Big Data for Sustainable Development
A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Information Systems and Data Management".
Deadline for manuscript submissions: closed (31 August 2020) | Viewed by 9308
Special Issue Editors
Interests: applied statistics; big data; data mining; data science; supervised modelling; unsupervised modelling; data clustering; classification and association rules; self-organising maps; support vector machines; neural networks; decision trees; logistic regression
Interests: workplace democracy; social entrepreneurship; social economy; social enterprise; cooperatives
Interests: computational intelligence; machine learning; probabilistic reasoning & learning; image analysis; probabilistic databases
Special Issue Information
Dear colleagues,
Each of the 17 United Nation’s Sustainable Development Goals (SDGs) constitutes a potential Big Data source for development strategies. Their complex overlap provides both challenges and opportunities in identifying and modelling important data attributes relating to various aspects of our sustainability, as clearly highlighted by the hundreds of indicators associated with each goal. SDG indicator data from different countries portray a deep and wide diversity across the continent, underlining the need for unified and more co-ordinated activities. The challenges and opportunities presented by SDGs are pathways towards addressing issues of data generation, sharing, governance, policy and legislation. Research communities across disciplines and regions are called upon to engage in unified initiatives for identifying data challenges and opportunities as well as devising interdisciplinary frameworks, tools and methods to address them.
In the current era of Big Data, our data generation capacities far outpace our data processing abilities, leaving a lot of useful information buried in potential data attributes. Addressing real-world challenges requires engaging tools, skills and resources within a tripartite strategic framework centred on Data, Computing power and Information flow infrastructure (DCI). Interestingly, the three pillars are embedded within the SDG fabric. For example, an implementation strategy for addressing issues relating to agriculture, food security and nutrition will typically require data on local, regional and global conditions—be they on rainfall, soil fertility, crop rotation, number of field officers, market access, food storage methods, etc. Other associated factors such as the general health of the population, level and quality of education and geopolitical stability may also significantly and cyclically impinge on agricultural output, food security and nutrition. Thus, the solution can be seen to naturally derive from a knowledge-based operation, driven by DCI.
While we have seen a number of high-level initiatives and publications on SDGs in recent years, research work on identifying triggers of SDG indicators is still in its infancy. The recent publication of the SDGs Atlas by the World Bank, the Millenium Institute and Our World in Data has provided descriptive statistics and simulated patterns that are vital to understanding the levels of attainment of the 2030 Agenda globally. A step forward would be to add a predictive power to these tools, taking an interdisciplinary view of all SDGs as a multidisciplinary data fabric. By sharing and analysing data, information and knowledge over relevant tools and platforms, we can deliver a spatiotemporal Development Science Framework (DSF). For different countries, success will depend on the "will and ability" to invest in DCI, which in turn depends on existing levels of socioeconomic prosperity and integration.
Dr. Kassim Mwitondi
Prof. Dr. Rory Ridley-Duff
Dr. Barnabas Gatsheni
Prof. Dr. Charles Taylor
Guest Editors
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Keywords
- big data
- data visualisation
- predictive modelling
- supervised modelling
- sustainable development goals
- unsupervised modelling
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