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Schuurman N. 2009. Spatial Ontologies. In Kitchin R, Thrift N (eds) International Encyclopedia of Human
Geography, Volume 1, pp. 377–383. Oxford: Elsevier
ISBN: 978-0-08-044911-1
© Copyright 2009 Elsevier Ltd.
Author's personal copy
Spatial Ontologies
N. Schuurman, Simon Fraser University, Burnaby, BC, Canada
& 2009 Elsevier Ltd. All rights reserved.
Glossary
Formalization It is the process through which ideas,
text, and spatial entities are translated into digital objects
so that they can be manipulated in a computer. It is the
process of rendering the nondigital world into code so
that it can be computed.
Interoperability It is the process of exchanging data
and code between diverse computing systems and
databases. It frequently involves conflation of near but
nonidentical categories or computing protocols.
Ontology It means something very different in
philosophy than it does in information systems. In the
former, it means the question of being. In computing and
information sciences, an ontology is a formal universe in
which each entity is precisely defined and its relationship
with every other entity in the specific categorical or
computing realm is also predetermined. Ontologies in
this context are the range of what is possible. They can
be thought of as simply a classification system or a data
dictionary.
Definition: Spatial Ontologies
The term ‘spatial ontologies’ is an amalgamation of two
words that point to very different concepts. Spatial can be
distinguished from geography as the former refers to
space while the latter refers specifically to the Earth’s
surface. Space is a concept that has many manifestations
at scales from the interior of cells to entire galaxy and
ultimately the universe. Space often refers to two dimensions but can be extended to three and even four
dimensions. Space can be mathematical (e.g., a set) or
philosophical (e.g., epistemological implications of a
conceptual framework). Space and the adjective spatial
are in effect complex descriptions of a concept that
embodies area at a range of scales and epistemologies.
Adding the noun ontology to the adjective spatial is a
complicating strategy. Ontology is multifaceted term as it
is understood very differently across disciplines. Human
geographers and social scientists have interpreted ontology in an Aristotelian manner. In this guise, ontology
signifies a foundational reality and is often referred to as
the essence of an object or phenomena. Alternatively,
ontologies answer the question: what must the world be
like – or what conditions must exist – for us to perceive
and know something. Computing scientists and systems
theorists, on the other hand, use an engineering
interpretation of the word ontology. In this view, an
ontology is a complete and internally logical system (e.g., a
classification system). In cartography and geographic information systems (GISs), ontology is interpreted in the
information science tradition. Each ontology in this guise
is a unique statement of logic, a way of describing spatial
entities from one perspective or knowledge system.
Formal ontologies comprise a logical universe, and an
ontology in a formal environment is equivalent to a logical theory – but is often expressed as a classification
system in databases. Formal or systems ontologies have
three levels of convention: representational, communication protocol, and content specification. The realm of
existence is limited to that which can be represented
(often in a database). Domain knowledge must be formally declared and this becomes its own world of discourse. Ontologies thus described appear circumscribed.
They lack the mystery of philosophical ontologies yet
this requirement of exact specification is a means of
communicating different epistemologies within an information system.
Each spatial ontology is a product of an epistemology –
or a way of knowing the world. There is a tradition of
essentialism in information sciences which argues that the
world is divided into natural classes that reflect an essential reality and these classes can be discerned. This
epistemological view relies on the power of human observation to divide artifacts into natural classes. There is
increasing recognition, however, that scientific classification is a mixture of metaphysics and epistemology and
that the classification systems we use to represent the
world are dependent on perspective, measurement techniques, and agenda. In geographic information science
(GIScience), many scholars now acknowledge that context
shapes the formation and selection of categories. Changes
in context – or point of view – lead to a shift in perceived
and/or enumerated categories. Each representation of
categories leads potentially to an alternative ontology that
could be implemented in a computational environment.
Though there are GIScientists who would argue that
categories are based on naturally occurring, stable categories, most people agree that GIS must be flexible
enough to represent more than one system
of classification. Multiple ontologies permit multiple
stakeholders with divergent agenda to be represented in a
single GIS.
