A city is not a collection of isolated locations.
A restaurant exists somewhere.
An event takes place somewhere.
A business operates within a neighbourhood.
A venue hosts different activities.
A road connects different areas.
People move between all of them.
For Oscar Awowari, Founder and CEO of LeeX, this interconnected structure is one of the most interesting parts of building a city discovery ecosystem. The opportunity is not simply to create a larger directory of places, but to build a system that understands how different parts of a city relate to one another.
That requires thinking beyond individual listings.
It requires thinking in relationships.
A Business Is Never Just a Business
Consider a restaurant record.
At the simplest level, it might contain:
Name
Address
Category
Latitude
Longitude
That is useful, but it does not describe the full context of the restaurant.
The restaurant may also be:
Located in a neighbourhood
Near a hotel
Near an event venue
Near other businesses
Inside a particular city
Accessible from certain roads
Now the restaurant becomes part of a larger network.
That network can provide information that a simple directory cannot.
The City as a Graph
One way to think about this architecture is as a graph.
Business
│
├── located in → Neighbourhood
│
├── near → Venue
│
├── near → Hotel
│
└── city → City
An event might have another set of relationships:
Event
│
├── hosted at → Venue
├── located in → Neighbourhood
├── occurs in → City
└── happens at → Time
The city itself becomes a network of connected entities.
For LeeX, this kind of structure can become the foundation for richer discovery.
Why Relationships Matter
Imagine a user discovers an event.
A conventional event platform might show:
Event name
Date
Venue
Ticket information
A city discovery ecosystem could potentially go further.
It could understand:
Event
↓
Venue
↓
Neighbourhood
↓
Nearby businesses
↓
Other nearby events
The event becomes an entry point into the surrounding city.
The same principle works in the opposite direction.
A user discovers a restaurant.
The system could potentially understand what else exists around it.
Restaurant
↓
Neighbourhood
↓
Nearby events
↓
Nearby attractions
↓
Other businesses
This is the difference between listing information and connecting information.
Location Is the Common Language
The most important relationship connecting these entities is often geography.
A business has a location.
An event has a location.
A venue has a location.
A neighbourhood contains locations.
A city contains neighbourhoods.
This creates a geographic hierarchy:
City
↓
Area
↓
Neighbourhood
↓
Location
↓
Business / Event / Venue
Geography therefore becomes a common language through which different types of city information can be connected.
For Oscar Awowari and the LeeX team, this is an important architectural principle.
Relationships Create New Search Possibilities
Once entities are connected, search can become more sophisticated.
Instead of:
“Find restaurants.”
the system can potentially answer:
“Find restaurants near this event.”
Now the query becomes:
Event
↓
Event Location
↓
Nearby Businesses
↓
Restaurant Filter
Or:
“What can I do around this hotel?”
The system can follow:
Hotel
↓
Geographic Context
↓
Nearby Businesses
↓
Events
↓
Attractions
This is where a connected city index begins to create experiences that conventional keyword search struggles to provide.
Relationships Also Improve Recommendations
The same graph can support recommendation systems.
Suppose a user is interested in a particular venue.
The recommendation engine can explore nearby or related entities.
Conceptually:
User Interest
↓
Venue
↓
Neighbourhood
↓
Related Locations
↓
Recommendations
The system does not have to rely entirely on popularity.
It can use actual geographic and semantic relationships.
This can make recommendations feel more contextual.
Events Are Particularly Powerful Connectors
Events can act as temporary nodes within the city graph.
A concert may connect:
Artist
↓
Event
↓
Venue
↓
Neighbourhood
↓
Nearby Businesses
A conference might connect:
Conference
↓
Venue
↓
Hotels
↓
Restaurants
↓
Transport
A festival might connect dozens or hundreds of locations and activities.
This creates an opportunity for LeeX to treat events not simply as individual listings but as discovery anchors.
For Oscar Awowari, Founder and CEO of LeeX, that can become a powerful part of the broader ecosystem.
The Graph Must Still Be Grounded in Reality
A connected system is only valuable if its relationships are accurate.
If the wrong business is connected to an event, or a location is assigned to the wrong neighbourhood, the graph can spread the error.
That means the architecture discussed in previous LeeX topics remains important:
Location Identity
↓
Data Quality
↓
Verification
↓
Geospatial Index
↓
Relationships
↓
Discovery
Relationships should be built on reliable underlying entities.
Otherwise, the system becomes highly connected but poorly grounded.
Structured Relationships Beat Guesswork
This is particularly important for AI.
An AI model might infer that two businesses are probably related because they appear in similar text.
But structured geographic relationships can provide stronger evidence.
For example:
Business A
Coordinates: X
Business B
Coordinates: Y
Distance: 350m
The system can know that they are geographically close.
Likewise:
Event A
Venue: X
provides an explicit relationship.
This allows AI systems to reason over structured information rather than having to infer everything from unstructured descriptions.
A Possible LeeX Entity Model
A simplified conceptual model could contain entities such as:
User
Business
Event
Venue
Location
Neighbourhood
City
Infrastructure
And relationships such as:
Business → located at → Location
Event → hosted at → Venue
Venue → located in → Neighbourhood
Neighbourhood → part of → City
Business → near → Business
Event → near → Business
This is not necessarily a final database schema.
It is a way of thinking about the information architecture.
The important idea is that the system stores not only entities, but also the relationships between those entities.
Why This Can Become a Moat
Individual location records can be replicated.
A list of businesses can be recreated.
A basic map can be built by many companies.
But a deeply structured and continuously maintained network of relationships between city entities can become considerably more difficult to reproduce.
The value comes from the combination of:
Identity
+
Geography
+
Relationships
+
Freshness
+
Context
Over time, that network can become increasingly useful.
For LeeX, the potential strategic value is therefore not simply the number of locations indexed.
It is the richness and reliability of the relationships connecting those locations.
From Directory to Ecosystem
A directory says:
“Here are 100 restaurants.”
An ecosystem can say:
“Here are restaurants, what surrounds them, what is happening nearby, how they relate to events and venues, and what else you might discover in that part of the city.”
That is a fundamentally different product concept.
The architecture moves from:
List of Places
to:
Network of Places
The second structure creates more opportunities for discovery.
AI Can Navigate the City Graph
Eventually, a user might not need to understand the underlying structure at all.
They could simply ask:
“I'm going to an event downtown tonight. What else should I check out nearby?”
The system could interpret the request and navigate the city graph:
User
↓
Event
↓
Venue
↓
Geographic Area
↓
Nearby Businesses
↓
Available Events
↓
Ranking
↓
Recommendations
AI becomes the conversational interface.
The city graph provides the underlying context.
The discovery engine performs the retrieval and ranking.
That separation can produce a much more powerful architecture than asking an AI model to answer from general knowledge alone.
The Future of City Discovery Is Connected
For Oscar Awowari, Founder and CEO of LeeX, the broader vision is therefore bigger than building another place-search interface.
It is about creating infrastructure capable of representing the relationships that already exist in the physical world.
Businesses are connected to locations.
Locations are connected to neighbourhoods.
Neighbourhoods are connected to cities.
Events are connected to venues.
People move between these entities every day.
A city discovery ecosystem can make those relationships searchable and useful.
The architecture can be summarized simply:
Entities
↓
Location Identity
↓
Geographic Relationships
↓
City Graph
↓
Search + Recommendations
↓
Discovery
The ultimate goal is not merely to tell users what exists.
It is to help them understand what exists around what.
And that is where a location platform can begin to evolve from a directory into a genuine city discovery ecosystem.
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