Crime Reduction - Helping to Reduce Crime in Your Area

The following is a report of the speech given by Adrian McKeon, Infoshare Ltd, at the inaugural Information Sharing Network Conference, 10 September 2001.

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Mr McKeon said that the title of his presentation was rather formal and he wanted to talk about the role of technology in information sharing. Although he would focus on technology, without organisational support and staff training, technology would just gather dust in a corner.

Shared information provided better intelligence and everybody benefited; decisions were data based not data free, provided more effective use of taxpayers’ money, reduced crime and disorder and improved the quality of life. The type of information currently exchanged in successful partnerships consisted of intelligence and incidents, and was covered in detail by the Home Office Toolkits.

Exchanging intelligence was carried out by sharing e-mails, Word documents and pictures about an event. Incidents involved the sharing of operational information records which described a vast number of events and provided the time, date location, sex, name, address, unique reference of people or events. Collating information was essential when targeting resources.

The probation service might hold client files on a spreadsheet which might have been cross-referenced with information from social services, police and health services. The most common types of data being exchanged were: crime incidents, social services, accident and emergency, domestic violence, youth offenders, probation at risk, fire and rescue, Victim Support, drugs and alcohol, truancy/free school meals, repeat victimisation, ambulance call outs and 999 calls. As there were over 700 local data sets available it was important to choose carefully; 20% would probably satisfy the needs of 80% and the rest would be irrelevant. There had to be a sound reason for every data set used.

He showed a map of Harrow which provided a 24 hour animation of street crime patterns, created using one year of crime data. Red patches were crime hotspots and green patches were crime coldspots. This information could have been used to make all sorts of resource decisions, monitoring and evaluation actions, and to produce reports to inform policy. The analysis was bullet proof; the interpretation could be disagreed with but the accuracy of the underlying data could not be disputed.

The problem faced by a number of partnerships was that the data being exchanged was too vague and dirty to deliver crime information to the detail required for targeting resources and analysis. The biggest problem at the heart of government was the amount of information involved. It was also the main reason why effective data exchange was difficult.

In the UK there were 55 million people and around 3.5 million businesses spread across 25 million places, and 80% of public sector data related to this. Every month one million people moved, 60,000 businesses changed, 100,000 postcodes altered and 50,000 property grid references varied. The numbers might have been debatable but the message was clear: information went out of date quickly and it must be kept up to date if it was to be relied on.

This was the problem that data exchange technology resolved. Infoshare’s experience had shown that 97/98% accuracy could be achieved and the information could be kept that way. One clear benefit was knowing which records were good and could be relied on and which were bad. The big problem was that one inaccurate data set matched with another inaccurate data set magnified the inaccuracies; somebody had called this “junk squared”!

A major reason why partnerships never took off after the Morgan Report was not a refusal to exchange data, but the fact that the combined data created useless results. Only when viewed at ward level were the results useful, but that was not helpful for resource targeting, this needed to be done at postcode level.

Results generated from matching one 80% accurate data set with another one were only 64% reliable. The Police National Computer contained information from 50 forces and was only 14% reliable. The average partnership that had not tackled the data quality problem and shared four data sets, could only rely on 41% of its intelligence being reliable.

The Government had bigger problems; if people and events could not be authenticated, projects like smartcards for delivering health or controlling immigration, would fail. The Criminal Records Bureau had been criticised because it relied on the PNC for issuing certificates. For example, if there were 200 innocent people at the conference, 172 of them might have been issued with a certificate saying they had a criminal record. Alternatively, if the 200 people were criminals, 172 of them could have received a clean bill of health.

He showed a detail from the A to Z of London with blue dots where events were thought to have happened, red dots where the events happened after data cleansing, and yellow lines representing the error between the dots. In one of the events highlighted the error was over 1,600 metres and this was a common margin of error not an exception.

This was the main problem with most data sharing projects since the Morgan Report; they did not address the data quality problem. However, there were now many Jupiter type solutions throughout the UK, Southwark being a prime example where technology had helped their data exchange.

There were two partnership projects represented at the conference: Stan Dubeck discussed Southwark’s experience and Steve Radburn ran a workshop on Jupiter. Southwark was a single area case study and Jupiter a regional area one, but the approaches were similar.

