COMS2200 Big data & Society Week 2 Crowdsourcing

http://doi.org/10.22215/tplauriault.courses.2018.coms2200
COMS2200
Week 2:
Crowdsourcing and Digital
Humanitarianism
Big Data & Society
September 14, 2018
Class Schedule: Fridays, 8:30 - 11:30
Location: CO372
Instructor: Dr. Tracey P. Lauriault
E-mail: Tracey.Lauriault@Carleton.ca
Office: 4110b River Building
Office Hours: Thursdays 9-noon, Friday Afternoon by apt.
ORCID:0000-0003-1847-2738
CU IR: https://ir.library.carleton.ca/ppl/8

Week 1: Agenda
Administration
 Events
 Notetaker PMC
 Assessment
 Library
 Office Hours & Email
 Assignment 3 - marks
Data Description
Infographic A
Lecture

Administration

http://statsportsconsulting.com/OTTHAC18/

Note taker Paul Menton Centre
Currently the PMC is seeking a volunteer notetaker for this class,
This volunteer service is very easy for you to do and has many rewards.
Volunteers must take notes for all lectures and have them uploaded within 48 hours of the
lecture date.
Notes can be typed or handwritten notes can be scanned and uploaded via Carleton
Central. Volunteers who upload all notes in a timely manner will be eligible for a letter of
appreciation and CCR credit at the end of the term.
If this is an opportunity you would like to take advantage of please email
volunteer_notetaking@carleton.ca with your name, student number and complete course
code, or you can stop by our office in 501 University Centre."
We truly appreciate any help you can provide in this process, and will keep you updated
on our progress to find a volunteer in your class. Please let us know if you have questions
or if we can assist in any way.
Kind regards,
PMC Notetaking Team
Paul Menton Centre for Students with Disabilities
Carleton University
Phone: 613-520-6608
Fax: 613-520-3995
Email: Volunteer_Notetaking@Carleton.ca

13 Weeks – 36 Hours
Weeks Date Guests Assignment
Week 1 – Introduction Sept. 7
Week 2 – Crowdsourcing & Dig. Humanitarianism Sept. 14 Assignment 1: Description
Week 3 – Open Data Sept. 21 City of Ottawa
Week 4 – Moving, Locating and Sensing You Sept. 28
Week 5 – Counting You Oct. 5
Week 6 – Social Media You Oct. 12 Assignment 2: Remote Sensing
Week 7 – Sorting you Oct. 19 Assignment 3: Article
Study Break
Week 8 – Identifying You Nov. 2 Part 2: Inforgraphic Peer Review
Week 9 – Watching You Nov. 9
Week 10 – Big Data You Nov. 16 Assignment 4: Data Trail
Week 11 – Data Brokers and You Nov. 23
Week 12 – Remembering You Nov. 30 Parts 3 & 4: Infographic FINAL
Week 13 – Critical Data Studies & Review Dec. 7
Exam

Office Hours & Correspondence
 E-mail:
 Tracey.Lauriault@Carleton.
ca
 include COMS2200 in the
subject line.
 Office Hours:
 4110b Richcraft Hall
 Thursdays 9-12:00

Library
Dr Tracey P. Lauriault, School of Journalism and Communication Carleton Universityhttp://doi.org/10.22215/tplauriault.courses.2018.coms2200
Acquisitions
Journal subscriptions
Signing in
Reference Desk

Week 2 Readings

Ch 1
Haiti Earthquake
@

“The Marine Corps is using your site every second
of every day. This has saved
100’s of lives…”

Lieutenant General Blum
2nd in Command, NORTHCOM
“You are doing a remarkable job.
We all need to learn from you.”

Definitions

Definitions
1. Spatial Media
2. Geoweb
3. Volunteered Geographic
Information (VGI)
4. Locative Media
5. User Generated Content
(UGC)
6. Citizen Science
7. Participatory Mapping or
PPGIS
8. Crowdsourcing

1. Spatial Media
Geography is an ‘organizational logic of the web’
& the web has become a key means to mediate
space, location and sociality
spatial and locative technologies render virtually
everything located or locatable, and thus open to
navigation via maps or spatialisations and
interpretation through geographical analysis
Mediation of a diverse set of socio-spatial practices
– communications, interactions, transactions –
beyond traditional mapping
Kitchin, Lauriault & Wilson (2017) Understanding Spatial Media

2. Geoweb
spatial technologies (hardware, software, APIs, databases,
networks, platforms, cloud computing),
spatial content (geo-referenced and geo-tagged data)
internet-based mapping and location based
applications/services that they compose and enable
generally refers to new spatial technologies that are more
interactive, participatory, social and generative in nature

