Book Image

TIBCO Spotfire: A Comprehensive Primer. - Second Edition

By : Andrew Berridge, Michael Phillips
Book Image

TIBCO Spotfire: A Comprehensive Primer. - Second Edition

By: Andrew Berridge, Michael Phillips

Overview of this book

The need for agile business intelligence (BI) is growing daily, and TIBCO Spotfire® combines self-service features with essential enterprise governance and scaling capabilities to provide best-practice analytics solutions. Spotfire is easy and intuitive to use and is a rewarding environment for all BI users and analytics developers. Starting with data and visualization concepts, this book takes you on a journey through increasingly advanced topics to help you work toward becoming a professional analytics solution provider. Examples of analyzing real-world data are used to illustrate how to work with Spotfire. Once you've covered the AI-driven recommendations engine, you'll move on to understanding Spotfire's rich suite of visualizations and when, why and how you should use each of them. In later chapters, you'll work with location analytics, advanced analytics using TIBCO Enterprise Runtime for R®, how to decide whether to use in-database or in-memory analytics, and how to work with streaming (live) data in Spotfire. You'll also explore key product integrations that significantly enhance Spotfire's capabilities.This book will enable you to exploit the advantages of the Spotfire serve topology and learn how to make practical use of scheduling and routing rules. By the end of this book, you will have learned how to build and use powerful analytics dashboards and applications, perform spatial analytics, and be able to administer your Spotfire environment efficiently
Table of Contents (18 chapters)
Free Chapter
1
Section 1: Introducing Spotfire
6
Section 2: Spotfire In Depth
12
Section 3: Databases, Scripting, and Scaling Spotfire

Using a data function to assist with geocoding

Now that I have introduced feature layers in Spotfire, I 'd like to move on to an advanced topic and show you how you can use a feature layer and the power of Spotfire's built-in statistical engine, combined with open source code released by TIBCO, to solve the issue with geocoding the Airbnb data.

Recall that the last time we looked at the Airbnb data in the previous example, geocoding by the city column wasn't successful. We couldn't use the neighborhood column either, because Spotfire's default geocoding tables don't support coding by neighborhood.

However, we can fix this! We will be using a published Spotfire data function that is able to determine which markers (when positioned by latitude and longitude) fall within a polygon on a map chart. If we load a shapefile containing neighborhoods, we can...