Indexing Spatial Data

Exmaple:

// Define an index with a spatial field
class Events_ByNameAndCoordinates extends AbstractJavaScriptIndexCreationTask {
    constructor() {
        super();
        const { createSpatialField } = this.mapUtils();

        this.map('events', e => {
            return {
                name: e.Name,
                // Call 'createSpatialField' to create a spatial index-field
                // Field 'coordinates' will be composed of lat & lng supplied from the document
                coordinates: createSpatialField(
                    e.latitude,
                    e.longitude
                )
                
                // Documents can be retrieved
                // by making a spatial query on the 'coordinates' index-field
            };
        });
    }
}

class Event {
    constructor(id, name, latitude, longitude) {
        this.id = id;
        this.name = name;
        this.latitude = latitude
        this.longitude = longitude;
    }
}

// Define an index with a spatial field
class EventsWithWKT_ByNameAndWKT extends AbstractJavaScriptIndexCreationTask {
    constructor() {
        super();
        const { createSpatialField } = this.mapUtils();

        this.map('events', e => {
            return {
                name: e.Name,
                // Call 'createSpatialField' to create a spatial index-field
                // Field 'wkt' will be composed of the WKT string supplied from the document
                wkt: createSpatialField(e.wkt)

                // Documents can be retrieved by
                // making a spatial query on the 'wkt' index-field
            };
        });
    }
}

class EventWithWKT {
    constructor(id, name, wkt) {
        this.id = id;
        this.name = name;
        this.wkt = wkt;
    }
}

Syntax:

createSpatialField(lat, lng);
createSpatialField(wkt);

Exmaple:

class Events_ByNameAndCoordinates_Custom extends AbstractJavaScriptIndexCreationTask {
    constructor() {
        super();
        const { createSpatialField } = this.mapUtils();

        this.map('events', e => {
            return {
                name: e.Name,
                // Define a spatial index-field
                coordinates: createSpatialField(
                    e.latitude,
                    e.longitude
                )

                // Documents can be retrieved
                // by making a spatial query on the 'coordinates' index-field
            };
        });

        // Set the spatial indexing strategy for the spatial field 'coordinates' 
        this.spatial("coordinates", factory => factory.cartesian().boundingBoxIndex());
    }
}

Syntax:

class SpatialOptionsFactory {
    geography(): GeographySpatialOptionsFactory;
    cartesian(): CartesianSpatialOptionsFactory;
}

defaultOptions(circleRadiusUnits);
boundingBoxIndex(circleRadiusUnits);
geohashPrefixTreeIndex(maxTreeLevel, circleRadiusUnits);
quadPrefixTreeIndex(maxTreeLevel, circleRadiusUnits);

boundingBoxIndex(): SpatialOptions;
quadPrefixTreeIndex(maxTreeLevel, bounds);

class SpatialBounds {
    minX; // number
    maxX; // number
    minY; // number
    maxY; // number
}

BoundingBox

• The bounding box strategy is the simplest.
  Given a spatial shape, such as a point, circle, or polygon, the shape's bounding box is computed
  and the spatial coordinates (minX, minY, maxX, maxY) that enclose the shape are indexed.

• When making a query,
  RavenDB translates the query criteria to the same bounding box system used for indexing.

• Bounding box strategy is cheaper at indexing time and can produce quick queries,
  but that's at the expense of the level of accuracy you can get.

• Read more about bounding box here.

GeoHashPrefixTree

• Geohash is a latitude/longitude representation system that describes Earth as a grid with 32 cells, assigning an alphanumeric character to each grid cell. Each grid cell is further divided into 32 smaller chunks, and each chunk has an alphanumeric character assigned as well, and so on.

• E.g. The location of 'New York' in the United States is represented by the following geohash: DR5REGY6R and it represents the 40.7144 -74.0060 coordinates. Removing characters from the end of the geohash will decrease the precision level.

• The maxTreeLevel determines the length of the geohash used for the indexing, which in turn affects accuracy. By default, it is set to 9, providing a resolution of approximately 2.5 meters.

• More information about geohash uses, decoding algorithm, and limitations can be found here.

Geohash precision values:

QuadPrefixTree

• The QuadTree represents Earth as a grid consisting of four cells (also known as buckets). Similar to GeoHash, each cell is assigned a letter, and is recursively divided into four more cells, creating a hierarchical structure.

• By default, the precision level (maxTreeLevel) for QuadPrefixTree is 23.

• More information about QuadTree can be found here.

Quadtree precision values: