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Geospatial Search with ArangoSearch

ArangoSearch supports geospatial queries like finding coordinates and GeoJSON shapes within a radius or area

ArangoSearch can accelerate various types of geospatial queries for data that is indexed by a View. The regular geospatial index can do most of this too, but ArangoSearch allows you to combine geospatial requests with other kinds of searches, like full-text search.

Creating geospatial Analyzers

Geospatial data that can be indexed:

GeoJSON features such as Points and Polygons (with coordinates in [longitude, latitude] order)
Raw coordinates ([latitude, longitude] arrays or two separate attributes)

The data needs to be pre-processed with a geojson Analyzer in case of GeoJSON and with a geopoint Analyzer in case of raw coordinates. These Analyzers need to be created manually.

Custom Analyzers:

Create a geojson Analyzer in arangosh to pre-process arbitrary GeoJSON shapes. The default properties are usually what you want, therefore an empty object is passed. No Analyzer features are set because they cannot be utilized for Geo Analyzers:

//db._useDatabase("your_database"); // Analyzer will be created in current database
var analyzers = require("@arangodb/analyzers");
analyzers.save("geojson", "geojson", {}, []);

See geojson Analyzer for details.

Create a geopoint Analyzer in arangosh to pre-process raw coordinate arrays using the default properties, hence passing an empty object. No Analyzer features are set as they cannot be utilized for Geo Analyzers:

//db._useDatabase("your_database"); // Analyzer will be created in current database
var analyzers = require("@arangodb/analyzers");
analyzers.save("geo_pair", "geopoint", {}, []);

Create a geopoint Analyzer in arangosh to pre-process raw coordinates with latitude and longitude stored in two different attributes. These attributes cannot be at the top-level of the document, but must be nested in an object, e.g. { location: { lat: 40.78, lon: -73.97 } }. The path relative to the parent attribute (here: location) needs to be described in the Analyzer properties for each of the coordinate attributes. No Analyzer features are set as they cannot be utilized for Geo Analyzers:

//db._useDatabase("your_database"); // Analyzer will be created in current database
var analyzers = require("@arangodb/analyzers");
analyzers.save("geo_latlng", "geopoint", { latitude: ["lat"], longitude: ["lng"] }, []);

Using the example dataset

Load the dataset into an ArangoDB instance and create a View restaurantsViews as described below:

Dataset: Demo Geo S2 dataset

View definition:

{
  "links": {
    "restaurants": {
      "fields": {
        "location": {
          "analyzers": [
            "geojson"
          ]
        }
      }
    }
  },
  "neighborhoods": {
    "fields": {
      "name": {
        "analyzers": [
          "identity"
        ]
      },
      "geometry": {
        "analyzers": [
          "geojson"
        ]
      }
    }
  }
}

Search for points within a radius

Using the Museum of Modern Arts as reference location, find restaurants within a 100 meter radius. Return the matches sorted by distance and include how far away they are from the reference point in the result:

LET moma = GEO_POINT(-73.983, 40.764)
FOR doc IN restaurantsView
  SEARCH ANALYZER(GEO_DISTANCE(doc.location, moma) < 100, "geojson")
  LET distance = GEO_DISTANCE(doc.location, moma)
  SORT distance
  RETURN {
    geometry: doc.location,
    distance
  }

Search for restaurants with Cafe in their name within a radius of 1000 meters and return the ten closest matches:

LET moma = GEO_POINT(-73.983, 40.764)
FOR doc IN restaurantsView
  SEARCH ANALYZER(LIKE(doc.name, "%Cafe%"), "identity")
     AND ANALYZER(GEO_DISTANCE(doc.location, moma) < 1000, "geojson")
  LET distance = GEO_DISTANCE(doc.location, moma)
  SORT distance
  LIMIT 10
  RETURN {
    geometry: doc.location,
    name: doc.name,
    distance
  }

Search for points within a polygon

First off, search for the neighborhood Upper West Side in a subquery and return its GeoJSON Polygon. Then search for restaurants that are contained in this polygon and return them together with the polygon itself:

LET upperWestSide = FIRST(
  FOR doc IN restaurantsView
    SEARCH ANALYZER(doc.name == "Upper West Side", "identity")
    RETURN doc.geometry
)
FOR result IN PUSH(
  FOR doc IN restaurantsView
    SEARCH ANALYZER(GEO_CONTAINS(upperWestSide, doc.location), "geojson")
    RETURN doc.location,
  upperWestSide
)
  RETURN result

ArangoSearch geospatial query for points in a polygon

You do not have to look up the polygon, you can also provide one inline. It is also not necessary to return the polygon, you can return the matches only:

