Disorientation and GIS-Informed Wilderness Search and Rescue

Abstract

Nowadays, Wilderness Search and Rescue (WiSAR) operations revolve around the creation of probability maps using GIS planning tools (Doherty et al, Appl Geogr 47:99–110, 2014). Although this method has proven effective, there is a missing link between WiSAR theory and advances in other fields related to disorientation (e.g. psychology and neuroscience). A unified conceptualisation of disorientation is a crucial element for understanding the mind and behaviour of disoriented subjects. The central aim of this chapter is to explore how a unified conceptualisation of disorientation can contribute to GIS-informed WiSAR theory. The paper will open with a review of the work on disorientation coming from different fields, to then introduce the conceptual work that is needed for a unified account of disorientation. We will discuss two different approaches to WiSAR theory: a ring model and a Bayesian model. We end on a discussion on how conceptual work on disorientation and GIS-informed WiSAR theory can help each other advance.

Full text

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Disorientation and GIS-Informed
Wilderness Search and Rescue
Pablo Fernandez Velasco and Roberto Casati
Abstract Nowadays, Wilderness Search and Rescue (WiSAR) operations revolve
around the creation of probability maps using GIS planning tools (Doherty et al,
Appl Geogr 47:99–110, 2014). Although this method has proven effective, there is
a missing link between WiSAR theory and advances in other fields related to
disorientation (e.g. psychology and neuroscience). A unified conceptualisation of
disorientation is a crucial element for understanding the mind and behaviour of
disoriented subjects. The central aim of this chapter is to explore how a unified
conceptualisation of disorientation can contribute to GIS-informed WiSAR theory.
The paper will open with a review of the work on disorientation coming from different
fields, to then introduce the conceptual work that is needed for a unified account of
disorientation. We will discuss two different approaches to WiSAR theory: a ring
model and a Bayesian model. We end on a discussion on how conceptual work on
disorientation and GIS-informed WiSAR theory can help each other advance.
Keywords Disorientation Wilderness search and rescue Unified account of
disorientation

Disorientation Review
Again something dark appeared in front of him. Again he rejoiced, convinced that now it was
certainly a village. But once more it was the same boundary line overgrown with wormwood,
once
more the same wormwood desperately tossed by the wind and carrying unreasoning terror to his
heart.
–Tolstoy. Master and Man
Tolstoy’s story of a disoriented man in a snowstorm is not far off from actual reports of
lost person behaviour (Tolstoy 2015). After realising that he’s been walking in circles,
Vasili Andreevich tries to keep calm, but assailed by a mounting panic, he finds himself
unable to collect himself and stay put and instead starts to wander aimlessly, which is a
typical lost person behaviour (Hill 1998). In 1998, Kenneth Hill conducted over a
hundred interviews with subjects who had become lost in the wilderness, and
constructed a list of typical disoriented behaviour that has now become a central
reference in the study of lost person psychology.
Hill claimed that when lost, people followed (at least) one of the following patterns and
strategies: random travelling (often caused by confusion and high emotional arousal),
route travelling (i.e. following a given route in the hope of finding something familiar),
direction travelling (trying to follow a given direction), route sampling (using an
intersection as a base for exploring different routes), direction sampling (using a visible
landmark as a base for exploring different directions), view enhancing (aiming for a high
position in order to gain visibility), backtracking, using folk wisdom (e.g. follow streams
to civilisation) and staying put (which according to Hill is the most effective strategy if a
SAR operation is triggered).
There has been some work relating this taxonomy of lost person behaviour with
different categories of lost subjects (e.g. hunters are more likely than others to use
direction sampling, while hikers are more likely to use route following). Neverthe- less,
there isn’t any work relating the taxonomy to a broader conceptualisation of
disorientation (e.g. how disorientation arises and what makes subjects follow one
method over another) or to the work in other fields such as neuroscience.
In an effort to establish a link between the behavioural psychology and the neuro-
science of disorientation, Dudchenko holds that humans, unlike other animals, need
vision to stay oriented (Dudchenko 2010). This makes sense because landmarks are used
to update one’s orientation and position within a cognitive map (Scholl 1987; Knierim
and Hamilton 2011). The notion of cognitive maps was originally developed by Tolman as
a hypothesis to explain rat behaviour in labyrinths without reducing the said behaviour to

