Search icon CANCEL
Subscription
0
Cart icon
Cart
Close icon
You have no products in your basket yet
Save more on your purchases!
Savings automatically calculated. No voucher code required
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletters
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Analytics for the Internet of Things (IoT)

You're reading from  Analytics for the Internet of Things (IoT)

Product type Book
Published in Jul 2017
Publisher Packt
ISBN-13 9781787120730
Pages 378 pages
Edition 1st Edition
Languages
Author (1):
Andrew Minteer Andrew Minteer
Profile icon Andrew Minteer
Toc

Table of Contents (20) Chapters close

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Defining IoT Analytics and Challenges 2. IoT Devices and Networking Protocols 3. IoT Analytics for the Cloud 4. Creating an AWS Cloud Analytics Environment 5. Collecting All That Data - Strategies and Techniques 6. Getting to Know Your Data - Exploring IoT Data 7. Decorating Your Data - Adding External Datasets to Innovate 8. Communicating with Others - Visualization and Dashboarding 9. Applying Geospatial Analytics to IoT Data 10. Data Science for IoT Analytics 11. Strategies to Organize Data for Analytics 12. The Economics of IoT Analytics 13. Bringing It All Together

Storing geospatial data


There are many ways to store geospatial data. Depending on your intended use, a filesystem format or a relational database maybe the most appropriate. We will cover an introduction to both.

File formats

There are hundreds of file formats for storing geospatial data. The most common for vector data is ESRI shapefiles. A shapefile actually consists of multiple different files with the .shp extension for the main file. Most geospatially-aware software and Python packages know to look for the other needed files when given the location of the .shp file.

GeoJSON is another storage format that is human readable. It uses a defined JSON format to store vector data definitions as text. It is easily readable but can get large in size.

Another way to represent vector data, whether in a file or in code, is using the Well-known text (WKT) and Well-known binary (WKB) formats. WKT is human readable, while WKB is not. WKB offers significant compression in size, so is often a good choice...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €14.99/month. Cancel anytime