Geospatial Search with ArangoSearch
ArangoSearch supports geospatial queries like finding locations 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), for example:{ "location": { "type": "Point", "coordinates": [ -73.983, 40.764 ] } }Coordinates using an array with two numbers in
[longitude, latitude]order, for example:{ "location": [ -73.983, 40.764 ] }Coordinates using an array with two numbers in
[latitude, longitude]order, for example:{ "location": [ 40.764, -73.983 ] }Coordinates using two separate numeric attributes, for example:
{ "location": { "lat": 40.764, "lng": -73.983 } }
You need to create Geo Analyzers manually. There are no pre-configured (built-in) Geo Analyzers.
The data needs to be pre-processed with a
geojsonorgeo_s2Analyzer in case of GeoJSON or coordinate arrays in[longitude, latitude]order.For coordinate arrays in
[latitude, longitude]order or coordinate pairs using separate attributes, you need to use ageopointAnalyzer.
Custom Analyzers:
Create a geojson Analyzer in arangosh to pre-process arbitrary GeoJSON features or [longitude, latitude] arrays. 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.
You can use the
geo_s2 Analyzer instead of the geojson Analyzer to more efficiently index geo-spatial data. It is mostly a drop-in replacement, but you can choose between different binary formats. See Analyzers for details.Create a geopoint Analyzer in arangosh using the default properties (empty object) to pre-process coordinate arrays in [latitude, longitude] order. 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 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
db.restaurants.ensureIndex({ name: "inv-rest", type: "inverted", fields: [ { name: "location", analyzer: "geojson" } ] }); db.neighborhoods.ensureIndex({ name: "inv-hood", type: "inverted", fields: [ "name", { name: "geometry", analyzer: "geojson" } ] }); db._createView("restaurantsViewAlias", "search-alias", { indexes: [ { collection: "restaurants", index: "inv-rest" }, { collection: "neighborhoods", index: "inv-hood" } ] });{ "links": { "restaurants": { "fields": { "name": { "analyzers": [ "identity" ] }, "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 restaurantsViewAlias SEARCH GEO_DISTANCE(doc.location, moma) < 100 LET distance = GEO_DISTANCE(doc.location, moma) SORT distance RETURN { geometry: doc.location, distance }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 restaurantsViewAlias SEARCH LIKE(doc.name, "%Cafe%") AND GEO_DISTANCE(doc.location, moma) < 1000 LET distance = GEO_DISTANCE(doc.location, moma) SORT distance LIMIT 10 RETURN { geometry: doc.location, name: doc.name, distance }LET moma = GEO_POINT(-73.983, 40.764) FOR doc IN restaurantsView SEARCH LIKE(doc.name, "%Cafe%") 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 restaurantsViewAlias SEARCH doc.name == "Upper West Side" RETURN doc.geometry ) FOR result IN PUSH( FOR doc IN restaurantsViewAlias SEARCH GEO_CONTAINS(upperWestSide, doc.location) RETURN doc.location, upperWestSide ) RETURN resultLET upperWestSide = FIRST( FOR doc IN restaurantsView SEARCH doc.name == "Upper West Side" 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
