The immediate purpose of this handbook is to aid further research by stating, in a form providing handy reference, the facts concerning the Communist ideology in Hungary Following a narrative of the vicissitudes of that ideology prior to its power-phase - intended as a general introduction contributing to the proper assessment of the 1945-1965 period, which is the main concern of this book - the essential and relevant facts concerning the events, issues, organizations and opinions which have shaped post-war Hungarian Marxism Leninism are set out without indulging in lengthy commentaries and personal value-judgements. (Since even the 1956 revolution is treated thus - perhaps the most important, and certainly the most controversial single event of the above period - I should add that the reader interested in finding a detailed analysis and evaluation of the ideological relevance of that event may refer to my Individualism Collectivism and Political Power, The Hague, 1963, pp. 111-140. ) Despite the specificity of much of the data, sufficient translations of Hungarian titles, names and terms have been provided to render the present book useful for the investigator regardless of whether or not he reads Hungarian. But the fundamental purpose of this volume is to make a modest contribution to East-West understanding. It has arisen from the belief that the lessening of world-tensions is best served by understanding, and understanding is best served by objective information.

Geospatial information modeling and mapping has become an important tool for the investigation and management of natural resources at the landscape scale. Spatial Statistics: GeoSpatial Information Modeling and Thematic Mapping reviews the types and applications of geospatial information data, such as remote sensing, geographic information systems (GIS), and GPS as well as their integration into landscape-scale geospatial statistical models and maps. The book explores how to extract information from remotely sensed imagery, GIS, and GPS, and how to combine this with field data—vegetation, soil, and environmental—to produce a spatial model that can be reconstructed and displayed using GIS software. Readers learn the requirements and limitations of each geospatial modeling and mapping tool. Case studies with real-life examples illustrate important applications of the models. Topics covered in this book include: An overview of the geospatial information sciences and technology and spatial statistics Sampling methods and applications, including probability sampling and nonrandom sampling, and issues to consider in sampling and plot design Fine and coarse scale variability Spatial sampling schemes and spatial pattern Linear and spatial correlation statistics, including Moran’s I, Geary’s C, cross-correlation statistics, and inverse distance weighting Geospatial statistics analysis using stepwise regression, ordinary least squares (OLS), variogram, kriging, spatial auto-regression, binary classification trees, cokriging, and geospatial models for presence and absence data How to use R statistical software to work on statistical analyses and case studies, and to develop a geospatial statistical model The book includes practical examples and laboratory exercises using ArcInfo, ArcView, ArcGIS, and other popular software for geospatial modeling. It is accessible to readers from various fields, without requiring advanced knowledge of geospatial information sciences or quantitative methods.

This book presents the current trends, technologies, and challenges in Big Data in the diversified field of engineering and sciences. It covers the applications of Big Data ranging from conventional fields of mechanical engineering, civil engineering to electronics, electrical, and computer science to areas in pharmaceutical and biological sciences. This book consists of contributions from various authors from all sectors of academia and industries, demonstrating the imperative application of Big Data for the decision-making process in sectors where the volume, variety, and velocity of information keep increasing. The book is a useful reference for graduate students, researchers and scientists interested in exploring the potential of Big Data in the application of engineering areas.