Plenary panel discussion held at the 15th International Conference on Location-based Services. 11 November 2019 | Vienna, Austria

3. ICA Joint Commission Research Agendas: Some Examples

13. Cognitive Issues of Mobile
Cartography:
A Focus on Mobile Map Users
Amy L. Griffin (RMIT)
Tumasch Reichenbacher (UZH)
Liao Hua (HNU)
Wang Wangshu (TUW)
Wang Shengkai (BNU)
Cao Yinghui (QU)
LBS 2019, TU Wien,
11 November 2019

14. Relevant context elements
Griffin et al., 2017

15. Context Factors
Users
Spatial abilities
Disability
Affective and emotional
responses
Other individual
differences
Environment
Physical environment
Level of distraction
Activity
Task
Time-of-use
Individual/collaborative
Map
Characteristics of
information used in the
map
Map display device
Representation type
Interface components
employed
Interaction mode

16. Credit: Reichenbacher 2019

17. Challenge 1:
• What additional cognitive challenges
become important when mobile maps
require or support interactions with other
people, machines/sensors, spatial
information, or places?
• Opportunities?

18. Challenge 2
• How can we best synthesize the
evidence provided by field and lab
studies to provide valid information
about mobile map use and users?
• Opportunities?

19. Challenge 3
• Do people need a hybrid type
of map that is neither
reference nor thematic to help
them better understand places
for use cases like wayfinding,
entertainment, disaster
response, etc.? [deep mobile
maps?]
• Opportunities?
Credit: Anton Thomas, North America: Portrait of a Continent

20. Map-based Mobile Services - Data
Modeling and Processing
Haosheng Huang, Yi Cheng, Weihua Dong, Georg Gartner,
Jukka Krisp, Liqiu Meng
LBS 2019, Vienna, 11 Nov 2019
Research Challenges and Opportunities

21. Data in map-based mobile services
Data about mobile
users and their context
Data about the geo-
social environment
e.g., affordance of
the environment,
what can/do people
do there, what is
happening there, ...
e.g., user tasks, user
preferences, activity
she is doing, past
behaviors, mobile
devices, …
Adapted from Nivala, A.-M. and Sarjakoski, L.T. (2003)

22. Data processing pipeline
in map-based mobile services
Data about mobile
users and their context
Data about the geo-
social environment
Data Processing:
reasoning, filtering
and adapting
Relevant
information to be
communicated to
end users
Mobile maps, AR, XR, …
Overall question: How to derive relevant information that best fits a
user’s cognition capacity for her task/activity and current context
Guided by
“Mobile
Cognition”
Map design
Providing answers for “What are
needed to be communicated to
the user, in which form”

23. Mobile maps vs. GIS and web map applications
• Distinct characteristics of map-based mobile services
o Users and tasks: Users often have limited GIS expertise, and often
employ mobile maps to support their daily activities in space
o Dynamic and mobile: Often used in a dynamic and mobile
environment, and via mobile portable devices
o Context-awareness: Aware of the context their users are currently in,
and can adapt the information and their presentation accordingly

24. How do these distinct characteristics matter?
• Distinct characteristics of map-based mobile services
o Users and tasks: Users often have limited GIS expertise, and often
employ mobile maps to support their daily activities in space
 User profiling and task modeling
 “Naïve Geography”: quantitative & qualitative representation of
geo-social environment
 Privacy-preserving and ethically-aware data modeling and processing
o Dynamic and mobile: Often used in a dynamic and mobile
environment, and via mobile portable devices
 Modeling the dynamics of geo-social environment and user context
 Modeling the cognition capacity of users for time-critical tasks
 Distributing of data and processing over mobile devices and the cloud
o Context-awareness: Aware of the context their users are currently in,
and can adapt the information and their presentation accordingly
 Context modeling and context-aware adaptation
 Ubiquitous positioning

25. Key issues

26. Challenge 1: How can the geo-social
environment be modeled to effectively
support map-based mobile services?
Opportunities (potential key topics addressing the challenge)
o Indoor/outdoor seamless spatial modeling (high definition)
o Linked geodata and semantic web
o Modeling urban semantics and mobility
o Computational place modeling
o Ambient spatial intelligence (Geospatial IoT)
o “Naïve Geography”: quantitative & qualitative data modeling
o Mapping geo-social environments for visually-impaired or mobility-impaired
o …
Physical
layer
Social
layer
• Both physical and social environment
• Social layer: How people behave in, use, experience
and perceive the environment

27. Challenge 2: How can context of a mobile
user, as well as its dynamics be modeled?
Opportunities (potential key topics addressing the challenge)
o Ubiquitous positioning (e.g., indoor and outdoor)
o “Contextualizing” location (towards semantic location)
o User profiling and task modeling
o Sensor fusion for context modeling and inference
o Context reasoning (for deriving high-level context)
o Activity recognition and prediction
o Indoor behavior modeling
o Interoperability of context data (among different apps)
o …
• There is more to context than location.
Adapted from Nivala, A.-M. and Sarjakoski, L.T. (2003)
Adapted from Kessler et al. (2009)

28. Challenge 3: How can relevant information
be derived, matching the user’s tasks, and
context?
Opportunities (potential key topics addressing the challenge)
o Theory: relevance modeling
o Techniques of context-aware adaptation: similarity-based, collaborative filtering,
machine learning
o Time geography
o Trajectory-aware adaptation
o Uncertainty-aware reasoning
o Level of automation in the adaptation process
o Distributed computing: load balancing between mobile devices and cloud
(“Where to process?”)
o …
Personalization and context-
aware adaptation

29. Challenge 4: How can users’ (geo-)privacy be
preserved during data modeling and processing?
How can ethical issues be better addressed?
Opportunities (potential key topics addressing the challenge)
o Theory: (Geo-)privacy (e.g., Keßler & McKenzie 2018)
o Modeling the trade-off between service quality and (geo-)privacy
o GDPR compliant data processing (e.g., Atael, Degbelo & Kray 2018)
o Privacy-preserving techniques
o Ethically-aware data processing
o Governance: methods to identify privacy violations, mechanism to validate
privacy-preserving data processing
o Guideline on consequence ethics (training)
o …

30.  Enable 4A (Anywhere, anytime, for anyone and
anything) map services
Thank you!
Haosheng Huang (haosheng.huang@geo.uzh.ch)

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