Data Science for International Development

Sara-Jayne TerpOpenCrisis / ICanHazDataScience

@bodaceacat

International Humanity

Sudden-Onset Crisis• Fire, flood, heat, cold, tsunami, earthquake,

storm, tornado, hurricane, cyclone, refugees, bombings, election issues / violence etc

Slow-Burn Crises

Droughts, agriculture, food insecurity, conflict, education, disease, employment, shelter, trade, endemic violence, GBV etc.

“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

It’s all about people!

Pro: “Laboratory” = on behalf of

Per: “Community” = alongside

Para: “Grassroots” – by and within

DATA FEATURES

Velocity

DATAVELOCITY

DECISIONVELOCITY

Crisismapping

Development Data Science

Countryindicators

Slow(Years)

Fast(Sub-seconds)

Slow (Years)

Fast (Minutes)

Volume

ACCESS

SIZE

Companies, mobile phones

Off-gridCommunities

Open Closed

Small

Large

Individuals

NGOs, Govts

Social Media

(Closed because: privacy, competitive advantage, off-grid etc.)

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)

Veracity and Validity

Virtual Teams, Virtual PTSD

SUDDEN-ONSET TASKS

Mapping

Data Management

Classification

Geolocation

Summary

Image Tagging

SUDDEN-ONSET EXAMPLE

Pablo Deployment: Start

Team

Internal Tools

Internal View

External Views

Team Tasks

Geolocation Task

Classification Task

Map Output

SLOW-BURN EXAMPLE

Next Time!

HOW TO HELP

Data Nerding with…• Digital Humanitarian Network members, e.g.:– DataKind– Humanitarian OpenStreetMap– Standby Task Force– Info4Disasters

• School of Data• Sahana, Ushahidi, Taarifa, RHOK

Help to Automate

BOTSHUMANS

Good at: complex analysis, heuristics, pragmatic

translations, creative data finding, sudden onsetNot 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

BTW, You’re Already Digital Humanitarians

QUESTIONS?