Geospatial queries basics in MongoDB - Mini Project: Build & Apply

Geospatial Queries Basics with MongoDB

📖 Scenario: You are building a simple location-based service that stores places with their geographic coordinates. You want to find places near a specific location.

🎯 Goal: Create a MongoDB collection with place data including coordinates, set up a geospatial index, and write a query to find places near a given point.

📋 What You'll Learn

Create a MongoDB collection named places with documents containing name and location fields.

The location field must be a GeoJSON point with exact coordinates.

Create a 2dsphere index on the location field.

Write a query using $near to find places near a specific coordinate.

💡 Why This Matters

🌍 Real World

Location-based services like finding nearby restaurants, parks, or stores use geospatial queries to provide relevant results based on user location.

💼 Career

Understanding geospatial queries is important for roles involving location data, such as backend developers, data engineers, and GIS specialists.

1

Create the places collection with place documents

Insert three documents into the places collection with these exact entries: { name: "Central Park", location: { type: "Point", coordinates: [-73.9654, 40.7829] } }, { name: "Times Square", location: { type: "Point", coordinates: [-73.9855, 40.7580] } }, and { name: "Empire State Building", location: { type: "Point", coordinates: [-73.9857, 40.7484] } }.

MongoDB

// Insert the three place documents into the places collection

Hint

Use db.places.insertMany([...]) with the exact documents.

2

Create a 2dsphere index on the location field

Create a geospatial index on the location field of the places collection using db.places.createIndex({ location: "2dsphere" }).

MongoDB

db.places.insertMany([
  { name: "Central Park", location: { type: "Point", coordinates: [-73.9654, 40.7829] } },
  { name: "Times Square", location: { type: "Point", coordinates: [-73.9855, 40.7580] } },
  { name: "Empire State Building", location: { type: "Point", coordinates: [-73.9857, 40.7484] } }
])

// Create a 2dsphere index on the location field

Hint

Use db.places.createIndex({ location: "2dsphere" }) to enable geospatial queries.

3

Write a query to find places near a specific point

Write a query to find places near the coordinate [-73.9850, 40.7589] using db.places.find({ location: { $near: { $geometry: { type: "Point", coordinates: [-73.9850, 40.7589] } } } }).

MongoDB

db.places.insertMany([
  { name: "Central Park", location: { type: "Point", coordinates: [-73.9654, 40.7829] } },
  { name: "Times Square", location: { type: "Point", coordinates: [-73.9855, 40.7580] } },
  { name: "Empire State Building", location: { type: "Point", coordinates: [-73.9857, 40.7484] } }
])

db.places.createIndex({ location: "2dsphere" })

// Write a query to find places near [-73.9850, 40.7589]

Hint

Use db.places.find({ location: { $near: { $geometry: { type: "Point", coordinates: [...] } } }) with the exact coordinates.

4

Assign the query result to a variable to complete the setup

Assign the result of the db.places.find query to a variable named nearbyPlaces to complete the geospatial query setup.

MongoDB

db.places.insertMany([
  { name: "Central Park", location: { type: "Point", coordinates: [-73.9654, 40.7829] } },
  { name: "Times Square", location: { type: "Point", coordinates: [-73.9855, 40.7580] } },
  { name: "Empire State Building", location: { type: "Point", coordinates: [-73.9857, 40.7484] } }
])

db.places.createIndex({ location: "2dsphere" })

// Assign the query result to nearbyPlaces

Hint

Assign the query to nearbyPlaces using const nearbyPlaces = db.places.find(...).

Practice

1. What does the $near operator do in MongoDB geospatial queries?

easy

A. Finds documents with locations closest to a specified point

B. Deletes documents within a certain distance

C. Updates documents with new coordinates

D. Sorts documents alphabetically by location name

Solution

1. Step 1: Understand the purpose of $near

   The $near operator is used to find documents that are geographically close to a given point.

2. Step 2: Compare options with $near functionality

   Only Finds documents with locations closest to a specified point correctly describes finding documents near a location. Other options describe unrelated actions.

3. Final Answer:

   Finds documents with locations closest to a specified point -> Option A

4. Quick Check:

   $near = Finds nearby points [OK]

Hint: Remember: $near finds closest points to a location [OK]

Common Mistakes:

• Thinking $near deletes or updates documents
• Confusing $near with sorting operators
• Assuming $near works without a geospatial index

2. Which of the following is the correct syntax to find documents near a point using $near in MongoDB?

easy

A. { location: { $near: { $geometry: { type: 'Point', coordinates: [-70, 40] } } } }

B. { location: { $near: { $geometry: { type: 'Point', coordinates: [40, -70] } } } }

C. { location: { $near: { $geometry: { type: 'Point', coordinates: [70, 40] } } } }

D. { location: { $near: { $geometry: { type: 'Point', coordinates: [-40, 70] } } } }

Solution

1. Step 1: Recall coordinate order in MongoDB

   Coordinates must be in [longitude, latitude] order, so longitude comes first.

