The Aggregate Of Data Is Called
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Thematic Cartography, Thematic Cartography and Transformations
A thematic map is a map that illustrates more than simply geographical relationships or locations, but rather also portrays themes, patterns, or data relating to physical, social, medical, economic, political, or any other aspect of a region or location. Examples include maps that show variations of population density, climate data, wealth, voting intentions, or life expectancy with geographical location. These tools have become central to the work of scientists, practitioners, and students in nearly every field, from epidemiology to political science, and are familiar to members of the public as a common means of expressing complicated and multivariate information in easily understood graphical formats. This set of three volumes on Thematic Cartography considers maps as information constructs resulting from a number of successive information transformations and the products of decision stages, integrated into a logical reasoning and the order of those choices. It thereby provides a thorough understanding of the theoretical basis for thematic mapping, as well as the means of applying the various techniques and methodologies in order to create a desired analytical presentation. This first volume introduces the basics of thematic cartography. The authors present the transformations necessary to the production – using a scientific approach – of any thematic map. Four stages are detailed: from geographic entities to cartographic objects; the [XY] transformation; the [XYZ] cartographic transformations; and the semiotic transformation. Technical aspects giving map-reading keys are also included.
Data Warehouse Systems
With this textbook, Vaisman and Zimányi deliver excellent coverage of data warehousing and business intelligence technologies ranging from the most basic principles to recent findings and applications. To this end, their work is structured into three parts. Part I describes “Fundamental Concepts” including multi-dimensional models; conceptual and logical data warehouse design and MDX and SQL/OLAP. Subsequently, Part II details “Implementation and Deployment,” which includes physical data warehouse design; data extraction, transformation, and loading (ETL) and data analytics. Lastly, Part III covers “Advanced Topics” such as spatial data warehouses; trajectory data warehouses; semantic technologies in data warehouses and novel technologies like Map Reduce, column-store databases and in-memory databases. As a key characteristic of the book, most of the topics are presented and illustrated using application tools. Specifically, a case study based on the well-known Northwind database illustrates how the concepts presented in the book can be implemented using Microsoft Analysis Services and Pentaho Business Analytics. All chapters are summarized using review questions and exercises to support comprehensive student learning. Supplemental material to assist instructors using this book as a course text is available at http://cs.ulb.ac.be/DWSDIbook/, including electronic versions of the figures, solutions to all exercises, and a set of slides accompanying each chapter. Overall, students, practitioners and researchers alike will find this book the most comprehensive reference work on data warehouses, with key topics described in a clear and educational style.
Introduction to Data Systems
Encompassing a broad range of forms and sources of data, this textbook introduces data systems through a progressive presentation. Introduction to Data Systems covers data acquisition starting with local files, then progresses to data acquired from relational databases, from REST APIs and through web scraping. It teaches data forms/formats from tidy data to relationally defined sets of tables to hierarchical structure like XML and JSON using data models to convey the structure, operations, and constraints of each data form. The starting point of the book is a foundation in Python programming found in introductory computer science classes or short courses on the language, and so does not require prerequisites of data structures, algorithms, or other courses. This makes the material accessible to students early in their educational career and equips them with understanding and skills that can be applied in computer science, data science/data analytics, and information technology programs as well as for internships and research experiences. This book is accessible to a wide variety of students. By drawing together content normally spread across upper level computer science courses, it offers a single source providing the essentials for data science practitioners. In our increasingly data-centric world, students from all domains will benefit from the “data-aptitude” built by the material in this book.