3. Volunteered Geographic Information (VGI)
New relations and practices of geographic
production and consumption & a new form
of producing geography
Web 2.0, ‘non-expert’ use tools to generate,
map & share spatial data & spatial apps
people interact w/& help build the geoweb
by adding georeferenced data
prosumption adding crucial value in the
creation of a product or delivery of a service,
which is also actively consume
the public creates & contributes facts to
websites where the facts are synthesized into
geo-databases
Citizens as sensors Kitchin, Lauriault & Wilson (2017) Understanding Spatial Media

4. Locative Media  Subsection of the geoweb
 situating users in time & space and mediate
interactions w/ locations
 underlying data, practices, & services are
location-orientated
 navigation & routing apps, LBS, and ad
practices where users are recommended
options w/ respect to activities based on
their present location, & location-based
social media
 Five categories:
1. social check-in sites (e.g., Foursquare);
2. social review sites (e.g., Yelp, Tellmewhere,
Groupon);
3. social scheduling/events sites (e.g.,
Meetup).
4. social real-time traffic & navigation
recommendations (E.g. Waze)

5. User Generated Content
Users contribute data to an
application / platform
It may or may not be spatial
Often tied with Location Based
Service on your phone –
(Device generated Content?)

6. Augmented Spatial Media
Real-world geography becomes interactive
Space is augmented with digital
information, real locations are tagged with
RFID, or phone number, or your LBS
recognizes is tied to an app that
recognizes a location and sends you
information

7. Citizen Science (CS)
process whereby citizens are involved in
science as researchers:
 concerned citizens
 government agencies
 industry
 academia
 community groups, and
 local institutions
collaborate to monitor, track and respond
to issues of common community concern.
not “scientists using citizens as data
collectors,” but rather, “citizens as scientists”
Conrad, Cathy C., and Krista G. Hilchey (2011)

Types of Citizen Science
Passive sensing:
 relies on participants to provide a resource
that they own for automatic sensing. The
information that is collected through the
sensors is then used by scientists for
analysis
Volunteer computing:
 participants share their unused computing
resources & allow scientists to run complex
computer models during the times when
the device is not in use
Volunteer thinking:
 uses ‘cognitive surplus’, participants
contribute their ability to recognise patterns
or analyse information that will then be
used in a scientific project.
Environmental and ecological observation:
 focuses on monitoring environmental
pollution or observations of flora and
fauna, through activities
Participatory sensing:
 is similar to the previous type, but gives
the participant more roles and control
over the process. The process is more
distributed and emphasises the active
involvement of the participants in
setting what will be collected and
analysed
Community/Civic science:
 is initiated and driven by participants
who identify a problem and address it
using scientific methods and tools. The
problem, data collection and analysis
are often carried out by community
members or in collaboration with
scientists or established laboratories.

National Biodiversity Data Centre
http://www.biodiversityireland.ie/

Participatory Sensing in Ottawa

8. Participatory Mapping (PM)
• Approaches & techniques that
combines cartography w/participatory
methods to represent the spatial
knowledge of local communities.
• inhabitants possess expert knowledge
which can be expressed in a
geographical framework
• Often socially or culturally distinct
understanding w/information that is
not in official maps.
• customary land boundaries
• traditional natural resource management
practices
• sacred areas
• Traditional Place names
Brown and Kytta 2014
Ogiek Peoples visualizing their traditional lands
Nessuit, Kenya

Cybercartography
Gwich’in Social and
Cultural Institute
Ingrid Kritsch Collected
over 800 spoken place
names, photos and
videos w/elders on an
iPad
Data replicated back in
Ottawa in a matter of
hours
https://gcrc.carleton.ca/confluence/display/GCRCWEB/Overview

Cybercartographic Atlases
Atlas of
Indigenous
Perspectives &
Knowledge
Atlas of
Arctic
Bay
Lake Huron
Treaty Atlas
Inuit (Siku)Sea
Ice Use &
Occupancy
Project
Views
from the
North
Kitikmeot
Place Name
Atlas
https://gcrc.carleton.ca/confluence/display/GCRCWEB/Atlases

9. Crowdsourcing
• Involves people not normally in your
workspace to help collect information
• An organization has a task it needs
performed
• An online community voluntarily
performs the task
• The result is mutual benefit for the
organization and the online community
• NOTE – labour issues
Daren C. Brabham IBM Center for The Business of Government, 2013,
Using Crowdsourcing In Government