stimulus–response connections. Tolman supports the view that rats construct something
similar to a field map with results from different experiments, including shows of initial
latent learning that gets activated when reward is offered later (Blodgett 1929), rats
sampling of the environments (Tolman 1938) and rats finding shortcuts that they had
not learned through stimulus–response conditioning (Tolman 1948).
The discovery of place cells in the rodent hippocampus further supported the
idea of a cognitive map (O’Keefe and Dostrovsky 1971), which has now become central
in the study of spatial cognition. Place cells fire as a function of the rodent’s spatial
location. Other cells that may play a role in navigation are grid cells (which fire in a
hexagonal grid that corresponds with the environment floor, where the grid module is
correlated with the size of the animal; Hafting et al. 2005), head direction cells (which
fire depending on head orientation; Ranck 1985; Taube et al. 1990) and boundary vector
cells (which fire when the rodent, moving in a certain direction, gets to a specific
distance from a boundary element of the environment; Barry et al. 2006).
An observation is in order. Different classes of spatial features are coded by different
cell systems, as per the following table:
Cell type
Geometric feature
coded
Example
Place cells
Individual locations
The entrance of the maze, the fork, etc.
Grid cells
Metric properties
Distance covered based on number and
size
of grid modules
Head direction cells
Angles
Azimuth of a landmark relative to
body’s
central plane of symmetry
Boundary vector cells
Topological properties
The middle of the room, close to the wall
This suggests that the spatial representation system could work in a constraint
satisfaction mode, looking for solutions that take into account whichever topological,
metric, angular and location representations are available at a given moment.
Of course, an open question is if the same type of cells and the same type of cogni- tive
map exist in humans. Although the invasive single-cell recording techniques that are used
in rats have not been used in humans, there is some empirical literature to support the
idea that work on rodent spatial cognition can be extrapolated to humans to a substantial
degree (see Epstein et al. 2017 for a review). For instance, a much quoted fMRI study of
taxi drivers in London shows that they acquire larger right posterior hippocampi as a
result of the years-long training they undertake to learn the map of London streets

(Maguire et al. 2000).
If the underpinnings of human spatial cognition do not differ substantially from those
of rodent spatial cognition, then the brain can store different maps for dif- ferent
environments, and even represent different conditions of the same environ- ment.
Different place cells would then fire for different environments, following a ‘global
remapping’ as the subject changes environments. When the same envi- ronment changes
conditions, the place cells that fire stay the same, but the rate at which they fire changes
(Colgin et al. 2008). It should be noted that landmarks play an important role in
anchoring these cognitive maps (Yonder et al. 2011). Finally, when it comes to
navigation, the grid cell network computes the direction and dis- tance to the
navigational goal (Kubie and Fenton 2012), while the hippocampus and entorhinal cortex
work together to calculate the optimal path to the goal (Bryne et al. 2007).
It is beyond the scope of this paper to explore in-depth the empirical literature
on spatial cognition, but in addition to the brief review above, we should refer to some
of the work that is of special relevance for the study of disorientation. An example of
this work is the experiment exploring what happens to the rodent’s place field map and
HD cells when disorientation occurs. The geometry of the environment (disregarding
useful non-geometric cues) determines the realignment of the cognitive map, which
determines in turn the behaviour of the animal during navigation (Keinath et al. 2017). An
interesting distinction is to be made between orientation and context retrieval. Orientation
refers to one’s location and direction within a cognitive map of a given place, and
context retrieval refers to the retrieval of the specific map that is appropriate for the
place in question. In mice, context retrieval and orientation have been found to be
mediated by dissociable cognitive processes, and it is not inappropriate to think that the
same could be the case for humans (Julian et al. 2015). Although there is an increasingly
clear picture of human orientation coming from spatial cognition, the study of lost
person behaviour is not connected to this body of research. The result is that there is
no unified account of disorientation that can draw on the different fields that deal
with the phenomenon. Conceptual work is needed to link spatial cognition and the
psychology of lost person behaviour (see Casati and Fernandez Velasco, forthcoming).
Establishing this link will advance the understanding of disorientation and will allow
WiSAR to profit from the progress
coming from a broad variety of disciplines.

