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Composite development indices show an exponential movement of major economic indicators to identify and predict the overall trend of the national economy. However, the existing method of writing composite development indices is based on simple statistical methods using macroscopic data. Therefore, it presents limitations when grasping regional economic trends late. It is because the time of announcement of composite development indices is concentrated at the end of each month, quarter, and year. This study used the floating population estimated from smartphone data that can be collected in real-time to analyze how floating population patterns affect regional economic situations to compensate for these limitations. The primary purpose was to present a prompt development prediction methodology that reflects this meaningful relationship. A correlation and cross-correlation analysis was performed to exhibit a clear relationship between composite development indices and floating population value. In addition, a time series model and a multiple regression model analyses were applied to predict regional development indices. The results obtained facilitated the prompt selection of regional composite indices after choosing a model that exhibits high prediction accuracy and efficiency of the application. The selected regional development composite indices are expected to be used as a faster and more reliable prediction criterion than the existing development composite indices used to predict a specific city’s economic situation.

Background: Health technologies have the potential to support the growing number of older adults who are aging in place. Many tools include visualizations (data visualizations, visualizations of physical representations). However, the role of visualizations in supporting aging in place remains largely unexplored. Objective: To synthesize and identify gaps in the literature evaluating visualizations (data visualizations and visualizations of physical representations), for informatics tools to support healthy aging. Methods: We conducted a search in CINAHL, Embase, Engineering Village, PsycINFO, PubMed, and Web of Science using a priori defined terms for publications in English describing community-based studies evaluating visualizations used by adults aged ≥65 years. Results: Six out of the identified 251 publications were eligible. Most studies were user studies and varied methodological quality. Three visualizations of virtual representations supported performing at-home exercises. Participants found visual representations either (a) helpful, motivational, and supported their understanding of their health behaviors or (b) not an improvement over alternatives. Three data visualizations supported understanding of one’s health. Participants were able to interpret data visualizations that used precise data and encodings that were more concrete better than those that did not provide precision or were abstract. Participants found data visualizations helpful in understanding their overall health and granular data. Conclusions: Studies we identified used visualizations to promote engagement in exercises or understandings of one’s health. Future research could overcome methodological limitations of studies we identified to develop visualizations that older adults could use with ease and accuracy to support their health behaviors and decision-making.