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Transparency within graph-powered predictive policing

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Defined as a tactic that uses advanced analytics for proactive crime prevention and supporting investigations, predictive policing identifies intervention targets, solves past crimes, and makes effective crime prevention policies. Due to the adoption of knowledge graph databases and their rigorous nature, such analysis is starting to become commonplace.

Predictive policing refers to any policing strategy or tactic that develops and uses information and advanced analytics to inform forward-thinking crime prevention.”
Craig D. Uchida
NIJ paper: ‘A National Discussion on Predictive Policing’

It is important to note: contrary to the grandiose claims from the media, predictive policing is no crystal ball. The promise of “big data” and “AI” often touted to be what we see in the media is, in fact, an exaggeration.

Such techniques acknowledge that crime, especially organised crime, isn’t random but patterned. Knowledge graphs, because of their structural nature, are ideal for unearthing relations in criminal networks. Past studies show that criminal networks display patterns that are insightful for police investigations. Recognising this, algorithms detect crime patterns and associations from past data, offering predictions and insights. The primary tools to support this phase are:

  • Co-offending network analysis
  • Link prediction

Co-offending Network Analysis

A co-offending network represents a graph linking individuals who have committed crimes together. Such networks are pivotal for law enforcement in understanding criminal groups engaging in concealed illegal activities, from terrorism to drug trafficking. Instead of focusing solely on individual suspects, the analysis includes group behaviour. Although not always stored by organisations, this network can be easily extracted from the knowledge graph developed in earlier phases.

The next image shows the schema of the crime sub-graph and how the projection works over it to generate a co-offender network:

GraphAware Cooffending Network

Notably, these networks contain temporal data, allowing for time-based evaluations of crime, and analysing the where’s and when’s of criminal activity. Below we can see a simple example of a co-offending network:

GraphAware Complete/incomplete network

The value of these networks is immense, facilitating the identification of key players and guiding strategic decisions by law enforcement, in particular for organised crime. Analysis methods include:

Whole-network properties: Examining total offenders, average connections, and network connectivity measurements so we can understand how closely offenders are connected.

Subgraph discovery: Identifying organised crime groups, their evolution, and interactions.

Node analysis: Determining key players essential to the network’s function and its disruption, crucial for tackling organised crime and terrorism.

Network evolution characterisation: Tracking network changes, like new members, shifts in power, and overall structural transitions in criminal networks.

Link Prediction in Predictive Policing

Link prediction is an advanced technique we offer for predictive policing. It determines potential connections among entities. The methodology involves a training phase, using a graph to train a machine-learning model able to predict new or hidden links. Once trained, this model predicts link likelihoods between nodes. If this likelihood surpasses a 50% threshold, a link’s existence is assumed. The next image shows the basic principles of link prediction:

GraphAware Link Prediction

This predictive capability offers manifold applications:

Co-offending Network Analysis: Predicting evolving relationships within criminal networks.

Knowledge Graph Completion: Filling in missing data connections to enrich overall understanding.

Revealing Hidden Connections: Unearthing concealed interactions, especially within clandestine organisations.

Suspect Identification: Enhancing crime investigation by predicting additional culprits.

Our demonstrations highlight the transformative power of link prediction on co-offender networks, especially in revealing influential individuals. These are just a few of the possibilities that have been identified with our current customers, but others can be explored and verified. To prove the value of the link prediction in improving the quality of the knowledge, we have considered the following image:

GraphAware Before/After

The two images display the same co-offender network: the left before link prediction and the right after. Using the betweenness algorithm, we identified key individuals who bridge separate groups. Node size reflects its relevance, with larger nodes being more influential. Notably, after link prediction, nodes 25, 26, 62, and 49 in the middle-right gained significance. Revealing these hidden links alters the network, highlighting hidden patterns and individuals.

GraphAware Hume provides a comprehensive suite of features, available as out-of-the-box functionalities or through professional services – algorithms, visualisation tools and best practices to achieve the client’s goals.

Finally, the graph can serve as the primary source of data for extracting information in a format compatible with traditional machine learning algorithms. Hume provides features for converting graph data into tabular format, which can then be utilised for various downstream processing tasks. This opens up a realm of limitless possibilities for further analysis and applications.

Discover the power of Knowledge Graphs in Criminal Intelligence Analysis – download our ebook “Three steps to intelligence-led policing: How knowledge graphs make a safer world”.


One Response to “Transparency within graph-powered predictive policing”

  1. davidbfpo says:

    Given the very low detection figures in England & Wales how can this model work. For example the detection rate for burglary dwelling is IIRC 3%.

    The reality is that policing does NOT know who committed nearly all property crime. Most violent crime involves one offender.

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