Disorientation: Conceptual Issues
There are several impediments that keep the different fields concerned with disori-
entation from associating. An important one is that these fields operate at different
levels of analysis. Even within neuroscience, where most explanations appertain to
the neural level, there is a chasm between the single-cell study that is possible in non-
human cognition and the non-invasive neuroimaging studies done in human cog- nition.
Moreover, functional magnetic resonance imaging cannot be done while sub- jects travel
physically through space, which restricts investigation to non-ecological, purely
represented settings. Although some lost person psychology relies on first person
reports (Hill 1998), for the most part it is concerned with the third-person analysis of
behaviour. In the case of WiSAR, the separation from spatial cognition goes one step
further, as most of WiSAR work operates at the level of statistical patterns emerging
from lost person behaviour (Koester 2008).
Another important element that separates these fields is that they tend to draw from
different experimental and study settings. A big part of the work in psychology takes place
in non-ecological conditions (see Li, Mou and McNamara 2012 for an exam- ple).
WiSAR theory is based on ecological non-experimental conditions (i.e. analysis of
existing cases of lost people), and the work in cognitive science is usually simula- tion
based (Ruddle et al. 2011). Finally, as mentioned above, neuroimaging studies are
stationary, and thus based on virtual reality, spatial recollections or imagined
navigation. Consequently, in order to extrapolate results from one field to another, one
has not only to extrapolate from one level of analysis to another, but also from one
setting to another (e.g. from simulations to the actual behaviour of people lost in the
wilderness).
A symptom of this divergence among fields is a divergence among characterisa- tions
of the phenomenon of disorientation itself. Disorientation could be understood, among
others: as being unable to find one’s way (Dudchenko 2010); as the failure of the way-
finding process (Golledge 1999); or as not knowing the directions and distances to get
to a given point (Rieser 1999). These all seem to point to the same phenomenon, but it
should be noted that they each characterise disorientation in a proprietary way: as an
issue of ability, failure or knowledge, respectively.
Although the above characterisations each capture some of the features of disori-
entation, they all have certain shortcomings. A central issue here is that while being lost
in an objective condition, disorientation is a strongly subjective one. Being lost can be
easily characterised as being unable to find one’s way, but this characterisation does not

necessarily extend to disorientation. Take the following case, for instance:
Maggie is following a path through the woods that leads to the Dorlcote Mill. The path is very
hilly and sinuous, changing directions often, which makes Maggie feel disoriented.
Disregarding her disorientation, she keeps going and eventually gets to the Dorlcote Mill.
In this case, Maggie is not lost (i.e. she is able to find her way), but she is disori-
ented; a distinction that Duchenko’s characterisation fails to capture. And while Dud-
chenko’s characterisation fails to capture some cases of disorientation, Golledge’s
characterisation overshoots, mistakingly identifying certain cases of being lost as
disorientation. Let us look at the following example:
Pierre is confident that he is at Place Denfert Rochereau, when in fact he just emerged at the
Port
Royal crossing, because he skipped one metro stop.
Here, Pierre’s way-finding process failed (he failed to keep track of the stations when
he was in the metro), but he doesn’t feel disoriented. Golledge would charac- terise
Pierre as being disoriented, but a more precise characterisation would identify Pierre as
someone who is lost, not disoriented. Finally, below is a last case that raises some
problems for Rieser’s characterisation:
John is a tourist in Paris. He is outside the Metro stop Odeon and he wants to go to walking
to
Place de la Concorde. He is told that if he follows Boulevard St. Germain and then crosses the Seine
he will get there. Boulevard St. Germain curves north as it advances west, but this
is not a problem
for John. He just follows the street without knowing the exact direction of Concorde. He ends up
at the Seine, crosses the river and is at Place de la Concorde.
In the case above, John doesn’t know the direction or the distance of his destina- tion,
but he is neither lost nor disoriented. Overall, there are many elements related to
disorientation, such as knowledge of distances and directions, the way-finding process
or navigational ability. However, casting disorientation in such terms over- looks that
disorientation is primarily a feeling. Characterising disorientation as a feeling captures
the subjective aspects of disorientation, and it can serve to develop a conceptualisation of
the phenomenon that helps us understand how elements such as a failure in the way-
finding process can lead to the emergence of the feeling of disorientation.
Elsewhere, we have proposed to sharply distinguish being lost and feeling disori-
ented and have argued that disorientation should be better understood as the metacog-
nitive feeling of unconfidence in the subject’s online spatial representation (Casati and
Fernandez Velasco, forthcoming). That is, disorientation is a feeling that evaluates the level
of confidence in the cognitive subsystem responsible for spatial representation. Bridging

the gap among these different fields is not only an empirical endeavour, but a conceptual
one. An important first step is a characterisation of disorientation that

