Using GeoJSON

    The most simple structure in GeoJSON is a point. Below is an example point representing the approximate location of . Note that longitude comes first in a GeoJSON coordinate array, not latitude.

    Below is an example of a Mongoose schema where is a point.

    1. const citySchema = new mongoose.Schema({
    2.   name: String,
    3.   location: {
    4.     type: {
    5.       type: String, // Don't do `{ location: { type: String } }`
    6.       enum: ['Point'], // 'location.type' must be 'Point'
    7.       required: true
    8.     },
    9.     coordinates: {
    10.       type: [Number],
    11.       required: true
    12.     }
    13.   }
    14. });

    Using subdocuments, you can define a common pointSchema and reuse it everywhere you want to store a GeoJSON point.

    1. const pointSchema = new mongoose.Schema({
    2.   type: {
    3.     type: String,
    4.     enum: ['Point'],
    5.     required: true
    6.   },
    7.   coordinates: {
    8.     type: [Number],
    9.     required: true
    10.   }
    11. });
    12. const citySchema = new mongoose.Schema({
    13.   name: String,
    14.   location: {
    15.     type: pointSchema,
    17.   }
    18. });

    Polygons are tricky because they use triple nested arrays. Below is how you create a Mongoose schema where coordinates is a triple nested array of numbers.

    1. const polygonSchema = new mongoose.Schema({
    2.   type: {
    3.     type: String,
    4.     enum: ['Polygon'],
    5.     required: true
    6.   },
    7.   coordinates: {
    8.     type: [[[Number]]], // Array of arrays of arrays of numbers
    9.     required: true
    10.   }
    11. });
    13. const citySchema = new mongoose.Schema({
    14.   name: String,
    15.   location: polygonSchema
    16. });

    Mongoose queries support the same that the MongoDB driver does. For example, the below script saves a city document those location property is a GeoJSON point representing the city of Denver, Colorado. It then queries for all documents within a polygon representing the state of Colorado using the MongoDB $geoWithin operator.

    1. const City = db.model('City', new Schema({
    2.   name: String,
    3.   location: pointSchema
    4. }));
    6. const colorado = {
    7.   coordinates: [[
    8.     [-109, 41],
    10.     [-102, 37],
    11.     [-109, 37],
    12.     [-109, 41]
    13.   ]]
    14. };
    15. const denver = { type: 'Point', coordinates: [-104.9903, 39.7392] };
    16. return City.create({ name: 'Denver', location: denver }).
    17.   then(() => City.findOne({
    18.     location: {
    19.       $geoWithin: {
    20.         $geometry: colorado
    21.       }
    22.     }
    23.   })).
    24.   then(doc => assert.equal(doc.name, 'Denver'));

    MongoDB supports for speeding up geospatial queries. Here’s how you can define a 2dsphere index on a GeoJSON point:

    1. const denver = { type: 'Point', coordinates: [-104.9903, 39.7392] };
    2. const City = db.model('City', new Schema({
    3.   name: String,
    4.   location: {
    5.     type: pointSchema,
    6.     index: '2dsphere' // Create a special 2dsphere index on `City.location`
    7.   }
    8. }));
    10. return City.create({ name: 'Denver', location: denver }).
    11.   then(() => City.findOne().where('location').within(colorado)).

    You can also define a geospatial index using the Schema#index() function as shown below.

    1. citySchema.index({ location: '2dsphere' });

    MongoDB’s and $geoNear aggregation stage require a 2dsphere index.