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© College of Policing (2015) 1

Predictive hotspots policing An introductory guide for managers and senior leaders

Kristi Beak and Paul Quinton – College of Policing1

What is ‘predictive hotspots policing’? The term is a catch-all for a variety of

approaches. It typically involves prospective crime mapping (i.e. the use of quantitative analytical techniques to forecast where and when future crime is most

likely to take place), and targeted prevention (i.e. the police taking targeted action to reduce the risk of future crime in these places).

How should predictive hotspots policing be used? As the approach is largely untested, it should be used in a way that complements existing practices. Given its focus on scanning, analysis and targeting, predictive mapping might be best used as

one element of a broader strategy for problem-solving and hotspots policing and.

Does predictive hotspots policing ‘work’? There is strong evidence to show that

problem-solving and traditional hotspots policing are effective. There is also emerging evidence from force pilots that targeted police activity in predicted hotspots can, in some situations, reduce crime. There is, however, limited ‘what works’ evidence on the

overall impact of the targeting of police resources based on predictive analysis.

What can predictive hotspots policing do? There are a number of myths about

what a predictive policing is able to do. In practice, predictive hotspots policing:

highlights places that are at an increased risk of experiencing crime; does not identify specific addresses where crime will definitely be committed;

requires practitioners to use their professional knowledge to make sense of, and interpret, the analysis; and

is not necessarily expensive.

What crime types are most suitable for prospective crime mapping? The approach has been used to target a wide range of crime types, but is often thought to

be particularly well-suited to burglary (because of data availability, and evidence on the risk of repeat and near-repeat victimisation).

What helps predictive hotspots policing to be successfully implemented? Practitioners have highlighted factors they perceive to be critical to success, including: senior officer support; the availability of appropriate resources/data; effective working

relations with, and use of police analysts; and a good problem-solving infrastructure.

What mistakes can be made with implementation? Forces need to be aware of

the potential pitfalls with implementation, including: the analysis producing results than cannot be actioned; the use of poor quality data; mistaking correlation for causation; and failing to evaluate the impact of predictive hotspots policing.

What should be considered when selecting a commercial product? A number of products and services for prospective crime mapping are commercially available. Key

considerations before deciding whether to procure a product or service include: clarifying the goals of predictive hotspots policing; identifying what capability exists in-house or externally free of charge; exploring the suitability of local crime problems to

forecasting; and being clear on the start-up and maintenance costs, and ongoing support to be provided by the supplier.

© College of Policing (2015) 2

Introduction

This guide aims to give managers and senior leaders an overview of predictive hotspots policing. It highlights how a predictive approach could be used by forces to deploy

resources to particular places at particular times, and discusses the potential risks and limitations with doing so. The guide is not intended to be an exhaustive or detailed account of predictive hotspots policing or crime mapping. Equally, the guide does not

seek to describe the issues related to the use of statistical predictions or risk assessment to identify targets for focused police activity in other, non-geographical

contexts (e.g. offender management). Instead, the guide seeks to answers some introductory questions that may be raised in forces looking to implement predictive hotspots policing.

What is ‘predictive hotspots policing’?

Predictive hotspots policing is a catch-all term that typically refers to a two-stage process:

1. Prospective crime mapping – The use of a variety of quantitative analytical

techniques for making statistical forecasts about where future crime is most likely to take place.

2. Targeted prevention – The targeting of police resources, activities and interventions in the predicted hotspots with the aim of preventing problems emerging and minimising future crime risk (see, for example: Bowers et al.

2004; Johnson et al. 2007).

These techniques do not identify exact addresses or perpetrators of future crime, or

highlight places were crime will definitely occur.

The application of predictive analytical techniques to hotspots policing is still relatively new and largely untested in policing. However, the approach has the potential – when

used in conjunction with officers’ knowledge in the field – to inform deployment decisions, and help identify the best times and places for targeted policing activities.

Does predictive hotspots policing ‘work’?

A number of forces have piloted predictive hotspots policing, and there is emerging evidence to suggest that, in some situations, it is possible to reduce crime through targeted activity (see, for example, Fielding and Jones 2013). The evidence base about

the impact of predictive policing on crime, however, is small overall. There are a number of important questions about effectiveness for forces to consider when

adopting a predictive approach:

Do predictive analytical techniques forecast risk accurately? Is it possible to implement interventions or deploy scarce resources against

predicted problems? Does the targeting of predicted crime problems reduce crime?

Which interventions and tactics are effective at prevention, and when? Is the use of predictive analysis to target resource more cost-effective than

other policing strategies at reducing crime?

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How should forces use predictive hotspots policing?

Predictive hotspots policing is likely to be very appealing to some managers and police leaders. They may be attracted by its ‘scientific’ appearance, the ability to be data

driven, the marketing claims of commercial suppliers, and the opportunity to ‘get ahead’ of offenders. However, as a largely untested strategy, it should be used in a way that complements – but does not replace – existing police practices.

Despites its particular focus on the dynamic identification of high risk areas, predictive hotspots policing has much in common with problem-solving and more traditional

models of hotspots policing (which concentrates on current or retrospective crime problems). The approach has a good fit, for example, with the four stages of the SARA problem-solving model (see Clark and Eck 2003):

Scanning – examining a range of data sources to identify which crime problems might emerge in the future, and where they are most likely to be concentrated.

Analysis – understanding the nature, extent, and underlying causes of those predicted crime problems.

Response – implementing a targeted and tailored intervention against a

predicted crime problem to prevent it from emerging.

Assessment – exploring whether the intervention prevented the predicted

problem, whether and how predictions have changed as a result of the intervention, and what further action might be required.

As hotspots and problem-oriented policing have both been shown to be effective in

reducing crime (see: Braga et al. 2012; Weisburd et al. 2008), a predictive approach might be best used by the police as one element of a broader strategy of targeting

activities in high crime areas, and understanding and addressing the underlying causes of crime problems.

What are the common misunderstandings about prospective crime mapping?

There are various myths about the initial crime mapping stage of the predictive

approach to hotspots policing, and what it can do. The following have been derived from Perry et al. (2013):

Prospective crime mapping will tell you where and when a crime will be

committed – No. The analytical techniques used in predictive hotspots policing attempt to forecast the relative risk or likelihood of future events occurring in an

area (e.g. a neighbourhood). Predictive models, thus, identify times and places that are at an increased risk of experiencing crime, rather than determine whether a specific event will take place.

The computer does it all – No. People remain the most important element in the process. Software packages using complex algorithms can process the data,

but they rely, for example, on practitioners:

‒ collecting, formatting and inputting good quality and relevant data;

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‒ developing hypotheses and making decisions as to what analysis to carry out in response to changing crime conditions and local need;

‒ checking the analytical output for errors; ‒ using professional knowledge to make sense of, and interpret, the analysis;

and ‒ thinking through different courses of action and their implications, and

recommending an appropriate intervention.

Prospective crime mapping is expensive – Not necessarily. There are examples of police forces that have carried out predictive geographic analysis

without purchasing software (see Fielding and Jones 2012). Forces may find they have expertise in-house to perform some types of predictive analysis, or develop a local tool. While a variety of software packages are available at a range of

prices, forces may already have access to the functionality that is required to support many predictive methods as part of their standard software.

What crime types are most suitable for prospective crime mapping?

Predictive geographic models have been developed for, and used to forecast, a wide

range of crime types (see Johnson et al. 2006).

Burglary is often thought to be particularly well-suited for prospective crime mapping

because of the large volume of data that is required, the quality of police data on burglary (particularly in terms of geographic accuracy), and the evidence on repeat victimisation (see Pease 1998) and near-repeat victimisation (see, for example:

Johnson and Bowers 2004; Townsley et al. 2003) which is underpinned by criminological theory (see below). There is little evidence, however, as to which

statistical models work best for particular crime types. Forces will need to consider whether their crime patterns are suitable for predictive analysis. In respect of burglary,

for example, some areas may not have a problem with near-repeats.

Forces in England and Wales have shown an interest in applying prospective crime mapping to other crime types including sexual offences, violent crime, organised crime,

and vehicle crime. There would be value in forces conducting their own initial analysis to find out whether there are patterns or regularities in these crimes locally that might

make them predictable. While there is little evidence on the minimum volume of crime that is required, prospective crime mapping is unlikely to be helpful in lower crime areas. Improving the geographic accuracy of data on other crime types would further

increase the opportunities for predictive geographical analysis. Care is also required to ensure that analysis takes account of both short term ‘bursts’ – which could point to

spates of criminal activity and a linked series of events – as well as the historical level of crime and longer term trends.

