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2013 10-22 humanitarian data talk to data kind
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2013 10-22 humanitarian data talk to data kind

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  • 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.
  • Transcript

    • 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?

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