The data quality problem was resolved using data cleaning and validation software, which was then mapped using a GIS mapping package. Actions were taken based on the analysis such as resource targeting, which were monitored and evaluated, and this generated feedback, which informed policy. The technology was all proven; it was not complicated and anybody could use it. Extensive training was required and a solid partnership structure was needed to run it.

He showed the front page of a Home Office Crime Reduction Toolkit. They had a crime bias but the model would grow to encompass other closely linked issues such as youth, drugs and social exclusion etc. The toolkits represented a superb repository of information which was just the start. Delegates were encouraged to feed back their experiences over the next three years and the resource would be found invaluable. Future audits would be continuous, which was when toolkits would show how valuable they were.

The role of technology was important as dirty data could derail a partnership. Jupiter and Southwark showed how data exchange technology solved this problem. Once the data was obtained something had to be done with it. It was very important that staff with expert local knowledge were trained to analyse the data and take action.

Crime reduction was a local issue; actions were taken and outcomes monitored locally. Only local staff could do this and they needed to be in a well developed management structure. Without that support the technology was irrelevant. Similarly, unless political barriers to data exchange were resolved, the technology would again be irrelevant.

The IDeA planned a national land and property gazetteer which provided an index of property locations in the UK. Councils were supposed to use it even though the IDeA had decided to omit organisations from the gazetteer. One local authority insisted that its crime and disorder partnership used the gazetteer for data sharing despite the lack of organisations. It took 6 months and the police and chief council officers had to assert pressure to override the decision. Crime and disorder staff were kept waiting in limbo while these political barriers were removed.

Ideology and technology should not get in the way of commonsense. Technology for the sake of it was a waste of time. Partnerships had spent tens of thousands of pounds on sophisticated IT, then discovered they had a solution that needed a problem. NASA needing pens for astronauts when they filled in clipboard checklists on the Apollo Moonshot was a pertinent example of this. They developed a pen that did not need gravity to feed ink to the nib at a cost of $10 million; the Russians used a pencil.

Technology needed other types of support, one of which was data sharing protocols. There were two types of protocol; the legalistic ones used between organisations and the practical ones which defined how partnerships exchanged data. The latter were essential as they were living documents which developed as the partnership developed. Their primary role was to create trust. Technology should fit the protocol, not the other way round. Miracles should not be expected as mistakes would be made, but the protocol joined the whole data exchange process together.

Standards were needed for data exchange as they involved people, organisations and places. There were three national standards at the heart of the system:

• BS7666 – for locations

• BS8766 – for names

• BS7799 – for IT and data security

The standards were not perfect but they should be adhered to.

Data protection was not a barrier to data exchange; there was not a single example under crime and disorder where data sharing in the public interest was not allowed because of the Data Protection Act. If the process was transparent the public liked it. Privacy enhancing technology could be built into any existing routines, and technology existed that automatically removed all personal identifiers.

People who did not want change created problems and barriers that had to be overcome. Crime and disorder, YOTs and DATs were examples of joined up government that actually worked.

Technology and data exchange in the future would be quite different to what happened previously. Historically most technology investment in the public domain had been for huge IT infrastructures; big meant better. This had changed; the days of the big bang IT projects were numbered. The focus would now be on data. Once the data was right, systems should be designed around how staff wanted to exploit them. Growth would be modular and small scale, providing quick wins, and implementation would be rapid. Regional or national IT systems would be obtained by small systems being joined together.

Staff would become ever more important. Business would not fit the IT, but IT would be adapted to staff and developed as their needs varied. A common local data structure would emerge in each area which could be exploited for wider use and by a wider range of individuals and organisations. Local areas would join to form regions, which would join to give the national view.

Many joined up government projects would fail or severely under-perform unless they realised that data quality was a local issue, and it could not be tackled from the top down. Projects that failed would be reconstructed and data flows controlled and created by partnerships to be used by many of them.

Retail crime of £2.2 billion had been ignored by most partnerships because it was too difficult an issue to deal with. As technology enabled retail crime data to be shared with mainstream crime and disorder partnerships, this would change. Legislation would force public and private sector organisations to improve the quality of the data used in its sharing. Tens of millions of pounds had been put aside by some government departments and police forces to fund litigation payouts. One accurate measurement was worth a thousand expert opinions.

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