Spatial Statistical Analysis and Geostatistical Mapping of Offshore Magnetometric Acquisition Data

This paper provides a description of SAGE, a software package linked to the Arc/Info GIS that can be used to undertake spatial statistical analysis of area based data. The paper is written from the perspective of the user who wishes to undertake exploratory and confirmatory spatial data analysis. The paper discusses design aspects of the package and also the statistical analysis philosophy underlying its contents. The paper describes the statistical analyses which SAGE can perform and details on how it performs them together with some illustrative examples. Detail on visualisation aspects of SAGE are discussed in a separate paper (Haining et al. 2000). The wider contribution of the paper is to build on earlier developments in this area and identify the needs of software packages if they are to enable users to implement effective spatial statistical analysis.

Spatial applications will gain high complexity as the volume of spatial data increases rapidly. A suitable data processing and computing infrastructure for spatial applications needs to be established. Over the past decade, grid has become a powerful computing environment for data intensive and computing intensive applications. In this paper, we tested and analyzed the feasibility of using Grid platform for spatial analysis functionalities in Geographic Information System (GIS). We found that spatial interpolation, buffers, and spatial query can be easily migrated to Grid platform. Polygon overlay and transformation could achieve better results on Grid platform. To do network analysis and spatial statistical analysis on Grid platform could be no significant improvement of performance. The most un-suitable spatial analysis on Grid platform is the spatial measurement.

Petroleum Infrastructure is indispensable considering the current local and global energy demands, however explosions from such establishments often cause loss of lives and properties within the surrounding communities thereby posing great concern to government and the citizenry. This condition calls for research that would provide meaningful solutions to mitigate this menace. Unlawful siting of oil refineries, petrochemical plants, berthing terminals, pipelines, storage terminals, oil and gas retail assets results from non-consideration of environmental impact on growing human population, competition for customers and lack of enforcement of energy standards. The study aims to employ a multifaceted approach comprising of suitability, proximity and spatial statistical analysis in assessing viable areas for developing petroleum hubs in the district. This study further investigated the efficiency of the method and level of compliance to standards set by the Ministry of Energy, Environmental Protection Agency (EPA) and the Town and Country Planning Department through validation using the newly acquired land for petroleum hub and existing filling stations in the study area. Primary and secondary data were used for the study. The primary data consist of locations of oil and gas filling stations picked with the Garmin handheld GPS and surveyed boundary of the land. The secondary data was obtained from the Survey and Mapping division of the land commission of Ghana. It comprises of topographic data, geology and soil maps from which soil types, lithology, roads networks, water bodies, terrain slope and land use features of the area were extracted and used. The dataset was reclassified and weighted using Fuzzy AHP and VIKOR. Spatial analyses were carried out using ArcGIS software to show areas suitable or otherwise for siting petroleum hubs in the study area. Results shows 67.44% of the area are highly suitable for establishment of petroleum hubs, 32.33% of the area falls within moderate suitability zones whereas the least suitability zones occupied 0.23% of the total area. The newly acquired government land for the petroleum hub project fell within the highly suitable zone confirming the validity of the studies in comparison with studies from field experts via environmental impact assessment. The proposed petroleum hub covered areas dominated by very high and high area suitability for its establishment constituting 75.9 km2 (90.3%) of its entire area whereas the moderate suitability zones constituted 8.2 km2 (9.7%) of the remaining areas. Towns situated in very high areas includes; Bakakole Nkwanta, Ahobre, Nawule, Allowule, Tikobo No.1, Edu, Damofu, Ave lenu and Ebonloa, Mpatabo. High areas comprises of Kengen Kpokezo, Alenda wharf, Tekyinta. Anwonakrom, Nkwamta, Elubo and Agege are among the moderate and low area zones for hub and oil retail assets establishment 75% of the oil retail assets complied with the required set standards whiles 25% defaulted. The integration of GIS techniques with multi-criteria decision analyses has proved to be an effective tool for selecting suitable sites for petroleum hubs, oil retail assets and very useful in determining places of high fire risk for proper planning of the area. It is recommended that the authorities and stakeholders put measures in place to educate, perform site suitability assessment and enforce set standards in establishment of petroleum infrastructure.

