Talk at a workshop for the RECOGNITION project on 12 April 2011 at Cardiff University

1. Map-based orientation in urban
environments
David Peebles
Department of Behavioural and Social Sciences
University of Huddersfield
12 April 2011

2. Introduction
• The project
• Funded by OS (2005/08)
• Aim: ACT-R model of
map-based orientation
• Output: Data and high-
level process model
• This talk
• Define orientation
• 2 studies investigating
orientation strategies
• Implications for map
design & understanding
orientation
• A process model of
map-based orientation

3. Map-based orientation
• Key features:
• Urban environment
• Using an OS map
• Goal: identify which way
you are facing on map
(i.e., align map and
environment)
• Aim to discover which:
• Cognitive processes and
strategies used
• Map and environmental
cues used
• Map and environmental
factors influence
performance

4. An example

5. Why is this interesting and useful (particularly to OS)?
• Cognitive Science
• Complex real-world spatial reasoning task
• Involves matching egocentric and allocentric mental
representations
• Requires interaction between cognitive and perceptual
processes
• Practical implications — find relative importance of:
• 2D geometry (local geographic layout) represented on map
• If important then use semi-automated cartographic
interventions analysing mapped layout
• Add landmarks map in places where orientation hardest
• Visual 3D scene
• If important then add orienting landmarks to maps,
particularly most visually salient in scene (Winter, 2003)

6. Orientation in real-world environments
• Previous research
• Rural landscapes (e.g., Eley, 1988; Pick et al., 1995)
• Flying perspective (e.g., Wickens & Prevett, 1995;
Gunzelmann et al., 2004).
• Single building viewed from outside (Warren et al., 1990)
• Inside building or single room (Presson, 1982; Meilinger et
al., 2007)
• Relevant findings
• Consistent evidence that people use prominent landmarks
(single features & groupings) to solve task if possible
• Less evidence that geometry of scene layout is abstracted.
Hermer & Spelke (1994) found stronger effect of geometry
than landmarks
• Urban environments are complex in terms of 3D shape and
so important to know if and how landmarks are used

7. Experiment 1
• Stimuli
• 40 (+5 practice) scenes
and maps of
Southampton
• OS MasterMap
Topography Layer at
1:1250 scale.
• Participants
• 35 University of
Huddersfield students &
staff
• Measures
• Angle and response time

8. Experiment 1 results
• Response correct if within 15 degrees of true angle in either
direction
• Performance varied between scenes:
• 9 scene/graph pairs eliciting accuracy of less than 30%
• 10 pairs accurately processed over 70% of the time
• Incorrect responses typically clustered around specific features
• Focus of analysis:
• Identify factors creating common errors
• Two independent coders – only agreement of 65% or above
included
• Four main causes of error. . .

9. Scene Object Salience (SOS)
• Very visually salient object in the scene (e.g., tall or distinctive
building) that is not unambiguous on the map.
• 33.2% of erroneous responses in 10 scenes
• People identified salient blue building but did not identify it on
map
• All erroneous responses identified alternative buildings

10. Missed Ground-level Cue (MGC)
• Unambiguous ground-level cue in the scene (e.g., traffic
calming chicane, lawn or pavement) ignored
• 25.1% of erroneous responses in 32 scenes.
• People identified road on the map
• Did not notice the pattern of pathways on either side of the road

11. Misperceived Object Distance
• Incorrect response would be accurate if object in scene was
nearer or further away
• 22.4% of erroneous responses in 22 scenes
• E & S pointing red lines below — people identified view as lying
between two buildings
• Misjudged distance of at least one building from viewpoint

12. Left/Right Reversal (LRR)
• Incorrect response would be accurate if scene was left/right
reversed
• 17.2% of erroneous responses in 26 scenes.
• N pointing red line below — object configuration is mirror image
of what is viewed in the scene

13. Experiment 1 — conclusions
• Factors creating common errors:
• SOS — strong effect of visual salience in scene on object
selection
• MOD & LRR — matching objects but ignoring (or
misperceiving) relative depth or left-right asymmetry
• MGC — may be due to map used (more detailed than typical
street maps)
• Limitations of Experiment 1
• Photographs include obtrusive unmapped objects (e.g.,
parked cars and trees)
• Uncontrolled distractors in scenes, (e.g., salient colours
or unusual objects)
• Matchable objects hidden from view

