=Paper=
{{Paper
|id=None
|storemode=property
|title=A Land Use Identification and Visualization Tool Driven by OWL Ontologies
|pdfUrl=https://ceur-ws.org/Vol-849/paper_5.pdf
|volume=Vol-849
|dblpUrl=https://dblp.org/rec/conf/owled/GomesMUD12
}}
==A Land Use Identification and Visualization Tool Driven by OWL Ontologies==
https://ceur-ws.org/Vol-849/paper_5.pdf
A Land Use Identification and Visualization Tool
Driven by OWL Ontologies
Jorge Gomes1 , Nuno Montenegro2 , Paulo Urbano1 , and José Duarte2
1
LabMAg, Faculdade de Ciências da Universidade de Lisboa,
{jgomes,pub}@di.fc.ul.pt,
2
Faculdade de Arquitectura - UTL
{nmontenegro,jduarte}@fa.utl.pt
Abstract. We present a software tool for urban planing, driven by se-
mantic web ontologies. With this tool, the participants of the urban
development process can semantically annotate an intervention site with
knowledge about the use of the land. The tool provides the user as much
flexibility as possible in the choice of the land use standard that will be
used to perform the classifications. This flexibility is achieved by defining
extensible ontologies in which the concrete standards can be built upon.
This allows our tool to load external ontologies that define the taxon-
omy of the standard and the semantic relations between the land use
categories. The loaded ontologies seamlessly integrate in the application,
and are used through a friendly user interface. We also demonstrate how
ontologies can be used to perform automatic categorizations and con-
sistency checks in the geographical knowledge, aiding in the process of
identifying the land uses of a site.
Keywords: urban planning, semantic web, ontologies, OWL, GIS
1 Introduction
Urban and regional planners usually develop long and short term plans to guar-
antee efficient management of land use and to promote the growth and revitali-
sation of urban, suburban, and rural communities. Before making plans for the
development of these communities, planners study and report on the existing
land use for business, residential, and community purposes. Our work aims to
design and build a tool for the manipulation of land use knowledge, to assist
the urban development process. It is targeted at district planning and the ulti-
mate goal is to promote the generation of more sustainable urban environments.
To achieve this goal is important to have reliable and up-to-date georeferenced
information, firstly, in order to identify the requirements of the plan, and sub-
sequently to establish appropriate recommendations.
We have designed 4CitySemantics, a software tool that aims at aiding in
the city planning process, and heavily relies on semantic web technologies. The
main objective of this tool is to provide the urban planners with a sharable
semantic interpretation of a defined intervention site and surrounding areas. Be-
sides visualizing the semantic information associated with georeferenced spatial
2 Jorge Gomes, Nuno Montenegro, Paulo Urbano, and José Duarte
features, this tool provides the participants in the urban design process with
the possibility to identify and classify semantically the urban areas subject to
intervention.
The key principle behind the tool is to offer as much flexibility and cus-
tomization as possible, in order to be able to integrate smoothly with different
urban knowledge and different classification standards. Allowing the tool to work
with any land use classification standard is paramount because there are many
classification systems used in land use planning. Land use standards are usu-
ally defined by state regulations, and the urban planner is required to use the
standards existing in the intervention zone. Semantic web technologies play the
main role for attaining the desired flexibility. We defined base ontologies written
in OWL 2, which can be extended as needed to describe the standards that the
user wants in the tool. The tool will then interpret the knowledge present in
those extensions using the semantic structure laid out by the base ontologies.
With 4CitySemantics users are able to 1) load a georeferenced map from a
shapefile, 2) choose the intervention zone and define the surrounding region on
the map; 3) define any other zones on the map as necessary; 4) load a land use
standard from an OWL ontology; 5) visualize the standard and all the informa-
tion associated with it; 6) classify the parcels on the map in accordance with
that standard; and 7) query the map for the parcels classified with any land use
categories, taking advantage of the reasoning capabilities.
2 Related Work
The use of ontologies has been a hot topic in urban planning [1], mostly for urban
planning knowledge externalization, sharing, integration, and reuse. The main
objective of the European COST C21 Action [2], known informally as Towntology
was to “increase the knowledge and promote the use of ontologies in the domain
of urban development, in the view of facilitating the communications between
information systems, stakeholders and urban experts at a European level”.
