PirateBay Download | anselin l introduction spatial data science geoda vol 1 2024

anselin l introduction spatial data science geoda vol 1 2024

Type:: E-books
Files:: 1
Size:: 20.4 MB
Uploaded On:: May 1, 2024, 9:42 a.m.
Added By:: andryold1
Seeders:: 4
Leechers:: 2
Info Hash:: 050685D96D9662094329225B24377DF056E1B437

Textbook in PDF format 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 coverage in this chapter is aimed at novices who are not very familiar with spatial data manipulations. Most of the features illustrated can be readily accomplished by means of dedicated GIS software or by exploiting the spatial data functionality available in the R and Python worlds. Readers knowledgeable in such operations may want to just skim the materials in order to become familiar with the way they are implemented in GeoDa. Alternatively, these operations can be performed outside GeoDa, with the end result loaded as a spatial data layer. The libgeoda library has a clearly de?ned Application Programming Interface (API), which allows other C++ code to access its functionality directly. In fact, this is what currently happens under the hood for part of desktop GeoDa, and in the experimental web-GeoDa (jsgeoda, implemented through JavaScript). In addition to achieving a more ?exible interaction with different graphical user interface implementations, the API also allows other software, such as R or Python programs to access the functionality through well-de?ned wrapper code. The primary focus in this effort so far has been to create an R package, rgeoda, and a Python module, pygeoda. These provide easy access to the functionality in libgeoda through a native interface and designated middleware. The interaction between R and Python and the C++ library is implemented under the hood, so that from a user’s perspective, everything works natively as in any other R package or Python module. 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 website Preface Introduction I Spatial Data Wrangling Basic Data Operations GIS Operations II EDA and ESDA Geovisualization Statistical Maps Maps for Rates Univariate and Bivariate Data Exploration Multivariate Data Exploration Space-Time Exploration III Spatial Weights Contiguity-Based Spatial Weights Distance-Based Spatial Weights Special Weights Operations IV Global Spatial Autocorrelation Spatial Autocorrelation Advanced Global Spatial Autocorrelation Nonparametric Spatial Autocorrelation V Local Spatial Autocorrelation LISA and Local Moran Other Local Spatial Autocorrelation Statistics Multivariate Local Spatial Autocorrelation LISA for Discrete Variables Density-Based Clustering Methods VI Epilogue Postscript – The Limits of Exploration A Appendix A – GeoDa Preference Settings B Appendix B – Menu Structure C Appendix C – Scripting with GeoDa via the geodalib Library

Get This Torrent

Anselin L. An Introduction to Spatial Data Science with GeoDa Vol 1. 2024.pdf 20.4 MB

Details for: Anselin L. An Introduction to Spatial Data Science with GeoDa Vol 1. 2024

anselin l introduction spatial data science geoda vol 1 2024

Similar Posts: