This article was published in the third edition of The Crowd Magazine, a fantastic resource for all things crowd safety!
Whether we are aware of it or not, we are constantly conducting risk assessments in our everyday life. From crossing the road to walking down steps, subconsciously we are dynamically assessing the likelihood of a hazard occurring. Identifying and mitigating risk is the heart of our role in protecting the safety of crowds. With this at the centre of our industry, one would assume that there is a robust and standardised approach to the process, however reality shows this is not always the case. Crowds are a complex narrative of mathematics and psychology that require more than one linear approach to managing risks.
What does a risk assessment look like?
In the industry, there lies divided opinions on what risk assessment tools are appropriate for the task at hand. When one pictures a risk assessment, it may take the form of a Probability Impact Graph (PIG)/Risk Matrix and to many, it is risk management. PIGs are a popular and simplistic method of assessing risk with templates easily accessible online and in print. This format was informally adopted by the event industry from safety management practices of other industries, but do we accept it as fit for purpose? UK law states that a risk assessment must be completed, but apart from offering guidelines, it does not command what format it must be presented in. Academic research into risk management suggest that first pass risk quantification should be so easy that the usual resistance, based on lack of data with subjective probabilities, is overcome (Chapman and Ward, 2000).
Figure 1 Probability Impact Graph
Cresswell (2013) states that the fundamental challenge with this approach is that qualitative risk is based on a philosophy that ‘risk = probability x impact’. The flaw in this system is that it gives an average to the risk, which goes against the very nature of risk management. Using a numeric scale to assess risks in relevance of importance also offers no absolute meaning (Ward, 1999). If the scale goes from 1-25, is 25 relative to the end of the world where Bruce Willis needs to fly out to space to blow up an impending asteroid? Risks to crowd safety are complex and variable and the understanding of variability should be the main focus of risk analysis. It’s the difference between the statistician crossing a river that is three feet deep on average and getting to the other side or not.
There is a wealth of knowledge and research conducted in the area of risk management, and varying formats of approaching risk analysis. According to Graham (2016) there is a family of risk that should be assessed, including;
- External risks
- Legal and regulatory risks
- Strategic risks
- Organisational risks
- Operational risks
- Information risks
- Human Resource risks
- Technology risks
- Financial and Adminstrative risks
- Political risks
Dynamic crowds, dynamic risk
When it comes to crowd safety, we know it’s more than just the operational elements we interact with on the ground. We need to understand ever changing components such as stakeholders, marketing strategy, political environment, artist profile, ticketing, weather, technology, transport provision, catering plans and the audience profile that all play a part in the safety of the crowd. Research shows that there is rarely one reason but the interaction of minor failings that cause crowd disasters. For example, several factors contributed to the Love Parade disaster in 2010, however the information required to mitigate the risks were all present. Had this information been risk assessed using tools appropriate for crowd safety, the disaster may have been averted. The data was there, it needed to be worked on with the right tools and presented in a way we could ‘see’ the risk.
Chapman et al (2003) believe that ‘risk’ encourages an idea of threat and propose instead, the estimation and evaluation of ‘uncertainty’, providing a broader perspective accounting for variables such as stakeholder relationships. Ward (1999) highlights the importance of risks typically rest on factors other than probability and impact and recommends using an alphabetic scale to highlight the subjective nature, removing temptation to employ quantified ratings in ranking calculations. Graham (2012) categorises risks into a four-square table of high/low risk v high/low frequency. Using Klein (2017) Recognition Primed Decision Making Model (RPDM), Graham focuses mitigating efforts on ‘High Risk/Low Frequency’ events as they historically have the highest likelihood of the worst consequences. This is due to little or no prior experience or knowledge in how to deal with a situation quickly if it were to arise.
Figure 2 Graham's Risk v Frequency Table
First pass risk analysis models specifically designed for crowd safety include, among others, Fruin (2002) Force Information Space Time (FIST) and Still (2014) Design Information Management - Ingress Circulation Egress (DIM-ICE) and Routes Area Movement Profile Analysis (RAMP). They consider both mathematics and psychology; the space the crowd occupies, how they interact with that space, their behaviour and how we manage them. Tools such as software simulations can take a deeper focus on risks assessed via first pass analysis and provide further data on crowd movement.
Above are some approaches in risk assessment that can be employed for crowd safety. They outline the wealth of research into improving risk management, taking into the account the variable elements that surround the complex nature of crowds. Considering the options available to us and the challenges we face in effective risk assessments, do we still see the PIG appropriate in effective risk management in crowd safety? Can we understand there are other tools in the toolbox? There are more efficient approaches to better reduce the risk to people, property and reputation and enhance the safe delivery and positive experience to our crowd. One thing we know for sure is that we need to make the risk assessment process as uncomplicated as crossing the road and getting to the other side, safely.
Chapman, C. and Ward, S. (2000) ‘Estimation and evaluation of uncertainty: a minimalist first pass approach’. International Journal of Project Management, January, pp. 1–15.
Chapman, C and Ward, S. (2003) ‘Transforming project risk management into project uncertainty management’. International Journal of Project Management, 21, December, pp. 97–105.
Cresswell, S. (2013) Qualitative Risk Assessment & Probability Impact Graphs. We Are Into Risk, pp. 1–13.
Fenton, N. and Neil, M. (2006) Measuring your risks. 11 May pp. 1–6. [Online] [Accessed on 19 October 2017] http://www.agenarisk.com/resources/white_papers/Measuring_Risks.pdf.
Fruin, J. J. (2002) The Causes and Prevention of Crowd Disasters. www.crowdsafe.com. 17 February pp. 1–10. [Online] [Accessed on 18 February 2016] http://www.crowdsafe.com/fruincauses.pdf.
Graham, G. (2016) REAL RISK MANAGEMENT. 4 May pp. 1–10. [Online] [Accessed on 22nd October 2017] http://www.lexipol.com/wp-content/uploads/2016/05/Lexipol_Real_Risk_Management_Part_1.pdf.
Graham (2012) High Risk/Low Frequency Events in the Fire Service [online video] [Accessed on 22nd October 2017] https://www.youtube.com/watch?v=Og9Usv82CdU&t=209s
Klein, G. A. (2017) Sources of Power. MIT Press.
Still, G. K. (2014) Introduction to Crowd Science. CRC Press.
Ward, S. C. (1999) ‘Assessing and managing important risks’. International Journal of Project Management, August, pp. 1–6.