In a world with multiple spatial ontologies, scientific
classification is just as vulnerable to error as lay classification. Increased recognition of the role of epistemology
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in creating ontologies is a radical departure from objectivism which is premised on one ‘real world’ independent of the human mind and social context. When
we talk of multiple spatial ontologies, we are incorporating recognition that each of those ontologies represents
an epistemology and a point of view.
How Do Spatial Ontologies Look Like?
A spatial ontology then is a formal list of objects or terms
that are permitted within a specified range set by a group.
Medical data registries, for instance, contain fields (columns in the database) that are strictly controlled. Using
the example of a Trauma Registry, we find a number of
fields including injury date, ICD 10 codes, postal code of
patient’s usual residence, injury severity score (ISS), external cause code, etc. In combination, these database
fields comprise a classification system. Such a classification system, like many in common use, implicitly describes the developers’ epistemology – or ways of
knowing and state of understanding – in that discipline.
The ontology can be considered spatial because even
though it describes nonspatial attributes (such as patient’s
date of birth), the injury location is a critical element. A
Trauma Registry from a different country might include
quite different fields or more commonly the interpretation or scale of the data in individual fields may vary
widely. In one registry, location might refer to postal
code while in another it may be entered using Global
Positioning System (GPS) coordinates. Likewise, injury
description may be described using a different coding
system (e.g., ICD 9). One of the challenges of working
with multiple ontologies is that comparing and integrating them may be difficult because each is developed
based on differing assumptions about the world. And as
the state of understanding in a field changes, ontologies
change in subtle and often imperceptible ways.
A reasonable question at this point might revolve
around the difference between a simple classification
system and an ontology. In a computational interpretation of ontology, the only discernable product is the
database. But before an ontology is fully developed, the
stakeholders might consider the skeletal classification
system the ontology. Moreover, geologists consider a map
legend a fully fledged ontology while a GIScientist might
consider a range of spatial entities (e.g., mountain, volcano, valley, and foothills) an ontology. And a database
analyst may refer to a data dictionary as an ontology. The
truth is that there is a range of acceptable interpretation
of spatial ontology in GIScience as illustrated in Figure 1. But every ontology is linked by its relationship to a
particular epistemology and its role in determining the
range of acceptable categories in a classification.
Moreover, ontologies in information science are ultimately translated into formal terms that can be implemented computationally. Formalization is the process
through which ideas and objects are described in terms
that can be encoded digitally so that the objects can
be manipulated in a computer. It is this process of
formalization that separates organized but unstructured
thoughts and conceptual frameworks from computational
data structures populated with data. A necessary stage
of formalization is the creation of succinct coherent
ontologies.
Formalization and Ontologies
GIScientists frequently refer to ‘formalization’ as the
process of rendering concepts into a form that can be
ultimately represented in a digital environment. This is
more easily said than done. There is a great difference
between the abstract concepts that we routinely use to
describe the world and the formal language – and computing code – that is required to implement technology.
GIS, like all information sciences, is based on a hierarchical relationship between conceptualization and
formalization. If we think of conceptualization as a step
toward understanding spatial processes and relationships,
there remains a pressing need to express these relationships in a mathematical or formal notation as a precursor
to coding them. The trade-off, however, is information
loss associated with formalization. There is a danger that
we suppress detail in favor of abstract and formal relationships. On the other hand, abstraction – based on information loss – is a powerful way of understanding
structure.
The process of abstraction assists us in making sense
of the physical and human world and, in GIS, we use
abstractions to model reality. The process of formalization is based on abstraction of concepts into a symbolic
form.
A mechanism for moving between cognitive impressions of reality and database representations of it is required, however, to achieve this level of abstraction.
In a seminal paper by Jean Brodeur and others, the
researchers describe five ontological stages associated
with geospatial data and analysis. At the top level is
reality (R), followed by cognitive models of reality (R1).
The third stage consists of conceptual representations
and is referred to as R2. The fourth ontological stage (R3)
converts concepts to database representations. Finally,
the fifth ontological level (R4) is the set of spatial concepts that can be produced from the database. Figure 2
illustrates the conceptual model of ontologies.
