The Topological Weighted Centroid: A New Vision of Geographic Profiling

The Topological Weighted Centroid: A New Vision of Geographic Profiling PDF

Author: Massimo Buscema

Publisher: Springer Nature

Published: 2023-08-14

Total Pages: 175

ISBN-13: 3031289013

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This book introduces the Topological Weighed Centroid approach and describes some applications in the study of the dynamics of various spatial phenomena with a special emphasis on the spatial analysis of the relationship, influence, and dynamics of geographical phenomena. Offering a comprehensive introduction to the theory and illustrative examples from various kinds of geographical data, this book also takes an in-depth look at more complex case studies, such as the applications of the topological weighed centroid approach in the study of epidemic patterns, cultural processes, criminality, and environmental phenomena.

Data Mining Applications Using Artificial Adaptive Systems

Data Mining Applications Using Artificial Adaptive Systems PDF

Author: William J. Tastle

Publisher: Springer Science & Business Media

Published: 2012-08-27

Total Pages: 279

ISBN-13: 1461442230

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This volume directly addresses the complexities involved in data mining and the development of new algorithms, built on an underlying theory consisting of linear and non-linear dynamics, data selection, filtering, and analysis, while including analytical projection and prediction. The results derived from the analysis are then further manipulated such that a visual representation is derived with an accompanying analysis. The book brings very current methods of analysis to the forefront of the discipline, provides researchers and practitioners the mathematical underpinning of the algorithms, and the non-specialist with a visual representation such that a valid understanding of the meaning of the adaptive system can be attained with careful attention to the visual representation. The book presents, as a collection of documents, sophisticated and meaningful methods that can be immediately understood and applied to various other disciplines of research. The content is composed of chapters addressing: An application of adaptive systems methodology in the field of post-radiation treatment involving brain volume differences in children; A new adaptive system for computer-aided diagnosis of the characterization of lung nodules; A new method of multi-dimensional scaling with minimal loss of information; A description of the semantics of point spaces with an application on the analysis of terrorist attacks in Afghanistan; The description of a new family of meta-classifiers; A new method of optimal informational sorting; A general method for the unsupervised adaptive classification for learning; and the presentation of two new theories, one in target diffusion and the other in twisting theory.

Intelligent Data Mining in Law Enforcement Analytics

Intelligent Data Mining in Law Enforcement Analytics PDF

Author: Paolo Massimo Buscema

Publisher: Springer Science & Business Media

Published: 2012-11-28

Total Pages: 522

ISBN-13: 9400749147

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This book provides a thorough summary of the means currently available to the investigators of Artificial Intelligence for making criminal behavior (both individual and collective) foreseeable, and for assisting their investigative capacities. The volume provides chapters on the introduction of artificial intelligence and machine learning suitable for an upper level undergraduate with exposure to mathematics and some programming skill or a graduate course. It also brings the latest research in Artificial Intelligence to life with its chapters on fascinating applications in the area of law enforcement, though much is also being accomplished in the fields of medicine and bioengineering. Individuals with a background in Artificial Intelligence will find the opening chapters to be an excellent refresher but the greatest excitement will likely be the law enforcement examples, for little has been done in that area. The editors have chosen to shine a bright light on law enforcement analytics utilizing artificial neural network technology to encourage other researchers to become involved in this very important and timely field of study.

Geocomputation with R

Geocomputation with R PDF

Author: Robin Lovelace

Publisher: CRC Press

Published: 2019-03-22

Total Pages: 335

ISBN-13: 1351396900

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Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested in extending their skills to handle spatial data. The book is divided into three parts: (I) Foundations, aimed at getting you up-to-speed with geographic data in R, (II) extensions, which covers advanced techniques, and (III) applications to real-world problems. The chapters cover progressively more advanced topics, with early chapters providing strong foundations on which the later chapters build. Part I describes the nature of spatial datasets in R and methods for manipulating them. It also covers geographic data import/export and transforming coordinate reference systems. Part II represents methods that build on these foundations. It covers advanced map making (including web mapping), "bridges" to GIS, sharing reproducible code, and how to do cross-validation in the presence of spatial autocorrelation. Part III applies the knowledge gained to tackle real-world problems, including representing and modeling transport systems, finding optimal locations for stores or services, and ecological modeling. Exercises at the end of each chapter give you the skills needed to tackle a range of geospatial problems. Solutions for each chapter and supplementary materials providing extended examples are available at https://geocompr.github.io/geocompkg/articles/. Dr. Robin Lovelace is a University Academic Fellow at the University of Leeds, where he has taught R for geographic research over many years, with a focus on transport systems. Dr. Jakub Nowosad is an Assistant Professor in the Department of Geoinformation at the Adam Mickiewicz University in Poznan, where his focus is on the analysis of large datasets to understand environmental processes. Dr. Jannes Muenchow is a Postdoctoral Researcher in the GIScience Department at the University of Jena, where he develops and teaches a range of geographic methods, with a focus on ecological modeling, statistical geocomputing, and predictive mapping. All three are active developers and work on a number of R packages, including stplanr, sabre, and RQGIS.

Chemical Health Threats

Chemical Health Threats PDF

Author: Raquel Duarte-Davidson

Publisher: Royal Society of Chemistry

Published: 2018-10-26

Total Pages: 330

ISBN-13: 1782620710

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This book examines the European guidelines for the risk assessment and management of serious international public health threats.

Geographic Knowledge Graph Summarization

Geographic Knowledge Graph Summarization PDF

Author: B. Yan

Publisher: IOS Press

Published: 2019-08-08

Total Pages: 170

ISBN-13: 1614999899

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Geographic knowledge graphs can have an important role in delivering interoperability, accessibility and the demands of conceptualization in geographic information science (GIS). However, the massive amount of accompanying information and the enormous diversity of geographic knowledge graphs limits their applicability and hinders the widespread adoption of this useful structured knowledge. This book, Geographic Knowledge Graph Summarization, focuses on the ways in which geographic knowledge graphs can be digested and summarized. Such a summarization would relieve the burden of information overload for end users and reduce data storage, as well as speeding up queries and eliminating ‘noise’. The book introduces the general concept of geospatial inductive bias and explains the different ways in which this idea can be used in the summarization of geographic knowledge graphs. The book breaks up the task of summarization into separate but related components, and after an introduction and a brief overview of concepts and theories, Chapters 3, 4 and 5 explore hierarchical place type structure, multimedia leaf nodes, and general relation and entity components respectively. Chapter 6 presents a spatial knowledge map interface which illustrates the effectiveness of summarization. The book integrates top-down knowledge engineering and bottom-up knowledge learning methods, and will do much to promote awareness of this fascinating area and related issues.

Artificial Adaptive Systems in Medicine

Artificial Adaptive Systems in Medicine PDF

Author: Massimo Buscema

Publisher: Bentham Science Publishers

Published: 2009-11-05

Total Pages: 122

ISBN-13: 1608050424

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"New Theories and Models for New Applications - This Ebook covers the emerging and most important theories underlying artificial intelligence applications in a variety of medical problems. It is written for physicians, researchers, engineers, statisticians"

Applied Spatial Data Analysis with R

Applied Spatial Data Analysis with R PDF

Author: Roger S. Bivand

Publisher: Springer Science & Business Media

Published: 2013-06-21

Total Pages: 414

ISBN-13: 1461476186

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Applied Spatial Data Analysis with R, second edition, is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS and the handling of spatio-temporal data. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. Compared to the first edition, the second edition covers the more systematic approach towards handling spatial data in R, as well as a number of important and widely used CRAN packages that have appeared since the first edition. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information science and geoinformatics, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003.

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