A robust machine learning approach to SDG data segmentation

Abstract

In the light of the recent technological advances in computing and data explosion, the complex interactions of the Sustainable Development Goals (SDG) present both a challenge and an opportunity to researchers and decision makers across fields and sectors. The deep and wide socio-economic, cultural and technological variations across the globe entail a unified understanding of the SDG project. The complexity of SDGs interactions and the dynamics through their indicators align naturally to technical and application specifics that require interdisciplinary solutions. We present a consilient approach to expounding triggers of SDG indicators. Illustrated through data segmentation, it is designed to unify our understanding of the complex overlap of the SDGs by utilising data from different sources. The paper treats each SDG as a Big Data source node, with the potential to contribute towards a unified understanding of applications across the SDG spectrum. Data for five SDGs was extracted from the United Nations SDG indicators data repository and used to model spatio-temporal variations in search of robust and consilient scientific solutions. Based on a number of pre-determined assumptions on socio-economic and geo-political variations, the data is subjected to sequential analyses, exploring distributional behaviour, component extraction and clustering. All three methods exhibit pronounced variations across samples, with initial distributional and data segmentation patterns isolating South Africa from the remaining five countries. Data randomness is dealt with via a specially developed algorithm for sampling, measuring and assessing, based on repeated samples of different sizes. Results exhibit consistent variations across samples, based on socio-economic, cultural and geo-political variations entailing a unified understanding, across disciplines and sectors. The findings highlight novel paths towards attaining informative patterns for a unified understanding of the triggers of SDG indicators and open new paths to interdisciplinary research.

A Framework for Data-Driven Solutions with COVID-19 Illustrations

“…As noted above, this work was motivated by Big Data Modelling of SDG (BDMSDG) (Mwitondi et al 2020(Mwitondi et al , 2018a and, particularly, by the way COVID-19 has impacted our ways of life (Zambrano-Monserrate et al 2020, Bartik et al 2020. The complex interactions of the SDG, the magnitude and dynamics of their data attributes as well as the deep and wide socio-economic and cultural variations across the globe present both a challenge and an opportunity to the SDG project.…”

“…The work derives from statistical models like bagging and bootstrapping, which either rely on aggregation of classifiers or sample representativeness (Mwitondi et al 2019). The SMA algorithm's superiority lies in its built-in mechanics for efficiently handling data randomness (Mwitondi et al 2019(Mwitondi et al , 2020.…”

“…The study methodology is based on structured and unstructured data. Its basic ideas are in the Sample-Measure-Assess (SMA) algorithm originally developed for structured data (Mwitondi et al 2020.…”

“…As already noted, learning rules from data is inevitably associated with variations due to data randomness (Mwitondi & Said 2013, which can negatively affect model performance. For generalisation and robustness, we adopted the SMA algorithm (Mwitondi et al 2018a(Mwitondi et al ,b, 2020, taking multiple samples and conditioning each on the training and validation proportions as in Table 3. In this case, while they start with high accuracy, it isn't until about 25 epochs that the training accuracy converges while the validation accuracy continues to oscillate around 97%.…”

“…Novelty of the paper is embedded in the two algorithms-both adapted from the Sampling-Measuring-Assessing (SMA) algorithm for addressing data randomness, originally developed by Mwitondi et al (2018aMwitondi et al ( , b, 2020 for modelling structured data based on statistical model fitting and evaluation. The adaptation in Section 2, resonates with cross-disciplinary research discussions in tackling global challenges.…”