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Spatial Data Science
Spatial Data Science will show GIS scientists and practitioners how to add and use new analytical methods from data science in their existing GIS platforms.By explaining how the spatial domain can provide many of the building blocks, it's critical for transforming data into information, knowledge, and solutions. This book is for those using or studying GIS and the computer scientists, engineers, statisticians, and information and library scientists leading the development and deployment of data science.
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12pcs Spatial 3D Building Blocks Montessori Brain Development Spatial Reasoning Education Puzzle
12pcs Spatial 3D Building Blocks Montessori Brain Development Spatial Reasoning Education Puzzle
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An Introduction to Spatial Data Science with GeoDa : Volume 2: Clustering Spatial Data
This book is the second in a two-volume series that introduces the field of spatial data science.It moves beyond pure data exploration to the organization of observations into meaningful groups, i.e., spatial clustering.This constitutes an important component of so-called unsupervised learning, a major aspect of modern machine learning. The distinctive aspects of the book are both to explore ways to spatialize classic clustering methods through linked maps and graphs, as well as the explicit introduction of spatial contiguity constraints into clustering algorithms.Leveraging a large number of real-world empirical illustrations, readers will gain an understanding of the main concepts and techniques and their relative advantages and disadvantages.The book also constitutes the definitive user’s guide for these methods as implemented in the GeoDa open source software for spatial analysis. It is organized into three major parts, dealing with dimension reduction (principal components, multidimensional scaling, stochastic network embedding), classic clustering methods (hierarchical clustering, k-means, k-medians, k-medoids and spectral clustering), and spatially constrained clustering methods (both hierarchical and partitioning).It closes with an assessment of spatial and non-spatial cluster properties. The book is intended for readers interested in going beyond simple mapping of geographical data to gain insight into interesting patterns as expressed in spatial clusters of observations.Familiarity with the material in Volume 1 is assumed, especially the analysis of local spatial autocorrelation and the full range of visualization methods.
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An Introduction to Spatial Data Science with GeoDa : Volume 1: Exploring Spatial Data
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. The focus of the book is on intuitive methods to discover interesting patterns in spatial data.It offers a progression from basic data manipulation through description and exploration to the identification of clusters and outliers by means of local spatial autocorrelation analysis.A distinctive approach is to spatialize intrinsically non-spatial methods by means of linking and brushing with a range of map representations, including several that are unique to the GeoDa software.The book also represents the most in-depth treatment of local spatial autocorrelation and its visualization and interpretation by means of GeoDa. The book is intended for readers interested in going beyond simple mapping of geographical data to gain insight into interesting patterns.Some basic familiarity with statistical concepts is assumed, but no previous knowledge of GIS or mapping is required. Key Features:• Includes spatial perspectives on cluster analysis• Focuses on exploring spatial data• Supplemented by extensive support with sample data sets and examples on the GeoDaCenter websiteThis book is both useful as a reference for the software and as a text for students and researchers of spatial data science.
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Spatial Information Technology for Sustainable Development Goals
This textbook aims to develop a scientific knowledge base on spatial information technology to communicate the United Nations' Sustainable Development Goals (SDGs) among students, researchers, professionals and laymen.The book improves understanding of the spatial database and explains how to extract information from this for planning purposes.To enhance the knowledge of geoscientists and environmentalists, the book describes the basic fundamental concepts to advance techniques for spatial data management and analysis and discusses the methodology.The Geographic Information System (GIS), remote sensing and Global Positioning System (GPS) are presented in an integrated manner for the planning of resources and infrastructure.The management of these systems is discussed in a very lucid way to develop the reader's skills.The proper procedure for map making and spatial analysis are included along with case studies to the reader.Where thefirst part of the book discusses the conceptual background, the second part deals with case studies using these applications in different disciplines.The presented case studies include land use, agriculture, flood, watershed characterization and infrastructure assessment for the Sustainable Development Goals.
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Spatial Data Science : With Applications in R
Spatial Data Science introduces fundamental aspects of spatial data that every data scientist should know before they start working with spatial data.These aspects include how geometries are represented, coordinate reference systems (projections, datums), the fact that the Earth is round and its consequences for analysis, and how attributes of geometries can relate to geometries.In the second part of the book, these concepts are illustrated with data science examples using the R language.In the third part, statistical modelling approaches are demonstrated using real world data examples.After reading this book, the reader will be well equipped to avoid a number of major spatial data analysis errors. The book gives a detailed explanation of the core spatial software packages for R: sf for simple feature access, and stars for raster and vector data cubes – array data with spatial and temporal dimensions.It also shows how geometrical operations change when going from a flat space to the surface of a sphere, which is what sf and stars use when coordinates are not projected (degrees longitude/latitude).Separate chapters detail a variety of plotting approaches for spatial maps using R, and different ways of handling very large vector or raster (imagery) datasets, locally, in databases, or in the cloud.The data used and all code examples are freely available online from https://r-spatial.org/book/.The solutions to the exercises can be found here: https://edzer.github.io/sdsr_exercises/.
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Handbook of Qualitative and Visual Methods in Spatial Research
Listening, experiencing, drawing or interpreting spaces: narratives, experiences, visualizations and discourses can be helpful for the empirical investigation of spaces.This interdisciplinary handbook presents a broad spectrum of established methods and innovative method development to capture and understand different facets of spaces.Instructive explanations and concrete examples make the varied qualitative methods of spatial research understandable and applicable across disciplines.The theoretical and methodological aspects of qualitative spatial research form the framework of this handbook.
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Au Spatial
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