Objectives: 1. Gain an understanding of key trends in ICT innovation which are influencing/disrupting crisis informatics. 2. Be able to trace these trends through discussions later this semester, and understand their influence and potential. 3. Introduce visualization lab

1. Emergent Trends and Tools in
Crisis Informatics
Adam Papendieck
Jan 30, 2014

2. Objectives
1. Gain an understanding of key trends in ICT
innovation which are influencing/disrupting
crisis informatics.
2. Be able to trace these trends through
discussions later this semester, and
understand their influence and potential.
3. Introduce visualization lab

3. Conventional, Centralized Info
Management
Source: Harvard Humanitarian Initiative. Disaster Relief 2.0

4. Crisis Information Ecosystem:
Complex Adaptive System
• Some information needs are clear and
structured.
• But the actual information ecosystems in
emergencies are complex and changing
rapidly.
• Individual technologies, information
systems and their relationship to others are
ephemeral, even in the short-to-medium
term. They evolve.

6. Crisis Information Ecosystem:
Complex Adaptive System
• Rapid, adaptive collaboration can be
advantageous
• complex adaptive systems are built upon feedback
loops
• Shift from institutions to collaborations
• Closed, rigid institutions  Open, fluid
cooperation

8. 0
100
200
300
400
500
600
Five most common categories of reports to the Ushahidi Haiti Crisis Map
over the first 90 days
Food Shortage Shelter Needed Water Shortage Vital Lines Emergency
Reports

9. Information Feedback Loop

10. Data Capture
Curation
Analysis
Dissemination
Use
months,years
minutes,weeks
Agency effort,
resources
Agency effort,
resources
Value StreamConventional Emergent
Humanitarian Information Systems

12. Emergent ICT
(Trends)
1. Improved Global Connectivity
2. The Cloud
3. Semantic Web and Open Standard
4. Mobile
5. Networks
6. Data Mining

13. http://www.submarinecablemap.com/
TREND: Improved Global Connectivity
Global Submarine Cables (2012)

14. NSFNet (1986)

17. Mobile Phone Subscribers per 100
Individuals

18. TREND: The Cloud
• Centralization of computing power and
standardization of data
– Semantic Web, XML, APIs
– Subscription-based “Software as a Service” (SaaS)
Source: http://resource.onlinetech.com/cloud-computing-prompts-2012-data-center-expansion-plans/

19. Software as a Service (SaaS)

20. TREND: Semantic web
• Old characteristics of the web: HTML, text,
individual static web pages
• New characteristics: XML, APIs (Application
Programming Interfaces), webservices, open
standards, dynamic and interactive web pages

21. TREND: Semantic web
• Augments information on web pages with
machine-readable metadata and relationship
information.
• Enables the “mashup”
• Example tools: Yahoo pipes, Google API,
Calais, Yahoo Terms, Google Maps

22. How Systems Communicate: XML
Other standard formats: JSON, RDF, CSV

23. How Systems Communicate: APIs
(Application Programming Interfaces)

24. Yahoo Pipes

26. Mashups and Dashboards

27. TREND: Mobile Computing
• Proliferation of thin clients (our various
“screens”) to access cloud
– API’s, XML etc.
• Sensor devices connected to the cloud
– human sensors
– internet of things
– enables more continuous assessment across time
and geography than ever before

28. Informed Human Sensors

29. http://opendatakit.org/
http://www.amazonteam.org/index.php/193/Participat
ory_Ethnographic_Mapping_Mapping_Indigenous_Land
s

31. Report 295: Buras, LA. 05/13/2010
“I have a sore throat and headache!!!
And I can not go run my crab traps because
my fishing grounds are closed,
now for the 14th day..”
Report 1646: Plaquemines, LA. 08/02/2010
“While walking on a beach in an area heading toward
South pass, we got stuck in 2 "baby" sinkholes of oil
buried beneath the sand. We started poking the holes
with sticks and oil came oozing out. The first sinkhole
was 4 feet wide and 6" deep. The second sinkhole
was 2x2 diameter. The tide must be bringing in sand
covering up the oil.”
Report 130: California Point, LA. 05/06/2010
Crabman out of work.
“I have crabbed in the California Point area inside
Breton Sound for well over 20 years. I also fish shad
in this area and sell it for crawfish bait. I also shrimp
seasonally. I am 52 years old and I am very
concerned about the future of commercial fishing,
my family responsibilities, my livelihood and
way of life.”

