Exploring Spatial Knowledge Graphs
The biggest value of spatial knowledge graphs lies in its capacity to simplify complex data inquiries. This episode explores what are knowledge graphs, how they are built, and their benefits in geospatial analytics.
About The Guest
Vikram Gundeti is a distinguished engineer at Foursquare whose work involves unifying various datasets into a singular, cohesive platform. Interestingly, he had no previous geospatial experience prior to joining Foursquare. He previously worked at Amazon and was one of the first engineers on the Alexa voice assistant. For him, entering into geospatial was not without its share of challenges, facing a steep learning curve with new specialized tools. However, his shift in roles signified an exciting new journey, reassessing his preconceived notions about the applications of geospatial data, which he found to be much broader and influential than just mapping and routing.
What Are Knowledge Graphs?
Knowledge graphs are networks that encapsulate relationships between real-world entities. These entities could include people and places, with the edges representing different types of relationships. For instance, a person’s relationship with a place could denote their home, workplace, favorite restaurant, or neighborhood. The place itself can possess further information, such as being part of a mall, airport, or a chain like Starbucks. The knowledge graph aims to showcase these relationships in a graph-like representation.
Spatial Knowledge Graphs
A spatial knowledge graph is a knowledge graph that includes geographic, or spatial, information. In general, a knowledge graph is a network that encapsulates relationships between real-world entities. These entities can consist of people, places, and things, with the edges of the network representing different types of relationships. For instance, a person’s relationship with a place could denote their home, workplace, favorite restaurant, or neighborhood.
In the case of a spatial knowledge graph, these relationships also include a spatial or geographic component. For example, a person and a place node might be connected with a “lives in” relationship, and the place node might contain information about its location.
The concept of a geospatial knowledge graph revolves around introducing a geographic context to the data. It’s about adding a location attribute to the nodes in the graph. This allows the graph to model geographic relationships and properties like distance, connectivity, and directionality. For instance, this kind of graph can show which businesses are located in which neighborhoods, or how different locations are related geographically.
To represent this spatial data, it might use systems like the H3 grid system. By mapping data onto H3 nodes at the appropriate resolution, geospatial elements are introduced. For instance, a neighborhood represented at a higher resolution (e.g., H10) and a point of interest at a lower resolution (e.g., H14) would infer the relationship that a person lives in a particular neighborhood.
Here is a simplified comparison table of Graph Databases and Traditional (Relational) Databases:
|Feature||Graph Database||Traditional (Relational) Database|
|Structure||Nodes and edges (relationships)||Tables with rows and columns|
|Relationships||Built into the structure as first-class citizens||Require JOIN operations|
|Performance||High for complex and interconnected data||High for structured, tabular data|
|Data Model||Schema-less (flexible)||Schema-based (fixed)|
|Scalability||Horizontal||Both horizontal and vertical, but often vertical|
|Query Language||Typically proprietary (e.g., Cypher for Neo4j)||Standard SQL|
|Use Case||Complex networks, social media, recommendation engines, geospatial data||Business data, transactional data, structured data|
|Complexity Management||Handles complex relationships easily||Can struggle with highly interconnected data|
|Real-time Processing||Good||Depends on the use case and system|
|Spatial Support||Can be incorporated, depending on the system||Requires specialized spatial databases|
Infusing Fluid Parameters into Spatial Knowledge Graphs
A fascinating aspect of geospatial knowledge graphs is their capacity to integrate and update fluid parameters, like weather, regularly. For instance, weather polygons indicating forecast data can be mapped to H3 cells, allowing applications such as Uber to understand how weather forecasts might affect demand surges. By indexing all this information to the H3 cells, correlation and causality between different features or attributes can be inferred.
Building Knowledge Graph Relationships with Machine Learning
In determining the strength of relationships between nodes or the likelihood of an edge between different nodes, machine learning models play a crucial role. By evaluating patterns, such as the time spent at certain locations and the attributes of those locations, these models assign probabilities to relationships, effectively creating new edges on the graph. For instance, if a user spends a significant amount of time in a particular location with specific attributes, it may be inferred as their work location with a certain probability.
Leveraging Spatial Knowledge Graphs for Fast Data Insights
Despite the increasing amount of location data being gathered by location companies such as Foursquare, the process of converting this data into actionable insights is fraught with difficulties in aggregating various datasets and performing complex spatial joins – that require specialized tools and infrastructure. This process increases the time it takes to derive value from the data, often discouraging companies from fully leveraging their own location data.