Some crime types are likely to be less suitable for prospective crime mapping. Serious

crimes, for example, can be difficult to forecast because they occur less frequently and not in large enough numbers to identify a predictable pattern. Crimes where the

offender is more likely to know and target a particular victim (e.g. murder) may also be less suitable than crimes where the offender is looking for an opportunity to commit crime.

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What analytical techniques does prospective crime mapping use?

An increasing number of software packages are available for prospective crime mapping (see College of Policing 2015). These packages use a range of different

statistical techniques or models, typically, to estimate where crime is most likely to occur over a specified period in the near future. Some models are informed by criminological theory, while others base their crime forecasts solely on past events. For

example:

Repeat and near-repeat victimisation models – These models are based on

the idea that crime is communicable, and can spread like a disease. There is evidence, for example, that when a house is burgled, it and nearby houses face an increased risk of victimisation for a defined period. There is evidence to

suggest that this increased risk is because burglary offenders can be characterised as ‘optimal foragers’ in that – like animals hunting for food – they

try to maximise the resources they acquire, while minimising the risk to their safety (see: Johnson 2014; Johnson and Bowers 2004; Krebs and Davies 1987). Models based on these ideas typically require evidence of a near-repeat burglary

problem in order to estimate where there likely to be a higher risk of burglary (relative to other nearby locations) in the near future.

‘Leading indicator’ models – Some models rely more on the analysis of large datasets to identify patterns and trends, rather than theories about crime and offending. Often these approaches seek to identify and focus on particular

‘leading indicators’ – factors that are found to be statistically associated with changing crime levels (e.g. petty crime or social change with more serious

incidents). These indicators may be used alongside other police data (e.g. calls for service and arrests) to predict likely high risk locations in the future.

What could help predictive hotspots policing to be successfully implemented?

The following were perceived by police practitioners, who attended a national

workshop, as requirements for the successful implementation of predictive hotpots policing (see also Perry et al. 2013):

Senior officer support for, and long term commitment to, the approach.

Resources being made available for the tasks required for prospective crime mapping (e.g. good quality data, and the necessary IT and software).

The involvement of interested and enthusiastic staff. Effective working relationships between analysts and officers. Officers with responsibility for solving local crime problems, who have the

capacity and capability to carry out interventions. Good police/community relationships.

Implementation of intelligence-led or problem-solving approaches to policing.

Predictive hotspots policing does not just involve the forecasting of future crime levels using a range of analytical techniques; it also involves decisions to deploy police

officers and other resources to predicted hotspots to carry out a range of targeted, preventive activities (e.g. target hardening and cocooning). Some forces might find it

an implementation challenge to deploy scarce resources to locations that might face an increased crime risk in the future, but do not currently suffer from high levels of crime.

© College of Policing (2015) 6

Focus group research carried out by National Policing Improvement Agency suggested that neighbourhood officers felt that their knowledge of local crime problems was

better than the outputs from predictive analysis. Some also felt that being told to patrol an area simply because it was a predicted hotspot was a challenge to their

professional judgement. Previous research, however, has indicated that officers’ can have inaccurate knowledge about the location of crime hotspots (see Ratcliffe and McCullagh 2001). It may be difficult, therefore, to encourage officers to carry out

proactive work in these areas, when they have other demands, which they feel are more immediate and have more tangible benefits.

There is also a related issue about how much and what type of activity to deliver in predicted hotspots when set against the requirement to deal with current crime problems. Nevertheless, emerging evidence suggests that some areas have benefitted

from taking a predictive hotspots approach, relative to neighbouring areas that did not (see Fielding and Jones 2013).

What mistakes can be made when implementing predictive hotpots policing?

Forces need to be aware of the potential pitfalls they may encounter when

implementing a predictive approach to hotspots policing. The following have been identified by Perry et al. (2013):

Producing un-actionable analysis – Use of prospective crime mapping

techniques need to take account of practical and tactical issues. The unit of analysis, for example, needs to be proportionate to the geographic scale of the

likely police response. Identifying a large district as a future hotspot may be useful to help secure funding for additional resources, but is unlikely to be sufficiently accurate to plan a targeted crime prevention activity.

‘Rubbish in, rubbish out’ – The accuracy and usefulness of prospective crime mapping techniques will be dependent on the quality of the data that are used.

Poor quality data will inevitably lead to unreliable or misleading results. One common issue is the quality and level of crime geo-coding, which can affect the

accuracy of hotspot maps.

Mistaking correlation for causation – Predictive analysis may predict future crime hotspots and identify factors associated with increased risks of crime. Just

because two factors are statistically related does not mean, however, that one necessarily causes the other (e.g. ice cream sales and crime). Awareness of

potentially spurious relationships will be important. The relationship could be purely coincidental or shaped entirely by another, more important factor (e.g. hot weather). It can help to ensure there is a clear hypothesis as to why a

particular factor is likely to be associated with crime in a particular place, and further research carried out to explore how much of risk it poses.

Failing to evaluate impact – Given the general lack of evidence on the effectiveness of the police taking a predictive approach to hotspots policing, there is a strong case for forces to evaluate its use locally. There would be scope

for forces to examine the predictive accuracy of their chosen model by making use of historical data to see whether it would have forecasted current crime

problems. It would also be possible for forces to evaluate whether

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implementation of a predictive strategy, or the use of different prevention tactics in predicted hotspots, reduces crime and outperforms other approaches. Such an

evaluation would usually involve comparing areas before and after predictive hotspots policing is introduced, relative to similar areas where ‘business as

usual’ is maintained. Consideration should also be given as to whether any benefits outweigh the cost of implementation, and whether any cheaper but effective options are available.

What should be considered when selecting a commercial product for prospective crime mapping?

Commercial suppliers offer a range of products and services that could support the

implementation of predictive hotspots policing. Products usually consist of a computer-based solutions, which require the end-user to enter appropriate data and perform a

range of analytical functions. Some software packages can be purchased ‘out of the box’, while others require customisation by the developer before it can be used. Other suppliers provide more of an analytical service; analysing police data on- or off-site

and providing the end-user with the results.

Before deciding whether to procure a commercial product or service – and, if so, which

one – there is a need for forces to:

be clear about their goals; consider what capability already exists in-house to carry out prospective crime

mapping or might available from other sources at no cost; think about whether available products/services are significantly better than the

hotspots analysis already carried out in force; understand the nature of the crime problems they are seeking to predict; explore all the start-up and maintenance costs that could be incurred; and

satisfy themselves the supplier will continue to be in a position to provide ongoing maintenance support.

Once predictive capabilities are in place, forces will want to check how much crime has been predicted in particular locations and the level of predictive accuracy that was

achieved (as these can vary). Subsequently, forces will need to ensure that police officers and staff develop at least a basic understanding of how geographic forecasts are developed, and the implications of such forecasts. This understanding should help

end-users to assess the validity of any forecasts that are produced, and identify the most appropriate responses to the forecasted crime problem. By taking the time to

reflect on the above issues, forces are likely to be better placed to select a product or service that is more suitable for their unique needs, has greater utility, has the buy-in from officers and staff, and is more likely to be successful in predicting crime.

References

Bowers, K. J., Johnson, S. D. and Pease, K. (2004) Prospective Hot-Spotting: The Future of Crime Mapping? The British Journal of Criminology, 44 (5): 641-658.

Braga, A., Papachristos, A. and Hureau, D. (2012) Hot Spots Policing Effects on Crime.

Oslo: Campbell Collaboration.

Clarke, R. and Eck J. (2003) Become a Problem Solving Crime Analyst: In 55 Small

Steps. London: Jill Dando Institute of Crime Science, UCL.

© College of Policing (2015) 8

Fielding, M. and Jones, V. (2012) Disrupting the Optimal Forager: Predictive Risk Mapping and Domestic Burglary Reduction in Trafford, Greater Manchester.

International Journal of Police Science and Management, 14 (1): 30-41.

Johnson, S. D. (2014) How Do Offenders Choose Where to Offend? Perspectives from

Animal Foraging. Legal and Criminological Psychology, 19 (2): 193-210.