32. TREND 4: Social Networking
Proliferation of networking and collaboration tools

33. Clay Shirky: Social Media makes
Collaboration Efficient
• TV is uni-directional: we just consume.
• Social media is bi-directional: Participants
produce and consume information
• This transition yields a “cognitive surplus”
– All the time we used to spend consuming TV can
now be used for producing something new,
together.
Clay Shirky: How Cognitive Surplus Will Change the World http://goo.gl/ZIgA

34. So, what do we do with this surplus?

35. The “Long Tail” of Contributors

36. Institutions vs. Collaboration

37. Crowdsourcing and Micro-tasking

38. http://vimeo.com/9182869

39. Collaborative, Community-driven and
Open Source Development Models
• Depend upon:
– feedback loops
– some level of openness and sharing

40. Crowdmap: SaaS for Crowdsourcing

41. TREND: Data Mining
• Open data
• Big Data
– Our “digital exhaust”
• Emphasis on Trend Visualization
– Dynamic
– Info-graphics

42. Open Data
• Standardized formats, APIs
• Share, Share Alike
– CRED EM-DAT
– World Bank http://data.worldbank.org
– USG
– Ushahidi

43. Big Data
• web logs
• sensor networks (e.g. mobile phones, RFIDs)
• social networks (who’s connected to who)
• social data (what’s being shared and generated)
• teaching and learning management systems
• financial transaction data and large-scale e-commerce
• Internet text and documents
• Internet search indexing
• call detail records
• military surveillance;
• medical records;
• photography archives;
• video archives;
• astronomy, atmospheric science, genomics, biogeochemical, biological, and
other complex and/or interdisciplinary scientific research;

44. Importance of Innovation in Data
Visualization/Analysis
• Time, energy and resources are moving from data
collection & curation to data interpretation & use.
What information consumes is rather obvious: it
consumes the attention of its recipients. Hence a
wealth of information creates a poverty of
attention, and a need to allocate that attention
efficiently among the overabundance of
information sources that might consume it.
(Herb Simon, quoted in Scientific American, 2005)

45. Bengtsson, L., Lu, X., Thorson, A., Garfield, R., & Schreeb, J. von. (2011). Improved
Response to Disasters and Outbreaks by Tracking Population Movements with
Mobile Phone Network Data: A Post-Earthquake Geospatial Study in Haiti. PLOS Med,
8(8), e1001083. http://doi.org/10.1371/journal.pmed.1001083

46. FluTrends Video & Website: http://goo.gl/ZcgV
Source Paper: http://goo.gl/nMFvDz
Google Flu Trends

47. Global Pulse Research Case Studies
Cases:
1. Commodities E-Index: http://goo.gl/tzCb0z
2. Twitter & Crisis Stress: http://goo.gl/LVPtf
3. Unemployment & Social Media: http://goo.gl/RuRTb
Questions
1. How were things done before?
2. How is big data visualization being used to do things
differently or add something new?
3. Strengths, weaknesses, opportunities?

48. Bread Index based on online pricing
information
Source: UN Global Pulse

49. Source: UN Global Pulse
Twitter and Crisis-related Stress

50. Social Media and Unemployment
Source: UN Global Pulse

51. Open and Big Data will Require
Protection Measures
Source: Big Data, Communities and Ethical Resilience: A Framework for Action
(PopTech Fellows) http://goo.gl/LAKrjF

52. Interactive Data Visualization Lab
Background for Lab Exercises

53. Two Parts
1. Individual Assignment - Today
– Get to know software and data site
– Get started with independent work – Due Wed
2. Group Synthesis – Next week
– Expand on visualization types, data, tools
– Integrate with RLP

54. Visualization Lab Exercise
• On Blackboard:
– All instructions, links and submission details
• http://goo.gl/eZlh8z
– Visualization Lab Discussion Forum is for asking
questions and posting data and other resources.
• http://goo.gl/1VFHX6

55. Next time: Visualization Tools
• End-user cloud databases and services
– Google Maps, Google Spreadsheets,
– Zoho Creator
• Visualization APIs and Kits for the web
– D3, MIT Simile Project
• Web-based end-user geodata and mapping platforms
– Geocommons
• Web-based dynamic visualization platforms:
– IBM Many Eyes
– Infogr.am
• Desktop platforms for creating dynamic visualization
– Tableau (Windows only, OSX soon)

56. • Big Data open to the public
– http://www.quora.com/Data/Where-can-I-find-
large-datasets-open-to-the-public

57. Caveats
• Awareness of privacy / security is importany
• Protect your data!
– Keep an eye on your escape hatch
– Use open, established standards
• Limits: query rate and database size
• Attribution/Fair Use
• Engage the tools but be critical
– Pay attention to utility and face validity

58. Thanks!
• Adam Papendieck
• apapendieck@gmail.com
• 200 Broadway, Suite 203