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Slides from the 2013 International Conference of Crisis Mappers in Nairobi, Kenya. Learn more at crisismappers.net
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Panel 1@CrisisMappers #ICCM
What's so Big about
Big Data?
Sanjana Hattotuwa
@sanjanah
Jon Gosier
@jongos
Anahi Iayala Iaccuci
@anahi_ayala
Big Data it’s
not about the
Data
Big Data is
about the
process
Information Ecosystem
Trusted Sources
Channels
ToolsInformation Exchanged
Resilience
How Big is Big Data?
The (forgotten) Humanitarian Crisis in 2013
Yemen = 10.5 m
Chad = 1.8 m
Afghanistan = 5.7 m
DRC = 2.6 m
Somalia = 3.8 m
CAR = 4.6 m
Big Data needs to be culturally sensitive
What happens
with
Big Data?
Big Data + Context + Action
= Information
Information saves lives
Anahi Ayala Iacucci
Senior Innovation Advisor
Internews Center for Innovation & Learning
http://innovation.internews.org
@anahi_ayala
@info_innovation
Emmanuel Letouzé
@Data4Dev @manucartoons
4 questions on the Big Data-Rich Future of Humanitarian Assistance
Emmanuel LetouzéFellow, Harvard Humanitarian Initiative
PhD Candidate, UC Berkeley
Non-Resident Adviser, International Peace Institute
Nairobi, November 21st, 2013
5th International Conference of Crisis MappersPanel I—What is so Big about Big Data?
(Reference..)
1. What is ‘Big Data’ about—and not about?
① Big Data as data == “traces of human actions picked up by digital devices” (Letouzé, Meier and Vinck)
1. “Digital breadcrumbs” (Sandy Pentland)2. Open web data (social media, online news..)3. Sensing (satellite, meters..)
② Big Data as data is not ‘about’ size—it’s a primarily qualitative shift
③ Big Data is “not about the data” (Gary King)
1. What is Big Data about—and not about?
Movement of an individual in Rwanda over 4 years (Source J. Blumenstock)
• Big Data as data doesn’t have to be big to be different• Big Data as data is about very many very small data
produced by / about connected individuals (big data is small data—it can also be slow data)
• Big Data takes intent and capacities
2. How will Big Data grow & age?
Stock of world data, circa 2020?Stock of world data, circa 1980 (assume)
2. How will Big Data grow & age?
1 10 1000 10000
50 years
90 days
Unknown data
① Descriptive analysis (e.g. maps)② Predictive analysis (proxying vs. forecasting)③ Diagnostics (causal inference)
3. How has / may it be used for humanitarian assistance purposes?
Source: Letouzé and Prydz, 2013
Pattern recognition + anomaly detection: Violent event in ACLED data vs. cellphone call volume in Ivory Coast
3. How has / may it be used for humanitarian assistance purposes?
NationalStatistical
Institutes carryout surveys
Telefonica teamused their data to‘predict’ SELs fromCell Phone Usage
Predict the present(SELs for non-
surveyed regions) and monitor the
future (trackchanges over time)
Survey from “a major city in Latin
America”
Example: “Prediction of Socio-Economic Levels Using Cell-Phone Records” (Telefonica research, 2011)
i. Main risks are① Creation of a ‘new’ digital divide
=>Recentralization of decision-making, reversing recent trends/efforts
② Dehumanization / de-democratization of decision-making (cf drones, killer-robots)
③ Confidentiality / security: e.g. CDRs de-anonymization and identification
4. What are the traps and priorities ahead?
ii. Main challenges/questions are① Political: Engaging with & empowering at-risk / affected
people and communities for community resilience, feedback loops, agile response..(urgency vs. sustainability?)
② Legal-institutional: Devising principles and frameworks for ‘responsible’ data sharing and analysis (D4D team)
③ Theoretical-methodological: further research / progress to take place on
1. Sample bias correction 2. Privacy: erasable future, noise in data3. Models of human response to emergencies 4. Causal inference
4. What are the traps and priorities ahead?
All data collectedall data shared
Extreme societal considerations / Open Data
society
All data collected,No data shared
Extreme commercial considerations / surveillance
No data collected,No data shared
Extreme individual consideration / Full privacy
% PERSONNAL DATA COLLECTED
% PERSONNAL DATA SHARED
Right Balance?Right Balance?
%personal data shared
%personal data collected
Source: Letouzé and Vinck, 2013
Jon Gosier
@jongos
Anahi Iayala Iaccuci
@anahi_ayala
Emmanuel Letouzé
@Data4Dev @manucartoons
Thanks for participating!@CrisisMappers #ICCM
Many thanks to our sponsors!
CrisisMappers 2013