The term ‘born digital data’ was coined by Taylor and Schroeder in 2015 to denote “data that are digital from the start rather than starting out in non-digital form”. ‘Born digital data’ can be ‘consciously volunteered data’ or ‘data in the wild’ (pp. 504-505).
The Haitian humanitarian crisis that followed the 2010 earthquake also highlighted the fact that real time data could now feature in humanitarian responses. In their2011 Haitian study, Bengtsson and his colleagues demonstrate that data could, in principle, be obtained for continuous and extended periods and in near real time, and that data were readily available.
During the 2011 World Conference on Social Determinants of Health, the Rio Political Declaration on Social Determinants of Health was adopted. The declaration expressed a global political commitment for the implementation of a social determinants of health (SDH) approach to reduce health inequities. Social determinants of health are defined by the World Health Organisation (WHO) as the conditions in which people are born, grow up, live, work and age. These conditions influence a person’s opportunity to be healthy, risk of illness and life expectancy. Social inequities in health – the unfair and avoidable differences in health status across groups in society – are those that result from the uneven distribution of social determinants. All of these drive health inequity – systematic disparities in health between social groups who have different levels of underlying social advantage or disadvantage such as food, shelter, clean water, sanitation, proper clothing and have limited access to medical care, education and finance.
Video: Dr Hans Rosling’s 200 Countries, 200 Years, 4 Minutes – use of data to visualize social determinants of health across the globe.
What does ‘development’ mean to data scientists, and how does that determine what data science can achieve within the field of international development? This essential question has been raised, in relation to certain D4D (Data for Development) projects, by some experts who further state:
Data science conducted with the aim of informing development policy must, by definition, involve an understanding of the policy area in question, and importantly the analysis must be combined with understanding of the local context. Without these characteristics, research only informs the field of data science rather than development policy.
(Taylor & Schroeder, 2015, pp. 508 & 514)
‘Data science must involve an understanding of the policy area and the local context’. Here is an interesting statement to begin with. So, let’s start with a video from the Geospatial Revolution Project.
The Sustainable Development Goals (SDGs) aim to address inequalities with an objective of “reaching the unreachable”. As mobile technology becomes more affordable, more powerful, and more accessible in low-income regions, it presents even more opportunity for governments to achieve these goals, even more so in public health.
“Social media, data and development”… It didn’t take me long to choose a focus within that theme: spatial data and mapping will be my common thread in the next few weeks.
Gathering geographical data about a crisis area is considered a traditional data-gathering target (Read etal., 2016, p. 6). According to some experts, the most mentioned application of ‘big data’ in developing countries is the possibility of mapping problems, for instance tracking and modelling the spread of diseases, through novel ways (Hay et al., 2013 cited in Spratt & Baker, 2015, p. 14).