can be effectively applied to different fields. As for the gap between levels of analysis, it can be
bridged indirectly. That is, results at one level can be used to speculate and produce
hypotheses at other levels. Ultimately, one would want to have an overall theory of
disorientation that draws on the progress from the variety of fields dealing with disorientation
into a unified conceptualisation of the phenomenon that can serve to generate testable
hypothesis for different settings and levels of analysis.
In what follows, we will look at different models that have been developed for
WiSAR theory, and speculate how novel conceptualisations of disorientation can
contribute to the models generating better predictions.
GIS-Based WiSAR Models
When a person becomes lost in the wilderness, the WiSAR manager faces a difficult
challenge, which is to figure out where the different SAR teams are more likely to locate
the lost person. The areas that need to be covered are often vast areas of difficult terrain, and
the SAR team needs to coincide with the lost person not only in space, but also in time (i.e. if
the SAR team and the lost person pass the same place at different times the lost person will
not be found). These difficulties should not be overlooked. WiSAR operations often have
limited resources, and past 2 days, or about 50 h of search, the lost person’s chances of
survival decrease significantly (Adams et al. 2007).
To guide the search effort, the most common strategy is to develop a probability map
stemming from the Initial Planning Point (IPP), which is usually the point where the lost
person was last seen or known to be (i.e. relying on cues). Geographic Information Systems
(GIS) provide WiSAR the planning tools (such as the ArcGIS planning tool, see Ferguson
2008) to create and update probability maps (Doherty et al. 2014). Here, we will discuss two
methods of establishing probability maps: a ring model and a Bayesian model.
The ring model, based on mathematical search theory (Koopman 1980), is the most common
way of establishing probability areas. The main dependent variable of the ring model is
Euclidean distance from IPP. The statistics of past WiSAR cases are analysed to produce four
concentric rings stemming from IPP. These rings represent probability areas, and are
established at the distances equivalent to the probability quartiles 25, 50, 75% and then at
95%. That is to say, within the first concentric ring, the probability of finding the lost person
is 25%, within the second concentric ring, the probability of finding the lost person is 50%
and so on.
An important finding for WiSAR is that different subject categories correspond to different

probability distributions. For example, the 25% probability area of a hiker is much larger
than that of a child, because the hiker can cover bigger distances and tends to follow either
trails or directions rather than staying put or walking randomly. Syrotuck first analysed 242
cases from New York and Washington states and established probability distributions for eight
subject categories (Syrotuck 2000). Koester has recently created ISRID, a large database
unifying thousands of cases from around the world and dividing them into 41 categories based on
scenario, age, medical or mental status, and activity (Koester 2008). Although other
parameters (such as the type of terrain) can be added, the subject category is the main
determinant of the distribution of the probability map that will guide the search efforts.
A recent development in WiSAR is Lin and Goodrich’s Bayesian approach (Lin and
Goodrich 2010). The main parameters of their model are the topography, vege- tation
coverage and local slope of the terrain. One of the innovative elements of their model is that it
uses expert opinions to determine the probability of the lost subject transitioning from one
terrain type to another (e.g. a WiSAR manager’s estimation of the likelihood of a given lost
subject moving from a plain to a hill). This opinion- based probability is expressed in the
form of mean and variance (to account for uncertainty). These probabilities are used as priors
to create a state transition matrix (specifying the probability of the subject transitioning from
each state to all other states) from which to generate a predictive probability distribution map.
As a person can only travel to adjacent cells, all of the cells that are not adjacent to the
current cell have probability zero. Of course, as the person moves, new cells will become
adjacent. Assuming a process in which only present states affect the probability of future
states (i.e. assuming a first-order Markov process), it is then possible to predict the lost person’s
trajectory as time progresses (i.e. the continuous posterior beliefs of the lost person’s
position at different times).
While in the ring model, the terrain is divided in concentrical rings, in Lin and Goodrich’s
model the whole terrain gets divided into tessellated hexagons. In their hypothetical scenario,
these hexagons are 24 m wide. Each state in the transition matrix is a hexagonal cell in the
tessellation. Each row in the matrix is composed of the transitional probabilities of
transitioning from one cell to each cell in the tessellation (and of staying in the same cell).
Given a point at which the person was last seen (in which the probability of the person being
in that cell at the time when she was last seen is (1), it is possible to compute the probability
of the person being in different cells at different points in time, thus generating a probability
distribution map to guide WiSAR efforts.
One advantage of Bayesian models is that existing observations (e.g. GPS track logs and
their associated terrain) can be used to update prior beliefs and thus reduce experts’

