Your search found 3 records
1 Havinga, I.; Bogaart, P. W.; Hein, L.; Tuia, D. 2020. Defining and spatially modelling cultural ecosystem services using crowdsourced data. Ecosystem Services, 43:101091. [doi: https://doi.org/10.1016/j.ecoser.2020.101091]
Ecosystem services ; Cultural factors ; Spatial analysis ; Modelling ; Assessment ; Biodiversity ; Economic aspects ; Diffusion of information ; Social media ; Landscape ; Observation / Netherlands / Texel
(Location: IWMI HQ Call no: e-copy only Record No: H049754)
https://www.sciencedirect.com/science/article/pii/S2212041620300334/pdfft?md5=a0a68b7cc968f1a2e98b56ff6193556e&pid=1-s2.0-S2212041620300334-main.pdf
https://vlibrary.iwmi.org/pdf/H049754.pdf
https://vlibrary.iwmi.org/pdf/H049754.pdf
(4.65 MB) (4.65 MB)
Cultural ecosystem services (CES) are some of the most valuable contributions of ecosystems to human well-being. Nevertheless, these services are often underrepresented in ecosystem service assessments. Defining CES for the purposes of spatial quantification has been challenging because it has been difficult to spatially model CES. However, rapid increases in mobile network connectivity and the use of social media have generated huge amounts of crowdsourced data. This offers an opportunity to define and spatially quantify CES. We inventoried established CES conceptualisations and sources of crowdsourced data to propose a CES definition and typology for spatial quantification. Furthermore, we present the results of three spatial models employing crowdsourced data to measure CES on Texel, a coastal island in the Netherlands. Defining CES as information-flows best enables service quantification. A general typology of eight services is proposed. The spatial models produced distributions consistent with known areas of cultural importance on Texel. However, user representativeness and measurement uncertainties affect our results. Ethical considerations must also be taken into account. Still, crowdsourced data is a valuable source of information to define and model CES due to the level of detail available. This can encourage the representation of CES in ecosystem service assessments.
2 Gaffoor, Z.; Pietersen, K.; Jovanovic, N.; Bagula, A.; Kanyerere, T. 2020. Big data analytics and its role to support groundwater management in the Southern African development community. Water, 12(10):2796. (Special issue: The Application of Artificial Intelligent in Hydrology) [doi: https://doi.org/10.3390/w12102796]
Groundwater management ; Data analysis ; SADC countries ; International waters ; Aquifers ; Data mining ; Machine learning ; Remote sensing ; Monitoring ; Technology ; Hydrological data ; Water levels ; Water storage ; Uncertainty ; Precipitation ; Social media ; Models / Southern Africa
(Location: IWMI HQ Call no: e-copy only Record No: H050040)
(1.58 MB) (1.58 MB)
Big data analytics (BDA) is a novel concept focusing on leveraging large volumes of heterogeneous data through advanced analytics to drive information discovery. This paper aims to highlight the potential role BDA can play to improve groundwater management in the Southern African Development Community (SADC) region in Africa. Through a review of the literature, this paper defines the concepts of big data, big data sources in groundwater, big data analytics, big data platforms and framework and how they can be used to support groundwater management in the SADC region. BDA may support groundwater management in SADC region by filling in data gaps and transforming these data into useful information. In recent times, machine learning and artificial intelligence have stood out as a novel tool for data-driven modeling. Managing big data from collection to information delivery requires critical application of selected tools, techniques and methods. Hence, in this paper we present a conceptual framework that can be used to manage the implementation of BDA in a groundwater management context. Then, we highlight challenges limiting the application of BDA which included technological constraints and institutional barriers. In conclusion, the paper shows that sufficient big data exist in groundwater domain and that BDA exists to be used in groundwater sciences thereby providing the basis to further explore data-driven sciences in groundwater management.
3 Carneiro, B.; Resce, G.; Laderach, P.; Schapendonk, F.; Pacillo, G. 2022. What is the importance of climate research? An innovative web-based approach to assess the influence and reach of climate research programs. Environmental Science and Policy, 133:115-126. (Online first) [doi: https://doi.org/10.1016/j.envsci.2022.03.018]
Climate change ; Research programmes ; CGIAR ; Food security ; Climate-smart agriculture ; Diffusion of information ; Innovation ; Internet ; Social media ; Digital technology ; Network analysis ; Text mining ; Stakeholders ; Policies
(Location: IWMI HQ Call no: e-copy only Record No: H051061)
https://www.sciencedirect.com/science/article/pii/S1462901122001058/pdfft?md5=ed4fd9f06b7706fcb16a0699d66ba94d&pid=1-s2.0-S1462901122001058-main.pdf
https://vlibrary.iwmi.org/pdf/H051061.pdf
https://vlibrary.iwmi.org/pdf/H051061.pdf
(7.72 MB) (7.72 MB)
Many parts of the world are increasingly experiencing the effects of climate change, making climate adaptation of rural livelihoods crucial to secure social and economic resilience. While the past two decades have witnessed a significant evolution in climate adaptation policy, evaluating the impact of climate science on policy has remained a challenge. This study employs the Digital Methods epistemology to explore the dynamics of agriculture-focused climate science and changes in attitude towards Climate Smart Agriculture (CSA) and climate change, using the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) as a case study. By considering online networks and narratives as evidence of “offline” influence, it effectively repurposes publicly available data from digital sources such as social media and websites by employing text mining and social network analysis to assess the influence and reach of the program among stakeholder at various levels. Results show that CCAFS has supported increased public awareness of CSA; that it actively engages with key actors within a network of stakeholders with more than 60 thousand members; that it has positively shifted the debate on climate adaptation among strategic partners through increased message alignment and space in the policy agenda; and that the program’s reach is potentially amplified to 5.8 M users on Twitter.
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