Unprecedented technical capabilities to collect data about physical processes, environmental conditions, industrial practices, transportation flows and citizens’ habits and preferences are providing us with the possibility... [ view full abstract ]
Unprecedented technical capabilities to collect data about physical processes, environmental conditions, industrial practices, transportation flows and citizens’ habits and preferences are providing us with the possibility of scrutinizing multiple aspects of our cities and characterizing them with huge size digital footprints. These records offer invaluable opportunities for sustainable management sciences, as they can be used to obtain precious information about current conditions and to build predictive models that can forecast the effects of possible interventions along improvement routes. Though, processing large and diverse data sets to extract meaningful and statistically valid evidences entails challenges from both the conceptual and technical viewpoints, and requires the rigorous and disciplined application of techniques from Data Analytics. Data Analytics includes several processes, all being increasingly supported by the aid of specialized systems and software applications, which implement an end-to-end chain from data acquisition, cleaning and validation, processing for descriptive, predictive and prescriptive purposes, until aggregated value in the form of information is produced and made available to decision-makers. The formalization of the above process steps guarantees that conclusions are drawn about the information already contained in the data, limiting subjective judgement and ensuring repeatability. This work wants to present the experience gained in an on-going data analytics project being carried out at the Universidad de los Andes. A team composed by undergraduate, graduate students and professors has been dealing with the historical dataset of air quality measurements of the city of Bogotá, with the aim of understanding the impacts that air pollution control strategies have had over time. This project offers several themes for consideration. On one side, in the case of Colombia, urban air quality data analysis from public authorities and from the academia have been drawing distinct and sometimes contracting results from the same datasets. It is usual that each study builds on specific and often not defined data processing rules, which turns out in scant reproducibility of results. Under these circumstances, defining agreed baseline measurements and air quality control interventions is a very hard task. On the other side, our research unveils the urgent necessity of establishing scientific collaborations between the academia and environmental and public health authorities, to create stakeholder engagement along synergistic bi-directional exchanges. The academia holds very advanced knowledge about Data Analytics. This opens important opportunities to push forward educational programs that can make up for the data science formation gaps, while promoting its use for decision making towards a sustainable urban development. Finally, the project wants to underline that the combination of the knowledge and expertise of the academia, with the experience in management of the city government, translates into benefits for the citizens, who have tools to be more and better informed.
1c Role of academia (advocacy and education in sustainable development science)
