Smart planet governance : analyzing the role of big data for monitoring the Sustainable Development Goals
(2018) In Master Thesis Series in Environmental Studies and Sustainability Science MESM02 20181LUCSUS (Lund University Centre for Sustainability Studies)
- Abstract
- Achieving the 17 Sustainable Development Goals (SDGs) depends on timely and reliable information. However, standardized methodologies and regularly available data exist for only 40% of the 232 indicators. The “Data Revolution for Sustainable Development” is supposed to bridge these data gaps by
harnessing ongoing developments in information technology, particularly big data. Currently existing pilots draw on sources such as satellite imagery, mobile phone data, and social media to calculate SDG indicators related to poverty, hunger, and health in real-time and disaggregated by demographic
groups and locations. These approaches represent a new mode of knowing about people and planet whose consequences are yet to be understood. This work... (More) - Achieving the 17 Sustainable Development Goals (SDGs) depends on timely and reliable information. However, standardized methodologies and regularly available data exist for only 40% of the 232 indicators. The “Data Revolution for Sustainable Development” is supposed to bridge these data gaps by
harnessing ongoing developments in information technology, particularly big data. Currently existing pilots draw on sources such as satellite imagery, mobile phone data, and social media to calculate SDG indicators related to poverty, hunger, and health in real-time and disaggregated by demographic
groups and locations. These approaches represent a new mode of knowing about people and planet whose consequences are yet to be understood. This work draws on the concept of cognitive assemblages to argue that representational technologies have knowledge and governance effects in terms of how problems are understood and addressed. It argues that inherent affordances of big data could exacerbate and obscure the knowledge and governance effects of the SDG indicator system and proposes six possible future trajectories. On this base, a combination of text mining and document analysis is used to identify and investigate 22 indicators that are currently supported by various big data approaches. It is shown that the uptake of big data in the SDGs is gradual and irregular rather than revolutionary. The inherent tendency of big data towards all-encompassing vision raises questions for the understanding of sustainability in the Anthropocene. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8947430
- author
- Bauer, Tim LU
- supervisor
- organization
- course
- MESM02 20181
- year
- 2018
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- sustainability science, SDGs, indicator governance, big data, assemblage
- publication/series
- Master Thesis Series in Environmental Studies and Sustainability Science
- report number
- 2018:022
- language
- English
- id
- 8947430
- date added to LUP
- 2018-06-10 13:46:36
- date last changed
- 2018-06-10 13:46:36
@misc{8947430, abstract = {{Achieving the 17 Sustainable Development Goals (SDGs) depends on timely and reliable information. However, standardized methodologies and regularly available data exist for only 40% of the 232 indicators. The “Data Revolution for Sustainable Development” is supposed to bridge these data gaps by harnessing ongoing developments in information technology, particularly big data. Currently existing pilots draw on sources such as satellite imagery, mobile phone data, and social media to calculate SDG indicators related to poverty, hunger, and health in real-time and disaggregated by demographic groups and locations. These approaches represent a new mode of knowing about people and planet whose consequences are yet to be understood. This work draws on the concept of cognitive assemblages to argue that representational technologies have knowledge and governance effects in terms of how problems are understood and addressed. It argues that inherent affordances of big data could exacerbate and obscure the knowledge and governance effects of the SDG indicator system and proposes six possible future trajectories. On this base, a combination of text mining and document analysis is used to identify and investigate 22 indicators that are currently supported by various big data approaches. It is shown that the uptake of big data in the SDGs is gradual and irregular rather than revolutionary. The inherent tendency of big data towards all-encompassing vision raises questions for the understanding of sustainability in the Anthropocene.}}, author = {{Bauer, Tim}}, language = {{eng}}, note = {{Student Paper}}, series = {{Master Thesis Series in Environmental Studies and Sustainability Science}}, title = {{Smart planet governance : analyzing the role of big data for monitoring the Sustainable Development Goals}}, year = {{2018}}, }