behaviors of a scattered partially polarized spatially and spectrally partially coherent electromagnetic pulsed beam irradiating on a deterministic medium. The analytical formula for the cross-spectral density matrix elements of this beam in the spherical coordinate system is derived. Within the framework of the first-order Born approximation, the effects of the scattering angle θ, the source parameters (i.e., the pulse duration T0 and the temporal coherence length Tcxx), and the scatterer parameter (i.e., the effective width of the medium σR) on the spectral density, the spectral shift, the spectral degree of polarization, and the degree of spectral coherence of the scattered source in the far-zero field are studied numerically and comparatively. Our work improves the scattering theory of stochastic electromagnetic beams and it may be useful for the applications involving the interaction between incident light waves and scattering media.

Statistical study on the spatial - temporal distribution features of the arctic sea ice extent

On the basis of the arctic monthly mean sea ice extent data set during 1953-1984, the arctic region is divided into eight subregions,and the analyses of empirical orthogonal functions, power spectrum and maximum entropy spectrum are made to indentify the major spatial and temporal features of the sea ice fluctuations within 32-year period. And then, a brief appropriate physical explanation is tentatively suggested. The results show that both seasonal and non-seasonal variations of the sea ice extent are remarkable, and iis mean annual peripheral positions as well as their interannu-al shifting amplitudes are quite different among all subregions. These features are primarily affected by solar radiation, o-cean circulation, sea surface temperature and maritime-continental contrast, while the non-seasonal variations are most possibly affected by the cosmic-geophysical factors such as earth pole shife, earth rotation oscillation and solar activity.

RANDOM SYSTEMS OF HARD PARTICLES:MODELS AND STATISTICS

This paper surveys models and statistical properties of random systems of hard particles. Such systems appear frequently in materials science, biology and elsewhere. In mathematical-statistical investigations, simulations of such structures play an important role. In these simulations various methods and models are applied, namely the RSA model, sedimentation and collective rearrangement algorithms, molecular dynamics, and Monte Carlo methods such as the Metropolis-Hastings algorithm. The statistical description of real and simulated particle systems uses ideas of the mathematical theories of random sets and point processes. This leads to characteristics such as volume fraction or porosity, covariance, contact distribution functions, specific connectivity number from the random set approach and intensity, pair correlation function and mark correlation functions from the point process approach. Some of them can be determined stereologically using planar sections, while others can only be obtained using three-dimensional data and 3D image analysis. They are valuable tools for fitting models to empirical data and, consequently, for understanding various materials, biological structures, porous media and other practically important spatial structures.

Limited feedback SDMA schemes based on statistical CSI

This paper investigates the multi-beam selection algorithms for transmit correlation channels by using statistical channel state information （SCSI） and instantaneous channel state information. Unlike the conventional codebook-based transmission scheme, the proposed multi-beam selection with the single channel quality indicator （CQI） feedback （MBS- SCF） algorithm determines the preferred beam vector by exploiting the SCSI and only feeds back CQI at each timeslot. The performance of the MBS-SCF algorithm is nearly the same as that of the conventional scheme. In order to further improve the average sum rate, a novel multi-beam selection with the dual CQIs feedback （MBS-DCF） algorithm is proposed, which determines dual preferred statistical eigen- directions and feeds back dual CQIs at each timeslot. The theoretical analysis and simulation results demonstrate that the MBS-DCF algorithm can increase the multiuser diversity and multiplexing gain and exhibits a higher average sum rate.

GIS Analysis of Spatial Distribution of Crop Incidence

Using GIS spatial statistical analysis method, with ArcGIS software as an analysis tool, taking the diseased maize in Hedong District of Linyi City as the study object, the distribution characteristic of the diseased crops this time in spatial location was analyzed. The results showed that the diseased crops mainly dis- tributed along with river tributaries and downstream of main rivers. The correlation between adjacent diseased plots was little, so the infection of pests and diseases were excluded, and the major reason of incidence might be river pollution.