Johnson, S. D. and Bowers, K. J. (2004) The Burglary as Clue to the Future: The Beginnings of Prospective Hot-Spotting. European Journal of Criminology, 1 (2): 237-

255.

Johnson, S. D. and Bowers, K. J. (2004) The Stability of Space-Time Clusters of

Burglary. British Journal of Criminology, 44 (1): 55-65.

Johnson, S. D., Birks, D., McLaughlin, L., Bowers, K. J. and Pease, K. (2007). Prospective Mapping in Operational Context. Home Office: London.

Johnson, S. D., Summers, L. and Pease, K. (2006) Vehicle Crime: Communicating Patterns of Risk in Space and Time. Report to the Home Office.

Pease, K. (1998) Repeat Victimisation: Taking Stock. Home Office: London.

Perry, W. L., McInnis, B., Price, C. C., Smith, S. C. and Hollywood, J. S. (2013) Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations.

Santa Monica: RAND.

Ratcliffe, J. H. and McCullagh, M. J. (2001) Chasing Ghosts? Police Perception of High

Crime Areas. British Journal of Criminology, 41 (2): 330-341.

Townsley, M., Homel, R. and Chaseling, J. (2003) Infectious Burglaries: A Test of the

Near Repeat Hypothesis. British Journal of Criminology, 43 (3): 615-633.

Weisburd D., Telep, C., Hinkle, J. and Eck, J. (2008) The Effects of Problem-Oriented Policing on Crime and Disorder. Oslo: Campbell Collaboration.

1 The authors would like to thank Professor Shane Johnson (UCL Jill Dando Institute of Security and Crime Science, University of London) for his comments on an earlier draft.

This revised paper includes a number of changes to clarify that its main focus is predictive hotspots policing rather than other uses of predictive analysis in policing.

A summary of models and software

for prospective crime mapping

Based on an international review by the Urban Institute

© College of Policing Limited (2015). This publication is licensed under the terms of the

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Where we have identified any third party copyright information, you will need to obtain

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and, to support this commitment, this document can be provided in alternative formats.

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contactus@college.pnn.police.uk.

2

Contents Page

Acknowledgements 3

1. Introduction 4

2. Example statistical models for prospective crime mapping 7

3. Example software packages for prospective crime mapping 13

4. Description of software packages 17

Appendix. Search and review methods 27

3

Acknowledgements The College of Policing would like to thank Dr Meagan Cahill, Senior Research Associate at the Urban Institute, for her thorough and professional work in conducting the review of predictive

crime mapping models and compiling the report, on which this summary is based.

The assistance of Dr Nancy La Vigne, Director of the Justice Policy Center at the Urban Institute is also acknowledged. Nancy, along with Dr Elizabeth Groff of Temple University, laid the foundation for this work over a decade ago with their seminal review of prospective

mapping for law enforcement.

The College would also to thank the independent academic who peer reviewed an earlier draft of the summary report.

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1. Introduction

The summary report and the Urban Institute’s 2012 review

Aim and scope

The aim of this summary report is to help police forces make informed choices when they are exploring the possible use of prospective crime mapping in predictive hotspots policing. The

report is based on an international review of crime mapping models and software carried out by the Urban Institute in 2012. This wider review sought to:

systematically identify the range of tools that were available then for prospective crime

mapping; understand how these tools operated, specifying the underlying statistical models; and establish whether the tools had been tested.

The Urban Institute examined a total of nine statistical models and 14 software packages used for predictive mapping. The models and software packages were reviewed against 11 criteria, including:

their theoretical basis;

the crime types for which they were most likely to be effective; the time periods for which forecasts could be developed;

whether they were then in use; and whether any evaluation had been carried out.

Further details about how models and software packages were identified and reviewed are set

out in the Appendix.

This paper seeks to draw out some of the key information for practitioners from the Urban Institute’s review. The next chapter presents a series of tables that summarise the main statistical models used for prospective crime mapping. Chapter 3 provides an overview of the

software packages that were reviewed by the Urban Institute, and sets outs which statistical models they used. The final chapter provides a brief description of the individual software

packages. The Urban Institute’s more detailed technical evaluations of the models and software packages are not presented in this summary, but available on request.

Limitations

This summary paper, and the Urban Institute’s review on which it is based, discuss models and software only in relation to prospective crime mapping. The methods and tools that may be more appropriate for other types of statistical predictions in policing (e.g. offender risk

assessment, survey analysis) have not been explored.

The review and summary paper are unlikely to be an exhaustive account of all models and software packages for prospective crime mapping. Both provide a snapshot of some of the

tools and techniques that were available when the review was carried out in 2012, and new developments will have been made since the work was originally carried out. The College of Policing has attempted to update some of factual information in the summary, to take

5

account of recent developments.1 The College can be contacted if any information presented

in this summary is inaccurate or not up-to-date at the time of publication.

Some software packages were tested by the Urban Institute as part of their review. However, due to accessibility issues, most of their assessments were based on academic work that provided the most in-depth information on the models or software, minimising reliance on

marketing material.

No attempt has been made by the College of Policing to verify the information in the Urban Institute’s review and, therefore, the summary findings in this report do not necessarily

represent the College’s view. The findings are thought to be accurate at the time the review was originally carried out.

Prospective crime mapping and predictive hotspots policing

‘Prospective crime mapping’ refers to a range of techniques that can be used to forecast future crime levels at different geographic scales (e.g. neighbourhood, street) or predict

hotspots where and when the risk of future crime being committed is likely to be high. Outputs from these analytical techniques can be used in predictive hotspots policing, an

overarching approach that also involves the targeting of police resources to reduce the risk of future crime in predicted hotspots.

The analytical techniques used in prospective crime mapping do not identify exact addresses or perpetrators of future crime, which would conjure up – for many – Minority Report-like

images of arrests before crimes had been committed. Although there are an increasing number of software packages for prospective crime mapping available, they use differing

approaches and, while some are grounded in criminological theory, others are atheoretical (e.g. predicting future crime solely on the previous crime levels). These approaches are still relatively new, but have the potential – when used in conjunction with officers’ knowledge in

the field – to inform deployment decisions, and help identify the best times and places for targeted policing activities. A number of forces have piloted predictive hotspots policing, and

there is emerging evidence to suggest that, in some situations, it is possible to reduce crime through targeted activity (see, for example, Fielding and Jones 2013). There is limited evidence, however, on whether different models for prospective crime mapping are able to

predict risk accurately, and whether the targeting of police resources based on predictive analysis is effective at reducing crime and more effective than other policing strategies.

The difference between ‘models’ and ‘software’

As the language to describe the tools and techniques for prospective crime mapping is sometimes used inconsistently, the following terms have been adopted.

Models – The term ‘model’ is used to refer to any mathematical equation (or set of equations) used to simulate real world conditions and events – in this case crime. The models discussed, therefore, provide the mechanics of the forecasts; they are how the

forecasts are actually created. The models included below range from the simplistic (such as basing expected crime on past crime) to the sophisticated (such as agent-

based models, which simulate individual behaviour in the context of a city in order to make predictions about crime).

1 This included: updating all hyperlinks; checking references; adding new information where available and relevant; confirming where the Urban Institute had evaluated software.

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In many cases, officers and crime analysts do not need to understand the algorithms underlying the models; they simply have to know how to interpret and use the output.

Being aware, however, of how forecasts are made can be valuable in assessing how much weight to give a forecast. Therefore, while most police forces will not be involved

in the actual modelling of crime, providing a conceptual understanding of the models behind the software can prevent software packages and their forecasts from feeling like a ‘black box’. And, if forces have personnel skilled in the required methodologies,

they can take greater control of the forecasting process.

Software – Software packages, on the other hand, are a more commonly understood concept. The term is used here to refer to any computer-based programme that was

created for use by any force. Some of these (such as HunchLab) are commercially available, while others (such as ProMap) are custom-designed solutions for specific

police forces. Some are ‘out-of-the-box’ solutions (like CrimeStat or the Near-Repeat Calculator), while some commercially available packages require a fair amount of customisation by the developer or vendor before they are ready for implementation

(e.g. SPSS Predictive Analytics and COPLINK). As with prospective mapping models, a range of products were reviewed, from the simple to the complex. In addition, all of

the software packages reviewed implemented at least one of the models discussed in some way, and some packages even give users the option to employ different types of models for different analysis or forecasting needs.