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], [-73.96147779901374, 40.79865415643638], [-73.96286980146162, 40.79923967661966], [-73.96571144280439, 40.80043806998765], [-73.96775900356977, 40.80130351598543], [-73.967873797219, 40.801351698184384], [-73.96798415954912, 40.80139826627661], [-73.9685836074654, 40.80163547026629], [-73.9700474216526, 40.8022650103398], [-73.97021594793262, 40.80233585148787], [-73.9702740046587, 40.80235903699402], [-73.97032589767365, 40.802384560870536], [-73.97150381003809, 40.80283773617443], [-73.97250022180596, 40.80321661262814], [-73.97257779806121, 40.803247183771205], [-73.9726574474597, 40.803276514162725], [-73.9727990775659, 40.803329159656634], [-73.97287179081519, 40.80335618764528], [-73.97286807262155, 40.80332073417601], [-73.97292317001968, 40.80324982284384], [-73.97303522902072, 40.803073973741895], [-73.97307070537943, 40.8030555484474], [-73.97312859120993, 40.80297471550862], [-73.97320518045738, 40.802830058276285], [-73.97336671067801, 40.80263011334645], [-73.9735422772157, 40.802356411250294], [-73.97361322463904, 40.80229685988716], [-73.97372060455763, 40.80216781528022], [-73.97377477246009, 40.802045845948555], [-73.97389989052462, 40.80188986353119], [-73.97394098366709, 40.801809025361415], [-73.97414361823468, 40.80151689534114], [-73.97448722520987, 40.801057428896804], [-73.97466556722725, 40.80081351473415], [-73.97483924436003, 40.800558243262664], [-73.97496436808184, 40.80036963419388], [-73.97508668067891, 40.800189533632995], [-73.97526783234453, 40.79993284953172], [-73.97554385822018, 40.79952825063732], [-73.97576219486481, 40.79926017354928], [-73.97579140130195, 40.799209627886206], [-73.97578169321191, 40.799156256780286], [-73.97583055785255, 40.799092280410655], [-73.97628527884252, 40.798435235410054], [-73.97639951930574, 40.79827321018994], [-73.97685207845194, 40.79763134839318], [-73.97779475503205, 40.79628462977189], [-73.97828949152755, 40.79558676046088], [-73.9792882208298, 40.79417763216331], [-73.98004684398002, 40.79311352516391], [-73.98013222578662, 40.79299376538315], [-73.98043434256003, 40.79259428309673], [-73.980537679464, 40.79245681262498], [-73.9809470835408, 40.79186789327993], [-73.9812893737095, 40.79137550943302], [-73.98139174468567, 40.79122824550256], [-73.98188645946546, 40.79051658043251], [-73.98234664147564, 40.789854780772636], [-73.98369521623664, 40.7879152813127], [-73.98461942858408, 40.78658601634982], [-73.98513520970327, 40.7865876183929], [-73.98581237352651, 40.78661686535709], [-73.98594956155667, 40.786487113463934], [-73.98594285499497, 40.78645284788032], [-73.98589227228327, 40.78642652901958], [-73.98574378790113, 40.78657008235215], [-73.98465507883022, 40.78653474180794], [-73.98493597820354, 40.786130720650974], [-73.98501696406497, 40.78601423719563], [-73.98513163631205, 40.786060297019965], [-73.98516263994709, 40.78602099926717], [-73.98521103560748, 40.78603955488367], [-73.98520511880037, 40.78604766921276], [-73.98561523764435, 40.78621964571528], [-73.98567072256468, 40.786242911993796], [-73.98563627093053, 40.786290150146684], [-73.9856828719225, 40.78630978621313], [-73.98576566029752, 40.786196274858625], [-73.98571752900456, 40.78617599466878], [-73.98568388524215, 40.78622212391968], [-73.9852400328845, 40.786035858136785], [-73.98528177918672, 40.785978620950054], [-73.98524962933016, 40.78596313985583], [-73.98524273937655, 40.78597257215073], [-73.98525509797992, 40.78597620551157], [-73.98521621867376, 40.786030501313824], [-73.98517050238989, 40.786013334158156], [-73.98519883325449, 40.785966552197756], [-73.98508113773205, 40.785921935110444], [-73.98542932126942, 40.78541394218462], [-73.98609763784941, 40.786058225697936], [-73.98602727478911, 40.78622896423671], [-73.98607113521973, 40.78624070602659], [-73.98612842248588, 40.78623900133112], [-73.98616462880626, 