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Hands-On Graph Analytics with Neo4j

You're reading from   Hands-On Graph Analytics with Neo4j Perform graph processing and visualization techniques using connected data across your enterprise

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Product type Paperback
Published in Aug 2020
Publisher Packt
ISBN-13 9781839212611
Length 510 pages
Edition 1st Edition
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Author (1):
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Estelle Scifo Estelle Scifo
Author Profile Icon Estelle Scifo
Estelle Scifo
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Graph Modeling with Neo4j
2. Graph Databases FREE CHAPTER 3. The Cypher Query Language 4. Empowering Your Business with Pure Cypher 5. Section 2: Graph Algorithms
6. The Graph Data Science Library and Path Finding 7. Spatial Data 8. Node Importance 9. Community Detection and Similarity Measures 10. Section 3: Machine Learning on Graphs
11. Using Graph-based Features in Machine Learning 12. Predicting Relationships 13. Graph Embedding - from Graphs to Matrices 14. Section 4: Neo4j for Production
15. Using Neo4j in Your Web Application 16. Neo4j at Scale 17. Other Books You May Enjoy

Representing spatial attributes

In this section, we are going to use the point built-in data type from Neo4j. Before that, we need to understand a few concepts purely related to spatial data.

Understanding geographic coordinate systems

The spatial data we'll be talking about in this chapter are points on the Earth's surface. There is no unique way to assign coordinates to each point.

Coordinates of a point P on a fixed radius sphere rely on two angles: the longitude, the angle from a reference meridian (θ in the following diagram), and the latitude, the angle with respect to the equator (δ in the following diagram):

Changing the longitude involves moving in a north-south direction, which corresponds to the y-axis in other projection systems. This means in a general case that longitude is denoted with an x and latitude with a y, which is counter-intuitive.

But, as you may know, the Earth is not a perfect sphere. Indeed, the radius close to the equator is higher than...

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