Special Issue "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).

Special Issue Editors

Dr. Kassim S. Mwitondi
E-Mail Website
Guest Editor
Industry & Innovation Research Institute, College of Business, Technology & Engineering (BTE), Sheffield Hallam University, Sheffield S1 2NU, UK
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
Prof. Dr. Rory Ridley-Duff
E-Mail Website
Guest Editor
FairShares Institute for Cooperative Social Entrepreneurship, Sheffield Business School, Sheffield Hallam University, Arundel Gate, Sheffield S1 1WB, UK
Interests: workplace democracy; social entrepreneurship; social economy; social enterprise; cooperatives
Dr. Barnabas Gatsheni
E-Mail Website
Guest Editor
Department of Applied Information Systems, College of Business and Economics, University of Johannesburg, Auckland Park 2006, South Africa
Interests: computational intelligence; machine learning; probabilistic reasoning & learning; image analysis; probabilistic databases
Prof. Dr. Charles Taylor
E-Mail Website
Guest Editor
Department of Statistics, University of Leeds, Leeds West Yorkshire, LS2 9JT UK
Interests: statistical learning; spatial statistics; circular data; statistical smoothing; statistical bioinformatics; data mining

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big data
  • data visualisation
  • predictive modelling
  • supervised modelling
  • sustainable development goals
  • unsupervised modelling

Published Papers (2 papers)

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Article
Does Land Use and Landscape Contribute to Self-Harm? A Sustainability Cities Framework
Data 2020, 5(1), 9; https://doi.org/10.3390/data5010009 - 21 Jan 2020
Cited by 2 | Viewed by 954
Abstract
Self-harm has become one of the leading causes of mortality in developed countries. The overall rate for suicide in Canada is 11.3 per 100,000 according to Statistics Canada in 2015. Between 2000 and 2007 the lowest rates of suicide in Canada were in [...] Read more.
Self-harm has become one of the leading causes of mortality in developed countries. The overall rate for suicide in Canada is 11.3 per 100,000 according to Statistics Canada in 2015. Between 2000 and 2007 the lowest rates of suicide in Canada were in Ontario, one of the most urbanized regions in Canada. However, the interaction between land use, landscape and self-harm has not been significantly studied for urban cores. It is thus of relevance to understand the impacts of land-use and landscape on suicidal behavior. This paper takes a spatial analytical approach to assess the occurrence of self-harm along one of the densest urban cores in the country: Toronto. Individual self-harm data was gathered by the National Ambulatory Care System (NACRS) and geocoded into census tract divisions. Toronto’s urban landscape is quantified at spatial level through the calculation of its land use at different levels: (i) land use type, (ii) sprawl metrics relating to (a) dispersion and (b) sprawl/mix incidence; (iii) fragmentation metrics of (a) urban fragmentation and (b) density and (iv) demographics of (a) income and (b) age. A stepwise regression is built to understand the most influential factors leading to self-harm from this selection generating an explanatory model. Full article
(This article belongs to the Special Issue Big Data for Sustainable Development)
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Emissions from Swine Manure Treated with Current Products for Mitigation of Odors and Reduction of NH3, H2S, VOC, and GHG Emissions
Data 2020, 5(2), 54; https://doi.org/10.3390/data5020054 - 18 Jun 2020
Cited by 8 | Viewed by 1590
Abstract
Odor and gaseous emissions from the swine industry are of concern for the wellbeing of humans and livestock. Additives applied to the swine manure surface are popular, marketed products to solve this problem and relatively inexpensive and easy for farmers to use. There [...] Read more.
Odor and gaseous emissions from the swine industry are of concern for the wellbeing of humans and livestock. Additives applied to the swine manure surface are popular, marketed products to solve this problem and relatively inexpensive and easy for farmers to use. There is no scientific data evaluating the effectiveness of many of these products. We evaluated 12 manure additive products that are currently being marketed on their effectiveness in mitigating odor and gaseous emissions from swine manure. We used a pilot-scale system simulating the storage of swine manure with a controlled ventilation of headspace and periodic addition of manure. This dataset contains measured concentrations and estimated emissions of target gases in manure headspace above treated and untreated swine manure. These include ammonia (NH3), hydrogen sulfide (H2S), greenhouse gases (CO2, CH4, and N2O), volatile organic compounds (VOC), and odor. The experiment to test each manure additive product lasted for two months; the measurements of NH3 and H2S were completed twice a week; others were conducted weekly. The manure for each test was collected from three different farms in central Iowa to provide the necessary variety in stored swine manure properties. This dataset is useful for further analyses of gaseous emissions from swine manure under simulated storage conditions and for performance comparison of marketed products for the mitigation of gaseous emissions. Ultimately, swine farmers, the regulatory community, and the public need to have scientific data informing decisions about the usefulness of manure additives. Full article
(This article belongs to the Special Issue Big Data for Sustainable Development)
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