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
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
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)
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
Data science for international development is all about people. It’s about spotting when there’s a problem that needs help, and working with the people affected to help solve it. These are your counterparts in Haiti. They’re all great young people, and they were all affected by the 2010 Haiti earthquake. In this photo, they’ve just finished designing and building a data system for gender-based violence counsellors.
What’s special about development data? PTSDMultiple languagesNo dataNo mapsTerrible formatsRapidly changing situation
This is a t-shirt printed after the 2010 Chile earthquake. The message on it reads “plz send help to 1712 estacion central, santiagochile. im stuck under a building with my child. #hitsunami #chile we have no supplies”.
Both manual and automated classification and geolocation of messages.