2. Step 2: Check each option's coordinate order

   { location: { $near: { $geometry: { type: 'Point', coordinates: [-70, 40] } } } } uses [-70, 40], which is [longitude, latitude]. Others have latitude first or wrong signs.

3. Final Answer:

   { location: { $near: { $geometry: { type: 'Point', coordinates: [-70, 40] } } } } -> Option A

4. Quick Check:

   Coordinates = [longitude, latitude] [OK]

Hint: Coordinates always [longitude, latitude] in MongoDB [OK]

Common Mistakes:

• Swapping latitude and longitude order
• Using positive latitude as longitude
• Forgetting to use $geometry inside $near

3. Given a collection with a 2dsphere index on location, what will this query return?

{ location: { $near: { $geometry: { type: 'Point', coordinates: [-73.97, 40.77] }, $maxDistance: 500 } } }

medium

A. All documents regardless of location

B. Documents with locations within 500 meters of [-73.97, 40.77]

C. Documents with locations more than 500 meters away

D. Documents with locations exactly at [-73.97, 40.77]

Solution

1. Step 1: Understand $near with $maxDistance

   The query finds documents near the point within the maximum distance specified (500 meters).

2. Step 2: Interpret the query result

   It returns documents whose location is within 500 meters of the given coordinates, not exactly at or beyond that distance.

3. Final Answer:

   Documents with locations within 500 meters of [-73.97, 40.77] -> Option B

4. Quick Check:

   $near + $maxDistance = nearby points within distance [OK]

Hint: Use $maxDistance to limit how far results can be [OK]

Common Mistakes:

• Thinking $maxDistance means exact match
• Assuming it returns points beyond maxDistance
• Ignoring the need for a 2dsphere index

4. You run this query but get an error:

{ location: { $near: { $geometry: { type: 'Point', coordinates: [40.77, -73.97] } } } }

What is the most likely cause?

medium

A. Coordinates are in wrong order; should be [longitude, latitude]

B. Missing $maxDistance parameter

C. The location field is not indexed with 2dsphere

D. The type should be 'Polygon' instead of 'Point'

Solution

1. Step 1: Requirements for $near with $geometry

   The $near operator with $geometry requires a 2dsphere index on the location field.

2. Step 2: Identify the most likely error cause

   Without a 2dsphere index, MongoDB throws an error for geospatial queries like $near. Coordinate order affects results but does not cause an error.

3. Final Answer:

   The location field is not indexed with 2dsphere -> Option C

4. Quick Check:

   $near requires 2dsphere index [OK]

Hint: Always put longitude before latitude in coordinates [OK]

Common Mistakes:

• Swapping latitude and longitude
• Assuming $maxDistance is required
• Confusing geometry types

5. You want to find all restaurants within 1 km of a user's location stored as { type: 'Point', coordinates: [-122.42, 37.77] }. Which query correctly finds these restaurants assuming a 2dsphere index on location?

hard

A. { location: { $near: { $geometry: { type: 'Point', coordinates: [-122.42, 37.77] }, $minDistance: 1000 } } }

B. { location: { $geoWithin: { $centerSphere: [[-122.42, 37.77], 1] } } }

C. { location: { $nearSphere: { $geometry: { type: 'Point', coordinates: [37.77, -122.42] }, $maxDistance: 1000 } } }

D. { location: { $near: { $geometry: { type: 'Point', coordinates: [-122.42, 37.77] }, $maxDistance: 1000 } } }

Solution

1. Step 1: Identify correct coordinate order and operator

   Coordinates must be [longitude, latitude], so [-122.42, 37.77] is correct. The $near operator with $maxDistance finds points within distance.

2. Step 2: Evaluate each option

   { location: { $near: { $geometry: { type: 'Point', coordinates: [-122.42, 37.77] }, $maxDistance: 1000 } } } uses correct coordinates and $near with $maxDistance 1000 meters (1 km). { location: { $geoWithin: { $centerSphere: [[-122.42, 37.77], 1] } } } uses $geoWithin with radius in radians, not meters. { location: { $nearSphere: { $geometry: { type: 'Point', coordinates: [37.77, -122.42] }, $maxDistance: 1000 } } } swaps coordinates. { location: { $near: { $geometry: { type: 'Point', coordinates: [-122.42, 37.77] }, $minDistance: 1000 } } } uses $minDistance which finds points farther than 1 km, not within.

3. Final Answer:

   { location: { $near: { $geometry: { type: 'Point', coordinates: [-122.42, 37.77] }, $maxDistance: 1000 } } } -> Option D

4. Quick Check:

   $near + $maxDistance + correct coords = find nearby points [OK]

Hint: Use $near with $maxDistance and correct coords for nearby search [OK]

Common Mistakes:

• Swapping latitude and longitude
• Using $minDistance instead of $maxDistance
• Confusing meters with radians in $geoWithin