Human Development and the Data Revolution
Seiten
2025
Oxford University Press (Verlag)
978-0-19-887914-5 (ISBN)
Oxford University Press (Verlag)
978-0-19-887914-5 (ISBN)
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This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations.
Human Development and the Data Revolution explores the uses of large-scale data in the contexts of development, in particular, what techniques, data sources, and possibilities exist for harnessing large datasets and new online data to address persistent concerns regarding human development, inequality, exclusion, and participation.
Employing a global perspective to explore the latest advances at the intersection of big data analysis and human development, this volume brings together pioneering voices from academia, development practice, civil society organizations, government, and the private sector. With a two-pronged focus on theoretical and practical research on big data and computational approaches in human development, the volume covers such themes as data acquisition, data management, data mining and statistical analysis, network science, visual analytics, and geographic information systems and discusses them in terms of practical applications in development projects and initiatives. Ethical considerations surrounding these topics are visited throughout, highlighting the tradeoffs between benefitting and harming those who are the subjects of these new approaches.
The authors explore the potentials of big data analysis for development but also the challenges and limitations for its usefulness in contexts of development, illuminating how the measurement and analytical choices made in using big data analysis are far from being neutral or value-free considerations.
Human Development and the Data Revolution explores the uses of large-scale data in the contexts of development, in particular, what techniques, data sources, and possibilities exist for harnessing large datasets and new online data to address persistent concerns regarding human development, inequality, exclusion, and participation.
Employing a global perspective to explore the latest advances at the intersection of big data analysis and human development, this volume brings together pioneering voices from academia, development practice, civil society organizations, government, and the private sector. With a two-pronged focus on theoretical and practical research on big data and computational approaches in human development, the volume covers such themes as data acquisition, data management, data mining and statistical analysis, network science, visual analytics, and geographic information systems and discusses them in terms of practical applications in development projects and initiatives. Ethical considerations surrounding these topics are visited throughout, highlighting the tradeoffs between benefitting and harming those who are the subjects of these new approaches.
The authors explore the potentials of big data analysis for development but also the challenges and limitations for its usefulness in contexts of development, illuminating how the measurement and analytical choices made in using big data analysis are far from being neutral or value-free considerations.
Sanna Ojanperä is a quantitative researcher at the Oxford Internet Institute, University of Oxford, and The Alan Turing Institute. Eduardo López is Associate Professor and Director of Graduate Studies at the Department of Computational and Data Sciences, George Mason University. Mark Graham is the Professor of Internet Geography at the Oxford Internet Institute.
| Erscheint lt. Verlag | 13.3.2025 |
|---|---|
| Verlagsort | Oxford |
| Sprache | englisch |
| Maße | 153 x 234 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Wirtschaft ► Volkswirtschaftslehre ► Ökonometrie | |
| ISBN-10 | 0-19-887914-8 / 0198879148 |
| ISBN-13 | 978-0-19-887914-5 / 9780198879145 |
| Zustand | Neuware |
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