2013 10-22 humanitarian data talk to data kind
DESCRIPTION
TRANSCRIPT
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?