Village of Santu'un is one of the malaria endemic areas in sub district of Muara Uya. Annual Parasite Insidence (API) from 2007 to 2009 are 11.4%, 82.9%, and 25.8% respectively. The study was an observational analytic study with a case control study design. Total sample was 291 people. Data analysis with statistical analysis Chi square and spatial analysis with SaTscan.The results shows during the ysoy 2010, there are two clusters of malaria cases. The first clusters was located at coordinate 1.863500 S and 115.606700 E with a radius of 0.65 km. The total population in RT 3 and RT 4 was 42 people (p-value: 0.001). The second clusters was located in 1.838667 S and 115.607200 E (radius 0,67 km), with total population in RT 5 was 26 people (p=0.001). These results indicate that the clusters of Indonesia cases was statisfically significant with the RR : 3.18.

White matter (WM) microstructure may vary significantly in pediatric-onset (PO) and adult-onset (AO) patients with multiple sclerosis (MS), a difference that could be explained by the effects of an inherent plasticity in the affected pediatric brains early in the disease, and a phenomenon that does not occur later in life. This hypothesis would support the observation that disease progression is much slower in POMS compared to AOMS patients. To examine WM microstructure in the brain of adults with POMS and AOMS, using tract based spatial statistics (TBSS) analysis of diffusion-tensor imaging (DTI). Adults with relapsing-remitting (RR) POMS, who were diagnosed before age of 18 years (n = 16), were compared with age-matched (AOA, n = 23) and disease duration-matched (AOD, n = 22) RR patients who developed MS after the age of 18 years. Scans were analyzed using the FSL software package (Oxford, UK) and statistics were performed using TBSS to evaluate WM microstructure between groups based on the mean fractional anisotropy (FA) values obtained from the DTI. Widespread cortical and deep WM area differences characterized by increased FA values were seen in the AOAMS compared with POMS group (P < 0.05, TFCE corrected). Significantly increased FA values of posterior WM areas were detected in the AODMS compared with POMS group (P < 0.05, TFCE corrected). Increased FA values in WM areas of the AOMS compared with the POMS patients suggest that diffuse WM microstructure changes are more attributable to age of onset than a simple function of disease duration and age.

Spatial Statistics Analysis with Artificial Neural Network

1. Integration of Data for Resource Evaluation.- Application of recent developments of regression analysis in regional mineral resource evaluation.- Poisson regression analysis and its application.- Measurement, sampling and interpretation.- Levels of information and probability mapping.- Review of geomathematical applications for mineral resources evaluation in China.- 2. Statistical Analysis of Geochemical Data.- New approaches to the recognition of anomalies in exploration geochemistry.- Univariate patterns in the design of multivariate analysis techniques for geochemical data evaluation.- Application of robust statistics in the analysis of geochemical data.- 3. Statistical Mehtods for Oil and Gas Resource Evaluation.- U.S. Geological Survey assessment methodology for estimation of undiscovered petroleum resources in play analysis of the Artic National Wildlife Refuge.- Statistical evaluation of petroleum deposits before discovery.- On assessing dry probabilities in offshore oil and gas exploration: an application of Bayes's theorem.- Statistical analysis of oil and gas discovery data.- 4. Image Processing in Earth Sciences.- Automated integration of mineral resource data by image processing and artificial intelligence.- Principles of mathematical morphology.- The use of geological image analysis in remote sensing and seismic attribute integration for resource assessment.- Statistical image processing.- 5. Statistical Analysis of Spatial Data.- Spatial analysis of patterns of land-based and ocean-floor ore deposits.- Point processes for the earth sciences.- 6. Geostatistics.- An unconventional approach to geostatistical estimation.- Case studies on modelling complex sulphide orebodies for ore reserve estimation using geostatistical methods.- A Bayesian approach to surface estimation.- Conditional simulation of indicator data. Case study of a multi-seam coal deposit.- Factorial kriging as a method to include spatial structure into classification. A case study on a sulphide orebody.- Geostatistical techniques for interpreting multivariate spatial information.- 7. Other Statisitcal Methodologies for Geoscience Data.- The shape of Lloydminster oil and gas deposit attribute data.- Confidence bands for the distribution and quantile functions for truncated and randomly censored data.- Geostatistical crustal abundance resource models.- 8. Artificial Intelligence in Earth Sciences.- The usage of Artificial intelligence in remote sensing: a review of applications and current research.- GEOVALUATOR, an expert system for resource appraisal: a demonstration prototype for kaolin in Georgia, U.S.A.- GEMS: a microcomputer-based expert system for digital image data.- On the understanding of geological cross-sections by expert systems.- 9. Other Contributed Papers.- Examples of spatial analysis and management in the geographical and conformal projection domains.- A classification procedure for exploitable reserves.- Drainage and divide networks derived from high-fidelity digital terrain models.- An algebraic model for data structure and manipulation as a design basis for integrated geoscience processing systems.- Case study on application of qualitative data analysis techniques to an uranium mineralization.- A shell for microcomputer exploration software used in developing countries.- Optimal exploration strategies: the synthesis of resource assessments and operations research.- Advantages and limitations of discovery process modeling: the case of the Northern West Siberian gas plays.- Some aspects of multivariate analysis.- Workshop Reports.- Current problems and future developments in multivariate analysis.- Current problems and future developments in oil and gas resource modeling and forecasting.- Current problems and future developments in spatial statistics and image processing.- Current problems and future developments in mineral and energy resource expert system development.- Current problems and future developments in the use of microcomputers.

