Eric Pimpler – Spatial Analytics with ArcGIS

 

Spatial statistics has the potential to provide insight that is not otherwise available through traditional GIS tools. This book is designed to introduce you to the use of spatial statistics so you can solve complex geographic analysis. The book begins by introducing you to the many spatial statistics tools available in ArcGIS. You will learn how to analyze patterns, map clusters, and model spatial relationships with these tools. Further on, you will explore how to extend the spatial statistics tools currently available in ArcGIS, and use the R programming language to create custom tools in ArcGIS through the ArcGIS Bridge using real-world examples. At the end of the book, you will be presented with two exciting case studies where you will be able to practically apply all your learning to analyze and gain insights into real estate data.

What you will learn:

Get to know how to measure geographic distributions
Perform clustering analysis including hot spot and outlier analysis
Conduct data conversion tasks using the Utilities toolset
Understand how to use the tools provided by the Mapping Clusters toolset in the Spatial Statistics Toolbox
Get to grips with the basics of R for performing spatial statistical programming
Create custom ArcGIS tools with R and ArcGIS Bridge
Understand the application of Spatial Statistics tools and the R programming language through case studies

 

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Pinliang Dong, Qi Chen – LiDAR Remote Sensing and Applications

 

CRC, 2018
pdf, 221 pages, english
ISBN: 978-1-4822-4301-7

This is a unique book in that it smoothly combines LiDAR principles, data processing methods, applications, and hands-on practices, following an overview of remote sensing. An index map of LiDAR data and a list of abbreviations are also included to improve the readability of the book.

For forest applications, readers can find examples such as creating leaf-on and leaf-off canopy height models in Susquehanna Shale Hills, PA; identifying disturbances from lightning and hurricane in mangrove forests in Florida; and estimating aboveground biomass in tropical forests in Ghana.

For urban applications, readers can see examples such as road extraction, powerline corridor mapping, and population estimation in Denton, TX; parcel-based building change detection in Surrey, Canada; and road blockage detection in Port-au-Prince after the 2010 Haiti earthquake.

For geoscience applications, readers can explore samples such as measuring dune migration rates in White Sands, NM; analysis of offset channels associated with the San Andreas Fault in California; and trend surface analysis and visualization of rock layers in Raplee Ridge, UT. Undergraduate and graduate students will find that the 11 step-by-step GIS projects with LiDAR data can really help them understand LiDAR data processing, analysis, and applications, while professionals and researchers will benefit from various topics on LiDAR remote sensing and applications, along with over 500 references in the book.

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