: [Feature] Smart Location Intelligence: Geocoding & Geospatial Expense Analysis

[SatyamPandey-07]

Is your feature request related to a problem? Please describe.
Transactions currently lack spatial context. Users cannot visualize where they are spending money (e.g., "How much did I spend at the Downtown Mall vs. near home?").

Describe the solution you'd like
I propose adding a Location Intelligence layer to the backend. This involves geocoding transactions, enabling geospatial queries, and clustering expenses by physical location using MongoDB's geospatial features.

Key Technical Requirements (L3 Complexity):

1. Location Service (services/locationService.js):
   • Interface with a Geocoding Provider (Google Maps/Mapbox - using a mock adapter pattern for the open-source repo).
   • Auto-tag transactions with lat, lng, and formattedAddress based on merchant name inference.
2. Geospatial Models:
   • Update models/Transaction.js to store GeoJSON points.
   • Create models/Place.js to cache recognized locations (e.g., "Starbucks on 5th Ave").
3. Geospatial Analysis:
   • Implement findNearby(lat, lng, radius) queries.
   • Create a clustering algorithm to group expenses by neighborhoods or cities.
4. Background Processing:
   • jobs/geocodingJob.js: A background worker to asynchronously process historical transactions and backfill location data.

Files to be created/modified:

• services/locationService.js (New)
• models/Place.js (New)
• jobs/geocodingJob.js (New)
• routes/maps.js (New)
• models/Transaction.js (Update schema/indexes)
• utils/geoUtils.js (New)

Impact
Enables "Map View" features on the frontend and provides deeper insights into spending habits based on location.

[github-actions]

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