Modelling and prediction of spatially distributed data such as the secondary cassiterite mineral distributions are often affected by spatial autocorrelation (SAC); a phenomenon that violates attributes data independence in space, which leads to type1 errors in classical statistics and overfitting or underfitting in machine learning (ML) classification respectively. The concept of overfitting and underfitting of spatially distributed datasets in an ML classification has not been properly addressed by the traditional random holdout technique of model validation, and this is a challenge to the assessment of predictive spatial model performance in spatially distributed datasets.

The integration of geology with data science disciplines, such as spatial statistics, remote sensing, and geographic information systems (GIS), has given rise to a shift in many natural sciences schools, pushing the boundaries of knowledge and enabling new discoveries in geological processes and earth systems.

When computers were frst invented in the middle of the last century, nobody ever anticipated that the instruction sets to program them would ultimately evolve to the point where many of them could be accessed freely, without charge, in ways that would become widely available to nonexpert communities. Nor did we ever consider that computers would penetrate every corner of social life, revolutionizing our social behavior, our science, and our politics.

Современные методы 3D моделирования рудных месторождений, дающие количественную оценку пространственной изменчивости границ рудных тел и содержания металлов, необходимы как для принятия решений при освоении и разработке месторождений, так и для технико-экономического обоснования кондиций и утверждения запасов в ГКЗ. Подсчеты запасов, выполненные с помощью традиционных методов оценки, очень трудозатратны и как правило трудно проверяемы. Рассмотренные в статье современные методы компьютерного моделирования, используемые российскими геологами и экспертами ГКЗ, позволяют эффективно и менее трудозатратно получать более достоверные результаты оценки запасов месторождений <...>

QGIS is a crossing point of the free and open source geospatial world. While there are a great many tools in QGIS, it is not one massive application that does everything, and it was never really designed to be that from the beginning. It is rather a visual interface to much of the open source geospatial world. You can load data from proprietary and open formats into spatial databases of various flavors and then analyze the data with well-known analytical backends before creating a printed or web-based map to display and interact with your results. What’s QGIS’s role in all this? It’s the place where you check your data along the way, build and queue the analysis, visualize the results, and develop cartographic end products. This learning path will teach you all that and more, in a hands-on learn-by-doing manner. Become an expert in QGIS with this useful companion. <...>

Geography has always been important to humans. Stone-age hunters anticipated the location of their quarry, early explorers lived or died by their knowledge of geography, and current societies work and play based on their understanding of who belongs where. Applied geography, in the form of maps and spatial information, has served discovery, planning, cooperation, and conflict for at least the past 3000 years (Figure 1-1). Maps are among the most beautiful and useful documents of human civilization.

Welcome to GIS Tutorial for Pro 2.8. This fourth-edition step-by-step workbook focuses on ArcGIS Pro but also covers Online and some of its major apps for mobile computing, including
Dashboards, and Collector.

The natural resources on the earth seem to be randomly distributed but their variations over space and time are not all random. They exhibit a spatial correlation. This spatial correlation can be captured by geostatistics. Geostatistics deals with the analysis and modelling of geo-referenced data. The point observations are analyzed and interpolated to create spatial maps. For geostatistical interpolation, first the spatial correlation structures of the parameter of interest are quantified and then spatial interpolation is done using the quantified spatial correlation and optimal predictions at unobserved locations to create a map.