LET upperWestSide = {
  "coordinates": [
    [
      [-73.9600301843709, 40.79803810789689], [-73.96052271669541, 40.797368469462334],
      [-73.96097971807933, 40.79673864404529], [-73.96144060655736, 40.79611082718394],
      [-73.96189985460951, 40.79547927006112], [-73.96235980150668, 40.79485206056065],
      [-73.96280590635729, 40.79423581323211], [-73.96371096541819, 40.79301293488322],
      [-73.9641759852132, 40.79236204502772], [-73.96468540739478, 40.79166402679883],
      [-73.96517705499011, 40.79099034109932], [-73.96562799538655, 40.790366117129004],
      [-73.96609500572444, 40.78973438976665], [-73.96655226678917, 40.78910715282553],
      [-73.96700977073398, 40.78847679074218], [-73.96744908373155, 40.78786072059045],
      [-73.96792696354466, 40.78722157112602], [-73.96838479313664, 40.78659569652393],
      [-73.96884378957469, 40.78596738856434], [-73.96933573318945, 40.78529327955705],
      [-73.96983225556819, 40.7846109105862], [-73.97030068162124, 40.78397541394847],
      [-73.97076013116715, 40.783340137553594], [-73.97122292220932, 40.782706256089995],
      [-73.9717230555586, 40.78202147595964], [-73.97357117423289, 40.7794778616211],
      [-73.97406668257638, 40.77880541672153], [-73.97453231422314, 40.77816778452296],
      [-73.97499744020544, 40.777532546222], [-73.97783054404911, 40.77872973181589],
      [-73.98067365344895, 40.7799251824873], [-73.98140948736065, 40.780235418619405],
      [-73.98151911347311, 40.78028175751621], [-73.9816278736105, 40.780328934969766],
      [-73.98232616371553, 40.78062377270337], [-73.9835260146705, 40.781130011022704],
      [-73.98507184345014, 40.781779680969194], [-73.98536952677372, 40.781078372362586],
      [-73.98567936117642, 40.78031263333493], [-73.98654378951805, 40.780657980791055],
      [-73.98707137465644, 40.78090638159226], [-73.98730772854313, 40.781041303287786],
      [-73.98736363983177, 40.78106280511045], [-73.98741432690473, 40.7810875110951],
      [-73.98746219857024, 40.7811086095956], [-73.98799363156404, 40.78134281734761],
      [-73.98812746102577, 40.78140179644223], [-73.98804128806725, 40.78158596085119],
      [-73.9881002938246, 40.78160287830527], [-73.98807644914505, 40.78165093500162],
      [-73.98801805997222, 40.78163418881042], [-73.98796079284213, 40.781770987031514],
      [-73.98791968459247, 40.78183347771321], [-73.98787728725019, 40.78189205083216],
      [-73.98647480368592, 40.783916573718706], [-73.98625187003543, 40.78423876424543],
      [-73.98611372725294, 40.78443891187735], [-73.98561580396368, 40.78514186259503],
      [-73.98546581197026, 40.78536070057543], [-73.98617270496544, 40.786068452258675],
      [-73.98645586240198, 40.7859192190814], [-73.98707234561569, 40.78518963831753],
      [-73.98711901394266, 40.78521031850151], [-73.98649778102359, 40.78595120288725],
      [-73.98616462880626, 40.786121882448306], [-73.98612842248588, 40.78623900133112],
      [-73.98607113521973, 40.78624070602659], [-73.98602727478911, 40.78622896423671],
      [-73.98609763784941, 40.786058225697936], [-73.98542932126942, 40.78541394218462],
      [-73.98508113773205, 40.785921935110444], [-73.98519883325449, 40.785966552197756],
      [-73.98517050238989, 40.786013334158156], [-73.98521621867376, 40.786030501313824],
      [-73.98525509797992, 40.78597620551157], [-73.98524273937655, 40.78597257215073],
      [-73.98524962933016, 40.78596313985583], [-73.98528177918672, 40.785978620950054],
      [-73.9852400328845, 40.786035858136785], [-73.98568388524215, 40.78622212391968],
      [-73.98571752900456, 40.78617599466878], [-73.98576566029752, 40.786196274858625],
      [-73.9856828719225, 40.78630978621313], [-73.98563627093053, 40.786290150146684],
      [-73.98567072256468, 40.786242911993796], [-73.98561523764435, 40.78621964571528],
      [-73.98520511880037, 40.78604766921276], [-73.98521103560748, 40.78603955488367],
      [-73.98516263994709, 40.78602099926717], [-73.98513163631205, 40.786060297019965],
      [-73.98501696406497, 40.78601423719563], [-73.98493597820354, 40.786130720650974],
      [-73.98465507883022, 40.78653474180794], [-73.98574378790113, 40.78657008235215],
      [-73.98589227228327, 40.78642652901958], [-73.98594285499497, 40.78645284788032],
      [-73.98594956155667, 40.786487113463934], [-73.98581237352651, 40.78661686535709],
      [-73.98513520970327, 40.7865876183929], [-73.98461942858408, 40.78658601634982],
      [-73.98369521623664, 40.7879152813127], [-73.98234664147564, 40.789854780772636],
      [-73.98188645946546, 40.79051658043251], [-73.98139174468567, 40.79122824550256],
      [-73.9812893737095, 40.79137550943302], [-73.9809470835408, 40.79186789327993],
      [-73.980537679464, 40.79245681262498], [-73.98043434256003, 40.79259428309673],
      [-73.98013222578662, 40.79299376538315], [-73.98004684398002, 40.79311352516391],
      [-73.9792882208298, 40.79417763216331], [-73.97828949152755, 40.79558676046088],
      [-73.97779475503205, 40.79628462977189], [-73.97685207845194, 40.79763134839318],
      [-73.97639951930574, 40.79827321018994], [-73.97628527884252, 40.798435235410054],
      [-73.97583055785255, 40.799092280410655], [-73.97578169321191, 40.799156256780286],
      [-73.97579140130195, 40.799209627886206], [-73.97576219486481, 40.79926017354928],
      [-73.97554385822018, 40.79952825063732], [-73.97526783234453, 40.79993284953172],
      [-73.97508668067891, 40.800189533632995], [-73.97496436808184, 40.80036963419388],
      [-73.97483924436003, 40.800558243262664], [-73.97466556722725, 40.80081351473415],
      [-73.97448722520987, 40.801057428896804], [-73.97414361823468, 40.80151689534114],
      [-73.97394098366709, 40.801809025361415], [-73.97389989052462, 40.80188986353119],
      [-73.97377477246009, 40.802045845948555], [-73.97372060455763, 40.80216781528022],
      [-73.97361322463904, 40.80229685988716], [-73.9735422772157, 40.802356411250294],
      [-73.97336671067801, 40.80263011334645], [-73.97320518045738, 40.802830058276285],
      [-73.97312859120993, 40.80297471550862], [-73.97307070537943, 40.8030555484474],
      [-73.97303522902072, 40.803073973741895], [-73.97292317001968, 40.80324982284384],
      [-73.97286807262155, 40.80332073417601], [-73.97287179081519, 40.80335618764528],
      [-73.9727990775659, 40.803329159656634], [-73.9726574474597, 40.803276514162725],
      [-73.97257779806121, 40.803247183771205], [-73.97250022180596, 40.80321661262814],
      [-73.97150381003809, 40.80283773617443], [-73.97032589767365, 40.802384560870536],
      [-73.9702740046587, 40.80235903699402], [-73.97021594793262, 40.80233585148787],
      [-73.9700474216526, 40.8022650103398], [-73.9685836074654, 40.80163547026629],
      [-73.96798415954912, 40.80139826627661], [-73.967873797219, 40.801351698184384],
      [-73.96775900356977, 40.80130351598543], [-73.96571144280439, 40.80043806998765],
      [-73.96286980146162, 40.79923967661966], [-73.96147779901374, 40.79865415643638],
      [-73.9600301843709, 40.79803810789689]
    ]
  ],
  "type": "Polygon"
}
FOR doc IN restaurantsView
  SEARCH ANALYZER(GEO_CONTAINS(upperWestSide, doc.location), "geojson")
  RETURN doc.location

