Spatial approaches to information search
and Ed Parsons
Birkbeck, University of London, London, UK;
University of California, Santa Barbara, CA, USA;
Searching for information is a ubiquitous activity, performed in a variety of contexts and
supported by rapidly evolving technologies. As a process, information search often has a
spatial aspect: spatial metaphors help users refer to abstract contents, and geo-
referenced information grounds entities in physical space. Although information search
is a major research topic in computer science, GIScience and cognitive psychology, this
intrinsic spatiality has not received enough attention. This article reviews research
opportunities at the crossroad of three research strands, which are (1) computational, (2)
geospatial, and (3) cognitive. The articles in this special issue focus on interface design for
spatio-temporal information, on the search for qualitative spatial conﬁgurations, and on a
big-data analysis of the spatial relation “near”.
cognitive search; geographic information retrieval; information search; spatial information
Information search is a major component in many human activities. Web
search engines process billions of queries every day and determine the visibility
and accessibility of much online content. Scientists search for meaningful
patterns in increasingly large datasets, while consumers search for products and
services among many available options. The search for information has been
tightly intertwined with a spatial dimension (Todd, Hills, & Robbins, 2012).
Human and artiﬁcial agents traverse heterogeneous information spaces
searching for entities and their relations, in an analogy with how biological
organisms explore their physical environment to search for sources of
In this sense, there is a spatial component at the core of information search.
Most search technologies rely on spatial metaphors: for instance, we refer to
going to websites to search for fragments in an overwhelmingly large abstract
space of messages, documents, images, and videos. Computing technologies
spatialize abstract pieces of information into tangible interfaces and layouts.
The physical geographic space grounds information and helps reﬁne search
strategies, relying on the location of entities on the Earth’s surface to assess
Author copy – Spatial Cognition & Computation, 2016
Although this spatial dimension of information search is pervasive in
many disciplines, including computer science, geographic information science
(GIScience), and cognitive psychology, there has been limited interaction and
cross-fertilization of these ﬁelds. Hence, this special issue explores precisely the
spatial dimensions of, and approaches to information search from several
interdisciplinary perspectives. To deal with this broad area of inquiry, we focus
on the interplay between computational, geospatial, and cognitive research
strands (Ballatore, Heggarty, Kuhn, & Parsons, 2015). These strands are
thoroughly interconnected, and we do not propose them as a clear thematic
partition, but rather as centers with porous peripheries.
By the computational strand (1), we refer to approaches to information search
that are grounded in mathematical formalization and algorithms (see Section 2).
Starting from seminal work in artiﬁcial intelligence (Russell et al., 2009), the
computational strand focuses on developing efﬁcient methods to explore
large information spaces, when exploring all possibilities is not feasible.
Computational approaches have radically transformed information search,
resulting in the engineering of database management systems and search
engines, in the rich area of information retrieval. More recently, the explosion
of “big data”has opened up novel informational spaces, characterized by
heterogeneity and varying levels of semantic structure.
The geospatial strand (2) hinges on a particular search space, i.e. geographic
space, intended as the space near the surface of the Earth. This space is
particularly important as it provides a uniﬁed ground to anchor disparate
pieces of information, enabling search for information related to human and
non-human phenomena occurring in space and time, from the local to global
scale. Both computer science and GIScience have engaged with search
techniques tailored to geographic dimensions of information (Murdock, 2014;
Jones & Purves 2008). In particular, the area of geographic information
retrieval (GIR) has tackled computational challenges such as the determination
of geographic relevance of text documents, and the disambiguation of place
names (Section 3).
Finally, the cognitive strand (3) takes a different tack on information search,
focusing on how the human cognitive apparatus searches for information both
in physical space, for example using mechanisms of visual search (Eckstein,
2011), and how it retrieves information from memory (Todd et al., 2012).
Knowing how humans perform information searches is arguably crucial to
design better information retrieval systems, to support interaction design and
geovisual analytics, and to spatialize abstract spaces effectively (Pirolli, 2007).
A complementary issue concerns the impact of increasingly pervasive
search technologies on how humans cognize the physical-geographic reality
Without pretension to exhaustiveness, the remainder of this article identiﬁes
themes and threads at the intersection of these broad disciplinary areas,
highlighting linkages, synergies, fractures, and, above all, promising research
gaps that appear ripe for an interdisciplinary agenda. As observed in a specialist
meeting held in Santa Barbara in December 2014 (Ballatore et al., 2015), these
complementary perspectives can interact more extensively to reap mutual
beneﬁts for scientiﬁc and technological advances.