To human geographers, such a taxonomy might
seem mechanistic, but it is essential in GIS to be able to
express cognitive models in a formal language as a
Family Cebidae
Family Aotidae
Family Pitheciidae
Family Atelidae
Family Tarsiidae
Parvorder Catarrhini
Superfamily Cercopithecoidea
Family Cercopithecidae
Superfamily Hominoidea
Family Hylobatidae
Family Hominidae
Infraorder Simiiformes
Parvorder Platyrrhini
Infraorder Tarsiiformes
Suborder Haplorrhini
Infraorder Lemuriformes
Superfamily Cheirogaleoidea
Family Cheirogaleidae
Superfamily Lemuroidea
Family Lemuridae
Family Lepilemuridae
Family Indriidae
Infraorder Chiromyiformes
Family Daubentoniidae
Infraorder Lorisiformes
Family Lorisidae
Family Galagidae
ORDER PRIMATES
Suborder Strepsirrhini
Data Element
Patient—diagnosis date, DDMMYYYY
270544
NHIG, Standard 01/03/2005
The date on which a patient is diagnosed with a particular condition or disease.
Patient—diagnosis date
The date on which a patient is diagnosed with a particular condition or disease.
Patient
Diagnosis date
Date
Date/Time
DDMMYYYY
8
(d)
Source: Australian Institute of Health and Welfare (2006). National Health Data Dictionary, Volume 2.
Collection and usage attributes
Comments:
Classification systems, which enable the allocation of a code to the diagnostic information,
can be used in conjunction with this metadata item.
Source and reference attributes
Submitting organisation:
Cardiovascular Data Working Group
Relational attributes
Related metadata references: Supersedes Date of diagnosis, version 1, DE, NHDD, NHIMG, Superseded 01/03/2005
Implementation in Data Set Cardiovascular disease (clinical) DSS NHIG, Superseded 15/02/2006
Specifications:
Cardiovascular disease (clinical) DSS NHIG, Standard 15/02/2006
Data element attributes
Representational attributes
Representation class:
Data type:
Format:
Maximum character length:
Value domain attributes
Data element concept:
Definition:
Object class:
Property:
Data element concept attributes
Metadata item type:
Technical name:
METeOR identifier:
Registration status:
Definition:
Identifying and definitional attributes
DATE OF DIAGNOSIS
(c)
Figure 1 Multiple interpretations of ontology. (a) Illustrates a map legend. For many geologists and cartograhers, the legend represents the entire formal universe of an ontology. In (b),
data models represent different ontologies. (c) Is a classification systems or taxonomy – another interpretation of an ontology. (d) Illustrates a data dictionary that defines exactly what
terms can be represented spatially and how they are defined. Each of these are alternative iterations of ontology.
(b)
(a)
River/Lake
Private Land
Farming
Park
Sightings
Food/Gas
Camping
Protected
Roads
LEGEND
(c)
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Spatial Ontologies
R: Reality
R1: Cognitive models of reality
R2: Conceptual representations of reality
R3: Database representations of reality
Feature
Cabin
Storage
Road
Bridge
Lake
Stream
Woods 1
Woods 2
Bush 1
Bush 2
Bush 3
ID
0001
0002
0003
0004
0005
0006
0007
0008
0009
0010
0011
Type
Point
Point
Line
Line
Polygon
Line
Polygon
Polygon
Point
Point
Point
Length
50
7
70
-
Area
500
500
300
-
Man-made
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
4
R : Spatial concepts that can be produced from the database
Feature
Cabin
Storage
Road
Bridge
Lake
Stream
Woods 1
Woods 2
Bush 1
Bush 2
Bush 3
ID
0001
0002
0003
0004
0005
0006
0007
0008
0009
0010
0011
Type
Point
Point
Line
Line
Polygon
Line
Polygon
Polygon
Point
Point
Point
Length
50
7
70
-
Area
500
500
300
-
Man-made
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
Figure 2 Five levels of abstraction associated with computational ontologies. The highest level of reality is the world as it is – a
traditional philosophical interpretation of ontology. R1 represents the way that humans perceive the world – transformed through
cognition. When we conceptualize reality, we alter it again (R2). Database representations of reality (R3) introduce another profound
transformation and the spatial concepts that can be produced from a database (R4) are further restricted. In this illustration, we see how
at each step, representations of the world are altered (and limited) as we move through different levels of abstraction. For example, the
first viewer conceives the scene of the forest, house, pond, and bridge differently than she is able to convey it to the interlocutor. And
representations of reality are again transformed when articulated in databases and re-articulated as spatial concepts that can be
computationally displayed.
