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

 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

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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

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

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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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