Search for polygons within polygons

Define a GeoJSON polygon that is a rectangle, then search for neighborhoods that are fully contained in this area:

LET sides = {
  left: -74,
  top: 40.8,
  right: -73.93,
  bottom: 40.76
}

LET rect = GEO_POLYGON([
  [sides.left, sides.bottom],
  [sides.left, sides.top],
  [sides.right, sides.top],
  [sides.right, sides.bottom],
  [sides.left, sides.bottom]
])

FOR result IN PUSH(
  FOR doc IN restaurantsView
    SEARCH ANALYZER(GEO_CONTAINS(rect, doc.geometry), "geojson")
    RETURN doc.geometry,
  rect
)
  RETURN result

ArangoSearch geosptial query for polygons in a polygon

Searching for geo features in a rectangle is something you can use together with an interactive map that the user can select the area of interest with. Take a look at the lunch break video about the ArangoBnB demo project to learn more.

Search for polygons intersecting polygons

Define a GeoJSON polygon that is a rectangle, then search for neighborhoods that intersect with this area:

LET sides = {
  left: -74,
  top: 40.8,
  right: -73.93,
  bottom: 40.76
}

LET rect = GEO_POLYGON([
  [sides.left, sides.bottom],
  [sides.left, sides.top],
  [sides.right, sides.top],
  [sides.right, sides.bottom],
  [sides.left, sides.bottom]
])

FOR result IN PUSH(
  FOR doc IN restaurantsView
    SEARCH ANALYZER(GEO_INTERSECTS(rect, doc.geometry), "geojson")
    RETURN doc.geometry,
  rect
)
  RETURN result

ArangoSearch geospatial query for polygons intersecting a polygon

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