BIG WEB UPDATE

ai/test_queries.py

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+import os
+from pymongo import MongoClient
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv('.env.local')
+
+# MongoDB connection
+MONGO_URI = os.getenv('MONGO_URI')
+client = MongoClient(MONGO_URI)
+db = client['crashes']
+collection = db['crashes']
+
+print("=== MongoDB Geospatial Query Examples ===\n")
+
+# 1. Count total documents
+print("1. Total crash records in database:")
+total_count = collection.count_documents({})
+print(f"   {total_count} crash records\n")
+
+# 2. Find crashes within a radius (near the White House)
+print("2. Crashes within 500 meters of the White House:")
+white_house = [-77.0365, 38.8977]
+nearby_crashes = list(collection.find({
+    "location": {
+        "$nearSphere": {
+            "$geometry": {
+                "type": "Point",
+                "coordinates": white_house
+            },
+            "$maxDistance": 500  # 500 meters
+        }
+    }
+}).limit(5))
+
+for crash in nearby_crashes:
+    print(f"   - {crash['crashId']}: {crash['address']} (Severity: {crash['severity']})")
+print()
+
+# 3. Find crashes within a bounding box (downtown DC area)
+print("3. Crashes within downtown DC bounding box:")
+downtown_crashes = list(collection.find({
+    "location": {
+        "$geoWithin": {
+            "$box": [
+                [-77.05, 38.88],  # Southwest corner
+                [-77.01, 38.92]   # Northeast corner  
+            ]
+        }
+    }
+}).limit(5))
+
+for crash in downtown_crashes:
+    print(f"   - {crash['crashId']}: {crash['address']} (Ward: {crash['ward']})")
+print()
+
+# 4. Aggregation with geoNear for fatal crashes
+print("4. Fatal crashes near Capitol Hill (within 1km):")
+capitol_hill = [-77.0090, 38.8899]
+fatal_nearby = list(collection.aggregate([
+    {
+        "$geoNear": {
+            "near": {
+                "type": "Point",
+                "coordinates": capitol_hill
+            },
+            "distanceField": "distance",
+            "maxDistance": 1000,
+            "query": {"severity": "Fatal"},
+            "spherical": True
+        }
+    },
+    {"$limit": 3}
+]))
+
+for crash in fatal_nearby:
+    distance_m = round(crash['distance'])
+    print(f"   - {crash['crashId']}: {crash['address']} ({distance_m}m away)")
+print()
+
+# 5. Count crashes by severity within a specific area
+print("5. Crash severity breakdown in Ward 1:")
+severity_breakdown = list(collection.aggregate([
+    {"$match": {"ward": "Ward 1"}},
+    {"$group": {"_id": "$severity", "count": {"$sum": 1}}},
+    {"$sort": {"count": -1}}
+]))
+
+for item in severity_breakdown:
+    print(f"   - {item['_id']}: {item['count']} crashes")
+print()
+
+# 6. Find crashes involving speeding within a polygon area
+print("6. Speeding-involved crashes near DuPont Circle:")
+dupont_circle = [-77.0436, 38.9094]
+speeding_crashes = list(collection.find({
+    "location": {
+        "$nearSphere": {
+            "$geometry": {
+                "type": "Point",
+                "coordinates": dupont_circle
+            },
+            "$maxDistance": 800
+        }
+    },
+    "circumstances.speeding_involved": True
+}).limit(3))
+
+for crash in speeding_crashes:
+    print(f"   - {crash['crashId']}: {crash['address']}")
+    print(f"     Vehicles: {crash['vehicles']['total']}, Severity: {crash['severity']}")
+print()
+
+print("=== Geospatial queries completed successfully! ===")
+
+client.close()