14. Experiment 1 — conclusions
• Factors creating common errors:
• SOS — strong effect of visual salience in scene on object
selection
• MOD & LRR — matching objects but ignoring (or
misperceiving) relative depth or left-right asymmetry
• MGC — may be due to map used (more detailed than typical
street maps)
• Experiment 2
• Replicate Exp 1 using simplified scenes:
• Reduce salience of scene objects
• Presence of salient 3D cue and/or distinctive 2D ground
layout cue manipulated.
• Collect verbal protocols and eye movements

15. Experiment 2
• Stimuli
• OS 3D buildings model over OS MasterMap Topography
Layer on OS Land-Form PROFILE terrain model
• 20 (+5 practice) scene/map pairs used in Exp 1
• Participants
• 49 University of Huddersfield students & staff

16. Experiment 2 results
• Errors
• 2D ground cue decreased errors (F(1, 48) = 5.47, p < .05)
• 3D cue increased errors (F(1, 48) = 40.35, p < .0001)
• Interaction effect:
• Performance improvement with 2D cue only occurred in
absence of 3D one (F(1, 48) = 5.26, p < .05)
• Response times
• 2D ground cue slowed responses (F(1, 48) = 9.28, p < .005)
• 3D cue slowed responses (F(1, 48) = 29.7, p < .0001)
• Interaction effect:
• Slowing effect of 2D cue small except where a 3D cue
was also present F(1, 48) = 4.37, p < .05)

17. Conclusions
• Experiment 2
• A strong 2D ground cue can improve accuracy (at a time
cost)
• Unless there is a salient 3D landmark
• This may confuse people as to which cue to use (slowing
them down more)
• People typically opt to use the 3D cue and make as many
errors as when the 3D cue appears on its own
• General
• Identified sources of orientation errors
• People tend to seize on visually salient objects in scene
• Even when 2D geometry would be a less ambiguous &
error-prone cue

18. Conclusions
• Experiment 2
• A strong 2D ground cue can improve accuracy (at a time
cost)
• Unless there is a salient 3D landmark
• This may confuse people as to which cue to use (slowing
them down more)
• People typically opt to use the 3D cue and make as many
errors as when the 3D cue appears on its own
• Recommendations
• Best cartographic innovation to help people would be
independent of 2D geometry
• Provide symbols at points that can be instantly and
unambiguously matched to most salient landmarks

19. A process model of map-based orientation
• A distillation of three approaches:
• Warren, Rossano and Wear (1990)
• Pick, Heinrichs, Montello, Smith, Sullivan, & Thompson
(1995)
• Gunzelmann (2008); Gunzelmann & Anderson (2006);
Gunzelmann, Anderson, & Douglass (2004)
• Key assumptions:
• Select features from scene & map (affected by visual
salience)
• Produce one or more hypotheses
• Test hypotheses
• Sample additional map/scene features
• Attempt to match until sufficient evidence accumulated to
decide

20. A process model of map-based orientation
Match
Attempt to identify
the selected feature
or configuration on the
other representation
Match found?
Testing
current
hypothesis?
Select
Select a feature
or configuration
for matching
Scan
Scan scene/map for
candidate features
or configurations to
use for mapping
Hypothesis
supported?
Sufficient evidence
to make decision?
Make
decision
Hypothesize
Generate hypothesis
about viewpoint
direction
Yes
No
No
Yes
Yes
No
Yes
No
Cognitive factors
underlying variation
in performance
Environmental factors
underlying variation
in performance
Focus initial
reconnaissance
on scene
Organise features
into configurations
Mental
imagery/rotation
ability
Generate and
evaluate multiple
hypotheses
Compare and test
hypotheses using
disconfirmation
procedure
Salience of
feature or
configuration
Scene/map
allignment
Depth cues
Response
criteria Speed/accuracy
tradeoff

21. Things are changing rapidly

22. Reference
• Davies, C. & Peebles, D. (2010). Spaces or scenes: Map-based
orientation in urban environments. Spatial Cognition and
Computation, 10, 135–156.