Japan is a tourism-rich country with abundant water resources, and its archipelago landscapes are particularly important features. To capitalize on the trend of tourism recovery, the management and improvement of archipelago landscapes are crucial. However, in recent years due to fiscal constraints, many tourist areas have neglected green space management. The growth of trees and other vegetation has a significant impact on the landscape. This paper focuses on the Kujūku Islands as the main research subject. Additionally, this paper focuses on green space management, using the identifiable number of islands as an indicator to evaluate archipelago landscapes. Using mathematical models such poisson point process, we demonstrate that when the central field of view overlooking the landscape is obstructed, the number of visible islands decreases significantly. The influence of tree growth on the visible sea surface area and the visible islands is shown.

To achieve sustainable urban digital transformation (DX) in Japan, the development of accessible 3D city models and Digital Urban Twins is essential. To address this need, Project PLATEAU was launched, and successfully achieved specification development, data creation, quality management, OSS development, use case development, and publication of large-scale Open Data within a short one-year timeframe. This paper analyzes the factors that led to its success, with a focus on the utilization of Open Standards, and proposes “StandardsOps” framework which integrates Standardization and Operations, and facilitates semantics localization and concurrent system development alongside data creation in collaboration with Open Communities. The proposed methodology and practical results demonstrate how these approaches accelerated the rapid social implementation in a nationwide project.

Improving the imbalance between supply and demand for nursery schools is an important issue in recent years. This paper aims to build a machine-learning model that estimates which nursery school each household will choose. We train the model, LightGBM, using (i) detailed information on the actual choice of nursery schools by each household collected through a Web questionnaire (conducted on 1,032 households across Japan) and (ii) attribute information of nursery schools all over Japan gathered from open data. The F-score for the trained model was 68.7％. Furthermore, based on the SHAP values calculated using the trained model, we demonstrate the quantitative impact of each explanatory variable (such as household attributes, facility characteristics, and commuting conditions) on the household nursery school choice.