How Long Before the Next Crime? A Better Basis for Crime Prevention

The Challenge of Crime Statistics

For both the public and professionals, deciphering crime statistics can be a perplexing puzzle. The current approaches often sow the seeds of misunderstanding and unwarranted fear. These misinterpretations have repercussions across various sectors, leading to ineffective and sometimes counterproductive crime prevention strategies.

Traditional Crime Reporting

Law enforcement agencies have traditionally reported crime in terms of recorded incidents. This means counting the number of specific crime types occurring in a particular area over the course of a year. While it makes sense for police record-keeping, it's not so helpful for the general public. It is challenging to compare crime levels in areas with different resident numbers.

Crime rates that standardizes crime numbers per 100,000 residents helps compare crime risk in areas with different numbers of resident . However, it doesn't work for places with visitors such bustling retail, transport hubs or tourism locations. Additionally, it subconsciously exaggerates the severity of crime. For example, a suburb with 5,000 residents might have 25 burglaries a year, but its equivalent police crime rate is 500 burglaries per 100,000 residents, sounding much more alarming.

Another approach has been percentage changes in crime incidents. This approach is problematic for rapidly changing conditions of population, development, and economic factors. It's particularly problematic for low-crime areas where even a minor change can be blown out of proportion. For instance, a shift from 20 to 25 burglaries in a suburb with very low crime rates could result in a scary-sounding 20% increase, despite the actual risk being minimal.

New Perspectives: What to Measure

A better metric for evaluating crime risks is "average time between incidents." By examining the time lapse between crimes, a clearer and more intuitive picture of crime risk emerges. Here's a simple example:

For any home in Smithfield, a small village of 3,000 people, the average time between burglaries is 45 years.

This time-based metric simplifies understanding and helps us better grasp the relative importance of the crime risk, and decide how much effort to invest in mitigating it.

Compare the above time-based measure to the conventional police crime rate:

In Smithfield, the burglary rate is 580 burglaries per 100,000 people per year.

The Police crime rate-based approach makes the actual crime risk seem much worse and creates unnecessary fear of crime. Additionally, to get even the simplest understanding of the actual crime risk in Smithfield, it necessitates additional calculations and makes it harder to gauge the significance of the crime risk.

Using 'Average time between crime incidents'

"Average time between incidents" can be adapted to all crime scenarios. For example:

• Average time between crimes on public transport can be assessed based transport use rather than the residential population.
• Average time between robberies at petrol stations can be assessed against the number of petrol stations.
• Average time between retail theft in different suburbs can be better compared using retail turnover or number of shops/malls, especially for areas like city centers and night-time economy districts with low residential populations.

The Power of Time-Based Crime Metrics

This paradigm shift to 'average time between crime incidents' offers many advantages:

• ? Easier Comprehension: It's straightforward to understand both for public and crime prevention professionals and doesn't require statistical expertise.
• ?? Adaptive to Change: 'Average time between crime incidents' automatically adjusts for variations in the number of properties and house occupancy.
• ?? Specific to Crime Types: It provides insights specific to different crime categories.
• ?? Cost-Effective: It lays the groundwork for resource allocation.
• ??? Community Safety: For local governments, increasing the 'average time between crime incidents' offers a justifiable basis for managing community safety programs.
• ?? 'Fear of Crime' Management: Understanding the 'average time between crime incidents' helps in campaigns to reduce fear of crime because it gives a much clearer picture than crime rates and crime incident counts.
• ?? Maximizing Resources: It provides a comparative basis for triage in making the most of limited resources for local governments and law enforcement.

Determining the Average Time between Crime Incidents is easy

Determining the average time between crime incidents is easy, as illustrated by the example of Subiaco:

• In 2021, Subiaco witnessed 61 burglary incidents.
• With 4,976 residences, the average time between burglaries for any single residence is 4,976/61 = 82 years

For any house in Subiaco, the average time between burglaries is 82 years.

The benefits in understanding are clear. This time-based metric offers a more relaxed and accurate perception of the crime risk.

In Conclusion

By shifting the focus to "average time between incidents," we gain a straightforward, easily understandable, reliable and robust basis for assessing crime risks for both public and crime prevention professionals. It offers a new era of transparency in crime prevention, enabling professionals to make more informed and justifiable decisions, allocate resources more efficiently, and ultimately create safer communities.

Unlocking a Safer Future

This innovative perspective of 'average time between crime incidents' invites us to embrace it as a transformation in crime prevention, particularly in Crime Prevention Through Environmental Design (CPTED). It's not just an idea; it's an invitation to usher in a safer and more informed society.

Let's re-imagine crime prevention, one time-based metric at a time.

Post also under the Design Out Crime and CPTED Centre on LinkedIn at

https://www.dhirubhai.net/.../how-long-before-next-crime...