In the context of Towntology, CityGML [3], an information model for the rep-
resentation of 3D urban objects, was successfully integrated with OSM, a soft-
mobility ontology, and with OTN, an Ontology of Transportation Networks [4],
both developed in OWL. OSM could complement the soft-mobility aspects that
were absent in OTN and CityGML was useful for visualization and communica-
tion. There were also cases of interoperability in the area of archeology [5] and
air quality [6].
KnowledgeScapes [7] is a visualization tool that links spatial features in a
spatial dataset to urban concepts expressed in an ontology. The users are able
to browse and select concepts and the corresponding spatial features are high-
lighted. In comparison with 4CitySemantics, the ontologies in KnowledgeScapes
are written using only RDF, with more limitations in terms of expressiveness
and inference and the users are not able to annotate themselves the spatial data
with semantic information, they can only visualize the selected spatial concepts.
A Land Use Identification Tool Driven by Ontologies 3
3 System Architecture
In 4CitySemantics, knowledge regarding geographical features and land use clas-
sification taxonomy are represented using OWL 2. The knowledge is separated
from the tool, and so it is fully independent of any land use standard, as long
as it is represented as an ontology. Designing a system where the user can de-
fine its own concepts, which our tool should be able to correctly interpret them,
raises some challenges: In one hand, it must be given enough liberty for the user
to define its own concepts, allowing rich descriptions and fully supporting the
OWL 2 semantics. On the other hand, the application must be able to bring into
play these foreign concepts, imposing some (preferably minimal) restrictions to
the user in the construction of the ontology.
The compromise between these goals is achieved through a small set of con-
cepts that are defined in non-modifiable ontologies in which the tool relies.
We call these ontologies the skeleton ontologies, because they are the common
ground where different ontologies will be built upon. To create an extension on-
tology defining a concrete land use, the user must extend the concepts of the
LandUseSkeleton ontology, with all the expressiveness he wants, as long as the
connection points between the ontologies are kept, so the tool can interpret it.
The land use skeleton ontology can be viewed as a schema for the definition of
a land use, which itself is also a schema. The extension provided by the user
together with the skeletons provided by the application form the T-Box of the
knowledge base. They define the schema that is used to assert information about
the parcels of the map.
The semantic information will target geographical features from a map (loaded
via a shapefile), so there must be some correspondence between the map and the
ontology individuals. This is specified in the CoreGIS ontology, that defines the
classes Feature and Zone and some properties related to them. A geographical
feature is any element in the map, which may be a polygon, line or point. A zone
is an aggregate of geographical features. For each geographical feature present
in the map, there will be a corresponding OWL individual of the class Feature,
linked to the map by the identifier of the feature. The ontologies are not intended
to replace the shapefile, just complement it with semantic information, so it is
not necessary to express the features geometry in the ontologies.
The semantic information created in the tool is stored in a separate OWL
ontology respective to the current project. This ontology is representative of
the A-Box. It can be used to 1) perform future modifications and additions to
the information by opening the ontology in the tool 2) share the information,
exploiting the advantages offered by the Semantic Web. The articulation of all
the ontologies described in this section is summed up in Figure 1.
The tool provides a friendly user interface for the loaded land use ontology
and the georeferenced map. The user can annotate semantic information about
the map parcels without worrying about the implementation details of the on-
tologies. If it is already built an ontology representing the desired land use,
the participants of the urban planning process can use the tool without even
understanding OWL – and simultaneously take advantage of it.
4 Jorge Gomes, Nuno Montenegro, Paulo Urbano, and José Duarte
T-Box A-Box
Land Use Concretization Project Ontology
LandUseSkeleton.owl
OWL
4CitySemantics
API
CoreGIS.owl
Fig. 1. The ontology modularity that allows the tool to use any land use system. The
solid arrows represent ontology imports. The dashed arrows represent the dependencies
of the application.
4CitySemantics is developed in Java 7 over Netbeans Rich Client Platform 1 .