Adopting knowledge graphs makes it possible to weave together multiple datasets into a unified platform, creating a more streamlined and efficient approach to leveraging the power of location data. Take an example of trying to ascertain the popular lunchtime restaurants among people working in a specific location, the task would require understanding user work locations, joining that data with local restaurant information, and computing visits. A tedious process with expansive spatial joins.
However, with a geospatial knowledge graph, this entire operation simplifies into a series of graph traversals. Starting with a neighborhood, users for whom that area is a work neighborhood are identified. Following this, all the restaurants mapped to the same neighborhood are detected. Finally, the number of visits from these specific users to those restaurants are computed, delivering the desired answer seamlessly.
How Do Precision and Accessibility Influence the Adoption of Knowledge Graphs?
The incorporation of knowledge graphs into data analysis seems intuitive. However, their widespread application has been slow to come. The reason is a delicate balance between precision and accessibility.
Historically, many applications were largely focused on precision, which traditional data structures could handle efficiently. But over the years, the focus has broadened to applications where ease of access trumps absolute precision, making knowledge graphs a more fitting choice.
Democratizing Data Access with Knowledge Graphs
Building a central repository where all data is accessible and new datasets can be continuously added, democratizes access to a rich cache of spatial datasets. By moving away from individual, custom pipelines to a unified, shared resource, teams no longer need to build custom pipelines to ingest the data into their own pipelines. Instead, the information is readily available in a single, accessible location.
Popular Usage of Knowledge Graphs
Popular examples of knowledge graph applications include The Internet Movie Database (IMDB), which contains the movie knowledge graph – the filmography of cast members, movie details, etc. They have even exposed a graph API to answer film-related queries.
Social graphs, often spoken about in the same breath, are indeed a type of knowledge graph, where an individual acts as the central identity. For location platforms, location is the central identity.
Real-time Responsiveness in Knowledge Graphs
An unparalleled benefit of knowledge graphs is their capability to facilitate on-the-fly solutions. Instead of having to predict and pre-arrange the database for a query, users can ask questions spontaneously, and the graph-based infrastructure can provide an instant response. The power of this real-time data access is profound: users no longer need to know what questions to ask. The moment a question arises, it can be translated into a tangible answer atop the knowledge graph.
Geospatial knowledge graphs are undeniably revolutionizing the big data landscape. By offering a tool to dissect, understand, and utilize the overwhelming wealth of available data, they’re ushering in a new era of accessible and flexible data analytics.
Points of Interest data
Questions people ask about storing spatial data in graph databases or about graph databases in general
What is a Graph Database?
A graph database is a type of NoSQL database that uses graph theory to store, map, and query
How Does a Graph Database Work?
Graph databases work by storing data in a graph structure with nodes, edges, and properties to represent and store data.
What is a Spatial Graph Database?:
A spatial graph database is a graph database that also includes support for spatial data and related operations.
What are the Advantages of Graph Databases?
Graph databases offer several advantages including handling of complex relationships, high performance for querying interconnected data, flexibility, real-time insights, and simplicity of representation for certain types of data.
How are Spatial Data Stored in a Graph Database?
In a spatial graph database, geographic or spatial entities are stored as nodes, with their relationships as edges. Spatial properties, such as location coordinates or areas, can be stored as node or edge properties.
What is the Difference Between Spatial Databases and Graph Databases?
Spatial databases are designed to store and query data that represents objects defined in a geometric space, while graph databases are designed to store data and the relationships between them. Spatial graph databases combine these capabilities.
Can Graph Databases Handle Large Amounts of Data?
Yes, graph databases are designed to handle large datasets. They excel at managing data with complex relationships and can scale as the dataset grows.
What are Some Use Cases for Graph Databases?
Use cases for graph databases range from social networking (where they can track the complex relationships between users), to recommendation engines (where they can map the relationships between customers and products), to spatial analytics (where they can handle the geographic relationships between different locations).
Are Graph Databases Fast?
Yes, for certain types of queries, especially those that involve traversing relationships, graph databases can be much faster than traditional relational databases.
How Difficult Is it to Transition from a Traditional Database to a Graph Database?
The difficulty of transition depends on the complexity of the existing data and relationships. However, once set up, a graph database can often simplify the representation and querying of complex data relationships.