The rapid growth of multivariate longitudinal data across diverse industries necessitates advanced analytical strategies for uncovering complex patterns and enhancing predictive accuracy. This paper introduces an adaptive window segmentation strategy that dynamically adjusts based on statistical variability, including covariance, skewness, and kurtosis, tailored to the unique characteristics of industry-specific datasets. Extending prior work, the enhanced methodology overcomes the limitations of traditional approaches by optimizing segmentation parameters for multivariate contexts. Empirical evaluations across the finance, retail, and healthcare sectors demonstrate significant improvements in forecasting precision. This work provides a scalable, context-aware solution for big data analytics, refining the quantitative boundaries of data bigness and enabling more effective, data-driven decision-making.

This study proposes a framework to manage HISUI data (spatiotemporal and spectral big data) as spatial statistics regarding time and spectrum. This study focuses on spatial Big Data infrastructure for developing and deploying interactive Web data applications of HISUI hyperspectral data from a constructive point of view.The study aims to construct a system for spatiotemporal spectrum analysis and visualization based on the proposed technique. Such a spatial Big Data infrastructure assists in developing algorithms that enable users to interactively analyze, classify, and detect temporal changes on the Earth’s surface. As a result, users can conduct statistical analysis of spatio-temporal and spectral characteristics on hyperspectral images.The World Grid Square statistics on descriptive statistics of reflectances for each band are produced by parallel computation with high-speed calculation methods and provided via Web API on MESHSTATS. The response is formatted as CSV with World Grid Square code, sample size, and descriptive statistics selected by type for 185 bands.The proposed method enables users to analyze hyperspectral satellite data captured as multi-channel raster data with different observation directions, times, and weather conditions by converted into Grid Square statistics. It is concluded that our data analytics platform enables users to conduct analysis on Grid Square statistics about HISUI hyperspectral satellite data with statistical significance.

This study examines relationships between environmental management efforts and corporate performance based on databases on financial items in Japan using ten years of financial data for Japanese listed 227 firms and ten years of environmental management scores from Nikkei Research’s "Environmental Management Score Survey" report. We have constructed a scalable data analysis pipeline using these databases for ten years. As a result, we clarified some positive correlations between environmental management efforts and corporate performance. Specifically, we found the statistical significance of the Product Measures in the environmental management efforts for financial performance. Our data analysis method with a systemic pipeline helps design an ESG investment portfolio.

This article proposes how to capture characteristics of cities based on world grid square statistics about the number of properties. First, how to compute grid square statistics from point data is explained, and a definition of the world grid square coding system is introduced. Second, a method to characterize cities based on grid square statistics about the number of properties is proposed. Empirical analysis for seven selected types of properties (automated teller machines (ATM), hostels, toilets, banks, post offices, hotels, and cafes) in Kyoto, Japan, and Erevan, Armenia, is conducted. Both the ordinary least squares (OLS) and reduced major axis (RMA) regression analyses for the linear relationship among the numbers of properties in terms of world grid square are used with the t-test in order to measure co-occurrence among properties. Moreover, a method to compare two cities based on matrices of regression coefficients is proposed. The comparative analysis between the OLS regression analysis and the RMA regression analysis is conducted. We discuss a relationship between the proposed method and statistical disclosure control (SDC) methods introduced in official statistics and an application of the proposed method to private information regarding confidentiality of private information with geographical positions.

Grid square statistics enable us to both protect privacy and analyze socioeconomic activities and compress an amount of data comparing original point data. We can compare and merge different grid square statistics and recalculate new grid square statistics from different grid square statistics without regard to privacy issues. Japan has industrial and government standards to define grid square codes to generate grid square statistics, which are defined in the Japan Industrial Standard X0410. This paper proposes a novel procedure to define global grid square codes for six levels, hierarchically modifying Japanese grid square codes. We show a procedure to extend JIS X0410 to grid square codes for worldwide usage, which we call world grid square codes and an example of grid square data focused on administrative areas for 252 countries and regions. We explain three case studies to employ grid square statistics and discuss how to apply several anonymization models to generating grid square statistics.

