Radionuclides Contamination in Soil: Effects, Sources and Spatial Distribution

Radionuclide concentrations in the soil depend on the geological and anthropogenic activities of an area. They influence level of gamma radiation in the environment, which can cause significant health risk in humans. Due to the non-uniform distribution of radionuclides in the soil, various measurement methods have been adopted to monitor our environment. The challenges involved in collecting environmental samples, duration, technicality and cost of measurement have led to various models for predicting unmeasured locations. This article presents geostatistical method using kriging techniques, which adopt the theory of regionalized variables, to characterize the spatial distribution of radionuclide in un-sampled locations using data obtained from sampled locations. Among other things, this paper presents results of application of this method to a study area. Spatial distribution of radionuclides reveal the non-uniform distribution in the study area. Though the radionuclides level in the study area are below global average set by United Nations Scientific Committee on the Effects of atomic Radiation (UNSCEAR), the spatial distribution map can be used to provide adequate information needed by regulatory authorities in identifying the contaminated area in need of remediation.

ANALISIS KRIGING UNTUK MENDETEKSI POLA SPASIAL KASUS DBD DI KABUPATEN TANAH LAUT

Geostatistics is a data processing in geological field that contains spatial information in it. Spatial information is information that identifies geographical location, characteristics of natural conditions and boundaries of the earth. Geostatistics is used to handle regionalized variables. One of the method that used to handle regionalized variables is the kriging method. The kriging method has a lot of expansion in its development, including the Simple Kriging method and the Cokriging method. Both of these methods will be applied in case studies of spatial patterns of dengue in Tanah Laut District. The purpose of this study was to estimate the distribution pattern of DHF in Tanah Laut District and compare the results of the RMSE method of Simple Kriging and Cokriging. The smallest RMSE value was compared and selected, followed by estimation using the Cokriging and Simple Kriging methods. From the two methods used the smallest RMSE value is in the Simple Kriging method. But when you looked from the thematic map of the distribution of dengue patients with the Cokriging and Simple Kriging method, it can be seen that the Cokriging method has a more diverse pattern. Keywords: geostatisticts , Cokriging , Simple Kriging , DHF

Spatial Component Models with Artificial Neural Networks for Spatially Constrained Regionalization

<p><strong>Abstract.</strong> The authors have investigated into different geostatistical point data modeling approaches for regionalization purposes that employ the Artificial Neural Network (ANN) techniques. Regionalization is a spatially constrained adjacency classification problem. In this study, regionalization is viewed as classification of spatial objects (non-uniformly distributed points) into a smaller number of geographic regions defined by their spatial and attributive characteristics or regionalized variables. For regionalization, we take into consideration the non-stationarity and autocorrelation properties of the spatial data.</p>

Volunteered Geographical Information and Digital Social Networks applied to Tourism Dynamics in the “Alta and University of Coimbra” (UNESCO world Heritage Site)

Spatial modeling in Geographic Information Systems (GIS) always involves choices. The existence of constraints, either of a financial nature or related to the specifics of the software itself, to the algorithms, the uncertainty and even the reliability of the data, the purposes and the applications of the studies, make this a kind of guiding compass for GIS analysts. Building on a previous exercise of data acquisition (check-ins) based on two Digital Social Networks (DSN — Facebook and Foursquare) and on the awareness of the use of Volunteered Geographic Information (VGI) generated by tourists sharing their topophilic ties through DSN, the present analysis aims to evaluate the contribution of modern techniques of spatial analysis applied to tourism in the “Alta and University of Coimbra” area. Concepts and procedural tasks related to density determination, cluster analysis and identification of patterns associated with regionalized variables have thus been implemented with the purpose of evaluating and comparing the results obtained through the application of two techniques of spatial analysis, Kernel Density Estimation (KDE) and Optimized Hot-Spot Analysis (OHSA) &amp; Inverse Distance Weighting (IDW) Interpolation.

