Gralhix OSINT Exercise #019

O Caller, Where art Thou?

The Challenge: A One-Sided Conversation

On a sunny July afternoon, my OSINT skills were put to the test by Gralhix. The challenge? To pinpoint the location of a caller based solely on a one-sided phone conversation. As an OSINT enthusiast, I couldn’t resist diving into this unique puzzle. Let me take you through my investigative journey, sharing the techniques, tools, and thought processes that led to the solution.

The Mysterious Transcript

Before we begin our investigation, let’s examine what we’re working with — a partial transcript of a phone call:

Decoding the Clues: Initial Analysis

As I read through the transcript, several key points jumped out at me:

1. Date: July 22, 2023 (line 1)
2. Location Type: A place of worship, likely a mosque (lines 8–10)
   — The use of the term “prayers” suggests communal religious activity
   — In my experience, “prayers” often refers to Muslim prayer services, while Christians or Jews might more commonly say “service” or specify a ceremony
   — The speaker is chastised for joking about swimming shorts, indicating a high level of respect for the space, which aligns with mosque etiquette
3. Recent Renovations: New carpeting, possibly blue or green (line 6)
4. Urban Setting: City with good public transportation (line 12)
5. Multilingual Environment: German is useful but not primary (lines 14–18)
6. Geographical Features:
   — Near a body of water, described as “a river or something” (line 24)
   — About 90 minutes from the sea by public transport (line 22)
   — The local waterway is directly connected to the sea (lines 26–27)

These clues painted a picture of a multilingual urban area with access to both a river and the sea, likely home to a significant Muslim community. But where in the world could this be?

Narrowing Down the Search

With these clues in mind, I began to formulate a hypothesis. The combination of a multilingual environment (particularly the usefulness of German), proximity to the sea, and urban setting strongly suggested the Benelux region (Belgium, Netherlands, Luxembourg) as our search area.

To efficiently process the vast amount of geographical data, I employed AI assistance to compile a list of Benelux cities meeting our criteria: about 90 minutes from the coast with direct waterway connections to the sea.

Benelux cities suggested by Claude AI based on my analysis of the call transcript. Those circled were the remaining candidates after visualization by the author in Google Earth Pro.

After visualizing the AI-provided suggestions, I narrowed our focus to four prime candidates:

1. Amsterdam
2. Antwerp
3. Ghent
4. Rotterdam

These cities best fit our criteria of having direct waterway connections to the sea and matching the described urban environment.

Deep Dive through the Shortlist

Now that we had a shortlist, it was time to go deeper. I conducted a thorough analysis of each city, paying particular attention to:

• Canal systems and their connections to the sea
• Public transport options and travel times to the coast
• Linguistic environments, especially the prevalence of German
• Urban characteristics and proximity to places of worship

As I sifted through the data, one city began to stand out: Ghent, Belgium.

A canal connects #1 Ghent, Belgium with #2 Terneuzen, Netherlands

Why Ghent?

1. Canal Connection: The Ghent-Terneuzen Canal provides a direct link to the Western Scheldt and then to the North Sea.
2. Travel Time: The journey to Terneuzen (where the canal meets the sea) takes about 1.5–2 hours by public transport, aligning with the “90 minutes” mentioned in the transcript.
3. Linguistic Environment: Ghent’s multilingual setting, where German is present but not primary, matches the description in the transcript.
4. Urban Setting: The city’s well-developed public transportation system fits the speaker’s comments.
5. Speaker’s Attitude: The relative inconvenience of traveling to the sea from Ghent (compared to Antwerp, Amsterdam, or Rotterdam) better explains the speaker’s reluctance to make regular trips.

The Power of Overpass Turbo: Narrowing Things Down Even More

With Ghent as our prime suspect, it was time to bring out the big guns. Enter Overpass Turbo, a powerful tool for querying OpenStreetMap data. Our goal? To find mosques near mass transit stations in Ghent.

Here’s the query I repurposed from previous exercises (to run the query yourself click link):

[out:json][timeout:25];
// Find mass transit stations
(
  nwr["public_transport"="station"]({{bbox}});
)->.stations;

// Find mosques near stations
(
  nwr["amenity"="place_of_worship"]["religion"="muslim"]
  (around.stations:500);
)->.mosques_near_stations;

// Output results
.mosques_near_stations out geom;
.stations out geom;

Let’s break down this query:

1. We first identify all mass transit stations in the area.
2. Then, we look for places of worship (specifically mosques) within 500 meters of these stations.
3. Finally, we output both the mosques and the stations on the map.

It’s important to note that the 500-meter radius used in this query is a judgment call. While it seemed reasonable for our purposes, it’s an arbitrary limit that could potentially exclude relevant results. For instance, a mosque 501 meters away from a station would be missed by this query, even though it might still be considered “near” in practical terms.

