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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

Adding points to a spatial layer

Once the spatial layer is created, we can add data to it, which will also update the spatial index. The following query adds all nodes with the POI label to the newly created spatial layer with the name 'pointLayer':

MATCH (n:POI)
CALL spatial.addNode('pointLayer', n) YIELD node
RETURN count(node)

This operation adds two more properties to the node:

  • point attribute, similar to the built-in Neo4j type
  • bbox attribute used for querying the data with a spatial index, as discussed earlier in this chapter

We will see how to use this data in the following sections. Before that, we are going to deal with other types of geometries, so as to take full advantage of neo4j-spatial compared to built-in Neo4j types.

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