Participation, Participants,
Quality & Authority

Typology of Participation
Muki Haklay, Citizen Science and Volunteered Geographic Information: Overview and Typology of Participation. (2013)

Spectrum of VGI Contributors
Knowledge of Geographic Information
Degree of
VGI
Contribution
Neophyte
Interested
Amateur
Expert
Amateur
Expert
Professional
Expert
Authority
GeoConnections
Volunteered Geographic
Information (VGI)
Primer (2012)

Issues for Government
• Interaction type
• Trigger event
• Domain
• Organization
• Actors
• Data sets
• Process
• Feedback
• Goal
• Side effects
• Contact point
• Policy
• Legal
• Standards
• Data quality
• Technology
• Sustainability
• Credibility of the
source
• Preservation
• Security
http://discovery.ucl.ac.uk/1433169/

Government of Canada
http://www.nrcan.gc.ca/earth-sciences/geomatics/canadas-spatial-data-
infrastructure/8904http://spatialinformation.files.wordpress.com/2013/04/white-paper-final-version.pdf

VGI Quality Control & System Openness
OpenRestricted VGI System Openness
Quality
Control
Formality
The Crowd
Professionals
GeoConnections Volunteered
Geographic Information (VGI)
Primer (2012)

Citizen Science as a Springboard to
Engagement
• VGI, Citizen Science, Participatory Mapping &
Crowdsourcing
• Co-governance
• Deliberative democracy
• Evidence informed decision making
• Policy development

Beijing Air Tracks: Tracking Data for Good
http://www.spatialinfor
mationdesignlab.org/pr
ojects/beijing-air-tracks
http://www.nytimes.com/int
eractive/2008/08/16/spor
ts/olympics/20080816-c0-
graphic.html

Structure of the paper
Intro
Literature review
Methodology
Background
 Who are the volunteers
 Professional background
 Motivation
 Geographic experience
 Organizations
 Professional volunteer organizations
 Humanitarian volunteers
Conclusion
Notes
Reference
Elizabeth Resor, 2015, The Neo-Humanitarians, https://doi.org/10.1002/poi3.112

Methodology
Surveyed vol. crisis mappers
Interviews with key experts
 Board members, professionals
Founds respondents on listserves
Text analysis of key online resources –
Bulleting Boards & Blogs
Practical documentation of event
Training material
Professionalized vs transparent

Research Question
Who are crisis mapping volunteers?
Do they have credibility to contributed to formal humanitarian
response?
Can they meet professional standards of engagement,
production and analysis?

History of Humanitarian Mapping
 Sept. 11, 2001
 GIS + remote sensing
 No protocol
 Emergency Mapping and Data Centre, Pier 92
 Indian Ocean Tsunami, 2004
 Satellite and radar imagery donated by vendors
 Online fundraising
 Hurricane Katrina, 2005
 Google Maps, SCIOPIONUS mashup
 No trust in FIMA
 Websites, message boards, relief centre locations, supplies, damaged
infrastructure
 Kenyan Elections, 2010
 Ushahidi
 SMS of post election violence
 Haiti, 2010
 OpenStreetMap
 Google Maps, Google Earth
 Lack of integration
 Digital Humanitarian Network, 2012
 Typhoon Pablo, Phillippines, 2012
 UN OCHA outreach
 SBTF, DHN

Premise
 Volunteer crisis mapping
 Technological advances
 Online mapping tools
 Social media
 Interactive website
 Global platforms
 Online communities
 Volunteerism
 Collect data in response to
a crisis
 Lack of affiliation with formal
humanitarian actors
 United Nations
 Quality of amateur mapping
 Neogeography
 Neo-Humanitarians
 Remote support
 Work done outside of formal
humanitarian response

What was provided?
“they were organized and fueled by volunteers using an open,
collaborative production model; they provided information
that was not otherwise available to humanitarian actors in a
very short period of time, and they applied very recent
developments in online mapping technologies” (p.36)

Kinds of data & Software
 Geographic information system (GIS)
 QGIS
 ESRI
 Ushahidi plaftorm
 Open Street Map (OSM) platform
 Geodata
 User generated content (UCG)
 Volunteered geographic information
(VGI)
 Social media
 Unstructured data
 Sometimes w/location
 Cleaned twitter data – events
 SMS
 Emails
Data were:
 Verified
 Categorized
 geotagged

What do they create?
Maps of building damage
Information about
trapped victims
Location of resources and
aid
Locations of armed
conflict
Location of tanks and
equipment
Extent of damage, floods,
etc.

