URBANIT
Urban Ontologies to Support Informed Urban Development and Planning
John Barton
City Futures Research Centre, Faculty of Built Environment
University of New South Wales, Sydney, Australia
Hairong Yu
School of Information Systems, Technology and Management, Australian School of Business
University of New South Wales, Sydney, Australia
Keywords: Spatial decision support, Ontology, Urban planning, Urban modelling.
Abstract: When dealing with complex and multi-faceted urban design challenges, the sheer weight of the information
available can make discerning the ‘bigger picture’ challenging. It is the suggestion of this paper that there is
a requirement for intelligent tools and mechanisms to assist in the capturing, comprehending and
communication of solutions to such problems whilst keeping in mind the consensus of the aims and targets.
To make knowledgeable decisions, there is a need to access the most relevant sources of information
possible. Quality intelligence requires a quality foundation of data. This paper will outline some fundaments
of how to best structure urban components and then examine how these can be applied to assist in
improving design and planning of urban precincts. In conclusion, some next steps are proposed for the
development of these tools and their application within an urban context.
1 INTRODUCTION
This paper is reporting on the UrbanIT project
undertaken at the City Futures Research Centre at
the University of New South Wales. The project has
been undertaken in partnership with the City of
Sydney Council, the NSW Department of Planning
and Landcom, a government development
corporation. A component of this research was to
investigate how the physical objects, concepts and
systems that comprise urban environments can be
defined and structured using an ontological, or
semantic, approach.
This paper presents existing technologies used in
structuring urban information, their interconnections
and potential applications. We then look specifically
at the Australian context regarding available
government datasets and present applied examples
of how these sets can be combined to present urban
information for decision support.
One challenge presented is when sources are
incomplete or inconsistently structured. An
ontological approach has the capacity to provide a
unified, connective framework.
2 ONTOLOGIES AND URBAN
PLANNING
In computer science, an ontology is a formal explicit
description of concepts in a domain of discourse
(Gruber 2009). Ontologies are designedly flexible
and consequently, can adapt to a particular domain’s
knowledge structure to serve a number of purposes.
An ontology defines a common vocabulary and
structure for those who need to share mutual
understanding of their domain information. The
process of logically ordering and organising this
knowledge makes it explicit and easily
communicated. Because it is structured, this can be
recognised by both humans and machines. It
provides the bridge between high-level human
conceptualisation and low-level physical or logical
database schema (Uschold and Gruninger 2004).
224
Barton J. and Yu H..
URBANIT - Urban Ontologies to Support Informed Urban Development and Planning.
DOI: 10.5220/0003087202240228
In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2010), pages 224-228
ISBN: 978-989-8425-29-4
Copyright
c
2010 SCITEPRESS (Science and Technology Publications, Lda.)
Once a framework has been established, inter-
relationships, rules and logical rationales can support
reasoning and knowledge discovery. These machine-
interpretable definitions of essential concepts and
relationships fulfil a great need for human-centred
modelling services that are pursued by modern urban
planners (Katranuschkov, Gehre et al. 2003).
An ontology adopts an open-world assumption
(OWA): put simply, there is an appreciation that just
because a concept might not be explicitly defined, it
may be just as valid. This approach lends itself well
to handling unstructured or semi-complete datasets.
In our own work we have employed Protégé, an
open-source knowledge-base framework with
ontology editing features (Protégé, 2010). However,
how the ontologies connect to existing government
database systems containing real-world content
becomes a pressing issue that needs technical
resolution to provide a working example as
described in section 5.
3 FRAMEWORKS FOR URBAN
MODELLING
The Semantic Web Stack, as proposed by Tim
Berners-Lee (Berners-Lee, Hendler et al. 2001),
represents a layer-based model that takes low-level
machine data and ‘humanises’ it toward the higher
levels. The ontologies are expressed as an
intermediary layer. The technical elements in data or
low-level schema can be mapped to higher concepts
a person can easily understand, or in our case, share
a common understanding with others about the built
environment.
The Open Geospatial Consortium (OGC)
specifies the standards and protocols to facilitate
geospatial web-based services. The 3D Information
Management (3DIM) working group is concerned
with developing the technologies for 3D urban
environments:
“A framework of data interoperability should
exist across the lifecycle of building and
infrastructure investment: planning, design,
construction, operation, and decommissioning.
