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Analytics for the Internet of Things (IoT)

You're reading from   Analytics for the Internet of Things (IoT) Intelligent analytics for your intelligent devices

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781787120730
Length 378 pages
Edition 1st Edition
Languages
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Author (1):
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Andrew Minteer Andrew Minteer
Author Profile Icon Andrew Minteer
Andrew Minteer
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Table of Contents (14) Chapters Close

Preface 1. Defining IoT Analytics and Challenges FREE CHAPTER 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...

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