Benefits
Tools are easily scalable, free or low cost, free labour
Open opportunities for participation
Involve young people
Friendly government
Rich data source
Local knowledge representation
Democratizing mapmaking

Pitfalls/concerns
 Reinforce existing inequalities
 Digital divide
 Quality control
 Bypass humanitarian response
 Poor user interface
 Lack of training in GiScience
 Privacy - geo
 Consent
 Reliability
 Ideology
 Copyright / IP
 Google maps vs OSM
 Data affect peoples survival & safety
 Weight of the decisions
 Publishing the location of relief
workers
 Which side of a conflict
 Language
 Skewed decision making
 Data overload for responders
 Familiarity
 Path dependent

Volunteers
Trust?
Expertise?
Affiliation?
Connection to formal actors?
Most are skilled and experienced in map making
Very little experience in humanitarian work

Motivation
Why do people get involved?
Data standards and data quality?
Most cared about the issue more than gaining skills
Most are aware that their work affects people
More news, interest in foreign affairs, social networks, career
goals
Crisis mapping might be making volunteers more aware of
humanitarianism

Geographic experience
Geo experience in the affected countries?
Geo knowledge?
Geo bias?
Language?
Local knowledge?
Mixed results
Most volunteers had demonstrated attributes related to trust
and evidence of expertise

Professional volunteers
GISCorps & URISA, 2003
46 countries, hundreds of projects
3000 volunteers
Remote and in the field volunteers
 Building databases
 Spatial analysis
 Modeling
 Teaching & capacity building
 App dev
 Project evaluation
Job specs, requirements
 CVs
 GISCorps Code of Conduct
What of protecting sources? Danger of data bias? Misallocation of
relief?

Humanitarian Volunteers
 SBTF, 2010
 Patrick Meir, Ushahidi, Harvard Humantiarian Initiative, Qatar
Foundation’s Computing Research Institute
 UN OCHA
 Tasks:
 Cleaning twitter data
 Categorizing
 Geotagging images
 Finding maps
 OS geodata
 Stanby Code of Conduct
 Do not harm
 Data quality
 Open data, open source
 Comparable with the formal sector

Infographic Project

INFOGRAPHIC B – (Due
Week 3 Sept. 21)
 Find 2 infographics in the library, online or
anywhere else about any topic.
 The infographics can be about concepts,
processes, a paper, a story, and should include
data, etc.
 Cite and share an image of these in the CULearn
Forum.
 In a few words, explain why you selected these,
how you found them, why you think they are
good, discuss if there is room for improvement?
 What kind of visualization techniques did they use?
What would you do differently?

Infographic Project
 You will produce an informative, relevant, accurate, purposeful, fun, and creative
infographic about open data at the City of Ottawa.
 It can be about:
 how open data came to be,
 any dataset in the open portal,
 the open data licence, policy or directive,
 open data applications, contests,
 open government,
 key performance indicators,
 mapping,
 or crowdsourcing projects at the City.
 We will look at many examples in-class and do exercises to get you
ready. You can discuss a process, findings in the data, an issue that uses any City
data, compare things, show a dataset flowline, tell a story with a dataset, unpack
the pieces of a dataset, discuss data found in a report, etc.. It can be digital or it
can be done by hand.
 See curated resources for you here https://traceyplauriault.ca/dataviz/.
 To ensure your success you will have a small activity every week that helps you
build up to the final project and these will be posted in the CULearn Class
Forum.

Next Week

Week 3 (Sept. 21) – Open Data & Guest
Lecture Darrel Bridge, City of Ottawa
 Kitchin, Rob,
(2014) Open
and Linked
Data,
chapter 3 in
the Data
Revolution,
Sage.
Open Data Videos Resources:
 Open Data City of Edmonton
 https://www.youtube.com/watch?v=Yuh_pnuIiGU
 City of Ottawa Smart Cities Challenge
 https://www.youtube.com/watch?time_continue=79&v=gvpZdNpFLK4
City of Ottawa Open Data Resources:
 City of Ottawa – Open Data Council Report (May 12, 2010)
 http://ottawa.ca/calendar/ottawa/citycouncil/occ/2010/05-
12/csedc/08-ACS2010-COS-ITS-0005-Open%20data%20(2).htm
 Municipal Freedom of Information and Protection of Privacy
Act
 https://www.ontario.ca/laws/statute/90m56
 City of Ottawa Accountability & Transparency Policy
 https://ottawa.ca/en/city-hall/your-city-government/policies-and-
administrative-structure/administrative-policies#accountability-and-
transparency-policy
 City of Ottawa Smart City 2.0
 https://documents.ottawa.ca/sites/documents.ottawa.ca/files/smart_cit
y_strategy_en.pdf

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