2. The spatial dimension of information search
In computer science and in artiﬁcial intelligence, search is seen as foundational
for problem solving and planning (Russell, Norvig, Canny, Malik, & Edwards,
2009). Problems are conceived as abstract spaces, in which intelligent
agents search for solutions, without having the possibility of an exhaustive
search, having to rely on heuristics to narrow down the search to a manageable
Papadimitriou (2014) aptly noted that many fundamental problems in
computer science are search problems, deﬁned as “given an input, call it x,ﬁnd
a solution ysuch that xand ystand in a particular relation to each other that is
easy to check”(p. 15881). Many of such fundamental search problems consist
of ﬁnding paths in spaces structured as networks (e.g., shortest path, Hamilton
path, Clique, and the Min-cut). In this context, information search is seen as a
complex process reducible to a sequence of basic computational operations
(e.g., read/write), identiﬁable through an algorithm (or, in difﬁcult classes of
problems, not decidable).
As Franklin and Andrade point out (Franklin & Andrade, in Ballatore,
Kuhn, Hegarty, & Parsons, 2014, pp. 16 –18), the remarkable increase of
available memory in computational systems is also changing which data
structures are appropriate to solve search problems. Although many retrieval
operations have become trivial, ﬁnding objects that co-occur in the search space
is still a challenge in very large databases. Research in database management
systems has generated data structures and indices to enable more efﬁcient
search, for spatial and non-spatial dimensions, such as array and nonrelational
databases, going beyond relational databases that have dominated the
landscape for 40 years (Brown, 2010). Similarly, linked data and semantic
web technologies offer a platform to integrate disparate and heterogeneous data
spaces into a uniﬁed, searchable space, structured as a dynamic network of
triples (Kuhn, Kauppinen, & Janowicz, 2014).
3. Geographic information search
Geographic space is particularly important in information search, as it pervades
informational content, providing ground for linking different data spaces.
Among all search spaces, geography emerges as a particularly important one.
Core concepts of spatial information, such as objects, ﬁelds, events, and
networks provide a suitable conceptual infrastructure to organize, integrate,
and search geographic information (Kuhn, 2012). Geospatial information has
also been proposed as a facilitator for discovery and interdisciplinary
collaboration in the context of scientiﬁc libraries (Laﬁa, Jablonski, Kuhn,
Cooley, & Medrano, 2016).
In GIScience, three dimensions of information (spatial, temporal, and
thematic) remain ubiquitous in framing the complexities of geographic
information, as well as search of geographic information (Yuan, 1999).
However, in the social sciences and the digital humanities, a new focus has
emerged on the notion of place, i.e., a socially and culturally constructed object,
rather than a merely topological and spatial entity associated with some
thematic description. For example, the description of a city as a place includes
a nexus of complex human agents, activities, processes, and relations, well
beyond the enumeration of the location of its roads and buildings. The
advantages of indexing information with respect to place is apparent for
exploratory search as well as for analysis (Grossner, in Ballatore, Kuhn,
Hegarty, & Parsons, 2014, pp. 26 –28).
As information is increasingly consumed through mobile devices, the geo-
location of the users has gained prominence to reﬁne the search process, as well
as an important element of user-generated content (Graham, Schroeder, &
Taylor, 2014). In recent years, novel sources of geographic information have
erupted, resulting in large and dynamic datasets of geo-tagged photographs,
messages, videos, and check-ins (Murdock, 2014). To extract insights from
such information and make the information more searchable, computational
models are including explicit locational information, increasing relevance and
level of personalization.
One of the most prominent efforts to support geographic information search
in GIScience can be seen in geographic information retrieval (GIR) (Jones &
Purves, 2008). This interdisciplinary area focuses on the geographic content
of text documents, harnessing concepts and techniques from computational
linguistics and natural language processing. The cogency of GIR lies also in the
increased availability of very large text corpora that contain rich spatio-
temporal information (Michel et al., 2011). A notable challenge in GIR is the
recognition and disambiguation of place names in text, which remains difﬁcult
for fully automated systems. Moreover, as pointed out by Purves (Purves, in
Ballatore et al., 2014, pp. 72–76), user interfaces for geographic search have not
improved substantially beyond the display of results as points (or polygons) on
generic base maps. Although difﬁcult to obtain, query logs in search engines are
still an unsurpassed tool to better understand how users interact and express
spatial needs on real systems. Given the still limited interdisciplinary
interaction between GIR and spatial cognition, tangible beneﬁts could be
brought about to better understand how users formulate spatial queries and
how they acquire spatial knowledge.
4. Information search and spatial cognition
As search is a fundamental activity of human and animal mental life, cognitive
psychologists have investigated the structures and processes that govern it.
