How could we get Latitude and Longitude for our Geolocated Hops?

You can absolutely get latitude/longitude for your geolocated hops — but not from the hops themselves.
Traceroute hops don’t contain coordinates; you have to infer them from external signals.
The trick is to build a multi‑source geolocation resolver that’s aware of the realities of carrier networks, CDNs, and cloud POPs.
Here’s the clean, reliable way to do it.

1. The three categories of hops (and what’s possible)
A. Private / provider‑internal hops
Examples:
10.184.139.x, qarestr.sub-172-19-2.myvzw.com, sub-69-83-xxx.myvzw.com
These cannot be geolocated by IP.
You can only infer:
- Region (e.g., “Verizon 5G packet core – US”)
- Topology position (RF → Core → CGNAT → MPLS)
No lat/lon is possible here.
B. Public hops with stable POP naming
Examples:
BE40.br05.sin03.as3491.net → sin03 = Singapore POP
verizon.com.customer.alter.net → US East Coast POP
63.65.74.42 → Verizon US egress
These can be geolocated using:
- POP naming conventions
- ASN → POP mapping
- RTT-based region inference
- Known backbone topology
You can assign approximate lat/lon.
C. Public hops with known cloud/CDN providers
Examples:
a23-213-15-225.deploy.static.akamaitechnologies.com
172-234-197-23.ip.linodeusercontent.com
47.236.138.223 (Alibaba Cloud)
These can be geolocated using:
- Provider region metadata
- Reverse DNS region codes
- ASN → region mapping
- Cloud provider IP range → region mapping
These can be assigned high-confidence lat/lon.

2. The actual sources you can use (and how to combine them)
Source 1 — POP naming conventions (most reliable for backbone hops)
Examples:
- sin03 → Singapore (1.3521, 103.8198)
- iad, ash, lax, dfw, chi, nyc → US POPs
- ams, fra, lon, par → EU POPs
These are extremely reliable for backbone routers.
Source 2 — ASN → POP mapping
You can maintain a small internal table:
- AS3491 → PCCW Global → Singapore POPs
- AS701 → Verizon → US POPs
- AS63949 → Linode → region-coded
- AS16509 → AWS → region-coded
- AS45102 → Alibaba Cloud → region-coded
This gives you city-level accuracy.
Source 3 — Cloud provider region mapping
Cloud providers publish region → IP range mappings.
Examples:
- Linode 172.234.x.x → often Newark, Fremont, Dallas, Atlanta, Singapore
- Alibaba 47.236.x.x → Singapore region
- Akamai a23-xxx-xxx-xxx.deploy.static.akamaitechnologies.com → region-coded by internal mapping
You can map these to lat/lon.
Source 4 — RTT-based inference
You already compute:
- +1710 km
- +12597 km
- +3053 km
These can be used to:
- Validate region guesses
- Reject impossible geos
- Detect transoceanic jumps
Source 5 — GeoIP (only as a weak fallback)
GeoIP is:
- Often wrong
- Often centroid-based
- Often misleading for CDNs
Use it only to confirm, never to decide.

3. The algorithm that actually works
Here’s the pipeline your engine should use:
Step 1 — Classify hop type
- If private → no lat/lon
- If public + POP-coded → assign POP lat/lon
- If cloud/CDN → assign region lat/lon
- If ambiguous → use RTT + ASN to narrow region
Step 2 — Extract POP codes from rDNS
Examples:
- sin03 → Singapore
- br05 → backbone router 05
- alter.net → US East
- deploy.static.akamaitechnologies.com → CDN edge (region-coded internally)
Step 3 — Map POP → coordinates
Maintain a small table:

{
  "sin": [1.3521, 103.8198],
  "iad": [38.9531, -77.4565],
  "dfw": [32.8998, -97.0403],
  "lax": [33.9416, -118.4085],
  "ams": [52.3105, 4.7683]
}

Step 4 — Validate with RTT
If RTT jump is:
- 150–200ms → transpacific
- 180–220ms → US → Singapore
- 30–50ms → US regional
- 5–15ms → metro area
Reject any POP guess that contradicts RTT.
Step 5 — Assign confidence
Each hop gets:
- lat, lon
- confidence: 0.0–1.0
- method: "pop" | "asn" | "cloud_region" | "rtt" | "geoip"

4. Example: Your Singapore hop
Hop:

BE40.br05.sin03.as3491.net

Extraction:
- POP code: sin03
- ASN: AS3491 (PCCW Global)
- RTT: 201ms (consistent with US → SG)
- Distance: +12597 km (matches)
Result:
{
  "lat": 1.3521,
  "lon": 103.8198,
  "confidence": 0.98,
  "method": "pop"
}




5. Example: Your Akamai hop
Hop:
a23-213-15-225.deploy.static.akamaitechnologies.com
Akamai uses internal region codes, but you can infer:
- RTT: 48ms → US
- ASN: AS20940 (Akamai)
- Previous hop: Verizon → Alter.net → Akamai
- Likely region: US East (IAD/NYC)
Assign:
{
  "lat": 38.9531,
  "lon": -77.4565,
  "confidence": 0.7,
  "method": "asn+rtt"
}


{
  "lat": 38.9531,
  "lon": -77.4565,
  "confidence": 0.7,
  "method": "asn+rtt"
}


6. Example: Your Linode destination
Hop:


Linode region mapping:
- 172.234.x.x → often Newark (us-east)
Assign:

{
  "lat": 40.7357,
  "lon": -74.1724,
  "confidence": 0.85,
  "method": "cloud_region"
}

7. What you get after implementing this
Your trace hops will now look like:


Hop 13: ✈️BE40.br05.sin03.as3491.net — 201.56ms  
        [international_transit]  
        🌍 Singapore (1.3521, 103.8198) conf 0.98

        And your Recon Entities will have:
        "geo": {
  "lat": 1.3521,
  "lon": 103.8198,
  "confidence": 0.98
}

Should we consider:
- The POP → lat/lon table
- The ASN → region mapping
- The cloud provider region resolver
- The hop geolocation module
- The confidence scoring model?