Geographic information system(GIS)has advantages of geospatial analysis,spatial statistical analysis,data synthesis,precise positioning and visualization.It was originally applied in topography and environmental science,and has been introduced gradually to the field of public health.In recent years,GIS has become a powerful tool for the surveillance of sexually transmitted diseases(STDs)and acquired immunodeficiency syndrome(AIDS),and has shown unique superiority in identifying patterns of spatial distribution,clustering analysis,clarifying core areas and risk factors,and tracing infection sources of STDs and human immunodeficiency virus(HIV)infection/AIDS. Key words: Sexually transmitted diseases; Acquired immunodeficiency syndrome; Population surveillance; Geographic information systems

Review for "Effect of number of diffusion encoding directions in Diffusion Metrics of 5‐year‐olds using Tract‐Based Spatial Statistical analysis"

Review for "Effect of number of diffusion encoding directions in Diffusion Metrics of 5‐year‐olds using Tract‐Based Spatial Statistical analysis"

Profile soil moisture (PSM), the soil water content in the whole soil layer, directly controls the major processes related to biological interaction, vegetation growth, and runoff generation. Its spatial heterogeneity, which refers to the uneven distribution and complexity in space, influences refined spatial management and decision-making in ecological, agricultural, and hydrological systems. Satellite instruments and hydrological models are two important sources of spatial information on PSM, but there is still a gap in understanding their potential mechanisms that affect spatial heterogeneity. This study is designed to identify the spatial heterogeneity and the driving factors of two PSM datasets; one is preprocessed from a satellite product (European Space Agency Climate Change Initiative, ESA CCI), and the other is simulated from a distributed hydrological model (the DEM-based distributed rainfall-runoff model, DDRM). Three catchments with different climate conditions were chosen as the study area. By considering the scale dependence of spatial heterogeneity, the profile saturation degree (PSD) datasets from different sources (shown as ESA CCI PSD and DDRM PSD, respectively) during 2017 that are matched in terms of spatial scale and physical properties were acquired first based on the calibration data from 2014–2016, and then the spatial heterogeneity of the PSD from different sources was identified by using spatial statistical analysis and the semi-variogram method, followed by the geographic detector method, to investigate the driving factors. The results indicate that (1) ESA CCI and DDRM PSD are similar for seasonal changes and are overall consistent and locally different in terms of the spatial variations in catchment with different climate conditions; (2) based on spatial statistical analysis, the spatial heterogeneity of PSD reduces after spatial rescaling; at the same spatial scale, DDRM PSD shows higher spatial heterogeneity than ESA CCI PSD, and the low-flow period shows higher spatial heterogeneity than the high-flow period; (3) based on the semi-variogram method, both ESA CCI and DDRM PSD show strong spatial heterogeneity in most cases, in which the proportion of C/(C0 + C) is higher than 0.75, and the spatial data in the low-flow period mostly show larger spatial heterogeneity, in which the proportion is higher than 0.9; the spatial heterogeneity of PSD is higher in the semi-arid catchment; (4) the first three driving factors of the spatial heterogeneity of both ESA CCI and DDRM PSD are DEM, precipitation, and soil type in most cases, contributing more than 50% to spatial heterogeneity; (5) precipitation contributes most to ESA CCI PSD in the low-flow period, and there is no obvious high contribution of precipitation to DDRM PSD. The research provides insights into the spatial heterogeneity of PSM, which helps develop refined modeling and spatial management strategies for soil moisture in ecological, agricultural, and hydrological fields.