Implications from the Urban Institute’s review

At the heart of most tools described below is a regression model. While sophisticated

regression models do exist, and many developers used these sophisticated models in their software’s predictive methodology, there was not as much variation in modelling techniques as initially expected. Instead, models were more divided on whether they are univariate or

multivariate, and whether they account of the relationships between crimes and the geographic characteristics of an area (such as housing density).

In selecting and choosing a prospective mapping system, forces need to identify their goals

and, in some cases, the nature of the crime problem they are seeking to predict. They then need to explore the complete start up and maintenance costs of the options that fit their needs. They should also consider the longevity of any software developer they investigate,

especially if they are considering systems with proprietary models – forces will want to avoid being stuck with an expensive system that needs maintenance, and a developer or software

distributor that no longer exists to assist the force.

Finally, once predictive capabilities are in place, police officers and staff should develop at least a basic understanding of how forecasts are developed and the implications of such

forecasts (i.e. the mechanics of the forecast and the theory behind the model design). This understanding will help users to assess the validity of any information produced, and to understand how to react to the findings – the theory will help point to the most appropriate

responses to the forecast crime levels.

With this information, forces can determine which option is most appropriate for their unique context. Undertaking a reasoned approach will ensure the utility of the system that is

implemented, the buy-in from staff using the system, and the ultimate success of the system in predicting crime, resulting in more efficient and effective policing and crime prevention.

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2. Example statistical models for prospective crime mapping There are numerous models for prospective crime mapping, which can be grouped into the following categories:

Multivariate models predict outcomes of situations that are affected by more than one variable. They use multiple variables and their inter-dependencies to produce forecasts. An overview of these models is presented in Table 1 below.

Temporal univariate models use only one variable, previous criminality, to identify areas at increased risk of future criminality.

An overview of these models is presented in Table 2 below. Spatial univariate models use only one variable, event location, to identify areas at increased risk of future criminality. An

overview of these models is presented in Table 3 below. Simulation-based models are used to explore behaviours within a situation which are very complex to study directly. A

simulation-based model is set up to mimic the real situation as closely as possible in order to explore the possible effects following

alternative conditions or courses of action. An overview of these models is presented in Table 4 below.

Table 1. Examples of multivariate models

Model description Crime type and data requirements

Skill requirements Advantages Disadvantages Use in operational policing context

Artificial Neural Networks (ANNs)

In relation to crime analysis, ANN suggests that while crime may

appear random, its occurrence actually

follows a mathematical pattern, and modelling

can provide a prediction of future crime levels. Computer simulation is

used to develop models incorporating feed-back

of errors so that it ‘learns’ in order to reduce error.

Crime type – any provided high

volume as model requires a lot of (numerical) data

Knowledge of mathematics and

programming is required

Some ANNs have the potential to

produce better forecasts than standard

statistical methods (e.g.

Ordinary Least Squares

regression)

Potentially complex, and may

only reach full potential when used by a

trained/skilled quantitative crime

analysts Requires an

extremely large amount of data

Requires very

high computational

power It can be a

statistical ‘black

box’ where not

No evidence of practical use

8

much is known about how results

produced

Leading indicators Method relies on a

known relationship between a crime type of and another indicator

(e.g. an increase in a certain activity one

month leads to an increase in crime the next). Based on time

series analyses of at least one measure,

which will predict changes in future levels of the specified crime.

Crime type – any if there is an

observed relationship with another measure

Data – At least two years of

weekly or monthly data aggregated to an area level

Minimal skill required to use

mainstream software

A higher level of

skill required if using without

proprietary software

Simple and straightforward

model Better when there

are substantial

changes in crime levels

Limited evidence of accuracy

Has a short forecast period (about a month)

Possibly used by Chicago Police

Department, but no detailed information

available

Spatial discrete

choice Produces probabilities

that a certain crime would occur in a certain location, given the

characteristics of that location rather than the

geographical location itself. Assumes that characteristics of places

(e.g. distance to roads, average income,

presence of parks and police stations) are more useful for

predicting the

Crime type – has

been applied to low volume crime

and rare events (e.g. terrorism)

Data – point data

or data aggregated to a

geographical area (e.g. crime counts)

Moderate

statistical skill required

Useful if very little

data available (e.g. rare events

like terrorism) or a short time frame (e.g. three

weeks)

Does not account

for the spatial or temporal

clustering of crime (the importance of

which is well documented in

the literature) Possible that

predictive

accuracy has been over-

estimated

Highly likely that

the model has been adopted by

Virginia Police Departments, but there are no

published reports on its use

9

occurrence of crime than spatial distribution

of crime alone.

Spatial regression Regression modelling

techniques that explicitly take into account spatial

relationships among events (e.g. crimes and

characteristics of places). These models explicitly account for

space – more specifically, they

account for the distance between places and assume that nearby

places exert a greater influence than more

distant places.

Crime type – any that can be

spatially located Data – large sets

of point data with

mapping coordinates or

centroid coordinates if using area level

data (i.e. mid-point of the area)

Substantial statistical

knowledge required to understand the

theory and interpret the

output Software training

would be required

Explicitly takes into account

space More suited for in

small study areas

(e.g. city level or smaller)

Predictive power and accuracy has

not been tested Not good at

predicting short

term spikes of crime

No evidence of practical use

10

Table 2. Examples of temporal univariate models

Model description Crime type and

data requirements

Skill requirements Advantages Disadvantages Use in operational

policing context

Near-repeat analysis Based on near-repeat

phenomenon, where properties close to those that have been

victimised are deemed to be at higher risk for

future crime than targets further away. Models identify patterns

of near-repeat crimes in the area under study to

determine areas with elevated levels of risk over a specified (short)

time period.

Crime type – strongest with

property crime, particularly burglary

Data – historic point data; better

when data is available for a long time period

(e.g. 1-2 years)

Minimal skills required to use

the analytical packages

An understanding

of statistical significance useful

in interpreting the output

Most noticeably effective in areas

with high burglary rates or in areas experiencing a

sudden rise in burglary

Does not predict time of specific

future crimes, but does help to identify areas with

elevated levels of risk over a

specified period Model should be

focused on one

type of crime at a time

Tested in the East Midlands and

West Yorkshire Police

Currently being

tested by West Midlands Police,

and also being used by Greater Manchester Police

Univariate time series Commonly used for

basic crime forecasting. Not linked to the spatial relationship between

crime levels across areas. Simply look at

the level of a particular crime type over time in order to make

predictions of what may occur in the future.

Crime type – any but if low volume

precision of forecasts decrease

significantly Data – simplest

methods only need a few points from the

immediate past, or from the same

time last year. More advanced methods require

weekly or monthly

Basic understanding of

maths and crime data required using simplest

models Good maths skills

required to use more advanced methods (e.g.

seasonal decomposition, or

ARIMA)

Simple and easy to implement in

its basic form When crime is

stable, some

methods have been shown to

work as well as other more complicated

models Most accurate for

short and medium term forecasts

Not truly ‘forecasting’

because the models project the same levels,

trends, and patterns in crime

rates from the past into the future. If the data

has a trend, a change in trend,

or noticeable seasonality, then more complicated

univariate models

Basic crime forecasting has

been used widely by police analysts

More advanced

methods which can improve the

accuracy of the forecasts have not been used

extensively in policing

11

data for a minimum of 1-2

years

are needed to account for these

Table 3. Examples of spatial univariate models

Model description Crime type and data requirements

Skill requirements Advantages Disadvantages Use in operational policing context

Hotspots models Estimate or predict

values across space, identifying areas where

crime or other events cluster.

Crime type – any Data – amount of

data depends on the size of the

area and the density of events in that area, but

less than several hundred points

may make identification of a hotspot difficult

Models work better when a

year’s worth of data is used

Little analytical, statistical or

programming knowledge

required

Most useful for identifying areas

where crime is likely to be

persistent

Requires a stable spatial pattern of

crime; won’t predict changes in

crime patterns Not ideal for

identifying

changes over short periods of

time, such as from week-to-week or month-

to-month The model would

be least effective if highly specific or precise

forecasts were required

Models are used widely in policing

These models are not explicitly

predictive, but having identified a persistent

hotspot, can be used to inform

deployment decisions

Table 4. Overview of simulation based models

Model description Crime type and

data requirements

Skill requirements Advantages Disadvantages Usage for modelling

crime

Agent based modelling Computer simulations of

simple ‘agents’ which

Crime type – any with a theory for why crime is

committed

Sophisticated method requiring very highly skilled

analysts

Explores complexity of large numbers of

agents interacting

Extremely complex and unlikely to be

appropriate for

No evidence of practical use

12

can behave like people in a simplified

environment. The agents typically have a

small or basic set of rules to follow, some of which may be governed

by an element of chance.

