Infrastructure vandalism threatens economic development, service delivery, and institutional stability, yet evidence remains fragmented across contexts and insufficiently integrated into a coherent prevention framework. This systematic review examined 44 research articles and review studies published between 2015 and 2025 to develop an integrated synthesis of the existing evidence. Nine literature-derived typologies were identified: graffiti/tagging, acquisitive, vengeful/vindictive/political, ideological/protest, institutional/organisational, cyber, economic, malicious, and play vandalism, with their frequency varying across the reviewed studies. Recurring drivers included legal and regulatory weaknesses, limited community and stakeholder engagement, insecurity and inadequate guardianship, socio-economic deprivation, limited public awareness, and institutional and environmental deficiencies. The identified impacts encompassed physical infrastructure damage, economic losses, service disruption, environmental degradation, and institutional erosion. Prevention measures included social, situational, environmental, institutional, technological, and integrated approaches, although evidence on their implementation and effectiveness was uneven. The review proposes an integrated conceptual framework linking vandalism typologies and drivers with governance capacity, impacts, and differentiated prevention strategies. The framework provides a basis for context-responsive prevention and future empirical research, while its proposed relationships require further validation through comparative, longitudinal, and mixed-method studies.
This article highlights how context matters in the implementation of a crime prevention initiative, exemplified by the implementation of the SafeGrowth method at Drottninghög, Helsingborg, Sweden. Drottninghög is the first area in Europe where SafeGrowth has been implemented, and the article is based on a report of the implementation process. The report, published in 2022, was based on qualitative material collected during the implementation of SafeGrowth between 2021 and 2022. The learnings from the report have been re-analysed using content analysis, with a specific focus on contextual matters. In the results, we present six identified contextual matters that are of general interest, namely: educational context, theoretical context, juridical context, issues of moving between different crime-contexts, place context and organizational context. The first two matters are closely connected to the SafeGrowth method, and the latter four are more generic in crime preventive work.
High-profile incidents of police use of force have intensified public scrutiny of law enforcement, particularly regarding perceptions of procedural fairness and legitimacy. This study examined whether participation in live-action simulated police-public interactions (traffic stops and foot-patrol contacts) altered participants’ perceptions of procedural fairness, distributive fairness, and obligation to obey police authority. Seventy-four participants completed pre- and post-simulation surveys; 93
This study examines how newspapers frame artificial intelligence (AI) as a tool of crime prevention in contemporary media and governance contexts, analyzing a large corpus of English language news articles retrieved from Nexis Uni. Using computational text analysis combining longitudinal trends, sentiment profiling, and topic modeling, the study shows that AI is increasingly constructed as an institutional response to digital fraud, surveillance, and policing challenges. The findings suggest a recurring interpretive pattern in which AI is represented both as a source of new risks and as a mechanism for managing those risks, situating algorithmic systems within narratives of governance, control, and future security. Thematically, coverage privileges financial crime prevention and surveillance infrastructures, while ethical accountability and public deliberation appear less prominent within the corpus. This study contributes to communication research on media representations of emerging technologies, and insights are offered into how journalistic narratives shape public expectations and policy debates around AI deployment in security contexts, with crime prevention treated as a critical site of AI sensemaking. These patterns have implications for crime prevention and community safety by indicating how media discourse may shape the legitimacy, acceptance, and governance of preventive technologies and algorithmic systems.
