In early July, while the summer in Skopje started to sizzle, big data enthusiasts popped by for one big (data) reason: to reflect on, share, and explore how we can use better use data for development.
Our partners in crime, UN Global Pulse and UN Volunteers were also in the mix, contributing presentations on the current state of big data affairs: from the Nepal population estimates after the earthquake to the case of the Seoul “Owl” bus, to UNV’s own burgeoning online volunteers platform.
The social innovation lab at the Faculty of Computer Science and Engineering (FINKI) was definitely the coolest place in town that week.
So, what kind of big data queries are our colleagues asking?
Let’s take a closer look:
1. Sudan: Can energy consumption correlate with poverty?
More specifically can electricity consumption be used as a proxy for income?
The team is mining energy consumption data on national and regional levels on monthly, weekly, even daily bases. We were partly inspired by Dumsor report – the first scientific analysis of energy outages as experienced by citizens.
2. Kosovo*: Can 112 calls be an indicator of growing security and safety trends of a particular nature?
The hypothesis is that the spatial distribution of these emergency call records can map the demand for emergency services Consequently, the 112 service can better tailor the emergency response. Many cities globally are already turning records of 112 data into predictive ability to preposition police officers or prioritize restaurant inspections.
That’s the one looking to achieve peaceful inclusive society, access to justice, accountable and inclusive institutions.
UNDP in Tunisia is working with the Tunisian National Statistics Institute to explore how non-traditional sources of data, like social media, can contribute to the establishment of a baseline, and continued monitoring of our progress in reaching the goal. We’re hoping to find a way in which big data can complement traditional statistics to monitor citizens’ perception and attitude toward governments.
4. TfYR Macedonia: Does the way people use their phones indicate mobility patterns?
And how can this help real-time risk assessment?
Whether it is the Japanese looking to foresee where they may go in case of earthquakes, or Nepalese looking at the extent of internal migration to preposition food supplies, the way people use mobile phones provides insights into patterns of behaviour on the ground that can be life-saving.
5. Egypt: Can data collected from remote sensors and related data sources (weather, etc.) support more resilient agriculture?
The hypothesis is that insights gleaned from analysis of these data can help inform farmers about better practices for planting and irrigation planning.
6. Armenia: Same question.
We’re looking into using real-time, environmental sensors to measure, accumulate and eventually predict trends for mitigation of extreme weather events.
This would lead to more resilient environment and citizens. Both teams were inspired by efforts such as WeatherSafe, where big data meets agriculture and helps farmers make better decisions and improve yields.
Big data, new insights
A major take-away in the first few months of this work has been the extent to which new sources of data that, when mashed together, can strengthen traditional metrics and provide new insights.
Plenty of challenges from access to certain types of data to privacy issues remain.
We would like to thank the UNDP Innovation Facility for funding our big data for development work this year.
Stay tuned for more and please be sure to follow this blog and #UNDP4Future on Twitter for regular updates on the ongoing big data 4 dev journey!
*References to Kosovo shall be understood to be in the context of Security Council resolution 1244 (1999).