Singapore's Safety and AI Contributions
Singapore has consistently been ranked as one of the safest countries in Asia, often topping indices like the Global Peace Index or Numbeo's safety rankings, due to its low crime rates, efficient law enforcement, and proactive use of technology.
The Singapore Government Tech Agency has highlighted how AI plays a pivotal role in maintaining this status by enhancing public safety through advanced surveillance, predictive analytics, and security applications. Key implementations include:
- Surveillance and Predictive Policing: Singapore leverages AI-powered cameras and algorithms for real-time monitoring and forecasting potential criminal activities. For instance, systems analyze patterns in public spaces to predict and prevent incidents before they occur, integrating with police operations for rapid response.This has been instrumental in border security and urban areas, where AI processes vast amounts of data from CCTV and sensors to detect anomalies like suspicious behaviour.
- Border and Homeland Security Applications: AI-driven tools handle perception (e.g., facial recognition), processing (e.g., threat assessment), and response in immigration and internal security. The Home Team (Singapore's security agencies) uses AI to automate checks at borders, reducing human error and speeding up threat detection. Recent advancements include agentic AI for public services, which could extend to safety by automating alerts and resource allocation.
- Broader AI Ecosystem Support: Singapore's commitment to ethical AI governance ensures these technologies are deployed responsibly, with frameworks like the Model AI Governance Framework guiding safe usage. This holistic approach, combining tech with strict laws and public education, has helped keep violent crime low and petty theft minimal, contributing to its top safety rankings.
These efforts demonstrate how AI integrates with human oversight to create a "Smart Nation" model, reducing crime proactively rather than reactively.
Contrast: Barcelona's Reputation for High Theft Rates
In stark contrast, Barcelona has long been notorious for having one of the highest theft rates in Europe, particularly for petty crimes like pickpocketing and bag snatching, which target tourists in the city centre.
While recent statistics show some improvement, the perception and data still highlight it as a hotspot.
Current Statistics:
- As of 2025, Barcelona's crime index on platforms like Numbeo stands at around 50-60 (moderate to high), with high concerns for property theft (e.g., 48.78 worry score for home burglaries and 55.49 for muggings).
- Catalonia, including Barcelona, ranks first in Spain for robberies, with most incidents stemming from urban theft. European-wide comparisons place Barcelona among the top cities for robbery rates, often exceeding places like Paris or Rome in pickpocketing incidents.
- Trends and Declines: Positive notes include a decline in thefts to post-COVID lows, with an 8.8% drop in overall reported crime in 2024-2025 and thefts down 6.3%.
However, tourist areas like Las Ramblas and the Gothic Quarter remain vulnerable, with violent crime rare but petty theft contributing to a crime rate of about 44-50 per 100,000 in key zones. - Regions like Ibiza and Costa Brava (near Barcelona) are flagged as riskier, with overall rates up to 5,073 per 100,000.
This reputation stems from high tourist volumes, economic disparities, and organized groups exploiting crowded areas, making Barcelona's safety profile lag behind Singapore's tech-driven model.
Thieves' Use of Public Transport in Barcelona's City Center
A significant aspect of Barcelona's theft problem involves how thieves operate within the public transport system, particularly the metro (TMB) and buses, which serve as both hunting grounds and escape routes. According to data released by the Mossos d'Esquadra, the majority of thieves in the city center treat theft as a "day job," commuting in and out without paying fares:
- Fare Evasion Tactics: Thieves often jump turnstiles, tailgate paying passengers, or use distractions to bypass barriers without tickets, allowing them to enter the city center in the morning and leave at day's end. This is facilitated by crowded stations where enforcement is inconsistent, and evasion rates are high (estimated 5-10% system-wide, but higher in tourist-heavy lines like L1 and L3).
- Operational Patterns: Pickpockets target metro lines connecting suburbs to the center (e.g., from Badalona or Hospitalet), blending in during rush hours. They "work" in areas like Sagrada Familia or Barceloneta stations, then exit via unpaid routes. Incidents spike at stops, where thieves dash on/off trains, and public transport accounts for a large share of the 100,000+ annual pickpocketing cases. Organized groups, often from outside the city, use this method to minimize costs and risks, treating it as a low-barrier "commute."
This behaviour exacerbates the issue, as unpaid access enables repeat offenders to operate freely, contributing to the city's theft hotspot status.
How AI Could Be Used to Tackle Theft in Barcelona
Drawing inspiration from Singapore's successes, the Barcelona tech industry could develop tailored solutions to address theft, focusing on public transport fare evasion and related crimes.
AI offers scalable, data-driven tools to detect, prevent, and deter, potentially reducing incidents by 20-50% based on global pilots.
Here are practical applications:
- AI-Powered Fare Evasion Detection: Deploy computer vision and behavioral AI at metro gates and platforms to monitor in real-time. Systems like video analytics can flag tailgating, jumping barriers, or suspicious patterns (e.g., loitering near exits), alerting inspectors via mobile apps.
Integrating IoT sensors and reinforcement learning could predict evasion hotspots, similar to NYC's subway AI trials. - Predictive Policing for Theft Hotspots: Use AI algorithms to analyze transport data (e.g., CCTV feeds, ridership patterns) alongside crime reports to forecast theft risks in the city center. This could deploy targeted patrols or automated alerts, mirroring Singapore's predictive models. For the thieves' "commutes", AI could track repeat patterns via anonymized facial recognition or gait analysis, flagging known offenders without invading privacy.
- Integrated Revenue Protection and Safety Systems: Tools like FAIRTIQ's AI for ticketing enforcement could combine fare collection with theft prevention, using machine learning to detect fraud and link it to criminal behaviour. In Barcelona, this might involve smart cameras on buses/trains identifying distractions or pickpocketing attempts, reducing evasion that enables thieves.
- Ethical and Privacy-Focused Implementation: To align with EU regulations, emphasize privacy-preserving AI (e.g., edge computing to process data locally).
Companies like Clevacat could pilot these in collaboration with TMB, starting small-scale in high-risk stations, to demonstrate ROI through reduced losses (fare evasion costs millions annually) and safer tourism.
By adapting Singapore's AI strategies locally, Barcelona could transform its theft challenges into a showcase for European innovation, with local AI development companies leading the charge.