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precursor to coding it. R2 or the set of conceptual representations marks the fuzzy dividing line between the
conceptual and implementational realms. A structured
vocabulary must be used to describe objects and object
classes. Moreover, database representation requires a
method of embedding context. For example, we need to
know whether the word ‘crown’ refers to the degree to
which a tree blocks light in a forest or a metal adornment
intended to rest on a queen’s head. In everyday (nondigital) life, such meaning is extracted from verbal or
environmental context. In a digital environment, it must
be supplied. Context is necessary for understanding implied use, background, and common meaning of spatial
entities. Ontologies are a way of encoding context.
Ontologies can exist, be described and understood,
but this does not mean that they have been formalized.
Formalization is the critical step referred to by Brodeur’s
R3 and R4. Formalization allows us to take descriptive
concepts and render them digital; it makes them real in
computational terms. GIScientists are currently developing methods of formalizing and encoding diverse
ways of looking at the spatial world – a critical component of ontology research.
381
development of databases – as a means of ensuring that
the terms of classifications can be recognized in different
settings.
The goal of maintaining ontological context is consistent with one of the chief objectives of present-day
GIScience: interoperability. There is a trend toward
distributed digital knowledge with different institutions
housing different databases that are occasionally integrated for comparative or global analyses. Such institutions, including businesses, government agencies, and
university researchers, need ways to integrate spatial
information even when it is described from different
epistemologies at different levels of abstraction using
multiple schematics (e.g., different database organization). Sharing spatial data and attributes requires stability
of language across culture and geography – assumptions
that are seldom true. Researchers are forced to develop
methods of delivering intended meaning along with field
names in databases in order to allow ‘intelligent’ semantic
sharing. Semantic interoperability implies translating
concepts into code and back again. The work of semantic
interoperability is enhanced by thinking of each unique
set of spatial objects as part of an ontology – with its own
context.
Ontology Research in GIScience
Ontology research in GIScience began in the mid-1990s
when a number of scientific journal papers first explored
the issue. Predictably, there were many variations in interpretation of ontology but they were linked by the
assumption that (1) we need to represent multiple epistemologies; and (2) formalization is a constraint in representation at the computational level. There is a
common understanding that concepts must be filtered
through a formalization process. The process of formalization is the designated arena for fixing meaning – for
creating a fixed universe of discourse.
Representing multiple ways of seeing and understanding the world in a computer has been tackled by a
number of researchers in different ways. One way is to
use semantic networks that employ node–arc structures
from graph theory to achieve this goal. To achieve this,
nodes are equated with spatial entities or concepts and
arcs with relationships between the entities. Close concepts are physically close on the graph structure. The
goal of the semantic proximity approach is to identify
close or near concepts and label database elements (e.g.,
mountains or valleys) so that proximate concepts and
entities can be linked. Other researchers have developed
complex sets of inclusion rules that must be incorporated
during classification of entities. Whether or not a spatial
entity is included in a future classification can be theoretically determined by the rules. Each of these methods
is linked by emphasis on including context in the
Spatial Ontologies of Land Use: An
Example
Land-use categories are used by municipal planners to
organize and categorize public and private lands under
their purview. Land use refers to the activities occurring
on a particular piece of property or area of public land.
Land-use classification is intended to include both the
intent and the reality of how a given land area within a
metropolitan area may be altered by habitation and
human use. Land-use categories, however, vary widely
among communities and most have different taxonomies
(e.g., ontologies). Because it is complicated by semantic
heterogeneity, integration of land-use taxonomies is
complicated. Each municipality collects its land-use data
using different assumptions about categories. These
epistemological and ontological differences become encoded in the data – often with no record. Though the
distinctions are often subtle (e.g., skateboard parks might
be included as ‘green space’ in one community but excluded in another because they are concrete), they affect
the reliability of the data. Data always contain gray zones
of meaning. While they are being used in the context in
which they are collected, this is usually not a problem.
