Book Image

Unity 2018 Augmented Reality Projects

By : Jesse Glover
Book Image

Unity 2018 Augmented Reality Projects

By: Jesse Glover

Overview of this book

Augmented Reality allows for radical innovations in countless areas. It magically blends the physical and virtual worlds, bringing applications from a screen into your hands. Meanwhile, Unity has now become the leading platform to develop augmented reality experiences, as it provides a great pipeline for working with 3D assets. Using a practical and project-based approach, Unity 2018 Augmented Reality Projects educates you about the specifics of augmented reality development in Unity 2018. This book teaches you how to use Unity in order to develop AR applications which can be experienced with devices such as HoloLens and Daydream. You will learn to integrate, animate, and overlay 3D objects on your camera feed, before gradually moving on to implementing sensor-based AR applications. In addition to this, you will explore the technical considerations that are especially important and possibly unique to AR. The projects in the book demonstrate how you can build a variety of AR experiences, whilst also giving insights into C# programming as well as the Unity 3D game engine via the interactive Unity Editor. By the end of the book, you will be equipped to develop rich, interactive augmented reality experiences for a range of AR devices and platforms using Unity.
Table of Contents (16 chapters)
Title Page
Copyright and Credits
Packt Upsell

Statistics used with GIS

Statistics is heavily used in GIS, and there is a special branch of statistics that deals with field data specifically. This special branch of statistics uses spatial data with a continuous index. It is able to provide methods to model spatial correlation and predict values at arbitrary locations, which is also known as interpolation.

When geographic phenomena are measured, the observation methods can dictate the accuracy of any analysis. There are limits caused by the nature of some data, where there is a constant or dynamic degree of precision that is always lost in measurement. This loss of precision is determined by the scale and distribution of the data collection.

When determining the statistical relevance of any analysis, the average has to be determined so that the points outside of any measurement can be included to their predicted behavior. Applied statistics and data collection methods have a limitation to predicting the behavior of particles, points, and...