We developed a spatial database and tool for calculating properties of a river basin, such as the upstream basin area, land use, rice, and crop planting rate, at any point on the river in Japan using various FOSS4G tools. The data set deployed to the database was derived from Japanese National Land Numerical Information from the Ministry of Land, Infrastructure, Transport and Tourism and various statistics of agriculture from the Ministry of Agriculture, Forestry and Fisheries. The database was constructed with PostgreSQL and PostGIS. The database was converted to SpatiaLite format for using a calculating tool. The calculating tool was developed using QGIS API with Python 3. The tool and database are available via GitHub.

This paper demonstrates from a case study that the Python language can practically be used for network-distance-based spatial accessibility analysis. In the case study, we analyze the spatial accessibility to convenience stores in Hirosaki. First, we show that road network data provided by OpenStreetMap can easily be obtained with the OSMnx library. Second, we show the Python code snippet that calculates a road-network-based distance matrix. Finally, we calculate two types of accessibility measures: nearest neighbor distance and cumulative opportunities. We show that the calculation can be done very effectively by list comprehension in Python.

Previous studies on land use in Tokyo during the transition to the modern era focused on creating land use maps for the late Edo era and the Taisho era. However, land use maps for the early Meiji era have not been produced, and the relationship between land use before and after that era has not been clarified. This paper created a land use map of the early Meiji era and analyzed the transformation of land from the period before the early Meiji era to the period after it. This study found that imperial facilities and military land characterized the changes in urban space between the early Meiji and the Taisho period. The inner area changed significantly from the early Meiji era to the Taisho era, mainly due to the relocation of military land to outer area. Additionally, by analyzing the changes in land use, we have been able to quantitatively demonstrate that the use of former daimyo residences differed according to the inner-outer concentric area and highland-lowland topographic area.

In recent years, supply chain (SC) disruptions caused by production stoppages at bottleneck firms have been frequent due to disasters. To develop a business continuity plan (BCP) to prevent SC disruptions, it is necessary to identify the bottleneck firms. In this study, we developed a total of 7 models for extracting bottleneck firms using machine learning methods based on the centrality and geographic features of firms and transaction networks obtained from actual data. The results show that the bottleneck firms tend to be characterized by large distances between firms downstream, diverse industries. Finally, we validated the model and the accuracy of the model was high. Our method is expected to contribute to the improvement of resilience of the entire SC, such as BCP and early recovery of SCs after disasters.

The problem of vacant housing in Japan is becoming more and more pronounced. However, a uniform view of vacant houses will not solve the problem, and duration of vacancy can be an important indicator to understand the characteristics of vacancies. This study focused on the geographic distribution of vacant houses considering the duration of vacancies with the use of smart meters, and analyzed the relationship with neighborhood characteristics. The main finding indicates that there is a structural difference between vacant houses and neighborhood characteristics depending on urban areas. Some variables had statistically significant correlations regardless of the duration of vacancies, others changed their significance or the magnitude of estimated coefficients depending on the duration of vacancies. Moreover, this study demonstrates the usefulness of smart meter data, which has not been utilized in urban and housing studies.

This study clarified the spatial characteristics of the Tokyo office market through an analysis of office vacancy rates from 2000 to 2021, using data on rental office vacancy rates in the 23 wards of Tokyo on a 500m mesh basis, which the authors themselves developed.

The results indicated that each mesh is classified based on the vacancy rate of itself and surrounding meshes, which shapes the spatial patterns. And these patterns temporarily changed significantly during economic downturns, such as the financial crisis and the COVID-19 pandemic. The results also showed that the regions with high vacancy rates have a significant relationship with the concentration of specific industries and the characteristics of office buildings. These results may help investors and practitioners to understand the office market by region in more detail.