The meaning of green space in the dataset is implicit, for
example, in the offices of individual municipal governments. But if that database is moved to a different
municipality, workers there might interpret it differently
– most likely in the same way that their community
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Spatial Ontologies
presently understands the term. When data are integrated in broader datasets, however, or sold as secondary
datasets, their contextual meaning can be lost resulting in
the propagation of uncertainty in the attendant analysis.
Once subtle distinctions are encapsulated with the
data, users make assumptions about their meaning. For
example, neighboring municipalities might integrate their
land-use categories to develop a regional classification.
Superficial integration of the datasets based on the assumption that categories such as ‘green space’ or ‘recreation centre’ mean the same thing in every jurisdiction
embeds error into the resulting merged data. The problem, in effect, is one of different ontologies. Comparing
urban land use across municipal lines in the Greater
Vancouver metropolitan area of British Columbia (BC),
Canada, is a telling example of the problems associated
with different spatial ontologies that affect everyday GIS
and cartographic practices.
The Greater Vancouver Regional District (GVRD),
located within the province of BC, houses over 2 million
people residing within 21 municipalities. It constitutes a
single contiguous metropolitan area. Land-use classification is a municipal government responsibility in the
GVRD. There is no regional land-use classification
standard, and resulting taxonomies of land use are disparate and thereby incommensurable. For example, one
community may identify ‘single family’ as a land-use
designation; this designation may include two or three
different types of single-family types in a competing
classification (see Table 1).
Municipalities characterize land use by employing
codes to describe differences. Table 2 illustrates the
superclasses used by the neighboring cities of Vancouver
and Burnaby. The superclasses differ somewhat as would
be expected – but the differences are more profound than
might first appear. The city of Burnaby uses detailed
codes to identify land use at the scale of individual
properties. They give each property outline a very specific code. For example, the numerical code range 000–
099 constitutes the ‘residential series’ and describes
various housing types and their associated forms of
tenure. The city of Vancouver, by contrast, operates with
many fewer subclasses – and their database is much more
general as a result.
The general categories may be similar, but the problem
is that there is no way of digging deeper. Fundamental
ontological distinctions are a product of epistemological
differences in data collection as well as varying institutional cultures in the individual municipalities. When
these ontologies’ differences are accounted for, one realizes
that a trivial integration of the superclasses contains uncertainty. While the mapping presented in Table 2 is
schematically legitimate, it fails to convey the true (lack
of) symmetry between the land-use classes. The city of
Vancouver bases its land-use coding on municipal ‘zoning’,
a legal classification encoded into, and mandated by, city
policy (bylaws).
Zones are determined for large areas at a time, and
they encompass many individual properties. Each property is assigned a land-use ‘code’ based on which zone
their property lies in. Land-use information for Vancouver
is based on the ‘what should be there’ principle.
By contrast, the city of Burnaby determines based on
the activities associated with individual cadastral parcels –
as determined by the taxation authorities. The city of
Burnaby also determines its own municipal zoning. Unlike Vancouver, it does not consider zoning and land use
as equivalent. These epistemological differences in how
Table 2
Cadastral land-use classification for the cities of
Vancouver and Burnaby
Table 1
Number of different zoning designations for various
categories including of industrial, low-rise residential, high-rise
residential and single-family residential land use
City of Coquitlam
City of North
Vancouver
City of Port
Coquitlam
City of Richmond
City of Surrey
Industrial Res low
Res
high
Single
family
9
7
2
1
2
1
5
3
5
2
2
1
7
5
1
2
1
2
1
3
The table illustrates that the five sample communities have a varying
range of subclassifications. Each land use categories and its
subclassifications constitute an ontology. Schuurman, N., Leszczynski,
A., Fiedler, R., Grund, D. and Bell, N. (2006). Building an integrated
cadastral fabric for higher resolution socioeconomic spatial data
analysis. In Riedl, W., Kainz, A., & Elmes, G. (eds.) Progress in Spatial
Data Handling, 12th International Symposium on Spatial Data Handling.
Berlin: Springer.
