Over the last few weeks, I have been writing on spatial data and mapping. Those of you who read my previous posts, may have noticed my interest in Meier’s book Digital Humanitarians: How Big Data Is Changing the Face of Humanitarian Response. Last week, Meier sent a message on Twitter to remind his followers of his 2011 TED talk, titled “Changing the World… One Map at a Time”, and encourage them to watch it.
The presentation that you can watch below demonstrates examples of the recent history of digital crisis mapping, from the 2008 post-election situation in Kenya and the earthquakes in Haiti in 2010 and in Japan in 2011, to the 2011 protests in Libya. Meier concludes his talk by emphasising the use of live maps for activism purposes in several countries like Syria, Sudan, Egypt, Lebanon, Yemen and Tunisia.
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).
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.
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.
“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).