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Objectives: The objective of this study is to provide road centerline data for professionals of disaster medicine areas who are often beginners in GIS use. Methods: Newly developed vector tile format data were converted into shapefile format data, then were organized as second level medical districts to which medical professionals are accustomed. Results: Road centerline data in Japan is being prepared to release from Association for Promotion of Infrastructure Geospatial Information Distribution free of charge. Conclusion: Professionals of disaster medicine areas increased their accessibility of GIS. Logistic planning for evacuation activities and dispatching of rescue teams were improved.

Background: The present study aimed to estimate the numbers of short-stay service recipients in all administrative units in Hokkaido from 2020 to 2045 with the machine learning approaches and reviewed the changing trends of spatial distributions of the service recipients with cartograms.Methods: A machine learning approach was used for the estimation. To develop the model to estimate, population data in Japan from 2015 to 2017 were used as input signals, whereas data on the numbers of short-stay service recipients at each level of needs for long-term care (levels 1–5) from 2015 to 2017 were used as a supervisory signal. Three models were developed to avoid problems of repeatability. Then, data of the projected population in Hokkaido every 5 years from 2020 to 2045 were fed into each model to estimate the numbers of the service recipients for the 188 administrative units of Hokkaido. The medians of the estimations from the models were considered as the final results; the estimates for 188 administrative units were presented with continuous area cartograms on the map of Hokkaido.Results: The developed models predicted that the number of the service recipients in Hokkaido would peak at 18,016 in 2035 and the number of people at level 3 in particular would increase. The cartograms for levels 2 and 3 from 2020 to 2030 and level 3 for 2035 were heavily distorted in the several populated areas in Hokkaido, indicating that the majority of the service recipients would be concentrated in those populated areas. Conclusions: Machine learning approaches could provide estimates of future care demands for each administrative unit in a prefecture in Japan based on past population and care demand data. Results from the present study can be useful when effective allocations of human resources for nursing care in the region are discussed.

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This research aimed to clarify the factors relevant to the awareness regarding skin dryness in community-dwelling elderly. Method: A self-administered survey was conducted in May 2017 and the stratum corneum hydration was measured. The survey items comprised basic attributes, whether the subjects used moisturizing agents, nutritional status, and awareness regarding skin dryness. Forearms and lower legs were observed, and their stratum corneum hydration was measured. Results: The research was conducted on 58 subjects; 32 were women with an average age of 72.9 years. Factors that influenced the awareness regarding skin dryness were female sex, lower age, and alcohol consumption, and the awareness prompted the use of skin moisturizers. However, stratum corneum hydration was not associated with the awareness of skin dryness. Some of the elderly subjects did not use skin moisturizers. Discussion: The women's great concern for their skin could be the possible reason they are aware of skin dryness; the awareness of skin dryness in the younger elderly was thought to be the result of their maintenance of visual and tactile susceptibilities. Moreover, alcohol consumption was believed to be influenced by sex. Conclusion: The prevention of skin dryness should be greatly promoted to males and the late elderly.