Police Street Work and the Use of Information

This Dutch longitudinal study examines how frontline police work (“noodhulp” – uniformed patrol and response units) has changed between 1991 and 2023, and how the use of information has shaped daily practice. Researchers conducted systematic ride-along observations in Amsterdam, Groningen, and Wageningen across three decades.

Key Findings

• Nature of incidents:
  • Nearly half of modern street work (2022–23) involves traffic-related events (49%), mostly proactive checks.
  • Assistance calls (e.g. disputes, accidents, vulnerable persons) make up about 40%.
  • Crimes represent just 13%, mainly shoplifting and burglaries.
  • Incidents with vulnerable individuals (mental health, social problems) are rising steadily.
• Police initiative vs. citizen calls:
  • More than half of events (54%) are initiated by police officers, largely through traffic stops.
  • Proactive social engagement (chatting with citizens) is rare—about once per 12 hours—despite policy ambitions stressing community connection.
• Impact of digitalisation:
  • Since the 2000s, mobile databases and ANPR tools have shifted the balance toward more traffic control.
  • Officers now spend more time per call, in part due to identity checks and data scanning.
  • Digitalisation has intensified “data profiling”, where computer information (e.g. prior offences) strongly influences who gets stopped or fined.
• Styles of policing:
  • Behaviour varies by context and culture. Some teams emphasised strict ticketing, others a “noting and advising” style.
  • Officers rely heavily on situational judgment, not only on formal rules.
• Use of information:
  • Main sources: police databases, direct observation, and citizen input.
  • Data profiling is most visible in traffic enforcement.
  • For crimes or assistance, officers combine multiple sources (colleagues, citizens, documents).
  • The biggest challenge is “translating” information into concrete action in complex situations.
• Risks and issues:
  • Profiling can reinforce divisions between “known offenders” and “law-abiding citizens”.
  • Raises concerns about indirect ethnic profiling.
  • Information does not automatically lead to “better policing”—blind optimism in technology ignores complexity on the ground.

Future Scenarios

The authors sketch three possible directions:

1. Business as usual: more data-driven checks, stronger profiling, risk of inequality.
2. Fair & safe controls: context-based information use, algorithmic support, graduated fines.
3. Socially effective policing: less focus on control, more on community connection, intel-based local engagement, every officer having a thematic focus.

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This study underscores the tension between digitalisation, data-driven profiling, and community trust. It shows how frontline police work is shaped not only by tools and data but also by context, discretion, and societal expectations. For innovation in police tech, it highlights the importance of avoiding naïve optimism about technology and ensuring ethical, transparent, and community-oriented use of information.

📄 Full report (in Dutch): Politiestraatwerk en informatiegebruik – Stol, Strikwerda, Jansen & Schreurs (2025)