This document discusses how data science can be used for international development and humanitarian aid efforts. It outlines two main types of crises - sudden-onset crises like natural disasters which require fast response, and slow-burn crises like drought or conflict which evolve over longer time periods. The challenges of data for these efforts include varying data velocities, volumes, varieties and access depending on the context. Examples are given of how data science tasks like mapping, classification and geotagging can support rapid response to sudden crises, as well as longer-term work for slow-burn issues. The role of both human and AI capabilities are discussed, and ways for individuals to get involved through groups like DataKind or Standby Task Force are

1. Data Science for International
Development
Sara-Jayne Terp
OpenCrisis / ICanHazDataScience
@bodaceacat

2. International Humanity

3. Sudden-Onset Crisis
• Fire, flood, heat, cold, tsunami, earthquake,
storm, tornado, hurricane, cyclone, refugees,
bombings, election issues / violence etc

4. Slow-Burn Crises
“Human development is a process of enlarging people’s choices.
The most critical ones are to lead a long and healthy life, to be
educated and to enjoy a decent standard of living. Additional
choices include political freedom, guaranteed human rights and
self-respect – what Adam Smith called the ability to mix with
others without being ashamed to appear in publick” – UNDP
Human Development Report
Droughts, agriculture, food insecurity, conflict,
education, disease, employment, shelter, trade,
endemic violence, GBV etc.

5. It’s all about people!
Pro: “Laboratory” =
on behalf of
Per: “Community” =
alongside
Para: “Grassroots” –
by and within

6. DATA FEATURES

7. Velocity
Fast
(Minutes)
Crisis
mapping
DECISION
VELOCITY
Slow
(Years)
Development Data Science
Country
indicators
Slow
(Years)
DATA
VELOCITY
Fast
(Sub-seconds)

8. Volume
Large
Companies,
mobile
phones
Social Media
Off-grid
Communities
SIZE
NGOs,
Govts
Individuals
Small
Open
ACCESS
Closed
(Closed because: privacy, competitive advantage, off-grid etc.)

9. Variety
CSV, json, xml, excel, pdf, text,
webpages, rss, scanned pages,
images, videos, audiofiles, maps,
proprietary formats etc.
DR Congo in Data.UN.Org:
•
“Congo, Democratic Republic of the”, “Congo Democratic”,
“Democratic Republic of the Congo”, “Congo (Democratic Republic of
the)”, “Congo, Dem. Rep.”, “Congo Dem. Rep.”, “Congo, Democratic
Republic of”, “Dem. Rep. of Congo”, “Dem. Rep. of the Congo”
DR Congo in common standards:
•
“Democratic Republic of the Congo” (UN Stats), “Congo, The
Democratic Republic of the” (ISO3166), “Congo, Democratic Republic
of the” (FIPS10, Stanag), “180” (UN Stats), “COD” (ISO3166, Stanag),
“CG” (FIPS10)

10. Veracity and Validity

11. Virtual Teams, Virtual PTSD

12. SUDDEN-ONSET TASKS

13. Mapping

14. Data Management

15. Classification

16. Geolocation

17. Summary

18. Image Tagging

19. SUDDEN-ONSET
EXAMPLE

20. Pablo Deployment: Start

21. Team

22. Internal Tools

23. Internal View

24. External Views

25. Team Tasks

26. Geolocation Task

27. Classification Task

28. Map Output

29. SLOW-BURN EXAMPLE

30. Next Time!

31. HOW TO HELP

32. Data Nerding with…
• Digital Humanitarian Network members, e.g.:
– DataKind
– Humanitarian OpenStreetMap
– Standby Task Force
– Info4Disasters
• School of Data
• Sahana, Ushahidi, Taarifa, RHOK

33. Help to Automate
HUMANS
BOTS
Good at: complex analysis,
heuristics, pragmatic
translations, creative data
finding, sudden onset
Not so good at: high
volume, repetitive, 24/7
accurate
Good at: high volume,
repetitive, complex
pattern finding, long
term
Not so good at:
complexity, human
foibles

34. BTW, You’re Already Digital
Humanitarians

35. QUESTIONS?