Vancouver
‘District schedules’
Burnaby
Land-use ‘series’
One-family dwelling
Two-family dwelling
Multiple dwelling
Limited agricultural
Industrial
Commercial
- no corresponding class -
Residential series
Cultural and recreation
(a subclass)
Historic area
- no corresponding class Comprehensive
development
Farm series
Industrial series
Commercial series
Utility series
includes railways, pipelines, etc.
Institutional series
government buildings, etc.
- no corresponding class -
Note that the superclasses are not commensurate but can be manually
synced. From Schuurman, N. and Leszczynski, A. (2006). Ontologybased metadata. Tranactions in Geographic Information Science 10(5),
709–726.
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Spatial Ontologies
land use is assigned in two different communities reveal
ontological differences. The differences can be summarized as the distinction between ‘what should be there’
versus ‘what is actually there’. As a result, it is difficult to
directly compare land-use categories between Burnaby
and Vancouver. The two classification schemes are irreconcilable at a very basic level because they emerge
from two different institutional contexts – in other
words, they represent two separate, though overlapping,
ontologies.
The example of land use reveals the impact that different ontologies have on spatial data – such as land use –
that may otherwise seem unproblematic. If data users are
to understand their data from a qualitative perspective,
they need to think about ontologies. Are the two ontologies comparable based on their classification systems;
attribute characteristics; terminology; naming conventions; rationale for collection; scale; and measurement
systems? Collectively, such parameters constitute data
‘context’. Context is likewise the basis for understanding
how different spatial ontologies shape data.
Spatial Ontologies: Present and Future
In the past decade, GIScience researchers have become
increasingly concerned with optimal methods for representing multiple epistemological perspectives about the
same spatial events and entities. At the same time, there
is a pressing need to attach contextual information to
database elements in order to identify different ontologies in the interest of enhancing interoperability of data
across institutions and geography.
Spatial ontologies enable multiple stakeholders to
represent different scenarios, agenda, and interpretations
of the geographic world. Formal ontologies are a
means of implementing these diverse views in a structured digital environment. Acknowledging and understanding alternate perspectives on the same geographical
383
phenomena or spatial framework is a means of preserving
heterogeneity of perspective with implications for
democratic policy and governance. Spatial ontologies are
a means of achieving these goals in a computational
environment.
See also: Critical GIS; Critical Realism/Critical Realist
Geographies; Feminism, Maps and GIS; GIS and
Cartography; Maps.
Further Reading
Agarwal, P. (2005). Ontological considerations in GIScience.
International Journal of Geographical Information Science 19(5),
501--536.
Bowker, G. (1996). The history of information infrastructures: The case
of the international classification of diseases. Information Processing
and Management 32(1), 49--61.
Bowker, G. C. (2000). Biodiversity datadiversity. Social Studies of
Science 30(5), 643--683.
Gregory, D. (2000). Ontology. In Johnston, R. J., Gregory, D., Pratt, G. &
Watts, M. (eds.) The Dictionary of Human Geography. Oxford:
Blackwell.
Harvey, F., Werner, K., Hardy, P. and Yaser, B. (1999). Semantic
interoperability: A central issue for sharing geographic information.
Annals of Regional Science 33, 213--232.
Kuhn, W. (2001). Ontologies in support of activities in geographical
space. International Journal of Geographical Information Science
15(7), 613--631.
Schuurman, N. (2006). Formalization matters: Critical GIS and ontology
research. Annals of the Association of American Geographers 96(4),
726--739.
Schuurman, N. and Leszczynski, A. (2006). Ontology-based
metadata. Tranactions in Geographic Information Science 10(5),
709--726.
Schuurman, N., Leszczynski, A., Fiedler, R., Grund, D. and Bell, N.
(2006). Building an integrated cadastral fabric for higher resolution
socioeconomic spatial data analysis. In Riedl, W., Kainz, A. & Elmes,
G. (eds.) Progress in Spatial Data Handling, 12th International
Symposium on Spatial Data Handling. Berlin: Springer.
Smith, B. and Mark, D. M. (1998). Ontology and geographic kinds.
Paper read at The 8th International Symposium on Spatial Data
Handling. Vancouver, BC, Canada.
Winter, S. (2001). Ontology: Buzzword or paradigm shift in GI science?
International Journal of Geographical Information Science 15(7),
587--590.