The stellar growth of the smartphones and nearly gapless mobile network coverage in urban and suburban areas has widely extended the potential of the citizens’ observatories. The possibility to easily record observations made by mobile citizens, and to automatically enrich this information using the built-in and external sensors sounds like a dream come true of the scientists and decision makers alike. However, in 2013 only a tiny portion of the potential users really participated in the citizen observation programs and the usability of the received information is often below expectations. The learning curve is often too high, sensor quality too low, the societal importance and the added value for the users not easily understood by the end users. Some of these obstacles can be overcome by improving the interaction with the users through crowdtasking and microlearning, others by re-assessing the application design and expectations. In this paper, we shall present the best practice examples of the mobile observation usage in applications currently available on the market. We shall discuss the scope, potentials, limitations, obstacles, and ethical issues of these applications, compare them with the apps developed by the research community and reason

1. “ENVIROfying” the Future Internet
THE ENVIRONMENTAL OBSERVATION WEB
FOR THE CROSS-DOMAIN FI-PPP APPLICATIONS
State and trends in mobile observation
applications
iEMSs 2014, June 18-th 2014, San Diego
Denis Havlik and Gerald Schimak (AIT)

2. Why VGI?
2014 Denis Havlik, AIT Austrian Institute of Technology GmbH. 2

3. Human sensors?
2014 Denis Havlik, AIT Austrian Institute of Technology GmbH. 3

4. What do we expect from users?
Panor
amio
Sports-tracker
Waze ENVIROFI Zmapuj
TO
PocketL
AI
e-mobiliTI
User Geo-ref.
photos,
Geo-ref.
photos
Hazards
reports,
map
improve
ments,
fuel
prices…
Tree occur.
and
additional
observation
s; own
reactions to
environment
Positions
of illegal
dump
sites
Geo-ref.
photos,
inclination
, weather
conditions
comments
and
explanatio
ns for
travels
System Ranking Tracks;
Speed,
calories
hearth
rate (opt.)
Car
velocity,
Routing
Plausibility,
leaf
detection,
ambient
pressure
Land
ownership
Leaf area
index
Segments;
means of
transport
Peers Comme
nts,
tags
Comment
s
Confirm
ations
Confirmatio
ns,
developmen
t over time
Confirmati
ons?
- -
2014Denis Havlik, AIT Austrian Institute of Technology GmbH. 4

5. Motivation matters!
2014 Denis Havlik, AIT Austrian Institute of Technology GmbH. 5

6. Panor
amio
Sports-tracker
Waze ENVIROFI Zmapuj
TO
Pocket
LAI
e-mobiliTI
User
Benefit
Backup,
manage
and
present
photos;
progress
and
achievem
ents;
(own,
pears)
Lower
cost and
duration
of trips
Tree
identification,
own reactions
to
environment
Higher
quality of
life;
Track
crop
growth,
estimate
yields
Improved
awareness
of own
habits
Motivation
boost
Peer
reaction
s (ego)
Own
achievem
ents, peer
reactions
Points
for
achieve
ments
tasks,
personalized
alerts,
feedback
Municipa
lity and
peer
reactions
- by project
team
Goodies Photo
album
Fitness
diary
Scaveng
er hunt
Learning
about
environment
& health
Organise
own
actions
- -
Main
benefactor
user user user,
communi
ty
community,
user
communi
ty
user project
What do users gain?
2014 Denis Havlik, AIT Austrian Institute of Technology GmbH. 6

7. On giving, taking and (t)asking
View existing knowledge
•Map view
•Table view
•Detailed View
•Areas of Interest
View existing knowledge
•Map view
•Table view
•Detailed View
•Areas of Interest
Report observations
•“New” things, e.g. “here and now I
see a tree”
•Personal, e.g. “I have a headache”
•Obs. on existing thing, e.g. “this
tree currently blossoms
Report observations
•“New” things, e.g. “here and now I
see a tree”
•Personal, e.g. “I have a headache”
•Obs. on existing thing, e.g. “this
tree currently blossoms
Receive information (events!)
•Requests for more observations,
•Warnings, e.g. “pollen warning”
•Interests, e.g. “monumental tree in
vicinity”
Receive information (events!)
•Requests for more observations,
•Warnings, e.g. “pollen warning”
•Interests, e.g. “monumental tree in
vicinity”
Inform
Server
Backend
(or proxy)
Alert!
Request
Action!
2014 Denis Havlik, AIT Austrian Institute of Technology GmbH. 7

8. Crowd(t)ask and thou shall be
given?
8
Mobile
Users
Sensors
Automated
Tasking
External
Data
Manual
Tasking
Decision
maker
Experts
Algorithms
2014 Denis Havlik, AIT Austrian Institute of Technology GmbH.
Data export

9. Crowd(t)asking at work
Waze sucesfully combines
crowdtasking, geofencing
and scavenger hunt
elements
• „are you at a petrol
station?“ => petrol
prices
• „are you in congestion“?
=> congestion info,
accidents
• „Collect the goodies on
a map“ => road
condition
(It also features geo-specific
alerts)
2014 Denis Havlik, AIT Austrian Institute of Technology GmbH. 9

10. 2014 Denis Havlik, AIT Austrian Institute of Technology GmbH. 10

11. Microlearning and VGI
Classical:
large information intake,
well in-advance to use
Illustration from Flickr, by Dean+Barb
Illustration from Flickr, by Tulane Public
Relations
Incompatible with the
modern lifestyle!
Learning by doing:
trial and error method
Illustration from: The Black Cat Diaries
OK, unless it endangers
users.
Learning while doing:
just in time intake of
information in small
portions (microlearning)
“Danger, complex diagrams ahead”
Illustration from Flickr, by Matthew Rogers
Preferably disguised as
a “game”.
2014 Denis Havlik, AIT Austrian Institute of Technology GmbH. 11

12. „Learning while doing“ examples
Objective Possible approach
How to use the application? Tooltips or popup messages
on first use (implemented)
Training to recognise
objects
Scavenger hunt for known
and tagged objects
Learn to avoid
misidentifications
control questions &
feedback
A-posteriori feedback Notify user when more info
on the object is available
(implemented)
Classify data & assess
users knowledge
Generalized re-capcha
principle
Reviewing, preparing for
“tests”
Generalized flashcards
2014 Denis Havlik, AIT Austrian Institute of Technology GmbH. 12

13. 2014 Denis Havlik, AIT Austrian Institute of Technology GmbH. 13

14. 2014 Denis Havlik, AIT Austrian Institute of Technology GmbH. 14

15. Acknowledgements
1. The authors are in no way affiliated to any of the
programs used in comparison tables (except
ENVIROFI)
2. The ideas presented today were developed in the
scope of the European Community's Seventh
Framework Programme (FP7/2007-2013) under
Grant Agreement Number 284898 (ENVIROFI)
3. Our R&D currently concentrates on crowdtasking
for crisis management in the DRIVER project
(Grant Agreement Number n° 607798).
• Micro-training and tasking according to concrete
needs, users knowledge and capacities is a must in
crisis management applications.
2014 Denis Havlik, AIT Austrian Institute of Technology GmbH. 15

16. Thank you for your attention
Dr. Denis Havlik
denis.havlik@ait.ac.at
www.envirofi.eu
The research leading to these results has received funding from the European Community's Seventh
Framework Programme (FP7/2007-2013) under Grant Agreement Number 284898