The ontologies are read using the OWL API2 , and the reasoning throughout the
application (which will be detailed later) is provided by the Pellet Reasoner3 .
The ontologies were built using Protégé 4.14 . The georeferenced maps are loaded
and manipulated in the application with GeoTools5 .
4 Land Use Ontologies
In this section we describe the skeleton ontology that allows any land use stan-
dard to be used in the application. An extension of this skeleton will be exempli-
fied with the LBCS6 ontology, that was developed in previous work [8]. We also
present how the application uses reasoning to offer the users relevant advantages
in this domain.
4.1 Land Use Taxonomy
Through the study of multiple land use standards we identified a few common
characteristics that formed the skeleton ontology, which is the common ground
where the actual land uses will be built upon. Namely, we found that each
land use category has an identifier (code), a color for visual identification in
maps, a title and a description. To cope with these necessities, it was created a
LandUseCategory class and data properties for the color (hasColor) and iden-
tifier (hasIndentifier), both with String as range.
The land use standards typically show some degree of hierarchic organization,
so the land use categories should be represented as classes in order to map the
1
Generic framework for Swing applications – http://platform.netbeans.org
2
Java API for manipulating OWL Ontologies – http://owlapi.sourceforge.net
3
An OWL 2 DL reasoner – http://clarkparsia.com/pellet
4
Open source ontology editor supporting OWL 2 – http://protege.stanford.edu
5
Open-source Java GIS Toolkit – http://geotools.org
6
Land Based Classification Standards – http://www.planning.org/lbcs
A Land Use Identification Tool Driven by Ontologies 5
hierarchy of land use categories. To assert information about the categories,
namely through the hasColor and hasIdentifier properties, it should be used
punning. Punning7 is a form of meta-modeling supported by OWL 2 that makes
possible to use a class in places where an individual was expected (among other
possibilities). This is achieved in the ontology by declaring an individual with
the same IRI (name) as the class that is being punned. While the object shares
the same IRI, depending on its context, it is evaluated by an OWL reasoner as
a different thing (class or individual).
To create an ontology describing a land use standard, the user has to import
the skeleton ontology, create subclasses of the LandUseCategory class, associate
them with proper annotations for label and description, and fill the color and
identifier properties using punning in the classes. We can see an example of this
in Figure 2. Additionally, if there is already an ontology describing a land use
standard, it can be linked with the skeleton, by declaring the existing categories
as subclasses of LandUseCategory and defining relations between the existing
properties and the ones from the skeleton (e.g. equivalent properties).
To show the land use taxonomy in the user interface (Figure 3), the applica-
tion retrieves the subclasses of LandUseCategory and the respective properties
in the punned individuals. The categories are displayed with all the informa-
tion available. The user can select any of these land use categories and asso-
ciate them with geographical features through a friendly user interface. The ge-
ographical features are associated to land use categories by the object property
hasLandUseCategory (Feature → LandUseCategory). When the user classifies
some features, the expression hasLandUseCategory some C, where C is the se-
lected category, is added as a type to the corresponding OWL instances of the
geographical features selected in the map.
4.2 Advantages Offered by the Reasoning Capabilities
The most popular GIS software tools, such as GRASS GIS8 or Quantum GIS9 ,
rely on relational databases to represent the information associated with the map
parcels, including the land use. In these tools, the information is not semantically
related, and so the user has the burden of, for example, maintain the integrity
of the information, or develop more complex queries to deal with the relations
between the categories. Our tool takes advantage of reasoning over the ontologies
and can overcome some of these difficulties. It is important to note that the direct
representation of the land use taxonomy (in the previous section) is only the tip
of the iceberg. The land use ontology can also contain axioms defining any other
relations between the concepts. These axioms will be used by the reasoner and
will affect the actions of the user in the tool. This raises interesting possibilities
in this domain, some of which will be presented next.
7
Metamodeling in Domain Ontologies – http://techwiki.openstructs.org/index.
php/Metamodeling_in_Domain_Ontologies
8
GRASS GIS – http://grass.fbk.eu
9
Quantum GIS – http://www.qgis.org
6 Jorge Gomes, Nuno Montenegro, Paulo Urbano, and José Duarte
Fig. 2. A screenshot of Protégé, showing the LBCS ontology [8], extending the land use
skeleton ontology. The classes (left) in bold belong to the LBCS ontology, the others
belong to the LandUseSkeleton and CoreGIS ontology. The bottom image shows the
data properties assertions that are made in the punned individual of the selected class.
