04347nam a22005055i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001400153072001600167072001400183072002300197072001500220072001300235100007700248245019500325250001800520264007200538300006500610336002600675337002600701338003600727347002400763505023000787520215301017650001803170650002703188650004203215650002103257650003403278650007103312650003203383650009903415650003603514700007703550710003403627773002603661776003603687776003603723776003603759856004603795978-981-97-2296-9DE-He21320251030112038.0cr nn 008mamaa240512s2024    si |    s    |||| 0|eng d  a97898197229697 a10.1007/978-981-97-2296-92doi 4aQA276-280 7aPBT2bicssc 7aK2bicssc 7aBUS0610002bisacsh 7aPBT2thema 7aK2thema1 aSato, Aki-Hiro.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aEvaluation Platform of Sustainability for Global Systemsh[electronic resource] :bStatistical Approach to Geospatial Information for Big Data Integration /cby Aki-Hiro Sato, Hiroe Tsubaki.  a1st ed. 2024. 1aSingapore :bSpringer Nature Singapore :bImprint: Springer,c2024.  aXVI, 143 p. 51 illus., 27 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 aIntroduction -- Methods and Concepts about Evaluation -- Grid Squares and Grid Square statistics -- Statistical foundations about Grid Square statistics -- How to construct data infrastructure based on Grid Square statistics.  aThe authors of this book assert that Grid Square statistics, a method of aggregating data within a geographically defined Grid, may be an effective solution to approach geospatial data for big data integration. Grid Square statistics is a technique that allows us to collect and analyze data based on Grids and makes it easier to understand patterns and trends. Sustainability, a key concern for the future of our society, often involves balancing multiple independent objectives. These objectives and key performance indicators must be shared to solve multi-dimensional optimization problems with some constraints related to sustainability issues. However, many of these sustainability issues are challenges that are part of global systems, which are modeled as a complex system consisting of many components. Social, economic, and environmental aspects in our socio-economic systems require solutions designed for the sustainability of our society. These conditions imply that Big Data must contribute to evaluating our current situation, actions, and changes to continuously change our behavior and society based on a Plan-Do-Check-Action (PDCA) cycle. The PDCA cycle, also known as the Deming cycle, is a four-step management method used to improve processes and products continuously. We must understand how to construct a platform by using Big Data to do so. Thus, extracting meaningful information from Big Data is crucially important in the advanced information society. This book provides case studies based on Grid Square statistics. It shows their applications to socio-economic and environmental problems such as tourism, where we analyze visitor patterns to optimize resource allocation, SDGs indicator, where we monitor the sustainability of our socio-economic systems, and disaster management, where we face natural disaster to enhance preparedness. Finally, the book proposes a World Grid Square Statistics Reference Architecture for constructing data applications across organizations and domains and reports on design concepts, system architecture, and the implication of a data platform for World Grid Square data and statistics. 0aStatistics . 0aQuantitative research. 0aSocial sciencesxStatistical methods. 0aLabor economics. 0aPopulationxEconomic aspects.14aStatistics in Business, Management, Economics, Finance, Insurance.24aData Analysis and Big Data.24aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy.24aLabor and Population Economics.1 aTsubaki, Hiroe.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978981972295208iPrinted edition:z978981972297608iPrinted edition:z978981972298340uhttps://doi.org/10.1007/978-981-97-2296-9