(usually used for burglary)

Data – usually individual level

data but aggregate data can also be used

Amount of data depends on

context

in a common environment

Shows how systems behave

when aspects are altered (e.g. increased target

hardening or patrols)

regular application in a

force Primary purpose

not prediction but to develop and test theories

No evidence on predictive

accuracy Development of

models still at an

early stage

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3. Example software packages for prospective crime mapping There are numerous software packages available for prospective crime mapping. Table 5 below provides an overview of some of the most

well-known packages, based on the review conducted by the Urban Institute. More detailed descriptions of the software packages are presented in the next chapter.

Table 5. Examples of software packages

Software (and developer)

Package Predictive model(s)

used

Unit of analysis

Data requirement

Skill requirement

Advantages Disadvantages

ArcGIS (ESRI) Out-of-box (with add-

ons available)

Hotspots Spatial

regression

Address or area

Points or aggregated

crime levels

Beginner Flexible, powerful mapping

software that can accept add-ons by other

developers of predictive

mapping models to extend its utility

Limited in pre-programmed

predictive capabilities

COPLINK (i2) Out-of-box

and custom built

Data

mining

Address,

area, offender

Any police

data

Beginner Finds patterns

across disparate datasets

Customizable with different modules

Limited true

predictive capability

CrimeStat (Ned

Levine and Associates)

Out-of-box Spatial

descriptive

Address or

area

Dependent

on procedure used for predictive

analysis’

Moderate Freely available,

extensive descriptive tools

Limited true

predictive capability

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CrimeView (Omega Group)

Out-of-box Near-repeats

Hotspots

Address or area

Address data for near-

repeat analysis,

points or aggregated crime levels

Beginner Dashboard design accessible

to users

More focused on descriptive

analyses than prediction

Daily Crime

Forecast (Stephane

Contre)

Out-of-box Data

mining Univariate

time series

Individual

events

Individual

events (e.g. offences,

calls, arrests)

Beginner Accessible user

interface Univariate

models but can incorporate data from various

sources

Not transparent

regarding models used

Data Detective (Sentient

Information Systems)

Out-of-box Data mining

Spatial discrete choice

Area Combines broad,

disparate data (not just police

data)

Skilled Developed with police to

maximise utility to forces

Extensive

functionality

Works best when large amounts of

data from various sources are available

HunchLab (Azavea)

Custom built Data mining

Near-

repeats

Area Any police data

Beginner Developed with police to maximise utility

to forces Performs

multiple predictive tasks

No current mobile capabilities

Requires customisation by

vendor

LEA (Information

Builders Inc)

Custom built Custom built to

user need Limited

only by R capabilities

Address or area

Dependent on model

used

Skilled Dashboard design accessible

to users Use of R makes

software highly functional for

Requires forces to build own

models or purchase

statistical consulting

15

statistical analysis

Near-repeat Calculator (Jerry Ratcliffe)

Out-of-box Near-repeats

Address Address data Beginner to moderate

Freely available, simple to use

Output hard to work with and understand

Only conducts one type of

analysis

ProMap (Shane Johnson and Kate Bowers)

Provision of analytical service

Near-repeats

Point and grid

Combines broad, disparate

data (not just police

data)

Skilled Thoroughness of model

Accuracy of

predictions higher than

typical police analysis methods

Custom programmed

PredPol (George Mohler

et al.)

Provision of analytical

service

Near-repeats

Point and grid

Individual events (e.g.

offences, calls,

arrests)

Skilled for programmer;

beginner for end user

Can produce daily predictions

Conceptually easy for officers

in field

Custom programmed

Signature

Analyst (GeoEye

Analytics)

Out-of-box Data

mining Spatial

discrete choice

Spatial

regression models

Address or

area

Combines

broad, disparate

data (not just police data)

Skilled Thoroughness of

model Use of

sophisticated modelling techniques

Extensive data

mining capabilities could

be hindered by lack of consistently

updated data to use in modelling

16

SPSS Predictive Analytic

Enterprise (IBM)2

Custom built by user

Custom built by

user

Multiple Combines broad,

disparate data (not

just police data)

Skilled Use of SPSS makes software

highly functional for statistical

analysis In use in multiple

police forces

Custom programmed by

end user

WebCAT

(DaPro Systems)

Out-of-box Data

mining Spatial

discrete choice

Address or

area

Any police

data

Skilled None Reports from

forces where currently

deployed about difficulties searching, and

data structure requirements

2 Entry updated in June 2016 following additional information provided by Avon and Somerset Police.

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4. Description of software packages

ArcGIS

The application used for predictive mapping/spatial analysis within ArcGIS is called ArcMap 10

(see: www.esri.com). It uses tools/routines that Esri calls the ‘Spatial Statistics Toolbox’. These are available at all license levels of ArcGIS on Windows machines and, with limited analysis

functions, through mobile devices.

The software can be used with any type of crime that can be spatially located, including hotspot analysis. The unit of analysis is either the individual crime or any spatial unit to which crime

counts were aggregated; this depends on the selected method for identifying hotspots. In order to properly interpret the results of a hotspot analysis and to make the results actionable, it may help to be aware of the theoretical basis of explanations why crime occurs where it does, such as

routine activity theory.

The tool requires a minimum of several hundred observations, but this minimum also depends on the size of the study area and the density of points in the study area. It works best when a

year’s worth of data is used, and thus identifies areas where crime is consistently or repeatedly high over that one year period. If crime patterns fundamentally change, the hotspots method would not be able to predict those changes. This model does not take into account any

contextual characteristics of crime and therefore cannot be used to predict emerging areas. It is best used for longer term predictions, as it helps identify areas where crime is likely to be

persistent.

Using ArcGIS requires at least a basic knowledge of spatial concepts, which can be acquired to the extent needed via the Esri website. Those who are new to mapping and to ArcGIS often

experience a steep learning curve because of the complexity of the software. Importing data into ArcMap in the required format often requires significant resources.

This software has been used extensively in a large number of US police departments, although limited evidence exists regarding its use specifically for predictive mapping; it is often used for

data processing and other analysis tasks in police forces.

There are start-up costs associated with the use of Esri products like ArcGIS. ArcGIS and ArcMap offer functionality for mapping and analysis not touched on in the review, and investment in this

software is likely to be more cost-effective when used for more than just spatial statistics and/or prospective crime mapping.

COPLINK

Analyst’s Notebook 8 (part of Analysis Product Line) and COPLINK are both designed to be accessible to many different kinds of users (see: www.i2group.com). They appear to have utility

for very basic functions, such as performing a specific query, to more complex searching and matching functions for instance, for intelligence purposes.

Analyst’s Notebook 8 can link directly to Esri’s ArcGIS Server, adding mapping and geospatial

analysis to the application. Analyst’s Notebook 8 can be used ‘out-of-the-box’ and can integrate with COPLINK. The different modules under COPLINK include a mobile module, face recognition module, a repetitive querying module, and a CompStat module, among others. Analyst’s

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Notebook and COPLINK both emphasise bringing together data from different sources, but both seem mainly designed for data mining and data management, with little real predictive mapping

or forecasting functionality.

Data mining itself does not necessarily require much skill on the part of the analyst, but analysts are likely to require some familiarity with the data sources that are fed into the data mining

process in order to understand the output. The model can use any type of data, including text data and other types of non-numeric, non-structured data. The techniques can be used to aid in

investigations of serial or repeat offenders or individual investigations, deployment decisions, or intelligence-led policing strategies. Based on data mining, this tool is designed to find patterns that exist in the data. It uses very large data sets, and often multiple large data sets. The model

does not explicitly model future criminality. It is up to the user to pull out patterns from the data mining results that can be fed into predictive techniques.

The extensive adoption of the software among the law enforcement community is suggestive of

its potential value in performing complex data mining and data management tasks. It is likely to be most appropriate for agencies looking mainly to integrate a number of different data sources and to streamline data searching for investigative purposes. In addition, for forces who do not

desire sophisticated analysis functionality but value extensive visualisation functionality, this software could be appropriate.

