European cities face significant challenges, including terrorism and organised crime, but also incivilities, petty crime and most recently, public health risks, which all affect citizens’ feelings of safety. These challenges undermine the vibrancy and security of urban public spaces and threaten the well-being of European urban populations.
The last 30 years have seen considerable developments and advances in our understanding of urban security and the effectiveness or prevention strategies in European cities, from which we can identify a number of broad, cross-cutting developments over time.
Browse the key Trends, Tensions, Lessons and Knowledge Gaps in the field of urban security below.
Tensions, here, refer to enduring fault-lines, recurring issues and conflicting pressures that persist across time with regard to urban security and crime prevention.
Lessons, here, refer to the research-informed insights and learning derived from the knowledge base through the application and evaluation of urban security practices and interventions.
Compared to the field of healthcare and medicine, the urban security evidence base remains embryonic. While much has been learnt about the effectiveness and efficacy of urban security interventions over the past 30 years, there remain persistent knowledge gaps and uncertainties in the face of technological and social change. In the field of urban security where risks and harms are continuously changing, moving and evolving in dynamic fashion, there are both ‘known unknowns’ and ‘unknown unknowns’. Here, we focus on the former.
Predicting future crime and security trends and developments, given their dynamic nature is intrinsically difficult.
All evaluations produce knowledge of what worked (in the past) for a particular population, under specific circumstances, at a particular time and may not hold for a future population at a different place or time. The inferences that can be drawn are contingent.
The knowledge base with regard to causation and the causal interactions between multiple factors remains limited.
The role that social, educational and welfare provisions play in shaping the propensity for crime and criminal behaviours remains poorly understood.
Too little is known about and insufficiently robust data are collected concerning the processes of implementation that influence the effectiveness of urban security interventions.
There is insufficient understanding of the ways in which context shapes successful outcomes and the nature and extent to which particular preventive mechanisms are context-determined or context-dependent.
More can be learnt comparatively about the ways in which urban security interventions and their effectiveness are shaped by differing culture, social practices and legal, political and administrative frameworks.
There is a need to better understand the extent to which crime prevention lessons from the physical world translate into cyberspace and their possible application (or not) to online environments.
The implications for urban security of artificial intelligence (AI), machine learning and algorithms build into products, services and utilities are largely uncharted, as expert knowledge and processes of interpretation are replaced by machine learning and automated decision-making. What we do know is that these algorithms are not impartial but embed with different assumptions about behaviour and risk that are opaque and obscure. As such, they raise fundamental ethical and normative questions about the values that inform the future of urban security.
Climate change, an ageing population and growing social polarisation, diversity and inequality are all likely to interact with wider social and technological change in ways that are more complex, interconnected and interdependent, raising new challenges for the tense relationship between liberty, security and other social values.
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