Fig. 3. A screenshot of the tool. One land use category is selected (left), displaying
in the map (right) the features with that category. It shows some features selected in
the map (in yellow), and the user classifying them with the selected category. For the
selected category, it is shown the respective color, full name and description (left).
A Land Use Identification Tool Driven by Ontologies 7
Finding the features with the selected category The most obvious use
of reasoning is to find which features are directly or indirectly categorized with
a given category. This is achieved by asking the reasoner for the instances of
hasLandUseCategory some C, where C is the category. Note that this scheme
deals perfectly with hierarchically organized land use categories, for example if
we asked for Educational Services, we would also obtain features categorized
with College, High School, etc. The features belonging to the selected category
are then coloured in the map according to the hasColor property of its category.
It is also possible to conceive more general queries, such as asking for all
the features that have a defined Function (using the example of LBCS, Fig-
ure 2). This highlights in the map features with many different and disjoints
sub-categories. Each of these features is coloured according to the color of its
sub-category, resulting in a multi-colour map, useful for land use analysis.
Automatic categorization (inference) Automatic categorization of some
geographical features can be achieved through the use of equivalent classes ax-
ioms. We could for example state that all the features that have both the cat-
egory ResidenceFunction and PrivateOwnership should be categorized (are
equivalent to) as PrivateResidential. These equivalences can be anything else,
including universal and quantifier restrictions, and expressions that are not re-
lated to the land use standard itself, but some other property of the geographical
feature. This scheme can also be used to make correspondences between cate-
gories of different land use systems.
Consistency checks Through the creation of some equivalent (defined) classes
and disjoint axioms it is also possible to express some restrictions and incompat-
ibilities that will allow a more robust and error free classification. For example,
we can express that a School cannot also be a Hospital by specifying that the
classes SchoolFeature (equivalent to hasLandUseCategory some School) and
HospitalFeature (equivalent to hasLandUseCategory some Hospital) are dis-
joint. Whenever the user classifies geographical features in the application, the
consistency is checked by the reasoner. If the ontology becomes inconsistent,
that classification is immediately removed in order to revert the ontology to a
consistent state. The features that were the cause of the inconsistency are also
highlighted on the map, so the user can understand its error.
5 Conclusion
We have presented 4CitySemantics, a tool for urban planing based on semantic
web ontologies. Using the tool, the participants in the urban development process
may semantically annotate the intervention site with knowledge about the use
of the land. We show that ontologies can be useful when dealing with this type
of information and can offer some advantages over the more traditional tools,
for example the inference and consistency checks offered by the reasoning.
8 Jorge Gomes, Nuno Montenegro, Paulo Urbano, and José Duarte
We have shown how extensible OWL ontologies can be used to create a
flexible application, which adapts its behaviour based on an ontology that is
provided by the user. Despite using external ontologies, the application can inte-
grate seamlessly with them, and provides a user interface that hides the imple-
mentation details of the ontologies. Following this principle, 4CitySemantics was
designed to be independent of urban semantic standards, being easily adaptable
to different standards.
The development of the tool was followed closely by urban planning archi-
tects, the target of our application, and people familiar with geographic infor-
mation systems. It was also presented to people outside of the project. The users
considered the tool very interesting, and stressed that the possibility of using
any land use standard was important. Despite some initial scepticism from the
users familiar with the traditional GIS tools, they were pleased by the reasoning
capabilities offered in 4CitySemantics. It could effectively tackle problems they
had when working with the traditional tools, for example when dealing with
hierarchical land use taxonomies.
Acknowledgements This research originates in the City Induction project
supported by Fundação para a Ciência e Tecnologia (FCT), Portugal, hosted
by ICIST at the Technical University of Lisbon (PTDC/AUR/64384/2006) and
co-ordinated by Professor José Pinto Duarte. N. Montenegro is funded by FCT
with grant SFRH/BD/45520/2008.
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