Our Publications
Compact City AI advances urban intelligence by leveraging AI and spatial analytics to support smart, compact city development. Our research covers a range of topics—from platform capitalism–based land management to public participation models, AI‑GIS transit infrastructure, and hyper‑localized compact city indices. We explore how digital platforms and real-time data flows reshape land governance and enable dynamic urban decision-making. AI‑GIS integration enhances public transport planning through improved connectivity, efficiency, and equitable access. Our hyper-local compact city index, tailored for Japanese cities, combines real-time metrics on walkability, amenities, and transit proximity to offer practical advice to planners, citizens, and businesses.
Platform capitalism-based land management model for smart cities
Our research on platform capitalism-based land management explores how digital platforms reshape land use, ownership, and value in smart cities. By analyzing data-driven governance and platform-driven urban development, we propose new models for equitable and efficient land management. It enables real-time data transfer between citizens, planners, and platforms, facilitating dynamic decision-making for land use, mobility, and services similar in compact cities.
Applying Mobile-Based Community Participation Model in Smart Cities
This research examines how platform capitalism shapes land management in smart cities, shifting ownership, value, and planning decisions through digital infrastructures and data‑mediated transactions. It highlights the role of real‑time platform interactions in dynamically governing urban land use and enabling more responsive, market‑driven development. It supports a real-time model for compact cities by enabling continuous, data-driven land use optimization through platform-mediated interactions
AI-GIS based Public Transportation Infrastructure Management for Smart Cities
This research focuses on integrating AI and GIS technologies to enhance the planning, monitoring, and optimization of public transportation infrastructure in smart cities. By combining spatial analytics with machine learning, the system enables real-time decision-making to improve connectivity, efficiency, and service equity across urban transit networks. It supports the compact city model by optimizing public transit infrastructure to promote high-density, walkable, and transit-oriented urban development.
AI Model for Hyper-Localized Compact City Urban Management in Japanese Cities
This research develops an AI model to generate a Hyper-Localized Compact City Index by analyzing urban features at a fine spatial scale across Japanese cities. The model integrates real-time data from multiple sources to assess walkability, accessibility to amenities, and transit proximity around each station or neighborhood. It supports dynamic urban management by offering planners and citizens actionable insights for advancing compact, livable, and sustainable urban environments.