NDVI spatial pattern and its differentiation on the Mongolian Plateau

GIMMS NDVI database and geo-statistics were used to depict the spatial distribution and temporal stability of NDVI on the Mongolian Plateau.The results demonstrated that:(1) Regions of interest with high NDVI indices were distributed primarily in forested mountainous regions of the east and the north,areas with low NDVI indices were primarily distributed in the Gobi desert regions of the west and the southwest,and areas with moderate NDVI values were mainly distributed in a middle steppe strap from northwest to southeast.(2) The maximum NDVI values maintained for the past 22 years showed little variation.The average NDVI variance coefficient for the 22-year period was 15.2%.(3) NDVI distribution and vegetation cover showed spatial autocorrelations on a global scale.NDVI patterns from the vegetation cover also demonstrated anisotropy;a higher positive spatial correlation was indicated in a NW-SE direction,which suggested that vegetation cover in a NW-SE direction maintained increased integrity,and vegetation assemblage was mainly distributed in the same specific direction.(4) The NDVI spatial distribution was mainly controlled by structural factors,88.7% of the total spatial variation was influenced by structural and 11.3% by random factors.And the global autocorrelation distance was 1178 km,and the average vegetation patch length(NW-SE) to width(NE-SW) ratio was approximately 2.4:1.0.

Spatial epidemiology of diabetes: Methods and insights

Diabetes mellitus(DM)is a growing epidemic with global proportions.It is estimated that in 2019,463 million adults aged 20-79 years were living with DM.The latest evidence shows that DM continues to be a significant global health challenge and is likely to continue to grow substantially in the next decades,which would have major implications for healthcare expenditures,particularly in developing countries.Hence,new conceptual and methodological approaches to tackle the epidemic are long overdue.Spatial epidemiology has been a successful approach to control infectious disease epidemics like malaria and human immunodeficiency virus.The implementation of this approach has been expanded to include the study of non-communicable diseases like cancer and cardiovascular diseases.In this review,we discussed the implementation and use of spatial epidemiology and Geographic Information Systems to the study of DM.We reviewed several spatial methods used to understand the spatial structure of the disease and identify the potential geographical drivers of the spatial distribution of DM.Finally,we discussed the use of spatial epidemiology on the design and implementation of geographically targeted prevention and treatment interventions against DM.

Hydrogeochemical evaluation and statistical analysis of groundwater of Sylhet, north-eastern Bangladesh

To investigate the hydrogeochemical characteristics of groundwater 23 shallow, 30 intermediate and 38 deep wells samples were collected from Sylhet district of Bangladesh, and analyzed for temperature, pH, Eh, EC,DO, DOC, Na^+, K^+, Ca^(2+), Mg^(2+), Cl^-, SO_4^(2-), NO_3^-,HCO_3^-, SiO_2^-, Fe, Mn and As. Besides, 12 surface water samples from Surma and Kushiyara Rivers were also collected and analyzed to understand the influence into aquifers. Results revealed that, most of the groundwater samples are acidic in nature, and Na–HCO_3 is the dominant groundwater type. The mean value of temperature, EC,Na^+, K^+, Ca^(2+), Mg^(2+), Cl^-, NO_3^- and SO_4^(2-) were found within the range of permissible limits, while most of the samples exceeds the allowable limits of Fe, Mn and As concentrations. However, relatively higher concentration of Fe and Mn were found in deep water samples and reverse trend was found in case of As. The mean concentrations of As in shallow, intermediate and deep wells were 39.3, 25.3and 21.4 lg/L respectively, which varied from 0.03 to148 lg/L. From spatial distribution, it was found that Fe,Mn and As concentrations are high but patchy in northern,north-western, and south-western part of Sylhet region. The most influential geochemical process in study area were identified as silicate weathering, characterized by active cation exchange process and carbonate weathering, which thereby can enhance the elemental concentrations in groundwater. Pearson's correlation matrix, principal component analysis and cluster analysis were also employed to evaluate the controlling factors, and it was found that, both natural and anthropogenic sources were influencing the groundwater chemistry of the aquifers. However, surface water has no significant role to contaminate the aquifers,rather geogenic factors affecting the trace elemental contamination. Thus it is expected that, outcomes of this study will provide useful insights for future groundwater monitoring and management of the study area.