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Neo4j Spatial: Indexing Football Stadiums Using the REST API

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Neo4j Spatial: Indexing Football Stadiums Using the REST API

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Late last week my colleague Peter wrote up some documentation about creating spatial indexes in neo4j via HTTP, something I hadn’t realised was possible until then.

I previously wrote about indexing football stadiums using neo4j spatial but the annoying thing about the approach I described was that I was using neo4j in embedded mode which restricts you to using a JVM language.

The rest of my code is in Ruby so I thought I’d translate that code.

To recap, I’m parsing a CSV file of football stadiums that I downloaded from Chris Bell’s blog which looks like this:

"Adams Park","Wycombe Wanderers",10284,51.6306,-0.800299
"Almondvale Stadium","Livingston",10122,55.8864,-3.52207
"Amex Stadium","Brighton and Hove Albion",22374,50.8609,-0.08014

The code to process the file and index the stadiums in neo4j is as follows (and is essentially a translation of the find_geometries_within_distance_using_cypher test):

require 'csv'
require 'httparty'
require 'json'
  :body => { :layer => 'geom', :lat => 'lat', :lon => 'lon' }.to_json,
  :headers => { 'Content-Type' => 'application/json' } )
  :body => { :name => 'geom', :config => { :provider => 'spatial', :geometry_type => 'point', :lat => 'lat', :lon => 'lon'  } }.to_json,
  :headers => { 'Content-Type' => 'application/json' } )
contents = CSV.read(File.join(File.dirname(__FILE__), 'data', 'stadiums.csv'))
contents.each do |row|
  name, team, capacity, lat, long = row
  node_id = HTTParty.post("http://localhost:7474/db/data/node", 		
    :body => { :lat => lat.to_f, :lon => long.to_f, :name => name, :team => team, :capacity => capacity }.to_json,
    :headers => { 'Content-Type' => 'application/json' } )['self'].split("/")[-1]
    :body => { :key => 'dummy', :value => 'dummy', :uri => "http://localhost:7474/db/data/node/#{node_id}"}.to_json,
    :headers => { 'Content-Type' => 'application/json' } )

One change from the previous version is that I’m not indexing the stadiums using point based geometry rather than wkt.

If we want to find the number of stadiums within 10 km of Centre Point in London we’d write the following query:

START node = node:geom('withinDistance:[51.521348,-0.128113, 10.0]') 
RETURN node.name, node.team;
==> +--------------------------------------------+
==> | node.name          | node.team             |
==> +--------------------------------------------+
==> | "Emirates Stadium" | "Arsenal"             |
==> | "Stamford Bridge"  | "Chelsea"             |
==> | "The Den"          | "Millwall"            |
==> | "Loftus Road"      | "Queens Park Rangers" |
==> | "Craven Cottage"   | "Fulham"              |
==> | "Brisbane Road"    | "Leyton Orient"       |
==> +--------------------------------------------+
==> 6 rows

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