uncertainty. It is impossible to determine the continuous posterior beliefs about the lost
person’s position in closed form, so Lin and Goodrich employ a Markov Chain Monte Carlo
(MCMC) algorithm to approximate the posterior distribution needed to generate the updated
probability distribution map (following the methods of Gelman et al. 2013). The MCMC
method consists of a massive iteration of different transformations from prior distributions
(based on expert opinion) to observations (based on GPS track logs) to generate a series of
distributions (21 in their hypothetical scenario) that approximate the posterior distributions
(see Lin and Goodrich 2010 for mathematical detail). These posterior distributions are used
in the place of the original prior distributions to update the probability distribution map.
To conclude this section, the table below sums up some of the distinctions between Koester’s
ring model and Lin and Goodrich’s Bayesian model:
Model
Sources
Updating
Terrain subdivision
Ring model
Actual cases (ISRID)
Based on time
Concentric circles
Bayesian model
Expert opinion
Based on both time
and observations
Hexagonal tessellation
Conceptualising Disorientation for GIS WiSAR
The traditional ring model of WiSAR seems to be ‘blind’ to different conceptual- isations of
disorientation. What we mean by this is that what is used to generate the probability map
is the statistics of different subject categories, which are unaf- fected by different models of
lost person behaviour. For example, what determines the distribution of the probability map
is not whether a subject in a given category is perceived to be more likely to give preference
to a given reorientation strategy, but simply how far from the IPP subjects in the same
category were found in previous WiSAR cases. However, this doesn’t mean that
conceptualising disorientation plays no role in WiSAR operations that use the ring model.
After all, WiSAR teams use their beliefs about lost person behaviour (based on a certain
conceptualisation of disorientation) to prioritise the search within the sub-areas of a given
probability ring.
One of the ways in which conceptual work can help improve the ring model itself is by
determining which subject categories should be used for generating the probability map.
From a cohesive theory of disorientation, one can hypothesise the existence of new
categories, and then use GIS to see if these categories are statisti- cally relevant. Furthermore,
a good conceptualisation of disorientation is expected to generate predictions as to what

parameters (e.g. age, terrain…) might be statistically relevant for determining new subject
categories.
Progress in the conceptualisation of disorientation is much more promising when it comes to Lin
and Goodrich’s Bayesian model. In their example, expert opinion is used to generate a prior
distribution regarding the transitions between three dimensions of terrain (vegetation, slope
and topography). What is promising is that their method offers a way to incorporate different
sources for the generation of a prior distribution. One such source could be a unified theory of
disorientation or the opinion of an expert informed by such a theory.
An adequate conceptualisation of disorientation, together with Lin and Goodrich’s Bayesian
model offers a way in which developments coming from a broad range of fields can be
exploited for improving GIS-informed WiSAR operations. With a working theory of
disorientation, knowledge about the phenomenon could then be used for establishing the prior
distribution that determines the initial probability map. What is more, such distribution could
be updated with GIS observation, by using the observations such as GPS logs to generate a
posterior distribution. Such posterior distribution could then be used to discover elements of
disorientation that have been overlooked by the overall theory, thus highlighting ways in
which the theory can be improved.
Lastly, conceptual work can help us improve the models themselves and choose between
different models for different scenarios. For instance, Lin and Goodrich’s Bayesian model
assumes that the lost person’s travelling resembles a first-order Markov process, in which
only present states affect the probability of future states. This might not be the case for route
sampling, a strategy that involves the lost subject starting on an intersection and following
different routes for a while, always returning to the intersection, as a way to explore the
surrounding terrain. In this case, there is a certain length that the subject is likely to follow
(say 1 km), so that the likelihood of the subject turning around is less likely after she has
walked 10 m than after she has walked 10 km on the same path. The travelling of such an
individual would not resemble a first-order Markov process. It might well be that Lin and
Goodrich’s model works better for children than for hikers (as hikers are more prone to route
sampling), and a good theory of disorientation would give us the conceptual apparatus to tell
if this is the case.
Conclusions
We opened this paper by reviewing the progress in different fields related to disori- entation,
such as lost person psychology and the neuroscience of spatial cognition. We then outlined

several challenges to unite these separate fields (i.e. differences in levels of analysis and
differences in settings) to show that conceptual work was needed to connect these fields. We
defended the idea that an adequate conceptuali- sation of disorientation would permit us to
use the findings in one field to produce hypothesis in a different field. Afterwards, we
reviewed two different methods of GIS-informed WiSAR (a ring model and a Bayesian
model) to then analyse how an adequate conceptualisation of disorientation could help
WiSAR profit from the developments in a broad range of research areas. We concluded that
while for the ring model conceptual work can only help develop new subject categories, for
the Bayesian theory conceptual work can be used to generate more accurate probability maps.
Furthermore, these Bayesian-based probability maps can in turn be used as input for
improving our theories of disorientation. Finally, the main claim of our contribution is that
conceptual work can help us improve the various WiSAR models (by examining their
underlying assumptions) and choose between different models.
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