40.786121882448306], [-73.98649778102359, 40.78595120288725], [-73.98711901394266, 40.78521031850151], [-73.98707234561569, 40.78518963831753], [-73.98645586240198, 40.7859192190814], [-73.98617270496544, 40.786068452258675], [-73.98546581197026, 40.78536070057543], [-73.98561580396368, 40.78514186259503], [-73.98611372725294, 40.78443891187735], [-73.98625187003543, 40.78423876424543], [-73.98647480368592, 40.783916573718706], [-73.98787728725019, 40.78189205083216], [-73.98791968459247, 40.78183347771321], [-73.98796079284213, 40.781770987031514], [-73.98801805997222, 40.78163418881042], [-73.98807644914505, 40.78165093500162], [-73.9881002938246, 40.78160287830527], [-73.98804128806725, 40.78158596085119], [-73.98812746102577, 40.78140179644223], [-73.98799363156404, 40.78134281734761], [-73.98746219857024, 40.7811086095956], [-73.98741432690473, 40.7810875110951], [-73.98736363983177, 40.78106280511045], [-73.98730772854313, 40.781041303287786], [-73.98707137465644, 40.78090638159226], [-73.98654378951805, 40.780657980791055], [-73.98567936117642, 40.78031263333493], [-73.98536952677372, 40.781078372362586], [-73.98507184345014, 40.781779680969194], [-73.9835260146705, 40.781130011022704], [-73.98232616371553, 40.78062377270337], [-73.9816278736105, 40.780328934969766], [-73.98151911347311, 40.78028175751621], [-73.98140948736065, 40.780235418619405], [-73.98067365344895, 40.7799251824873], [-73.97783054404911, 40.77872973181589], [-73.97499744020544, 40.777532546222], [-73.97453231422314, 40.77816778452296], [-73.97406668257638, 40.77880541672153], [-73.97357117423289, 40.7794778616211], [-73.9717230555586, 40.78202147595964], [-73.97122292220932, 40.782706256089995], [-73.97076013116715, 40.783340137553594], [-73.97030068162124, 40.78397541394847], [-73.96983225556819, 40.7846109105862], [-73.96933573318945, 40.78529327955705], [-73.96884378957469, 40.78596738856434], [-73.96838479313664, 40.78659569652393], [-73.96792696354466, 40.78722157112602], [-73.96744908373155, 40.78786072059045], [-73.96700977073398, 40.78847679074218], [-73.96655226678917, 40.78910715282553], [-73.96609500572444, 40.78973438976665], [-73.96562799538655, 40.790366117129004], [-73.96517705499011, 40.79099034109932], [-73.96468540739478, 40.79166402679883], [-73.9641759852132, 40.79236204502772], [-73.96371096541819, 40.79301293488322], [-73.96280590635729, 40.79423581323211], [-73.96235980150668, 40.79485206056065], [-73.96189985460951, 40.79547927006112], [-73.96144060655736, 40.79611082718394], [-73.96097971807933, 40.79673864404529], [-73.96052271669541, 40.797368469462334], [-73.9600301843709, 40.79803810789689] ] ], "type": "Polygon" } FOR doc IN restaurantsViewAlias SEARCH GEO_CONTAINS(upperWestSide, doc.location) RETURN doc.locationLET upperWestSide = { "coordinates": [ [ [-73.9600301843709, 40.79803810789689], [-73.96147779901374, 40.79865415643638], [-73.96286980146162, 40.79923967661966], [-73.96571144280439, 40.80043806998765], [-73.96775900356977, 40.80130351598543], [-73.967873797219, 40.801351698184384], [-73.96798415954912, 40.80139826627661], [-73.9685836074654, 40.80163547026629], [-73.9700474216526, 40.8022650103398], [-73.97021594793262, 40.80233585148787], [-73.9702740046587, 40.80235903699402], [-73.97032589767365, 40.802384560870536], [-73.97150381003809, 40.80283773617443], [-73.97250022180596, 40.80321661262814], [-73.97257779806121, 40.803247183771205], [-73.9726574474597, 40.803276514162725], [-73.9727990775659, 40.803329159656634], [-73.97287179081519, 