Spatial Big Data, Mapping and Geographic Information Systems

Source:  GlobalGiving

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.

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Big Data: Blessing or Curse? Part 2

Source: Vishal Krishna/yourstory.com (27 June 2016)

Big Data are mostly discussed for two reasons. The first reason is whether those data that include personal information are safe or not. The second one is about how the data that are provided are used.

To begin with, I need to clarify what I am talking about when I use the term Big Data. CERN also uses Big Data but with another meaning for example. In this post I refer to the specific kind of Big Data that include private information and are mostly generated by humans through social media platforms or records of mobile phone lines.

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Spatial Big Data, Impact, Sharing and Ethics

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).

In my post published on February 22, 2017, I already wrote about ‘consciously volunteered data’ that are ‘born digital data’, namely crowdsourced data. Crowdsourcing has been proved to be particularly useful for humanitarian response (Meier, 2015). One of the first, and most emblematic, example of the power of crowdsourcing is the digital humanitarian response to the 2010 Haiti earthquake, in which the Ushahidi crisis-mapping platform played a critical role.

The Haitian humanitarian crisis that followed the 2010 earthquake also highlighted the fact that real time data could now feature in humanitarian responses. In their 2011 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.

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Spatial Big Data, Participation, Empowerment and Agency

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.

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Spatial Big Data, Crisis Response and International Development Policy

“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 et al., 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).

Before going any further, let’s start with a video from the Geospatial Revolution Project.

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