This limitation highlights a crucial aspect of OSINT work: the tools we use often require us to make decisions that can impact our results. In a more comprehensive investigation, it would be wise to run multiple queries with different radii (e.g., 500m, 750m, 1000m) to ensure we’re not missing potential matches.

Running this query returned two interesting results. It was time for some good old-fashioned digital detective work.

Annotated results are Overpass Turbo query results of Mosques located near train/bus stations

The Final Piece: Social Media Sleuthing

With our two mosque candidates, I turned to Google and social media platforms to find the missing piece of our puzzle: the new blue carpet mentioned in the transcript.

After some digging, I struck gold. The Moskee Okba Ibn Nafi (location #1 in the above map) had recently shared photos on their Facebook page (link) showcasing their newly installed blue carpet. This perfectly matched the description in our mysterious phone call transcript.

The new carpet likely explains the difference in expected appearance discussed during the call.

However, Gralhix had another complicating factor for this challenge: the other mosque, Yavuz Sultan Selim Moskee (location #2), also had a blue carpet, according to their Instagram page!

Individual on left is blurred for privacy

It seems we’re left with an investigative dilemma — what to do, what to do?

Case Closed: The Verdict

Based on this comprehensive investigation, I can conclude with moderate confidence that:

The speaker was in Ghent, Belgium on July 22, 2023, and had recently attended prayers at the Moskee Okba Ibn Nafi (located at coordinates 51.057988, 3.73677 — [Google Street View Link]). The exact location of the call is uncertain, but likely in the vicinity of the mosque.

The reasons for this conclusion and its confidence level are:

1. Both mosques fit our geographical and Overpass Turbo query criteria.
2. Both mosques have blue carpets, which aligns with the transcript.
3. The renovation at Moskee Okba Ibn Nafi is more recent (2021) compared to Yavuz Sultan Selim Moskee (2018), making it more likely to be described as “new” in a 2023 conversation.
4. However, we cannot rule out Yavuz Sultan Selim Moskee (location #2) entirely, as perceptions of what constitutes “new” can vary.

Lessons Learned: OSINT Insights

This investigation provided valuable insights into effective OSINT techniques:

1. Iterative Analysis: Constantly refine your hypotheses as new information emerges.
2. Tool Integration: Combine multiple OSINT tools and techniques for a comprehensive investigation.
3. Human-AI Collaboration: Use AI to enhance your investigation, but rely on human insight for critical thinking and final decision-making.
4. Attention to Detail: Small details, like carpet color or attitude towards travel, can be pivotal in narrowing down locations.
5. Embracing Uncertainty: Sometimes, OSINT investigations don’t lead to absolute certainty. It’s crucial to accurately report your confidence level and explain your reasoning.
6. Geographical Knowledge: A solid understanding of regional geography and urban characteristics is invaluable in OSINT investigations.
7. Tool Limitations: Be aware of the limitations of your tools and queries. Arbitrary parameters (like search radii) can significantly impact your results. Always consider how these limitations might affect your conclusions.

Ethical Considerations

As OSINT practitioners, it’s crucial to consider the ethical implications of our work. While this exercise used a fictional scenario, real-world OSINT investigations often deal with sensitive information. Always respect privacy, adhere to legal guidelines, and consider the potential consequences of your findings.

In this investigation, for example, I came across photos of individuals on one of the mosque’s social media posts. Even though these were publicly available, I took the time to blur out faces before including any such images in my analysis. This extra step helps protect individual privacy and demonstrates responsible OSINT practices.

Try It Yourself!

Now that you’ve seen the process, why not give it a try? Here are some exercises to hone your OSINT skills:

1. Use Overpass Turbo to find all the cafes within 200 meters of a train station in your city.
2. Practice “geoguessing” using Google Street View. Can you identify a location based solely on visual clues?
3. Create a fictional scenario similar to this one and challenge a friend to solve it using OSINT techniques.

Remember, OSINT is as much about the journey as it is about the destination. Each investigation is an opportunity to learn and improve your skills.

Conclusion: The Art and Science of OSINT

This investigation demonstrates the power of combining critical thinking, technical tools, and creative problem-solving in OSINT work. By methodically analyzing clues, leveraging appropriate tools, and thinking outside the box, we were able to transform a vague, one-sided conversation into a precise location.

As we continue to explore the fascinating world of OSINT, let’s remember that our goal isn’t just to find answers, but to develop a mindset of curiosity, persistence, and ethical consideration. Every challenge is an opportunity to refine our skills and contribute positively to the OSINT community.

Acknowledgments: A huge thank you to Gralhix for creating this challenging and engaging exercise. It provided an excellent opportunity to combine various OSINT techniques and showcase the potential of human-AI collaboration in investigations.

For those interested in further honing their geoguessing skills, I highly recommend checking out Rainbolt’s walkthrough of this exercise (link). Rainbolt’s one of the best of the best at geolocation/geoguessing, and there’s much to learn from their process.