As Todd et al. (2012) noted in their comprehensive survey, organisms perform
similar searches in a variety of contexts, highlighting the commonalities
(and indeed differences) between searching in visual, aural, spatial, social,
and memory spaces. The theory of information foraging draws a strong
analogy between search for food in the physical environment and search
for information in abstract and digitally mediated spaces, based on the
evolutionary assumption that search strategies evolved ﬁrst to ensure successful
physical foraging—and therefore survival (Pirolli, 2007). Spatialization is thus
an important methodology to make abstract spaces cognizable and searchable
in an intuitive way.
In a societal context, where search in digital informational spaces has
become crucial to carry out daily tasks, understanding how information search
occurs at a deep, cognitive and neural level can provide insights to build more
effective search tools. Although human-computer interaction and cognitive
psychology have a long and fruitful history (Card, Newell, & Moran, 1984), an
area where more interplay between the three strands is needed is geovisual
analytics, where visual search (Eckstein, 2011), and spatial language (Matlock,
Castro, Fleming, Gann, & Maglio, 2014) are paramount. In summary, little
interaction has occurred between the cognitive and other research strands to
systematically study and exploit the spatial dimensions of information search as
a cognitive task.
5. Challenges and opportunities
The interdisciplinary discussions at the Specialist Meeting in Santa Barbara
(Ballatore et al., 2015) have identiﬁed a number of promising research themes
and questions on information search at the intersection of the computational,
geospatial and cognitive strands. Hoping to stimulate further interest beyond
this special issue, we summarize them here.
5.1. Spaces and places
The humanistic notion of place is multifaceted and complex, and yet we cannot
easily search for places beyond very few and simplistic thematic dimensions
(e.g., “cities with more than a million inhabitants”). Better “platial”models are
needed to include the notion of place into geographic information systems,
which are traditionally (and successfully) built on topological spaces. The
challenges to place computing include the ad hoc, subjective, and mutable
nature of place. To a large extent, the information retrieval community still
ignores space and place, and more efforts from GIScience are needed to
make these perspectives more central to research on information search.
In particular, articulating and working on speciﬁc problems of place-based
search appears to be an opportunity for collaboration.
5.2. Visualization of big spatial data
To provide better organization of knowledge beyond lists of ranked documents
and traditional pins-on-maps visualizations, new visualization methods are
needed. From a cognitive perspective, knowledge about mental representations
of geographic and abstract spaces is essential to devise more effective
approaches to exploring, summarizing, and uncovering meaningful patterns in
large datasets. This challenge can beneﬁt from developments in database
technology, such as nonrelational, column, and array database management
systems in addition to research on how humans represent and search both
physical and information spaces.
5.3. Models of human search behavior
More research in cognitive psychology is needed to further illuminate the
strategies and heuristics deployed in search behavior in physical and information
spaces, which would deepen our understanding of how humans search for
patterns in stimuli and in memory. This information in turn could be used to
develop information systems that build on and augment human search abilities.
5.4. Benchmarking exploratory search
Compared with task-oriented search, the evaluation of exploratory search is
more challenging, because it is difﬁcult to establish objective criteria of
success. It would be valuable to design and curate test collections to be used
across different research communities. To date, there is a lack of benchmark
collections that allow evaluations, hindering reproducibility and comparison of
methods to explore informational spaces. The visual dimension, for example
through the collection of eye movements, can be used to evaluate users’search
strategies and behavioral patterns.
5.5. Georeferencing quality
Although commercial and open-source tools for georeferencing are available,
their quality varies dramatically. Better benchmarking and evaluations are
needed to support search for geographic information effectively. Mainstream
search engines need better topological and geographic knowledge bases to
produce more meaningful results. For example, a Google search for “distance
between Italy and France”returns 1,298 km, ignoring the topological structures
of the two adjacent countries, using their arbitrary centroids. In this sense,
deciding when a point location is adequate to solve a problem and when
extended footprints are needed is a largely unsolved problem.
5.6. Vagueness and ambiguity in spatial hierarchies and relations
Geospatial search involves the use of spatial terms, which are often intrinsically
vague and context-dependent. Notably, the deﬁnition of nearness varies
depending on the context, and place name disambiguation is a hard problem,
especially for vernacular place names not encoded in a gazetteer. As search in
the geographic domain is strongly affected by scale, organizing content in
hierarchies is beneﬁcial. However, spatial and thematic hierarchies constitute a
challenge for evaluation. These hierarchies should be made more explicit for
the user, in order to collect relevance feedback. Similarly, the development
of multiscale, context-sensitive spatial relations has the potential for greatly
improving search approaches.
5.7. Search in spatio-temporal networks
Many human and natural systems, such as urban transit and social media, can
be conceived as networks whose spatial structure changes over time. Their
properties are emerging from interdisciplinary research and novel techniques
are needed to search efﬁciently for paths, events, patterns, clusters, and outliers
in these complex networks. They will bridge established strands of network
analysis, such as social network analysis, with spatial and time-series analysis.
