Handling MongoDB GeoJson

Whenever we have to store the location in MongoDb we usually encounter a confusion as to how to store the location co-ordinates in the right way. Mongodb has a format called GeoJson that handles the location in a more readable and intutive way.

This is how we store the legacy coordinate pairs in MongoDB:

"location": [-73.856077, 40.848447]

This is the format of storing the location as a GeoJson in mongodb:

"location": {
    "type": "Point",
    "coordinates": [-73.856077, 40.848447]
}

In mongodb there are 2 Geospatial Indexes possible:

1. 2dsphere Indexes - for spherical Earth models
2. 2d Indexes - for flat Earth models

Spatial operators like $geoWithin, $geoIntersects, and $near allow for precise filtering and retrieval of data based on

• proximity,
• containment, or
• intersection with our data.

read more: here

🔗Examples:

1. Example Finding the 5 closest points to me Lat, Lon

   ################################################################################
   
   from pymongo import MongoClient
   
   # Connect to the MongoDB server
   client = MongoClient('mongodb://localhost:30000')
   
   # Access the database and collection
   db = client['johns_db']
   collection = db['points_collection']
   
   # User-defined latitude and longitude
   latitude = 30.2672
   longitude = -97.7431
   
   # Create the query to find the five nearest points
   query = {
       "location": {
           "$near": {
               "$geometry": {
                   "type": "Point",
                   "coordinates": [longitude, latitude]
               }
           }
       }
   }
   
   # Limit the results to the five nearest points
   projection = {
       "name": 1,
       "location": 1
   }
   
   # Execute the query and find the five nearest points
   results = collection.find(query, projection).limit(5)
   
   # Print the results
   print("Five nearest points:")
   for point in results:
       print(f"Name: {point['name']}, Coordinates: {point['location']['coordinates']}")
   
   ################################################################################
   

2. Following the example using $near we’ll perform a $geoNearaggregation (Note: This is too basic a use case for $geoNear so consider aggregation pipelines when you want to string together transformation and query capabilities)

   ################################################################################
   
   from pymongo import MongoClient
   
   # Connect to the MongoDB server
   client = MongoClient('mongodb://localhost:30000')
   
   # Access the database and collection
   db = client['johns_db']
   collection = db['points_collection']
   
   # User-defined latitude and longitude
   latitude = 30.2672
   longitude = -97.7431
   
   # Create the aggregation pipeline
   pipeline = [
       {
           "$geoNear": {
               "near": {
                   "type": "Point",
                   "coordinates": [user_longitude, user_latitude]
               },
               "distanceField": "distance",
               "limit": 5,
               "spherical": True
           }
       }
   ]
   
   # Execute the aggregation pipeline
   results = collection.aggregate(pipeline)
   
   # Print the results
   print("Five nearest points:")
   for point in results:
       print(f"Name: {point['name']}, Coordinates: {point['location']['coordinates']}, Distance: {point['distance']}")
   
   ################################################################################