Wednesday 16 October 2019 – Lunchtime Seminar with Dr Kassim S Mwitondi (Senior Lecturer in Applied Statistics/Data Mining)

Title: A Big Data Approach to Expounding Triggers of Sustainable Development Goals (SDG) Indicators
Date and time: Wednesday 16 October 2019, 1pm-2pm
Speaker: Dr Kassim S Mwitondi (Senior Lecturer in Applied Statistics/Data Mining, Sheffield Hallam University)

Technological advances in computing power and explosions in data generation, continue to trigger data–intensive research and related activities across disciplines, through, inter–alia, different applications aimed at addressing the challenges and opportunities of Big Data [1, 2, 3]. Across sectors and nations, the challenges and opportunities can be viewed as technical and application based. Technically, they are seen as pathways towards addressing issues ranging from data infrastructure, governance, sharing, modelling and security. From an application perspective, they potentially lead to influencial policies and improving decision making at institutional, national, regional and global levels, creating a Development Continuum (DC).

In particular, Big Data challenges and opportunities present potential knowledge for unlocking our understanding of the mutual impact–positive and negative, resulting from our interaction with our environment [4]. Triggers of species facing extinction, hunger and poverty, low productivity, land degradation, gender inequality or gaps in education quality and technological achievements span across sectors and regions. Sustainability of our livelihood and natural habitat requires an adaptive understanding of the triggers of known and potential positive and negative phenomena we face.

Image provided by Dr Kassim S Mwitondi

Image provided by Dr Kassim S Mwitondi

We present a consilient approach to expounding triggers of SDGs indicators based on the original ideas of Big Data modelling of SDGs and Development Science Framework–DSF [5, 6]. We adapt the well–documented root–cause analysis [7] and an automated observation mapping, modelled on existing knowledge systems and cross–sectoral governance arrangements, [8], to extract huge chunks of data from selected SDGs for identifying and modelling triggers of indicators across SDGs. Without loss of generality, we make a number of key assumptions, including regional homegeneity and heterogeneity, allowing data simulations based on one country’s real data to be applied to another, with adjustments.

The approach is designed to unify our understanding of the complex overlap of the SDGs by utilising data from different sources to achieve robust, consilient scientific consensus. The main idea is to create an SDG knowledge–unification context for application across the spectrum. The complexity of SDGs interactions and the dynamics through their indicators arise naturally from SDG–related studies. The search for triggers of the indicators encompasses the potential for uncovering what works in different sectors and countries.

Using data visualisation patterns, we demonstrated how collecting and standardising SDGs indicator–related data attributes, utilising locally authoritative data, carrying out comparative analyses across countries and interdisciplinary extraction of knowledge from data, moulds a unified understanding of available options for addressing the 2030 agenda.

Abstract adapted from DIRISA Research Data Workshop presentation.



  1.  Kharrazi, A. Challenges and Opportunities of Urban Big-data for Sustainable Development. Asia-Pacific Tech Monitor 2017, 34, 17–21.
  2. Kruse, C. S.; Goswamy, R.; Raval, Y.; Marawi, S. Challenges and Opportunities of Big Data in Health Care: A Systematic Review. JMIR Medical Informatics 2016, 4, e38.
  3. Yan, M.; Haiping,W.; Lizhe,W.; Bormin, H.; Ranjan, R.; Zomaya, A.;Wei, J. Remote sensing big data computing: Challenges and opportunities. Future Generation Computer Systems 2015, 51, 47 – 60.
  4. IUCN, In the spirit of nature, everything is connected. 2018
  5. Mwitondi, K.; Munyakazi, I.; Gatsheni, B. An Interdisciplinary Data-Driven Framework for Development Science. DIRISA National Research Data Workshop, CSIR ICC, 19-21 June 2018, Pretoria, RSA 2018
  6. Mwitondi, K.; Munyakazi, I.; Gatsheni, B. Amenability of the United Nations Sustainable Development Goals to Big Data Modelling. International Workshop on Data Science-Present and Future of Open Data and Open Science,12-15 Nov 2018, Joint Support Centre for Data Science Research, Mishima Citizens Cultural Hall, Mishima,Shizuoka, Japan 2018
  7. Ishikawa, K. Guide to Quality Control; Asian Productivity Organization, 1976.
  8. Primmer, E.; Furman, E. Operationalising ecosystem service approaches for governance: Do measuring, mapping and valuing integrate sector-specific knowledge systems? Ecosystem Services 2012, 1, 85 – 92.


Kassim Mwitondi obtained his PhD in Statistical Data Mining in 2003 from the School of Mathematics, University of Leeds. A well-rounded multi-disciplinary professional, he holds MSc degrees in Statistics, Computing and Finance with many years of experience in managing interdisciplinary research projects. His research interests are in extracting knowledge from multi–faceted data, with previous applications focusing mainly on solar–terrestrial, environmental, agro-forestry and business data. He has published extensively in those domains and presented at numerous international conferences.

His recent work focuses on interdisciplinary modelling of Sustainable Development Goals (SDGs)–seeking to uncover SDG indicator’s triggers by viewing each of the highly interacting SDGs as a Big Data node. He is actively in multinational research consortia promoting open-data for open-science.


See here for details of other seminars in the series.

All SHU staff and students are welcome to attend the C3RI Lunchtime Research Seminars. If you are from outside of the University and would like to attend a seminar, please email the C3RI Administrator to arrange entry.