This work is of interest to the geospatial
community in that there is a growing need for
technologies and information to effectively
interoperate between these domains to support a
range of vital services and decision support
needs”
(www.opengeospatial.org/projects/groups/
3dimwg)
The
OGC Web Services Phase 4 testbed was
conducted in 2006 to extend and demonstrate the
geospatial interoperability of urban modelling
technologies. This resulted in the development of
several new components that demonstrate the
integration of Building Information Modelling
(BIM) with the OGC Architecture. With increased
uptake and development of the semantic content of
information repositories, there is a need for systems
to be able to compare and translate this information
more intelligently. Stoter (2006) recommended the
employment and development of machine ontology
using the Web Ontology Language (OWL). There is
a growing body of research around the application of
ontologies to model and interpret urban information
(Teller, Billen et al. 2010). This work forms part of
that larger international initiative toward open
spatial data access; a key example is the UK
Ordnance Survey’s GeoSemantics work where a set
of ontologies describing the built environment are
available for download and development (Ordnance
Survey, 2010).
4 THE AUSTRALIAN CONTEXT
Sydney is facing the challenge of managing a
pressing level of population growth combined with a
tightly constrained geographic footprint. As such,
planners and designers need to design better, denser
urban environments to maximise economic,
environmental and social benefits. For the purposes
of demonstration, we have been focussing on five
different government resources:
1. Floorspace & Employment Survey (FSES)
2. Building Sustainability Index (BASIX)
3. Industry Foundation Classes (Ifc)
4. Strata titles database, containing property
ownership data, and
5. SEPP65: Planning policy to improve the
design quality of residential development.
5 DATA INTEGRATION USING
ONTOLOGIES
An ontological approach provides a way of
effectively browsing and retrieving concepts, their
properties, relationships, and even
geometric/geospatial representations. However,
effective tools to connect ontologies with the content
of the data sources are required. For the purposes of
our research, we have integrated selected tools under
development by TONES (2010).
URBANIT - Urban Ontologies to Support Informed Urban Development and Planning
225
Table 1: Semantic mapping across government resources and documents concerning the built environment.
CONCEPT FSES BASIX IFC SCHEMA STRATA SEPP65
SITE(s) Site_2006 tagcadastre IfcSite Address Context, Streetscape
BUILDING Building_2006 building_details IfcBuilding Firsthousenum Built form
STOREY Floor_2006 .storeys IfcBuildingStorey Floor_num Scale: Bulk, height
SPACE SpaceUnits_2006 Dwelling details IfcSpace, IfcZone Lot_/unit_num Units, Rooms
PROJECT Establishment project_details IfcProject Strata Plan (SP) Process
PERSON Tenant, Surveyor
Accredited
A
IfcPerson,
If O
Owner,
Oii
Social dimensions, Density
5.1 Data Integration Issue
Urban management involves various groups, and
their associated endeavours create detailed
repositories of digital information in various
forms. These are
complex, massive, fast updating and diverse. One
such dataset is the City of Sydney’s Floor Space and
Employment Survey (FSES). This repository is a 3D
geospatial dataset comprising of site, building and
space information and corresponding geometry.
These homogenous spatial clusterings have strong
parallel concepts described in the Ifc schema. The
Ifc schema forms an exhaustive taxonomy
describing a building in detail down to that of its
components. As such, this verbose list of elements
and concepts forms the base level for a ‘bottom-up’
approach. This serves as a foundation to attach
corresponding nodes and concepts found in other
datasets concerning the urban domain. A test of the
UrbanIT framework is to map these concepts across
these different datasets.
5.2 Application of Ontologies
The previous section illustrated a bottom-up
approach, however, when dealing with the
complexity of urban environments, distilling the
important elements into a manageable set of entities
helps focus the framework in a top-down way. When
using ontologies to connect the distinct datasets, we
can identify the common concepts and map their
terms together. In this case, the concepts that were
important for our application were simply sites,
buildings, storeys, and spaces.
5.3 Semantic Concept Mapping
Semantic mappings are used to bring the data
sources, classes and properties of the ontologies
together to assist users on data retrieval. For
instance, the schematic ‘IfcBuilding’ can be mapped
to the similar concept described in the FSES, listed
at ‘Building_2006’. Similarly, ‘IfcSite’ maps
directly to ‘Site_2006’ and so on. Once these
concepts are connected, we can map out how these
elements relate to each other: for instance, “The
Building sits on a Site”.
Table 1 illustrates the common concepts
spanning our different data and information sources,
and lists the corresponding concepts identifying
name from each data source (See section 4).
Sometimes there is a direct connection between
concepts, as with Ifc and FSES, but at other times
inconsistencies, ambiguities and indirect references
occur. This is mainly due to the fact that different
data has been structured and collected in different
contexts for different purposes. When the
ontological layer spans across these sources, it has
the capacity to homogenise them.
Figure 1: Framework Diagram.
5.3.1 OBDA
Ontology-Based Data Access (OBDA) is an area of
research in which the goal is to provide access to
data in heterogeneous sources through a semantic
layer formed by ontology (Calvanese et al, 2009,
DIG-OBDA, 2010). The main contribution of this
KEOD 2010 - International Conference on Knowledge Engineering and Ontology Development
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research is to provide an end-to-end system to build
a semantic layer for integration, to classify and
reason by an OBDA-enabled reasoner and retrieve
data through mapping. Another added value of
OBDA, which applies particularly to the domain of
urban planning, is that constraints expressed by the
ontology allow users to overcome incompleteness
that is present in the complex and fluid data captured
from urban processes.