This paper analyzes Grid Square Statistics on job opportunity ads collected from a Japanese Internet job matching site (“from A navi”). We confirm a relationship between the number of job opportunities and socioeconomic quantities (the population, the numbers of firms and workers) in each 1-km and 10-km Grid Square. The number of workers is the best variable to explain the number of job opportunities out of the three candidates of socioeconomic quantities in the 1-km Grid Squares, however explanatory powers of three socioeconomic quantities to the number of job opportunities are just slightly different from one another in the 10-km Grid Squares depending on observation dates. We propose that the power law exponent estimated from the daily cross-sectional relationship can be used as the production ratio of job opportunities to socioeconomic quantities. It is determined that the production ratios vary in time and show seasonality associated with the Japanese calendar. Moreover, we compute 1-km Grid Square Statistics about job opportunity data based on JIS X0410 and we clarify relationships between the number of job opportunities and the number of occupation types. We extracted industrial clusters in terms of job opportunities and the types of job occupations in Japan.

The Accommodation Survey in Japanese Tourism Statistics is a quarterly survey conducted by the Japan Tourism Agency of the Ministry of Land, Infrastructure, Transport and Tourism. The aim of the survery is to capture a whole picture of monthly travel and tourism trends in Japan and obtain data to inform for Japanese tourism policies. Data collected include the total number of travelers, the number of foreign travelers with their nationality and the number of Japanese travelers with their residential prefecture. These data are collected from hotels, inns, and other private and public accommodations listed in the establishment frame database defined in Article 27 of the Statistics Act. In this study, the 1-km grid square statistics data were generated by using micro-data from 50,802 accommodations that took place from January 2013 to June 2014 (18 months); use of this data is in accordance with Article 33 of the Statistics Act. These data were analyzed for spatio-temporal patterns of Japanese travel trends. The 1-km grid square statistics data were computed by converting postal addresses into geographical information expressed as latitude and longitude and encoding it into 1-km grid square code standardized in JIS X0410. The relationships between the total number of travelers and the total number of foreign travelers in each 1-km grid square were clarified. It was confirmed that the power-law relationship between the number of travelers and the number of foreign travelers exists. The power-law exponent is greater than 1 for 15 months of the observation period. This implies that foreign travelers tend to choose accommodations concentrated in several areas, including areas in which Japanese travelers frequently visit and stay. Furthermore, Japanese travelers tend to stay in some areas close to their residence prefecture.

This study proposes an epidemic model for Ebola virus disease (EVD) based on a combination between a delayed stochastic SIR model and a metapopulation network model. Our proposed model consists of a set of stochastic differential equations of state variables, such as the number of susceptible persons, the number of infected patients both inside and outside of isolation wards, and the number of recovered persons both inside and outside of isolation wards, the number of deaths. We collected socioeconomic, technological-environmental data, such as OAG aviation timetable data, grid statistics on world population estimates provided by the Socioeconomic Data and Applications Center (SEDAC), the number of cases and deaths by EVD reported in 2014 by the World Health Organization (WHO) and economic statistics provided from the World DataBank by the World Bank. Linking these databases, we calibrated model parameters. We then conducted a numerical simulation by using aviation timetables and calculated the potential numbers of infectious persons and deaths. We found that the pandemic would not be completely prevented in industrialized countries by medical efforts alone. We conducted sensitivity analysis for the numbers of cases and deaths in terms of possible scenarios for medical, socioeconomic, and transport dimensions. We conclude that the medical efforts in industrialized countries can control the pandemic rate and that international aviation transport can reduce the number of passengers from places where epidemic outbreaks occur, and delay the beginning of the pandemic. The numerical simulation model can be extended to epidemic diseases other than EVD.

This paper proposes a multi-objective optimization method for a Japanese domestic airline network using socioeconomic-environmental data obtained from Japanese governmental bureaus and NOAA Tsunami Data and Information. Japanese domestic air transportation information is extracted to construct an airline network constituting of airports and flights. When we define three kinds of metrics such as Risk (R), Economy (E) and Convenience (C) for an airline network based on several statistics and evaluate them, we find that there exists a tradeoff relationship between these metrics. It is shown that multi-objective optimization is possible to recover the airline network by decreasing the Risk (R) metric while simultaneously increasing the Economy (E) and Convenience (C) metrics, even in a severe situation such that some airports are damaged by tsunamis and their associated flights at the airports are all cancelled.