Hate crime, surely one of the most invidious offences out there, has surged in England and Wales over the last year, with the number of such incidents recorded by police increasing by 4,000 to reach a record high of more than 104,000. Depressing, right? There are many root causes for their increasing prevalence – and no doubt commentators and scholars, law enforcement officials and politicians, will have their say – but we believe that it’s time now for action.
And that’s why we and several of our colleagues teamed up to take part in the recent Police Rewired Hate Hackathon, a three-week event organised by volunteer coders who run regular hackathons to help the police. Forced virtual by the pandemic, this particular one was geared specifically towards identifying new ideas, prototypes, and solutions for stopping the spread of hate crime.
Drilling into the data
So, what can be done about the spread of hate crimes? Sadly, there is no silver bullet. Fighting back will take a number of approaches and methods, but as engineers ourselves, perhaps it’s not surprising that we believe that data has a key role to play.
We work with law enforcement clients in a range of sectors, and, as a result, believe that those receiving hate crime reports would get more value from data by being able to search and visualise it more effectively. The result would be a faster way to identify common perpetrators and make it easier to support victims.
Of course, as a company, BAE Systems often creates secure solutions, but for the Hackathon we decided to make our offering open source where possible – chiming with the spirit of the event as a whole. This meant creating a standalone environment that we could work in with the right tools, but also ensuring that more teams and more agencies could use it – more bang for buck, so to speak.
Our solution utilises existing data collection formats, transforms the data using COTs products, and creates a standardised set of attributes for the data to be presented in the user interface. This means that the user can garner the best insight through cross-pollinating data types. It aims to support both the charities and public services that tackle hate crime, especially as all are overwhelmed by ever-increasing caseloads.
Without wanting to get too techie, the solution uses NiFi (an automated data flow tool) to transform the data and Elasticsearch (a full-text search engine) to store the information. A Kibana interface (providing data visualisation) and a React web front-end (a javascript library) then allows non-technical users to search for information of relevance. Ultimately, it provides the capability to rapidly process and collaborate on reports across in-person and online hate crime.
What’s next?
Our idea went down well – judges from the Metropolitan Police, Amazon Web Services and the Alan Turing Institute awarded us the prize for ‘Improving Hate Crime Detection’.
Such plaudits are, of course, nice to receive, but what is of more interest to us is what happens next with our solution. We are hoping to collaborate with NGOs, law enforcement agencies and charities to take it forward in the future.
Our goal is to help victims of hate crime within numerous different communities. Using data in a more effective way is one way of taking this forward, and we are excited about its potential to make a real difference.
The hard work starts now.
About the authors
Chris Bull is a Business Consultant with BAE Systems Applied Intelligence
Tigs Knowles is a Software Engineer with BAE Systems Applied Intelligence
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