What is temporal and spatial analysis?
Temporal and spatial analysis helps analysts understand when events occur, where they occur, and how those patterns relate to one another.
Temporal analysis focuses on changes over time, helping identify sequences of events, trends, anomalies, and cause-and-effect relationships. Spatial analysis focuses on location, helping analysts understand geographic patterns, proximity, movement, and clustering.
When combined, temporal and spatial analysis provides a powerful way to understand how people, objects, locations, and events interact across both time and space. This approach is widely used in intelligence analysis, law enforcement, fraud detection, cybersecurity, public health, and logistics.
Knowledge graphs are particularly well-suited to temporal and spatial analysis because they model relationships between entities in ways that closely reflect real-world situations. By adding temporal and geospatial data to a graph, analysts gain a richer understanding of how activities unfold, who was involved, where events occurred, and how situations evolve over time.
Why temporal and spatial analysis matters
Many investigations become difficult because analysts can see relationships between entities but lack context on when and where the activities occurred.
Adding temporal and spatial dimensions helps analysts:
- Understand sequences of events
- Identify coordinated activity
- Detect unusual patterns and anomalies
- Validate or challenge investigative hypotheses
- Prioritise investigative leads
- Build a more accurate operational picture
Together, these capabilities help analysts move beyond understanding what happened to understanding how, where, and why it happened.
Spatial analysis: understanding where events occur
Spatial analysis examines the geographic aspects of data to identify patterns, relationships, and areas of interest.
By visualising events, people, assets, or activities on a map, analysts can identify:
- Crime hotspots
- Areas of repeated activity
- Proximity between people and locations
- Movement patterns
- Clusters of related events
- Resources located near an incident
For example, investigators might map mobile phone activity around a crime scene to identify devices present during a specific time period. Financial investigators could analyse the geographic distribution of transactions to identify unusual activity. Public sector organisations may use spatial analysis to understand how incidents are distributed across a region.
Adding location data provides valuable context that is often difficult to identify through traditional reports or spreadsheets alone.
Temporal analysis: understanding how events unfold over time
Temporal analysis uses timestamps to understand how events, behaviours, and relationships change over time.
By analysing the sequence and timing of events, analysts can identify:
- Emerging trends
- Recurring behaviours
- Event chains and dependencies
- Anomalies and outliers
- Triggering events that precede larger incidents
For example, a seemingly legitimate card transaction may be followed by a series of fraudulent purchases. Analysing the timing of those events can help investigators understand how the fraud developed and identify when suspicious activity began.
Temporal analysis is particularly valuable when analysing investigations, fraud cases, cybersecurity incidents, and other situations where understanding the order of events is critical.
Combining time and location: spatiotemporal analysis
When a dataset contains both timestamps and geographic information, analysts can perform spatiotemporal analysis.
This allows investigators to understand how activity evolves over time and across locations, creating a much more complete picture of real-world events.
Spatiotemporal analysis can help answer questions such as:
- Did two individuals repeatedly appear in the same location at the same time?
- Could a person realistically travel between two locations within the recorded timeframe?
- How did a network of activity spread geographically over time?
- Which event appears to have initiated a chain of subsequent actions?
- Were people, devices, or assets consistently located near a particular event?
By combining temporal and spatial perspectives, analysts can uncover patterns that would be difficult to identify using either approach independently.
Common applications of temporal and spatial analysis
Temporal and spatial analysis is used across a wide range of investigative and analytical disciplines, including:
Intelligence and law enforcement
Investigators use temporal and spatial analysis to identify crime hotspots, understand suspect movements, reconstruct timelines, and uncover links between people, locations, and events.
Fraud detection
Financial institutions can analyse transaction locations and timings to identify suspicious activity, detect coordinated fraud, and uncover organised criminal networks.
Cybersecurity
Security teams use temporal analysis to reconstruct attack timelines and understand how incidents spread across systems and networks.
Public health
Researchers can analyse the spread of diseases across regions and over time, helping identify trends and support intervention planning.
Logistics and supply chains
Organisations can monitor the movement of goods, identify bottlenecks, and understand how disruptions affect operations across locations and time periods.
How GraphAware Hume supports temporal and geospatial analysis
GraphAware Hume combines graph visualisation, temporal analysis, and geospatial analysis within a single environment for graph-powered intelligence analysis.
Investigators can:
- Visualise people, objects, locations, and events on an interactive map
- Explore activity over time using timeline controls
- Analyse movement patterns and recurring co-location events
- Identify assets present at a location during a specific period
- Correlate information across multiple data sources
- Run investigative workflows without requiring technical expertise
By bringing together graph visualisation, timeline analysis, and geospatial context in a single workspace, GraphAware Hume helps analysts uncover patterns, identify investigative leads, and move from data to insight more quickly.
See temporal and spatial analysis in action
GraphAware Hume enables investigators to analyse people, objects, locations, and events across both time and geography.
Watch the demonstration below to see how investigators use timeline analysis, geospatial mapping, and graph visualisation to uncover patterns, identify investigative leads, and accelerate decision-making.
Learn more about temporal graph analysis
Temporal and spatial analysis can also be applied to understand how entire networks evolve over time. In this presentation, we explore temporal graph analysis using a dataset of COVID-19 scientific publications to demonstrate how graph snapshots, temporal patterns, and changing network structures can reveal the emergence of new topics, shifts in influence, and evolving relationships between entities.
While the examples focus on scientific research, the same techniques can be applied to intelligence analysis, fraud detection, customer analytics, and organisational network analysis.
FAQs
What is the difference between temporal and spatial analysis?
Temporal analysis focuses on when events occur and how they change over time. Spatial analysis focuses on where events occur and the geographic relationships between people, objects, and locations.
How are temporal and spatial analysis used together?
Temporal and spatial analysis are often combined to understand how activity changes over time and across locations. This approach, known as spatiotemporal analysis, helps analysts identify movement patterns, recurring interactions, event sequences, and relationships that may not be visible when analysing time or location separately.
What is spatiotemporal analysis?
Spatiotemporal analysis combines time and location data to understand how events evolve across both dimensions. It helps analysts identify patterns, movements, and relationships that may not be visible when analysing time or location independently.
How is temporal and spatial analysis used in investigations?
Investigators use temporal and spatial analysis to reconstruct timelines, identify crime hotspots, analyse movement patterns, correlate events, and uncover relationships between people, locations, and activities.
Why are knowledge graphs useful for temporal and spatial analysis?
Knowledge graphs connect people, objects, locations, and events through relationships, making it easier to analyse activity over time and across geographies while maintaining context.
What industries use temporal and spatial analysis?
Common applications include intelligence analysis, law enforcement, fraud detection, cybersecurity, public health, logistics, transportation, and financial services.