40.80335618764528], [-73.97286807262155, 40.80332073417601], [-73.97292317001968, 40.80324982284384], [-73.97303522902072, 40.803073973741895], [-73.97307070537943, 40.8030555484474], [-73.97312859120993, 40.80297471550862], [-73.97320518045738, 40.802830058276285], [-73.97336671067801, 40.80263011334645], [-73.9735422772157, 40.802356411250294], [-73.97361322463904, 40.80229685988716], [-73.97372060455763, 40.80216781528022], [-73.97377477246009, 40.802045845948555], [-73.97389989052462, 40.80188986353119], [-73.97394098366709, 40.801809025361415], [-73.97414361823468, 40.80151689534114], [-73.97448722520987, 40.801057428896804], [-73.97466556722725, 40.80081351473415], [-73.97483924436003, 40.800558243262664], [-73.97496436808184, 40.80036963419388], [-73.97508668067891, 40.800189533632995], [-73.97526783234453, 40.79993284953172], [-73.97554385822018, 40.79952825063732], [-73.97576219486481, 40.79926017354928], [-73.97579140130195, 40.799209627886206], [-73.97578169321191, 40.799156256780286], [-73.97583055785255, 40.799092280410655], [-73.97628527884252, 40.798435235410054], [-73.97639951930574, 40.79827321018994], [-73.97685207845194, 40.79763134839318], [-73.97779475503205, 40.79628462977189], [-73.97828949152755, 40.79558676046088], [-73.9792882208298, 40.79417763216331], [-73.98004684398002, 40.79311352516391], [-73.98013222578662, 40.79299376538315], [-73.98043434256003, 40.79259428309673], [-73.980537679464, 40.79245681262498], [-73.9809470835408, 40.79186789327993], [-73.9812893737095, 40.79137550943302], [-73.98139174468567, 40.79122824550256], [-73.98188645946546, 40.79051658043251], [-73.98234664147564, 40.789854780772636], [-73.98369521623664, 40.7879152813127], [-73.98461942858408, 40.78658601634982], [-73.98513520970327, 40.7865876183929], [-73.98581237352651, 40.78661686535709], [-73.98594956155667, 40.786487113463934], [-73.98594285499497, 40.78645284788032], [-73.98589227228327, 40.78642652901958], [-73.98574378790113, 40.78657008235215], [-73.98465507883022, 40.78653474180794], [-73.98493597820354, 40.786130720650974], [-73.98501696406497, 40.78601423719563], [-73.98513163631205, 40.786060297019965], [-73.98516263994709, 40.78602099926717], [-73.98521103560748, 40.78603955488367], [-73.98520511880037, 40.78604766921276], [-73.98561523764435, 40.78621964571528], [-73.98567072256468, 40.786242911993796], [-73.98563627093053, 40.786290150146684], [-73.9856828719225, 40.78630978621313], [-73.98576566029752, 40.786196274858625], [-73.98571752900456, 40.78617599466878], [-73.98568388524215, 40.78622212391968], [-73.9852400328845, 40.786035858136785], [-73.98528177918672, 40.785978620950054], [-73.98524962933016, 40.78596313985583], [-73.98524273937655, 40.78597257215073], [-73.98525509797992, 40.78597620551157], [-73.98521621867376, 40.786030501313824], [-73.98517050238989, 40.786013334158156], [-73.98519883325449, 40.785966552197756], [-73.98508113773205, 40.785921935110444], [-73.98542932126942, 40.78541394218462], [-73.98609763784941, 40.786058225697936], [-73.98602727478911, 40.78622896423671], [-73.98607113521973, 40.78624070602659], [-73.98612842248588, 40.78623900133112], [-73.98616462880626, 40.786121882448306], [-73.98649778102359, 40.78595120288725], [-73.98711901394266, 40.78521031850151], [-73.98707234561569, 40.78518963831753], [-73.98645586240198, 40.7859192190814], [-73.98617270496544, 40.786068452258675], [-73.98546581197026, 40.78536070057543], [-73.98561580396368, 40.78514186259503], [-73.98611372725294, 40.78443891187735], [-73.98625187003543, 