CrimeStat

CrimeStat version 3.0 was developed by Ned Levine and Associates, it is available as a free download (see: www.icpsr.umich.edu/CrimeStat). It runs on Microsoft Windows machines. The

purpose of CrimeStat is descriptive spatial statistical analyses to help understand the spatial dynamics of crime. The predictive analyses available are relatively simple and straightforward.

Its predictive capabilities are limited and it is not mapping software.

The only procedures explicitly intended for prediction are correlated walk analysis and predicted crime travel trips. Correlated walk analysis uses past data on time and location of crime events

to predict the time and location of the next event. It also assumes that the recent spatial and temporal pattern of crime will continue, so the validity of the predictions hinge on the appropriateness of such assumptions. The predicted crime travel trips method models the

number of trips from one origin to a given destination for criminal activity. In terms of future predictions, it assumes that the relationship between observable neighbourhood characteristics

will continue.

CrimeStat software is based on the theoretical underpinnings of the hotspots model. All types of crime can be analysed, provided point data are used. Many of the analyses are specifically developed for serial offenders. The model can use as many independent variables as desired. In

terms of the amount of data on existing crime levels necessary for analysis, correlated walk analysis requires very little data, but the predicted crime travel trips method requires

considerably more data.

The CrimeStat manual is extensive, but some experience in spatial analysis (beyond mapping) would be beneficial to be able to use this software. For analysts familiar with spatial analysis, the

transformations necessary to prepare the data are fairly straightforward (assuming point data are available). However, because the program has no mapping capabilities, any analyst would also have to know how to use some form of GIS software (CrimeStat can be integrated with

almost any GIS software). On-going analysis time requirements are low, and the program

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mostly requires an initial investment to gain an understanding of when the methods are appropriate. If the software is already being used to monitor and examine crime trends,

extending its use for prediction would only require minimal additional effort.

CrimeView

CrimeView, developed by the Omega Group, offers the police basic crime mapping capabilities, along with querying, reporting and alerting (see: www.theomegagroup.com). Specifically, it provides hotspot mapping, near-repeat analysis (identifying increased risk for property crimes

over a one- to two-week horizon), threshold alerting, and visualisation (maps, charts, graphs) of various crime-related data. These can be directly imported from force computer aided dispatch

and record management systems. The software does not do explicit prospective mapping or sophisticated crime prediction modelling; its analysis capabilities fall into the category of CompStat analysis (i.e. doing descriptive analyses and data management tasks).

The software incorporates two applications: CrimeView Desktop; and CrimeView Dashboard. CrimeView Dashboard makes the CrimeView tools accessible to remote users via a web browser.

CrimeView does not require a minimum level of crime, however, as with all crime analysis tools, more crime events for analysis result in more accurate forecasting. It does not make any

assumptions about future criminality levels and does not require a stable pattern of future crime. It can analyse point data and data aggregated to an area (such as police districts).

The software is billed as ‘intuitive’ and ‘easy to use’ and is likely to present few challenges to

those who have at least a basic knowledge of mapping software. Common routines can be automated. The software integrates with ArcGIS server and other Esri applications, but no specific information on computing requirements is readily available from the website.

The first CrimeView application was released in 1996 and over that period has been adopted by many police forces. However, it is likely that some of the examples of use may not relate to the data management and analysis tools in CrimeView. Nevertheless, CrimeView has received

positive feedback from the forces who have adopted it and the software developers have reported examples of improvements in police effectiveness following its use. However, it is not

possible to distinguish between the effectiveness of the software itself and the intelligence-led policing strategies it was used with. In addition, because the software does not really perform predictive analyses, no evidence was found by the reviewer of the accuracy or effectiveness of

that type of functionality.

Daily Crime Forecast

The Daily Crime Forecast tool, developed by Stephane Contre, uses an algorithm to assess spatial and temporal patterns of crime, searching for patterns among crime events (data mining), and using those patterns to predict future levels of crime (see:

www.crimeforecast.com). The tools uses a univariate model with a time function included, taking into account time of day, day of week, day of month, and monthly temporal patterns. It was not

possible to determine exactly what model the software employs to create forecasts, however the description appears to be similar to what is being used by HunchLab software.

The software can read in data, in the form of individual events (e.g. calls, arrests, crimes reported), from a number of different sources, including computer aided dispatch and record

management systems. The forecast model works best with larger areas or for larger time

20

horizons, where more crime events are used to generate forecasts. Thus, the model works best for aggregate crime measures, such as all property crimes, and can be applied to any type of

crime. There is indication that the software can perform for up to two months ahead while maintaining the quality of the forecast.

Only one agency (in Canada) is known to have been using the software since 2006, to forecast

daily crime predictions to make deployment decisions, with success claims cited in the company’s website: the software’s predictions were found to be twice as predictive “than other

spatial and temporal programs in use” (crimeforecast.com). They also found a significant increase in officer-initiated calls and a significant drop in reactive call deployment, meaning officers were being more proactive based on predictions from the software. It is not clear,

however, whether the forecasts themselves were compared with a scenario of what would have occurred had police not proactively been at the scene of a forecast high-risk location

(counterfactual scenario). In short, more implementation and testing of the software are likely to be needed by police forces in order to make informed decisions about its use.

There may be some indication that the software is not very transparent, and users do not have much control over the searches and analyses that the software is conducting, and may not be

able to understand how to interpret output from the software. Nevertheless, the software resembles popular mapping software and is likely to be straightforward to get up to speed with

and present few challenges to those who have at least a basic knowledge of mapping software. In addition, functions can be automated and a web-based interface also makes automated results available to frontline staff.

Data Detective

DataDetective was produced by Sentient Information Systems in the Netherlands in conjunction

with police departments and was designed specifically for the policing context and has been used by Dutch Police (see: http://www.sentient.nl/?dden). It is mainly a data mining package (performing pattern searching and fuzzy matches – record linking technique), with extensive

additional functionality, to include link analysis (a type of social network analysis) and predictive modelling (similar to spatial discrete choice modelling which relates the possibility of crime to

characteristics of places). The software is also described as having ‘learning’ capabilities where it can continually adapt to changing inputs. Finally, the software provides basic mapping capabilities, or it can be integrated with MapInfo software to extend mapping functionality.

DataDetective claims that minimal a priori assumptions are required by users, as the data mining functionality looks for patterns based on mathematical properties and not on theory about crime and where it locates. This software has been applied to burglary and robbery and

could be applied to low volume crime and rare events. Users, however, do need to be able to sort through the patterns that are identified and ascribe meaning to those patterns using their

experience and knowledge to determine which patterns are relevant to the task at hand and which are inconsequential.

Many different crime types can be analysed using DataDetective, including white collar or organised crime. The model uses multivariate data from numerous sources (including weather,

time of day, events) and is expected to work best when large amounts of data are available. It is suggested that predictions work best and are more accurate when created for smaller areas,

such as neighbourhood or several-block areas.

21

Information from the developer suggests that participation in a two-day training seminar would ensure new user proficiency in the software. From then on it is likely that much of the time

getting up to speed would be spent on learning how to incorporate data from different sources in different formats, and understanding how patterns are chosen.

The only evidence available of the software’s predictive accuracy in the field are generic

measures provided by the developer itself. As the general findings appear to be positive, the software would benefit from additional testing and analysis

HunchLab

The web-based HunchLab software (see: www.azavea.com) can be accessed through standard web browsers and is directly integrated with a force’s data stores so that it can receive data

updates at a predetermined rate (hourly, daily). This allows users to access data from within the application instead of having to import data. The software contains several different

components, briefly discussed below.

The Crime Analysis component includes mapping, allowing users to visually examine changing spatial patterns over time. The Early Warning component examines temporal and geographic crime trends to identify emerging levels of activity that are unlikely to be random coincidence.

The software performs data mining and predictive analysis in order to detect such crime spikes (‘hunches’) and, when one is detected, analysts in the department are automatically notified so

that they can make decisions about whether and how to react in order to prevent crime from increasing further.

The Risk Forecasting component looks at near-repeat patterns of crime to identify areas at higher risk for specific types of crime (especially property crimes) and performs modelling to

forecast aggregate volumes of incidents in different areas.

