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.

This book is the first in a two-volume series that introduces the field of spatial data science. It offers an accessible overview of the methodology of exploratory spatial data analysis. It also constitutes the definitive user’s guide for the widely adopted GeoDa open-source software for spatial analysis. Leveraging a large number of real-world empirical illustrations, readers will gain an understanding of the main concepts and techniques, using dynamic graphics for thematic mapping, statistical graphing, and, most centrally, the analysis of spatial autocorrelation. Key to this analysis is the concept of local indicators of spatial association, pioneered by the author and recently extended to the analysis of multivariate data.

Spatial Statistics for Data Science: Theory and Practice with R describes statistical methods, modeling approaches, and visualization techniques to analyze spatial data using R. The book starts by providing a comprehensive overview of the types of spatial data and R packages for spatial data retrieval, manipulation, and visualization. Then, it provides a detailed explanation of the theoretical concepts of spatial statistics, along with fully reproducible examples demonstrating how to simulate, describe, and analyze areal, geostatistical, and point pattern data in various applications. <...>

Учебное пособие по курсу «Геоинформационные системы» посвящено применению ГИС-технологий и методов пространственного анализа для решения различных тематических задач.

Детально описаны существующие возможности работы с системами координат и проекциями в ГИС, включая создание и использование местных и локальных систем координат.

Nowadays, many surficial mineral deposits are being mined out, leaving only deep-seated mineral deposits for feeding raw materials into the industry. Therefore techniques applied to mineral exploration need to be revisited for discovering new mineral resources, which may be located in harsh and remote regions. Over the past decades, remote sensing technology and geographic information system (GIS) techniques have been incorporated into several mineral exploration projects worldwide. This aim is to bridge the knowledge gap for the geospatial-based discovery of buried, covered, and blind mineral deposits. This book details the main aspects of the state-of-the-art remote sensing imagery, geochemical data, geophysical data, geological data, and geospatial toolbox required to explore ore deposits. It covers advances in remote sensing data processing algorithms, geochemical data analysis, geophysical data analysis, and machine learning algorithms in mineral exploration. It also presents approaches on recent remote sensing and GIS-based mineral prospectivity modeling, which offer a piece of excellent information to professional earth scientists, researchers, mineral exploration communities, and mining companies <...>

The dissemination of digital spatial databases, coupled with the ever wider use of GISystems, is stimulating increasing interest in spatial analysis from outside the spatial sciences. The recognition of the spatial dimension in social science research sometimes yields different and more meaningful results than analysis which ignores it

Процесс пространственного анализа напоминает растяжение резиновой ленты, где долгая и тягучая работа по оцифровке, формированию баз данных, выявлению ошибок и учету нюансов проекций и координатных систем всплескивает, наконец, эффектным результатом или находкой наилучшего решения. Пространственный анализ многим пользователям часто кажется чем-то недоступным: слишком "математичный" для легкого восприятия и слишком сложный для быстрого осуществления. В условиях ощутимого дефицита на хорошие справочники и руководства он оставался уделом немногих специально подготовленных пользователей.

The history of applied spatial modelling has been a chequered one. If we ignore the elements of simple numerical reporting in the early commercial geographies of the nineteenth century, then we can probably trace the origins of applied quantitative spatial analysis to the fields of transportation modelling, especially at the University of Pennsylvania, in the early 1960s (Herbert and Stevens, 1960; Harris, 1962). Mathematical geography became prevalent in planning circles both in the USA and in Europe from this time onwards. The early 1960s also witnessed the emergence of statistical geography: the search for patterns or similarities in spatial data sets and the testing for significance, with the aim of providing universal spatial theories and laws (or at least theories that worked well in the real world). Although this was often very empirical in nature, it seldom extended beyond the academic geography community. There are excellent summaries in Haggett (1965) and Haggett et al. (1977).