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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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 Scifo Scifo
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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

Creating a spatial layer

Both junctions and streets have spatial attributes. Junctions have latitude and longitude properties and streets (relationships of the LINKED_TO type in our graph) have a geometry property containing the WKT representation of a LINESTRING object. We can create a spatial layer for each of these entities; however, remember that the GDS path finding algorithms work between nodes, not relationships. This means that, from the (latitude, longitude) user input, we will have to find the closest Junction node. So we need to create a spatial point layer to index our 4426 junctions. For now, there is no need to create a layer to hold the streets; we will create it later on if necessary.

Let's then create the point layer that will index the nodes with the Junction label:

CALL spatial.addPointLayer("junctions")

Now, add points to it:

MATCH (n:Junction)
CALL spatial.addNode("junctions", n) YIELD node
RETURN count(node)

After a...

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