Smart Parks & Incident Management

🏞️ Public Spaces
📍 Fujisawa City, Japan

About

Status:

âś… Finalized

🗓️ Start: 2026-03-24

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🗓️ End: 2026-03-25
Fujisawa City faced a surge in maintenance reports for its public parks, but fragmented data made it difficult to prioritize interventions. This challenge introduced a mobile reporting system for staff and citizens, integrated into a smart city hypervisor. By centralizing GPS-tagged incident reports and applying AI-driven analytics, the city transitioned from reactive repairs to proactive, data-driven management. This optimizes municipal resources while ensuring higher safety and quality of life in urban green spaces.

Description of the Challenge

Urban public parks are essential infrastructure for public health, social interaction, and urban biodiversity. However, maintaining these spaces and responding to incidents (vandalism, infrastructure decay) remains a significant logistical hurdle for municipal authorities globally.

Fujisawa City, Japan, has seen an increasing volume of park-related reports. The city recognized a need to modernize its park services by moving beyond simple data collection toward integrated data management and decision support.

Specific problem

Concrete manifestation: The city faced a growing number of reported incidents, such as damaged playground equipment, graffiti, and vegetation overgrowth. These issues were reported by various groups (staff and citizens), but the information remained scattered.

Who/What is affected: Park management teams (struggling to prioritize tasks), citizens (dealing with degraded public facilities), and the overall operational efficiency of the municipality.

Current situation and gap

Current situation: While Fujisawa began collecting incident data through digital reporting tools, the workflow remained largely reactive.

The Gap: Reports were fragmented and difficult to analyze collectively. There was a lack of integration between historical data, seasonal patterns, and real-time field reports. Without centralized visualization, the city could not effectively detect incident clusters or anticipate maintenance needs before they became critical infrastructure failures.

Expected Outcomes

The city needs to transition from reactive maintenance to a proactive, data-driven approach by centralizing all park-related data sources into a single operational view that supports real-time situational awareness.

Expected outcome

  • Desired solution: An integrated digital system consisting of a mobile reporting application and a centralized City Sensing Platform (Kentyou Eye).
  • Functional expectations: Ability to submit reports with GPS/images, automated clustering of duplicate reports, and AI-driven trend analysis to optimize maintenance schedules (e.g., grass cutting, equipment repair).
  • Visualizations: Intuitive dashboards enabling municipal officers to monitor park conditions and operational performance at a glance.
  • Expected impact:
    • Social: Increased citizen engagement and safer public environments.
    • Economic: Optimized allocation of resources and reduced long-term repair costs through early detection.

Operational: Faster response times and improved reliability of incident data.

Space for Solutions and Experimentation

  • Available experimentation space: Public parks within Fujisawa City, Japan.
  • Scale of experimentation: City-wide park network.
  • Available data/tools: Real-time incident reports, GPS location data, historical maintenance records, and technical support from a consortium including Keio University and NTT East.
  • Existing experience: The pilot successfully demonstrated that integrating citizen reporting with municipal platforms increases service responsiveness. It is now being considered for expansion to other urban service domains beyond parks.

The Pilot

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