40.78423876424543], [-73.98647480368592, 40.783916573718706], [-73.98787728725019, 40.78189205083216], [-73.98791968459247, 40.78183347771321], [-73.98796079284213, 40.781770987031514], [-73.98801805997222, 40.78163418881042], [-73.98807644914505, 40.78165093500162], [-73.9881002938246, 40.78160287830527], [-73.98804128806725, 40.78158596085119], [-73.98812746102577, 40.78140179644223], [-73.98799363156404, 40.78134281734761], [-73.98746219857024, 40.7811086095956], [-73.98741432690473, 40.7810875110951], [-73.98736363983177, 40.78106280511045], [-73.98730772854313, 40.781041303287786], [-73.98707137465644, 40.78090638159226], [-73.98654378951805, 40.780657980791055], [-73.98567936117642, 40.78031263333493], [-73.98536952677372, 40.781078372362586], [-73.98507184345014, 40.781779680969194], [-73.9835260146705, 40.781130011022704], [-73.98232616371553, 40.78062377270337], [-73.9816278736105, 40.780328934969766], [-73.98151911347311, 40.78028175751621], [-73.98140948736065, 40.780235418619405], [-73.98067365344895, 40.7799251824873], [-73.97783054404911, 40.77872973181589], [-73.97499744020544, 40.777532546222], [-73.97453231422314, 40.77816778452296], [-73.97406668257638, 40.77880541672153], [-73.97357117423289, 40.7794778616211], [-73.9717230555586, 40.78202147595964], [-73.97122292220932, 40.782706256089995], [-73.97076013116715, 40.783340137553594], [-73.97030068162124, 40.78397541394847], [-73.96983225556819, 40.7846109105862], [-73.96933573318945, 40.78529327955705], [-73.96884378957469, 40.78596738856434], [-73.96838479313664, 40.78659569652393], [-73.96792696354466, 40.78722157112602], [-73.96744908373155, 40.78786072059045], [-73.96700977073398, 40.78847679074218], [-73.96655226678917, 40.78910715282553], [-73.96609500572444, 40.78973438976665], [-73.96562799538655, 40.790366117129004], [-73.96517705499011, 40.79099034109932], [-73.96468540739478, 40.79166402679883], [-73.9641759852132, 40.79236204502772], [-73.96371096541819, 40.79301293488322], [-73.96280590635729, 40.79423581323211], [-73.96235980150668, 40.79485206056065], [-73.96189985460951, 40.79547927006112], [-73.96144060655736, 40.79611082718394], [-73.96097971807933, 40.79673864404529], [-73.96052271669541, 40.797368469462334], [-73.9600301843709, 40.79803810789689] ] ], "type": "Polygon" } FOR doc IN restaurantsView SEARCH ANALYZER(GEO_CONTAINS(upperWestSide, doc.location), "geojson") RETURN doc.locationSearch 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.right, sides.bottom], [sides.right, sides.top], [sides.left, sides.top], [sides.left, sides.bottom] ]) FOR result IN PUSH( FOR doc IN restaurantsViewAlias SEARCH GEO_CONTAINS(rect, doc.geometry) RETURN doc.geometry, rect ) RETURN resultLET sides = { left: -74, top: 40.8, right: -73.93, bottom: 40.76 } LET rect = GEO_POLYGON([ [sides.left, sides.bottom], [sides.right, sides.bottom], [sides.right, sides.top], [sides.left, sides.top], [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
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.right, sides.bottom], [sides.right, sides.top], [sides.left, sides.top], [sides.left, sides.bottom] ]) FOR result IN PUSH( FOR doc IN restaurantsViewAlias SEARCH GEO_INTERSECTS(rect, doc.geometry) RETURN doc.geometry, rect ) RETURN resultLET sides = { left: -74, top: 40.8, right: -73.93, bottom: 40.76 } LET rect = GEO_POLYGON([ [sides.left, sides.bottom], [sides.right, sides.bottom], [sides.right, sides.top], [sides.left, sides.top], [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