5.8. Effects of search technologies on spatial cognition
The pervasive availability of search technology is redeﬁning the process of
retrieval of geographic information, limiting the need for memorization. Beside
anecdotal evidence, little is known about how this new technological landscape
impacts spatial cognition. Fruitful investigations might focus on psychological
aspects, such as spatial awareness and wayﬁnding abilities, as well as on more
social, cultural, and political dimensions of how the geographic world is
collectively imagined and accessed.
5.9. Unstructured and subjective spaces
Current spatial search is largely conﬁned to structured spatio-temporal data,
and ideally search should be possible across large volumes of unstructured
spatial data, gathered from social media and other web sources (Hoffart,
Suchanek, Berberich, & Weikum, 2013). Thanks to recent advances in natural
language processing and machine learning, subjective experiences, emotions,
and opinions can become novel search spaces, unlocking new understandings
of social and urban dynamics.
5.10. Reference systems for abstract spaces
Web maps and time sliders provide a widely used mechanism to consume
information structured in the geographic space, but what about abstract spaces,
such as conceptual spaces (Gärdenfors, 2004)? We need more explicit semantic
reference systems for better ontological organization of search spaces. In this
context, the metaphor of the map projection can be deployed to represent
multiple spatial representations of the same abstract spaces, guiding the
development of coordinates systems, and the assessment of distortions in these
culturally embedded informational spaces. Cognitive research on how people
conceptualize information spaces may also lead to the development of other
5.11. Type instantiation
In geographic information retrieval (as in other searches), queries often refer to
instances of geographic entities by referring to their type (e.g., “the beach next to
University of California, Santa Barbara”when referring to Goleta Beach). Spatial
reasoning and geographic knowledge are needed to resolve this type of indirect
referencing, expanding traditional techniques of coreference resolution.
5.12. Search for aggregates and similarities
Searching for individual database records matching a set of criteria is not a
notable challenge anymore, even in very large datasets. However, the search for
complex aggregates, such as the co-occurrence of events in space and time is
still challenging, particularly when facing very large and diverse data sources.
Such aggregates include city neighborhoods, large public events, and
trajectories. Spatio-temporal datasets can also be conceptualized as special
kinds of aggregates, stored in data catalogues. In an ecological approach to
information search, the space to be searched is that of multiple interactions
between entities, stressing the need to be able to express and solve complex
queries for spatial, temporal, and thematic aggregates that emerge in physical
and abstract spaces alike. Searching for similar aggregates also represents a
worthwhile challenge, as aggregates rarely present exact structures and need
fuzzier mechanisms for comparison.
6. Summary of the special issue
The articles included in this special issue provide stimulating perspectives
centered on the spatial dimensions and approaches of information search,
addressing some of the aforementioned challenges. Bruggmann and Fabrikant
(2016) explore the potential of spatial information search for the digital
humanities. Harnessing techniques from GIScience, GIR, and the more recent
area of geovisual analytics, they propose an interdisciplinary methodology to
design usable interfaces for spatio-temporal analysis. In their case study, a text
corpus containing articles about Swiss history is processed with computational
tools. The resulting spatio-temporal references provide the data to be
consumed in an interactive interface, using spatialization to represent thematic
information, and providing detailed guidelines to design spatial interfaces,
aimed at the consumption and exploration of geographical and historical
datasets. The method successfully identiﬁes improvements for the complex
interface design, aimed at the reduction of the adoption barrier of spatio-
temporal search systems.
From the perspective of qualitative spatial reasoning, Fogliaroni, Weiser, and
Hobel (2016) focus on spatial conﬁguration search, an area largely neglected
by current geographic information systems. When a user wants to identify
conﬁgurations of objects in qualitative terms, their system solves the query by
formalizing it as a set of qualitative spatial predicates of arbitrary size. These
spatial constraints are then propagated through a hypergraph containing the
dataset expressed as qualitative predicates, identifying suitable solutions that
capture potentially very complex aggregates of spatial entities holding speciﬁc
Information needs are often expressed in ambiguous and vague natural
language (e.g., “hotels near the city center”). In their article, Derungs and
Purves (2016) take a big data approach to study these vague spatial relations
in informational and geographical spaces. Noting that the interpretation of
spatial queries in natural language remains a challenge, they inspect a large
dataset of linguistic sequences based on billions of web pages (the Microsoft
Web N-grams) focusing on the spatial relation “near.”Their work provides a
method to extract knowledge from n-grams, which are potentially powerful
resources, but difﬁcult to disambiguate to reduce noise and misinterpretation
of linguistic tokens. This investigation provides new empirical evidence for
the asymmetry and other characteristics of this ubiquitous spatial relation,
demonstrating the potential of more interaction between computational,
geospatial and cognitive research on information search.
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