Geostatistics and Digital Social Networks—A Study of Tourism Dynamics in “Alta and University of Coimbra” (UNESCO world Heritage Site)

Spatial modeling in Geographic Information Systems (GIS) always involves choices. The existence of constraints, either of a financial nature or related to the specifics of the software itself, to the algorithms, the uncertainty and even the reliability of the data, the purposes and the applications of the studies, make this a kind of guiding compass for GIS analysts. Building on a previous exercise of data acquisition (check-ins) based on two Digital Social Networks (DSN&mdash;Facebook and Foursquare) and on the awareness of the use of voluntary geographic information generated by tourists sharing their topophilic ties through DSN, the present analysis aims to evaluate the contribution of modern techniques of spatial analysis applied to tourism in the &ldquo;Alta and University of Coimbra&rdquo; area. Concepts and procedural tasks related to density determination, cluster analysis and identification of patterns associated with regionalized variables have thus been implemented with the purpose of evaluating and comparing the results obtained through the application of two techniques of spatial analysis, Kernel Density Estimation (KDE) and Optimized Hot-Spot Analysis (OHSA) &amp; Inverse Distance Weighting (IDW) Interpolation.

COMPARISON OF FRACTIONAL NOISE DISTRIBUTIONS FOR GENERATING HETEROGENEOUS PERMEABILITY FIELDS IN OIL RESERVOIR SIMULATIONS: A CASE STUDY

Prediction of reservoir rock properties, especially permeability distribution is needed for precise simulation of heterogeneous reservoirs. Interwell permeability fields have recently been considered for dynamic simulation using geostatistical models and fractal geometries. The geostatistical models employ experimentally observed variograms to characterize the spatial variability of regionalized variables such as permeability. Fractal models can be useful in assessing the spatial correlation of a property because their variogram can be characterized with a single parameter called the Hurst exponent. In this study, based on core permeability data of each well, Hurst exponent (using [Formula: see text] analysis) is assigned locally to each well by means of stream lines and as averaged value for interwell spaces. Then, permeability distributions are created using Fractional Brownian Motion (FBM) and Fractional Gaussian Noise (FGN) models by implementing fast Fourier transform (FFT). Through comparison between simulation results of these models, as well as real grid simulation results, the averaged distribution was shown to give better results over a locally assigned fractal distribution. Furthermore, predictions of field pressure using the FGN model were shown to function better than the FBM model for vertical wells.

Geostatistically based optimization of a rainfall monitoring network extension: case of the climatically heterogeneous Tunisia

Rainfall data are an essential input for many simulation models. In fact, these latter have a decisive role in the development and application of rational water policies. Since the accuracy of the simulation depends strongly on the available data, the task of optimizing the monitoring network is of great importance. In this paper, an application is presented aiming at the evaluation of a precipitation monitoring network by predicting monthly, seasonal, and interannual average rainfall. The method given here is based on the theory of the regionalized variables using the well-known geostatistical variance reduction method. The procedure, which involves different analysis methods of the available data, such as estimation of the interpolation uncertainty and data cross validation, is applied to a case study data set in Tunisia in order to demonstrate the potential for improvement of the observation network quality. Root mean square error values are the criteria for evaluating rainfall estimation, and network performance is discussed based on kriging variance reduction. Based on this study, it was concluded that some sites should be dropped to eliminate redundancy and some others need to be added to the existing network, essentially in the center and the south, to have a more informative network.

The Possibilities and Limitations of Geostatistical Methods in Real Estate Market Analyses

In the traditional approach, geostatistical modeling involves analyses of the spatial structure of regionalized data, as well as estimations and simulations that rely on kriging methods. Geostatistical methods can complement traditional statistical models of property transaction prices, and when combined with those models, they offer a comprehensive tool for spatial analysis that is used in the process of developing land value maps. Transaction prices are characterized by mutual spatial correlations and can be considered as regionalized variables. They can also be regarded as random variables that have a local character and a specific probability distribution. This study explores the possibilities of applying geostatistical methods in spatial modeling of the prices of undeveloped land, as well as the limitations associated with those methods and the imperfect nature of the real estate market. The results are discussed based on examples, and they cover both the modeling process and the generated land value maps.