The OBDA Protégé plugin is developed by
‘Knowledge Representation meets Databases’
(KRDB). It provides complete views to relational
database management systems (RDBMS), a
mapping editing and testing environment,
classification and reasoning built-in, and evaluated
at the mapping phase and SPARQL
(Protocol +RDF Query Language) queries reasoned
by OBDA-enabled reasoners.
5.3.2 QuOnto
QuOnto is a reasoner that supports an OBDA-
enabled ontology. QuOnto is able to use an RDBMS
as a repository for mappings between the data
(Poggi et al. 2008).
5.3.3 Architecture
Figure 1 presents an architecture of an application
driven by Protégé, OBDA and QuOnto.
5.4 High Order Reasoning for Data
Extraction
Mapping connections in well-structured datasets is
relatively straight forward, but the real strength of
semantic techniques comes into play when semi-
structured, messy or otherwise disaggregated data is
being interrogated. SEPP 65 is a (text-based)
government policy document prescribing a set of
guidelines to improve the design quality of
residential apartment buildings. The document deals
with objective and quantitative measures, such as the
bulk and height, but also covers the qualitative and
sometimes subjective aspects to urban
developments. For instance, the policy states that:
“Good design responds and contributes to its
context. Context can be defined as the key
natural and built features of an area.
Responding to context involves identifying the
desirable elements of a location’s current
character or, in the case of precincts undergoing
a transition, the desired future character as stated
in planning and design policies. New buildings
will thereby contribute to the quality and identity
of the area.”
(State Environmental Planning Policy No 65-
Design Quality Of Residential Flat Development
- Reg 9)
These elements and interrelationships can be
documented logically and systematised so that the
framework can carry out reasoning and logical
testing. For instance, the building’s context can be
deduced as ‘everything but the building, within an
area, location or precinct’. It also can encompass
time-based concepts such as ‘precincts undergoing a
transition’. At this point of development OpenCalais
is being trialled to automatically mark up
government documents such as SEPP65 for
integration by the UrbanIT framework.
5.5 Information Leverage &
Integration
Ontologies provide data access by presenting many
different channels: these could be a web portal,
knowledge acquisition system or an object-oriented
/relational database. For an end-user (e.g. an urban
planner, decision maker or an urban information
modeller), there is no need to be concerned about
which channels are employed and what the
connection is to each channel. Real-time, automatic
processing and reasoning is handled transparently,
so that the application acts as a one-stop experience
to provide as much information as the user requires,
through a service-oriented approach. This can be
extended to an inter-organisational scale, to better
provide support specific to advanced metropolitan
strategic planning. This enhances inter-connectivity
by fostering horizontal connections through open,
unified and user-defined channels. This creates an
opportunity for the development of a whole suite of
new computer tools that can undertake multiple
analyses at an urban level.
Figure 2 shows how energy use data might be
retrieved and visualised from the BASIX database.
The cooler colours (blue and green) reflect places
that might have energy efficient Air-conditioners
fitted, while the red spaces are less energy efficient.
The surrounding buildings are coded by the FSES
data to show actual context.
At the time of writing, the UrbanIT project is
working closely with KRDB on more effective
integration through VDB (Virtual Database, 2010)
and retrieval by reasoning with
incomplete/incompletely specified/missing data
(Calvanese and Giacomo, 2008), both of which are
URBANIT - Urban Ontologies to Support Informed Urban Development and Planning
227
Figure 2: Query result illustrating possible energy ratings
of air-conditioners.
poignant tools to meet the demands of improving the
accessibility and usefulness of existing urban data
repositories held by government.
6 CONCLUSIONS
These urban modelling exercises have indicated that
the adoption of an ontological approach is valuable
as it can assist in solving semi-structured urban
problems. As a powerful modelling and integration
tool, an ontology can be applied to the fundamental
data layers, through to the higher layers where end-
users are needing to make sense of otherwise
scattered information, and make decisions about
special urban challenges. A critical component of
this workflow is OBDA. This enables ‘vertical
drilling’ down through the ‘horizontal’ layers, which
plays a central role in bringing heterogeneous and
autonomous geographic and building information
together to support urban decision making.
ACKNOWLEDGEMENTS
The UrbanIT project has been funded by the
Australian Research Council (ARC) and undertaken
in partnership with City of Sydney, NSW
Department of Planning and Landcom. We are
grateful for the support from Mariano Rodríguez-
Muro and Diego Calvanese of the KRDB research
group at the Free University of Bozen Bolzano in
Italy by providing OBDA plugin and the QuOnto
reasoner.
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