Conference Agenda

Restaurants play a crucial role in shaping urban vibrancy, particularly in smart cities where they attract people and stimulate urban activities. While previous studies have primarily focused on the number of restaurants, the impact of restaurant genre diversity on urban vibrancy remains unclear. This study analyzes the relationship between restaurant genre diversity and the staying population around train stations using data from "Tabelog," Japan’s largest gourmet review website, along with public statistics.

A total of 111,104 restaurants in Tokyo’s 23 wards were classified into 16 genres, and Simpson’s Diversity Index (λ) was calculated for each 250-meter grid. Furthermore, an Ordinary Least Squares (OLS) regression analysis was conducted to examine the effect of genre diversity on the staying population while controlling for the number of restaurants, proximity to train stations, and business density. The results show that restaurant genre diversity significantly impacts urban vibrancy (coefficient = 0.674, p < 0.001). Additionally, areas with higher diversity indices and a larger number of restaurants tend to have higher staying populations, and proximity to train stations also contributes to increased urban vibrancy.

The findings suggest that enhancing restaurant genre diversity is as important as increasing the number of restaurants. Moreover, integrating private-sector gourmet data with public urban datasets is beneficial for optimizing urban planning and restaurant location strategies.

From a smart data perspective, the significance of this study lies in demonstrating that detailed urban analysis can be achieved using individual restaurant data from private gourmet information sites, which influences the increase in urban vibrancy.

The increase in vacant houses has become a serious social issue in many developed countries, including Japan. 　Therefore, to support mid- to long-term policy planning, there is a growing need to understand future vacancy distributions. In this study, we develop a machine learning model to predict future municipal-level vacancy rates by incorporating not only demographic and building information, but also spatial indicators related to road development conditions. First, we constructed a road mesh dataset at the 500-meter grid level by aggregating physical road indicators, block rectangularity, and the proportion of buildings with front roads. We then combined these variables with data from the Population Census and the Housing and Land Survey and developed a vacancy prediction model using LightGBM. The results show that incorporating road-related indicators improves prediction accuracy. In particular, we found that municipalities with a higher density of narrow roads, more irregularly shaped blocks, and a larger proportion of buildings lacking direct road access tend to have higher vacancy rates. This study demonstrates the value of road development information, which has received limited attention in previous research, in improving vacancy prediction, suggesting that road environments can influence the spatial distribution of vacant houses. Moreover, the findings of this study contribute to the early identification of areas at risk of high vacancy and the planning of preventive measures, thereby supporting urban management through the use of smart data.

A detailed map database containing attribute information of individual buildings is highly valuable and expected to be utilized in various fields, including urban planning, energy management, and disaster preparedness. However, obtaining such detailed map databases are significant difficulty, because of privacy concerns and their high cost. To address this issue, this research aims to construct an open building-level spatial database as the goal. In this study, as a first step toward achieving this objective, we developed a method to classify buildings into detached houses and other types of buildings by utilizing Foundation Geospatial Data and information derived from open data. First, we assigned explanatory variables to each building in the foundation geospatial data for Nagaoka City, Niigata Prefecture, and created training data using PLATEAU data as the ground truth. Based on this dataset, we developed a machine learning model to classify each building as either detached or other types of buildings. Furthermore, we extrapolated the machine learning model to Sanjo City, Niigata Prefecture. We selected buildings in a way that aligns with the number of detached households reported in the national census at the subregion level and identified these as detached houses. Finally, validation of the extrapolated results showed that the mean absolute error (MAE) at the subregion level was approximately 9 buildings, demonstrating that the model successfully reproduced the spatial distribution of detached houses and other types of buildings.

Video shooting and sharing at tourist destinations have become common activities among travelers. These videos, often enhanced with captions and audio, serve as a means to convey impressions and information about the visited locations, and they are widely disseminated through social media platforms. Such content not only enriches the personal travel experience but also contributes to promoting regional tourism and stimulating inbound demand. However, producing informative video content requires editing skills and the reliability of the information, which poses a challenge.

This study developed a new context-aware framework for caption generation in tourist video blogs by detecting tourists' points of interest (POI) using sensor fusion of GPS and camera orientation. The framework was evaluated and validated.

The results showed that by integrating camera orientation information along with geofencing, it was possible to estimate tourists' attention within the geofence with a certain level of accuracy, indicating new possibilities for guide systems.