RT Report
T1 Chapter 6: Data opportunities - Measuring Progress: Water-related Ecosystems and the SDGs
A2 Dou, Changyong
A2 Wang, Futao
A2 Liu, Jie
A2 Zuo, Lijun
A2 Wang, Meng
A2 Guo, Huadong
A2 Lu, Shanlong
A2 Li, Xiaosong
A2 Chen, Yaxi
A2 Chen, Yu
K1 big data
K1 data collection
K1 environmental indicator
K1 earth observation
K1 citizen science
K1 satellite image
PB United Nations Environment Programme
YR 2023
FD 2023-03
LK https://wedocs.unep.org/handle/20.500.11822/42101
UL https://wedocs.unep.org/handle/20.500.11822/42101
NO Big data sources are increasingly being recognized as new and innovative information sources for SDGs (MacFeely 2019; IAEGSDGS 2019; Tam and Van Halderen 2020). Many NSOs are already experimenting with big data in the production of official statistics, with initiatives catalogued by the United Nations Global Working Group on Big Data and the United Nations Global Pulse. Currently, the dominant big data types include Earth Observation (EO) data, citizen science, other sensor network data, commercial data, tracking data, administrative data, and opinion and behavioural data. Combined with advanced analytical techniques (e.g. machine learning, geospatial modelling and geostatistical modelling), they could contribute to the monitoring of 15 goals, 51 targets and 69 indicators (Allen et al. 2021), particularly those related to health and biodiversity.Full report is available at https://wedocs.unep.org/handle/20.500.11822/41997
NO https://www.unep.org/resources/report/measuring-progress-water-related-ecosystems-and-sdgs
DS UN Environment Document Repository
RD Sep 17, 2026