Gis Data Sources

Put the world of GIS data resources at your command-- GIS users routinely encounter key questions about the data needed for their projects: Where did the data come from? Is this the best data available? How can the data be loaded to make it work? What about creating original data? With a broad range of GIS data options to choose from, knowing how to find, select, and use the most appropriate resources for different purposes is absolutely essential in order to keep costs down and make the most of the technology. Filled with crucial information for today's GIS users, this book offers a comprehensive, straightforward reporting on GIS data sources--what they are, hot to find them, and how to determine the right source for a given project. Beginning with a thorough review of the basic GIS data types and groups, GIS Data Sources shows hot to define specific data needs for a project and accurately envision how the data will look and act once it is applied. The next step is to locate and obtain the data. Here the book presents a wealth of data sources, with added guidance on creating original data and important information on suitable applications for different types of data. Nuts-and-bolts material on data formats, media, compression, and downloading helps users acquire and use GIS data easily and avoid the technical snags that can slow a project down. In addition, the book's extensive resource listings provide details on where to find GIS information on the Internet, and a complementary Web site (www.gisdatasources.com) provides further data links and updates to help jump-start your projects. With invaluable time-and cost-saving advice and answers to a host of common GIS data questions, GIS Data Sources is a powerful new tool for users of the technology in any field. Drew Decker is Texas State Cartographer with the Texas Natural Resources Information System in Austin, Texas. He serves as Co-chair of the Texas Geographic Information Council's Technical Advisory Committee and is the Project Manager of the Texas Strategic Mapping Program.

Readers will understand how to find, evaluate, and analyze data to solve location-based problems. This guide covers practical issues such as copyrights, cloud computing, online data portals, volunteered geographic information, and international data with supplementary exercises.

The role open-source geospatial software plays in data handling within the spatial information technology industry is the overarching theme of the book. It also examines new tools and applications for those already using OS approaches to software development.

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.

Geographical Information Systems has moved from the domain of the computer specialist into the wider archaeological community, providing it with an exciting new research method. This clearly written but rigorous book provides a comprehensive guide to that use. Topics covered include: the theoretical context and the basics of GIS; data acquisition including database design; interpolation of elevation models; exploratory data analysis including spatial queries; statistical spatial analysis; map algebra; spatial operations including the calculation of slope and aspect, filtering and erosion modeling; methods for analysing regions; visibility analysis; network analysis including hydrological modeling; the production of high quality output for paper and electronic publication; and the use and production of metadata. Offering an extensive range of archaeological examples, it is an invaluable source of practical information for all archaeologists, whether engaged in cultural resource management or academic research. This is essential reading for both the novice and the advanced user.

This book presents the theory and methodology of geographical data acquisition, providing comprehensive coverage ranging from the definition of geo-referencing systems and transformation between these systems to the acquisition of geographical data using different methods. The material provides readers with a good understanding of the nature of spatial data, the accuracy of spatial data, and the theory behind various data acquisition methodologies.

Taking data from the real world, maps, reports, & other sources, Geographical Information Systems (GIS) synthesize, analyze, & model geographical decision media, providing tools that are revolutionizing urban & regional planning. This valuable introduction to GIS & remote sensing (RS) clearly explains how to take full advantage of this new technology. Thorough enough to offer valuable counsel to those already working with GIS & RS, yet presupposing no knowledge of computing, the book takes readers step by step from the simplest hows & whys all the way to the architecture, implementation, & application of a complete system.

This book introduces the usage, functionality, and application of data in geographic information systems (GIS) for geo-spatial analysis. It offers knowledge on GIS tools and techniques and explains how they can be applied in real-world project to architects and planners in the Indian and the Greater South Asian context using open-source software. The volume explains concepts on planning and architectural tasks, their data, methods and requirements followed, and includes GIS-related exercises on the same tasks. It takes the reader through the concepts of geo-spatial analysis and its referencing system while quoting examples from India. Further, the content of the book will help the planners involved in preparing GIS-based master planning for cities under the Atal Mission for Rejuvenation and Urban Transformation (AMRUT) scheme (see Glossary for details). A practical guidebook providing a step-by-step guide to learn open source GIS, this book will be useful for students, scholars and professionals from the field of architecture and planning, geography and other spatial sciences, instructors of GIS courses on planning and architecture, urban and regional planners, transport planners, urban design, landscape architects, environmental planners, departments of town and country planning, and development authorities. It will also be useful for anyone interested in the geospatial analysis.

This edited volume gathers the proceedings of the Symposium GIS Ostrava 2016, the Rise of Big Spatial Data, held at the Technical University of Ostrava, Czech Republic, March 16–18, 2016. Combining theoretical papers and applications by authors from around the globe, it summarises the latest research findings in the area of big spatial data and key problems related to its utilisation. Welcome to dawn of the big data era: though it’s in sight, it isn’t quite here yet. Big spatial data is characterised by three main features: volume beyond the limit of usual geo-processing, velocity higher than that available using conventional processes, and variety, combining more diverse geodata sources than usual. The popular term denotes a situation in which one or more of these key properties reaches a point at which traditional methods for geodata collection, storage, processing, control, analysis, modelling, validation and visualisation fail to provide effective solutions. >Entering the era of big spatial data calls for finding solutions that address all “small data” issues that soon create “big data” troubles. Resilience for big spatial data means solving the heterogeneity of spatial data sources (in topics, purpose, completeness, guarantee, licensing, coverage etc.), large volumes (from gigabytes to terabytes and more), undue complexity of geo-applications and systems (i.e. combination of standalone applications with web services, mobile platforms and sensor networks), neglected automation of geodata preparation (i.e. harmonisation, fusion), insufficient control of geodata collection and distribution processes (i.e. scarcity and poor quality of metadata and metadata systems), limited analytical tool capacity (i.e. domination of traditional causal-driven analysis), low visual system performance, inefficient knowledge-discovery techniques (for transformation of vast amounts of information into tiny and essential outputs) and much more. These trends are accelerating as sensors become more ubiquitous around the world.

The report describes potential applications of geographic information systems (GIS) and spatial analysis by HUD's Office of Policy Development and Research for understanding housing needs, addressing broader issues of urban poverty and community development, and improving access to information and services by the many users of HUD's data. It offers a vision of HUD as an important player in providing urban data to federal initiatives towards a spatial data infrastructure for the nation.