One needs only to look through YouTube, the best example of what came to be called Web 2.0 (Meikle, 2016, p. 14), to find several TED talks on spatial data and mapping. Today, I would like to share the following 2015 speech of Chris Grundy from the London School of Hygiene and Tropical Medicine about ‘opportunistic data collection’ in the field of public health.
TED (Technology, Entertainment, Design) talks are up to eighteen-minute Internet-streamed presentations in which speakers address important topics from any discipline with the aim of “spreading ideas” (TED, 2017).
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).