BackgroundAccording to UNSD, World Bank, and WHO, a 2020 joint report, 3 billion people who used solid fuel were found in low and middle-income countries. The burning of such type of fuel emits a variety of pollutants such as PM2.5, PM10, CO, NO2, organic compounds, and other substances which a had wide range of public health problems The evidence from the WHO report, indoor air pollution was attributed to around 3.2 million deaths each year in 2020, and more than 237 000 deaths of children under the age of five. This study aims to investigate solid fuel prevalence, predictors, and spatial variation in Ethiopia.MethodThis study was conducted in Ethiopia based on the fifth Ethiopian Demographic and Health Surveys 2019 dataset. 8,663 households were successfully interviewed at a response rate of 99%. Weighted by sampling weight was done to do a reliable statistical analysis. Fuel type was used as the outcome variable whereas sex of household head (male or female), wealth index (poor, middle, and rich), educational status (no education, primary, secondary, higher), having television and radio, a separate room used as a kitchen, were individual predictor variables and community level education(lower/higher), residence(urban/rural), community level media exposure (exposed/unexposed), region (pastoralist, semi-pastoralist, Agrarian, and City administration) were community level variables. All the above analyses were conducted using Excel Microsoft 2016, STATA 14, ArcGIS 10.7, and SaTScan 10.1 software.ResultsThe majority (72.62%) of household heads were males. The overall national level of solid fuel use was 87.13% (95% CI (86.4%-87.82%)). From this 87.13% of solid fuel use, 18.60% accounts for urban and 68.53% for rural parts of the country. Educational status, having television, accessing electricity, and wealth index were individual-level variables community-level education, type of residence, and region were community-level variables significantly associated factors towards solid fuel use in our study. Global (z-score = 33.109144, P-value <0.0001), local (hot spot, cluster, and outlier), and Spatial Scan statistical analyses revealed that there was a major geographical variation in solid fuel use across Ethiopia.ConclusionBased on this finding, the prevalence of solid fuel use was higher in Ethiopia. Educational status, having television, accessing electricity, and wealth index were individual-level variables and community-level education, type of residence, and region were community-level variables statistically significant factors in determining fuel choice for cooking. There was significant spatial variation in the prevalence of solid-fuel use across the country. In order to addressing such heavily dependent on solid fuel use, responsible bodies like health policy makers, national and international organizations, and public health researchers should work on showing health problems of solid fuel use and the means of increasing clean fuel use. Substantial policy modifications are desirable to reach access to clean fuels and technologies (SDG 7.1.2) by 2030 to address health inequities.

In general, the multifractal model provides more information about measurements on spatial objects than a fractal model. It also results in mathematical equations for the covariance function and semivariogram in spatial statistics which are determined primarily by the second-order mass exponent. However, these equations can be approximated by power-law relations which are comparable directly to equations based on fractal modeling. The multifractal approach is used to describe the underlying spatial structure of De Wijs 's example of zinc values from a sphalerite-bearing quartz vein near Pulacayo, Bolivia. It is shown that these data are multifractal instead of fractal, and that the second-order mass exponent (=0.979±0.011 for the example) can be used in spatial statistical analysis.

This article presents a study of the Third National Agricultural and Livestock Census data to examine the characteristics of family and industrial farming in Colombia using together, statistical and spatial analysis techniques. From the available anonymized census microdata, which was added up to a level of rural relevance called vereda, a selection of variables was made according to properties found out in the state of art as related to both agriculture types and to the established theoretical framework. From there on, a Principal Component Analysis allowed distinguishing association between the attributes of family and industrial agriculture, while an Exploratory Spatial Data Analysis lets identifying polarized geographical distributions of these two phenomena in the country. In this way, this study seeks to become a starting point and a benchmark for analyzing the Colombian agricultural sector, as well as demonstrating the importance of census information, data reduction and statistical and spatial exploration methods.

A Spatially Adjusted ANOVA Model