Understanding urban diversity is critical to inclusive planning for sustainable urban development. This study introduces a new Urban Diversity Index (UDI) based on global point-of-interest (POI) data for food-related establishments—defined here as facilities that offer food and beverage services, including various kinds of eating and drinking venues —covering 249 cities across 154 countries. The UDI integrates three components: Pielou’s Evenness Index (J′) to capture the balance of establishment types, a Coverage Ratio (C′) to measure global representativeness of establishment categories, and density (ρ′) to reflect spatial concentration. By applying concentric buffer analysis around city centers, we evaluate the spatial profiles of diversity in each city. Results show that while cities like London and Istanbul have similar index components, they exhibit significant differences in the spatial extent and pattern of high-diversity zones, reflecting their unique morphological and regulatory contexts. Furthermore, the analysis of “Peak Distance Buffer Zones”—areas where UDI remains above 95% of its maximum—reveals diverse urban forms, particularly in Asian megacities. Scatterplots of standardized UDI and peak distances identify distinct typologies of urban diversity structures. Notably, urban population size showed no significant correlation with UDI values. Overall, this study demonstrates the utility of globally standardized POI-based metrics in capturing the spatial heterogeneity of urban qualitative diversity and offers new insights into cross-city comparisons of urban complexity.

Japan is currently confronting the critical social problem of an increasing number of vacant houses, driven by its super-aging society and declining birthrate. The proliferation of vacant houses entails significant risks, including structural collapse, the deterioration of local landscapes, and elevated crime rates, all of which negatively impact community welfare. In recent years, various measures have been implemented in Japan to tackle this problem. However, assessing the current state of vacant houses often requires field surveys, which are time-consuming and costly, posing a substantial challenge for municipalities. To overcome these challenges, this study proposes a novel priority indicator of vacant housing control that enables municipalities to effectively allocate limited resources and implement targeted control against vacant houses. The proposed indicator utilizes nationwide statistical datasets, such as the Population Census and Housing and Land Survey, and incorporates machine learning techniques to predict priority areas for vacant housing control over a 3-year horizon. This approach is anticipated to allow municipalities to preemptively evaluate the scale and geographic distribution of measures related to the priority indicator of vacant housing control, optimize resource allocation, and facilitate proactive efforts to prevent the emergence of new vacant houses, potentially reducing reliance on costly and labor-intensive field surveys. While developed with Japan’s unique demographic and social conditions in mind, this methodology is adaptable to other countries experiencing similar challenges, such as population aging and urbanization. The framework presented in this study provides municipalities with a practical, evidence-based approach based on the priority of vacant housing control, thereby streamlining the process of tackling vacant house problems, promoting sustainable residential environments, and improving housing market functionality.