The software can be used with any type of crime and can be used to aid in investigations of serial or repeat offenders or individual investigations, deployment decisions, or intelligence-led

policing strategies. However, it requires that a large number of crime events be available for analysis in order to generate the most accurate forecasts. In general, the software’s forecasting

routines are most effective at the precinct, ward or district level. Smaller units of analysis would likely not have enough crime to use in creating accurate forecasts. The software can produce forecasts for four hours ahead, up to about a month into the future, and it can produce such

forecasts starting immediately or any period up to a month ahead. Forecasts for longer time periods, up to a month, are more accurate.

The software’s interface is designed to be accessible to even beginner users, including police

officers working in the field who are not trained as crime analysts. The software’s cost is based on the expected number of users and the level of customisation required, such as integrating data sources into the application, readying the tools of most interest to the client and setting up

standard queries/analyses and subsequent alerts to run at regular intervals. HunchLab meets Cohen and Gorr’s3 criteria for more successful predictive accuracy in that it incorporates both

3 Cohen, J and Gorr, W. L. (2005) Development of crime forecasting and mapping systems for use by police. Technical Report. Washington, DC: National Institute for Justice.

22

previous crime events and characteristics of places as well as distances between locations as a way of measuring influence of one event on another.

Law Enforcement Analytics (LEA)

Law Enforcement Analytics (LEA) software has been developed by Information Builders Inc (see:

www.informationbuilders.com/products/webfocus) and includes a range of business intelligence functions all in support of intelligence-led policing. The software includes WebFOCUS RStat which provides predictive modelling and data mining capabilities. It is this element of the software that

is described here. WebFOCUS RStat can either be used as a standalone component or be added to existing LEA software. It is a customizable ‘off-the-shelf’ application which can be accessed

via remote computers such as in-vehicle units.

WebFOCUS RStat:

WebFOCUS RStat requires users (forces) to design and code predictive models in R (an open source analysis programme). Information Builders can provide statistical consulting

services or the force can task a statistician with designing and implementing a predictive model.

The statistical functionality available is extensive and includes various forms of regression

models, neural networks and survival analysis, among others.

It has dashboard capabilities which allow users to make the functions used most often

readily available, and can include predictive models and regular reporting. Information Builders Inc works with agencies who are adopting the software to design dashboards unique to each agency.

The interface (using R) means that the engine is accessible without the need for scripting (i.e. can be run by anyone) but only after the initial model has been programmed by a

statistician or modeller. In addition, users would still have to understand how to interpret the results of the model, although the programmer or statistician can program the output to be interpretable by the lay user.

The software does not require a minimum level of existing crime to function – certain analyses can be done with smaller amounts of crime. However, the user needs to be aware of the minimum data requirements for the specific analysis that they are conducting.

The software is designed specifically according to user specifications and requirements meaning

that most of the following areas are all determined by the user (e.g. assumptions about future criminality, how far in to the future the software considers whether a stable pattern of future

crime is required, the unit and type of analysis, and the predictive models).

The software can access and incorporate the following information: criminal histories, incident reports, crime tips, emergency calls, computer aided dispatch and records management system data, weather patterns, and events.

The software would be most successful where a police department has skilled statisticians who can build predictive models for repeat use. No evaluations of the software’s implementation for predictive modelling purposes currently exist.

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Near-Repeat Calculator

The Near-Repeat Calculator was developed by Jerry Ratcliffe at Temple University, and is free to download (see: www.temple.edu/cj/misc/nr/). The software is designed to calculate the risk of

near-repeat events in crime data. The model searches for pairs of events in both space and time to try and identify two events that happened both geographically close to each other and within a short time period (usually two weeks). The model then determines if there are more of these

‘near’ pairs than would be expected based on a random pattern and if a pattern emerges that suggests near-repeats are common in the area under study.

The user can map this information to find clusters of originator (initial) events and also to

identify areas within a jurisdiction where near-repeats or a specific crime type are more likely. The results help to identify locations where patrols should be focused after an initial burglary in order to try to avoid near-repeats. The software does not include any mapping functionality, but

the analyses conducted can be combined with GIS software in order to generate maps. This can then contribute to tactical decision-making by police around patrol deployment. The software

can run on machines using Microsoft Windows.

The near-repeat model uses any type of point data, such as crime or calls for service. Property crime has been found to have the most likelihood of near-repeats. It does not require a specific

amount of data or that which covers a set time period, but data used covering shorter time periods will have lower levels of reliability than that covering more extensive time periods. Data aggregated to geographical areas or other independent variables such as policing districts cannot

be used with the model.

The model does not make any assumptions about future criminality. The model can receive new data at any time enabling it to analyse this information and determine whether the previously

identified patterns are changing at all.

It is stated that all types of crime can be used within this model, but it is most effective when used in relation to property crimes.

The software is straightforward and easy to operate, meaning that the skills required by the

analyst to use this method are minimal. It also requires little start-up time. The output is limited and requires additional software to enable mapping of the data obtained.

ProMap

ProMap was developed by Shane Johnson and Kate Bowers at the Jill Dando Institute of Crime Science, University College London. ProMap is not a commercially available software package but

an application that was developed, implemented, and tested in UK police force. It seems that the software has not been deployed on a server or on desktops. Calculation of the model occurs in several steps and is similar in theory (but not necessarily in calculation) to a density or hotspot

model and a spatial discrete choice model, whereby both spatial patterns of crime and characteristics of places are taken into account in assessing victimisation risk levels.

The application and model for prospective mapping were developed to assist with police

deployment decisions and focused only on burglary. It was based on the idea of near-repeat victimisation risk. The model looks at crime points in space and time in two ways. The first considers all the burglary events around the initial event over a certain time period using a

distance decay model (whereby closer events are given more weight and farther points less

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weight). The second method uses only the most recent and closest burglary to assess risk. A grid is overlaid on the map of burglary events and risk is calculated for each cell using one of the

two above methods.

This method is similar to a hotspot map created using kernel density estimation. However, risk is refined in the case of ProMap by taking into account the ‘environmental backcloth’ of each cell.

This backcloth includes the concentration of houses, the existing street network, and demographic characteristics, which are all used in calculating risk based on the theory that a

denser pattern of housing or streets would increase an area’s risk for burglary victimisation.

The model uses crime report data on burglaries, street network data, parcel data (to calculate the number of residences per cell), and demographic data to calculate affluence and race/ethnicity measures. It assumes that future burglaries will occur nearby to ones that have

recently occurred, but it does not assume a stable pattern of future crime. The software requires a minimum amount of data (e.g. enough burglaries having taken place to provide reliable

estimates but it is not specified what this minimum would be).

The developers have compared predictions made by ProMap to hotspot methods using kernel density estimation methods and found that ProMap made more accurate predictions.

PredPol

PredPol applies seismographic methods used within the study of the near-repeat phenomenon of earthquake aftershocks to a criminological context (see: www.predpol.com). The model

identifies patterns of near-repeat crimes in the area under study and possible elevated risks of crime over a time period during which the risk is elevated.

PredPol is similar to hotspot mapping but with a time element included. It identifies areas with

elevated risks for crimes and suggests the time period for which these risks are elevated, although it does not predict the time or location of specific crimes. It takes into account background risk such as characteristics of the crime location, the offender, the weather, the day

or time of offence. If a pattern emerges indicating that near-repeats are common in the area then the model can contribute to tactical decision-making by police enabling them to focus patrol

efforts on these areas. The model results can be used to help make patrol deployment decisions in order to prevent additional crimes occurring after an initial event. Application has mostly focused on property crime because the space-time patterns of this type of criminality most

closely resemble the pattern of an earthquake. It is stated that if other crimes are found to fit the pattern of this model then it can also be applied to them.

The model does not make any assumptions about future criminality, only finding patterns among

crimes that have already occurred. The model does not require a minimum amount of existing crime in order to be utilised but data covering shorter periods of time will elicit lower levels of reliability. It uses any type of point data, which means that data aggregated in to small

geographies such as policing districts cannot be used, and it is a univariate model meaning that no independent variables are included within the data analysed.

Setting up the model requires a highly skilled statistician, meaning that the start-up knowledge

required is high. If the model is used to create a software package that can easily be implemented then the skills required by end users will be minimal and interpretation of the

results should be straightforward. The strengths of the model lie in the frequency of forecasts, the ease of interpretation and the minimal computing requirements to create forecasts. PredPol

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is a cloud-based software where users upload crime data and receive the analysis output. As such it is more like a ‘black box’ for the police department that uses it. The algorithms

underlying the model have been published4, but creating a bespoke model would require skilled statisticians.

Signature Analyst

Signature Analyst is a software package made by GeoEye Analytics that can be used as a standalone client or extension to ArcGIS software to add spatial capabilities to ArcGIS’s existing

mapping and analysis functions.5 The developers describe the software as relying on geospatial preference modelling which assumes that human behaviour and other events are influenced by a

range of environmental, cultural and social factors, and analyses events within a spatial context in order to discover the nature of these relationships. The approaches to predictive modelling that the software employs are data mining, spatial discrete choice models and spatial regression

models. The software produces a density map that displays hotspots of increased risk for the crime being modelled. It mines huge datasets containing any number of different factors that

may influence where crime occurs and then uses the modelling techniques to assess the risk of crime occurring in specific locations.

Signature Analyst can be applied to any type of crime but predictions were found to be most

accurate when the crimes used as inputs were as homogenous as possible (e.g. gang-related shootings as distinct to drug-related shootings). The model appeared to function most accurately at a city or sub-city level, predicting hotspots of risk for a neighbourhood or other small

geographic area. The level of analysis is decided by the user, and can provide focus on short term predictions such as days or several weeks. Signature Analysis draws data from numerous

sources of data to incorporate as much knowledge about the environment as possible and is expected to work best when large amounts of data are available.

Users of the software would have to be skilled at programming or other complex software packages in order to learn this package quickly. The software runs on Microsoft Windows XP

Professional. Users have also reported using it on Windows 2000, Windows Vista and Windows Server 2003. The software may be difficult to learn, but appears to offer sophisticated and

research-based modelling. It would be most effective when there are large amounts of data available that can be fed in to the model.

SPSS Predictive Analytics Enterprise

The SPSS Predictive Analytics Enterprise suite by IBM is used in a number of different fields, including policing (see: www-03.ibm.com/software/products/en/spss-predictive-analytics-

enterprise). It comprises four main products, two of which explicitly offer predictive modelling capabilities: SPSS Modeler, and SPSS Collaboration and Deployment Services.

4 See: Mohler, G., Short, M., Brantingham, J., Shoenberg, F. and Tita, G. (2011) Self-exciting

point process modeling of crime. Journal of the American Statistical Association, 106 (493): 100-108. 5 Since the original review, GeoEye Analytics has merged with the DigitalGlobe Intelligence

Solutions, who still make the product available. The review was based on the old proprietary information which was correct at the time of writing. For further information see:

http://www.digitalglobe.com/products/insight/analytic-services#overview.

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SPSS Modeler performs a wide range of different data mining and predictive analysis techniques including time series analysis, regression and neural networks. SPSS Collaboration and

Deployment Services focus more strongly on sophisticated data mining techniques and on distributing the results of analysis to users or other personnel.

SPSS software can be integrated with other applications, including ArcGIS (which would add

mapping capabilities). It offers automated options to enable standard tasks to be programmed once and used repeatedly. SPSS Predictive Analytics also has a graphical user interface.

The software can use any number of different types of data including standard crime measures

such as crimes reported, computer aided dispatch data and records management systems. The software is custom built and the level of customisation and the functions it is set up to fulfil determines the data requirements and the level of skill required by the analyst to use it

effectively. The programme can utilise a number of different modelling routines.

The predictive power and accuracy of the software could not be assessed as the software is customised by the user according to their requirements. The predictive accuracy of each model

depends on its various characteristics.

The software has been deployed in a police operational context by a number of police departments. SPSS software provides the police forces with the ability to obtain the most

appropriate tool for their individual context because they will have a level of control over the design of the system. Police departments will need to specify the types of analysis they desire including the level of sophistication for each model, the kind of automated mapping and

reporting that they want, the number of users and how different their needs are from those obtained from a tool ‘out-of-the-box’.

WebCAT

Web-based Crime Analysis Toolkit (WebCAT) software was developed at the University of Virginia in close cooperation with the Virginia Department of Criminal Justice Services to

increase and improve data management, the sharing of data between jurisdictions, and the analytical capabilities of crime analysts. Comprehensive data sharing was seen as a crucial

aspect of the development of crime prevention efforts at the time the data was developed.

The software is now maintained and distributed by the software company DaPro Systems, which also distributes a number of other software packages aimed at public safety agencies (see: www.daprosystems.com).

WebCAT is a web-based software. It conducts analyses based on data mining, hotspot mapping, and spatial discrete choice models. It can use any type of data for data mining. It is stated as being easy to use, but it is suggested within the evaluation that training is necessary.

WebCAT has been implemented in many police departments within Virginia and found to be

useful for data sharing and mapping purposes, but there is little evidence of either the use of the predictive capabilities or the adoption of the software outside of Virginia.

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Appendix. Search and review methods

Search strategy

Groff and La Vigne’s (20026) review of 11 different models formed the foundation and starting

point for Urban Institute’s review, and provided initial direction for the search to identify models for subsequent evaluation. The following methods were used:

Internet searches – Searches was carried out to give a broad indication of the scope of

materials that were available, and allowed software to be identified that would not have been identified through the literature searches.

Literature searches – Extensive searches were carried out of library databases

cataloguing literature on range of relevant academic disciplines (such as geography, criminology, maths, and computer science).

Prior knowledge – The Urban Institute was also familiar with several packages, though not all were included in the final review due to a lack of information or relevance.

Consultation – The review team consulted with developers and software representatives, who provided further detail on their products and services.

The classification scheme used by Groff and La Vigne (2002) was adjusted to account of progress that had been made in predictive statistical modeling and availability of more software

options.

Data collection and evaluation criteria

The following criteria – developed by the National Policing Improvement Agency – were used by the Urban Institute to gather data on, and evaluate, each model and software package.

Model overview – Name and brief description of crime mapping model, including lead

research figures / institutions associated with it, and references of key research papers.

Crime type(s) targeted – The crimes the model was designed to predict, the intended context for deployment, the crimes examined in any empirical tests and planned future tests.

Theory – Any theoretical assumptions underpinning the model or its components, with

brief descriptions and references.

Crime levels – Assumptions/requirements of the model in terms of crime levels. For example:

‒ Does the model require a minimal level of existing crime, and if so what?

‒ Does the model make any assumptions about future criminality, and if so what?

6 Groff, L. and La Vigne, N. (2002) Forecasting the Future of Predictive Crime Mapping. Crime

Prevention Studies, 13: 29-57.

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‒ Does the model require a stable pattern of future crime, and if so what?

Data availability constraints – The data that the model accepts and/or requires. For example:

What types of data does the model use (e.g. crime figures, public calls)? How much data does the model use (e.g. univariate or multivariate)? What is the unit of analysis?

Scale – The temporal and spatial scales of the model. For example:

What is the model’s ‘bandwidth’ or level of focus? How far in the future does the model look?

Analyst skills – The skills required by analytical users, details of any preliminary work required before use (e.g. opportunity mapping), and evidence of relative ease of use from

an analytical perspective.

Computing constraints – Description of the equipment required. For example:

What computing power/equipment does the model require? What are the model’s software requirements?

Are there any constraints on the model being accessed via a mobile devices?

Predictive power / accuracy – Description of the model’s claimed predictive power / accuracy, and summary of evidence supporting or refuting claim (including descriptions of

evaluation contexts).

Evidence of operational usage – Summary of evidence about the model’s operational practicality. For example:

Has the model been deployed in a police operational context, and if so where?

From a process-focussed point of view, what were the findings of such trials?

Other comments

The information that was used to evaluate the various models and software programs came from a range of sources (e.g. websites promoting software, academic literature reviewing packages,

and conversations with the software developers). The assessment was, however, limited by the amount of information available. Much of the work on specific models or methods came from academic sources (e.g. journal articles), and mainly referred to those that had been used

programmed for the research study being reported and were not available as a software package.

It was not possible to carry out extensive, independent testing of the software packages as

many required customisation or were not available free of charge. Most of the assessments, therefore, were based on academic work that provided the most in-depth information on the model or software. The Urban Institute’s review team tried not to rely on marketing materials

where possible for statements about the accuracy of predictions or forecasts, due to assumed bias.