=Paper=
{{Paper
|id=Vol-518/paper-1
|storemode=property
|title=Spatial Planning on the Semantic Web
|pdfUrl=https://ceur-ws.org/Vol-518/terra09_submission_1.pdf
|volume=Vol-518
}}
==Spatial Planning on the Semantic Web==
https://ceur-ws.org/Vol-518/terra09_submission_1.pdf
Spatial Planning on the Semantic Web
Radboud Winkels1 , Rinke Hoekstra1,2 , and Erik Hupkes1
1
Leibniz Center for Law, Universiteit van Amsterdam
Kloveniersburgwal 48, 1012 CX, Amsterdam, The Netherlands
{winkels,hoekstra,ehupkes}@uva.nl
2
AI Department, Vrije Universiteit Amsterdam
De Boelelaan 1081a, 1081HV, Amsterdam, The Netherlands
hoekstra@few.vu.nl
Abstract. Land use regulations are an important but often underrated
legal domain. In densely populated regions such as the Netherlands, spa-
tial plans have a profound impact on both (local) governments and citi-
zens. This paper describes our work on a ‘Legal Atlas’. Using Semantic
Web technology we combine distributed geospatial data, textual data
and controlled vocabularies to support users in answering questions such
as “What activity is allowed here?”. Spatial norms are represented using
OWL 2 in a way that enables intuitive visualisation of their effects: map
based legal case assessment. Users can represent a (simple) case by se-
lecting or drawing an area on the map. Given a designation for that area,
the system can assess whether this is allowed or not. The same solution
also enables the comparison of two or more sets of spatial norms that
govern the same region.
1 Introduction
Land use regulations are an important but often underrated legal domain. Espe-
cially in densely populated regions such as the Netherlands, spatial plans have
a profound impact on both (local) governments and citizens alike. They deter-
mine whether building permits are granted, businesses can expand, roads can be
widened, housing projects can be built, and even determine the chance of sur-
vival of endangered species. Although spatial regulations can be characterised
as normal regulations – given their applicability within a spatially restricted ju-
risdiction (e.g. a country) – spatial plans regulate at a much more fine-grained
level, where individual norms may apply only to specific locations or regions
(often called zones). In practice this has led to a situation where the annotated
map of a spatial plan is the dominant source of law, rather than the textual
description of the plan.
Over the past decade, internet technology significantly improved the acces-
sibility of legal sources. This development not only meant the construction of
internet portals for accessing legal texts, but also involved a standardisation of
the structural description of legal sources in terms of both web standards – such
as XML – and interchange standards – such as CEN MetaLex [1, 2].1 Given the
1
See http://www.metalex.eu
prominence of maps in spatial plans, in order to improve access to these regu-
lations a combination of existing technology for disclosing legal texts with that
currently available in geographical information systems seems inevitable. Such a
combination allows the connecting of textual descriptions as in traditional legal
sources, to the more object oriented representation of the world employed by
the maps of zoning plans. The Legal Atlas [3, 4] was the first system that com-
bined MetaLex encoded texts with the corresponding maps expressed in GML2
by using Semantic Web technology, RDF in particular.3 In this paper we show
that this proves a solid approach for extending the functionality of Legal At-
las well beyond straightforward information integration; i.e. to support not just
concept-based information retrieval, but rather map-based normative reasoning.
Legal Atlas can serve several use cases and scenarios:
Assessment – A citizen or company or a civil servant consults the regulations
to see whether a particular plan at a specific location is allowed. In case there
is the option to compensate somewhere else in order for the original plan to be
approved, the system should indicate this on the map. In the spatial planning
domain it is sometimes allowed to compensate for violating a norm in one area
in another area, e.g. compensating the building of houses in a park by turning
another area into a new park
Planning – A citizen or company consults the regulations to see where a partic-
ular plan is allowed.
Evaluation – Someone wants to or needs to check the consistency of two sets of
spatial norms. The State Council in the Netherlands for instance, may need to
check whether a zoning plan of a city council fits the overall zoning plan of the
province. A second type of evaluation by a legislative drafter for instance may
be to see what the consequences of a new set of norms might be in practice.
Suppose the idea is to forbid “Dutch coffee shops”4 in the vicinity of schools (an
actual case in Amsterdam at the moment). If we have access to a database of all
schools in a certain area with geographical location, the consequence of various
implementations of these norms (e.g. not within a radius of 500 meters, or 1000
meters etc.) can be plotted on a map. We can for instance see if any areas remain
where coffee shops would be allowed at all (cf. the “Planning” scenario above). If
we also have a database of all existing coffee shops in the area, we can determine
the consequences for these establishments.
2 Requirements
It is clear that Legal Atlas has to deal with the intrinsic heterogeneity of the in-
formation it discloses: spatial plans are not just documents, but they are closely
tied to maps. These maps are created using advanced Geographic Information
Systems (GIS) technology, and are ideal candidates for standardisation. Geo-
graphical standardisation efforts focus primarily on exchange formats, such as
2
GML: the open Geography Markup Language, see http://www.opengis.net/gml/.
3
RDF: the Resource Description Framework, see http://www.w3.org/RDF
4
Well known for other products than coffee.
GML, KML and ESRI Shapefiles,5 but also on standard web-based service pro-
tocols, e.g. the Web Map Service (WMS) and Web Feature Service (WFS) pro-
tocols.6 To comply with these standards, geographical information servers are
required to implement basic facilities for spatial reasoning, such as determining
the overlap between two polygons (regions).
The free availability of web-based application programming interfaces, most
notably the Google Maps API, has exposed this technology to the open source
community at large, and has already led to various mash-ups of incredibly diverse
data and maps: geo-tagging of photos allows us to search for pictures of food
in Thailand, we can view map overlays of traffic conditions all over the world,
and even consult weather radar imagery. In all of these cases, the key means
to tie together the two types of information – photos and maps, traffic data
and maps, or legal texts and maps – is to determine the overlap of data that
is meaningful in both domains. Where for traffic data, the location (stretch of
road) and traffic density are obvious candidates, in spatial planning one typically
depicts the category of land use applicable to each area. Therefore, to connect
maps to texts, and vice versa, they should be annotated using the same set of
metadata [3].
However, for any given region a multitude of different regulations applies.
These regulations may be issued by different government bodies with overlapping
jurisdiction; government bodies that each maintain a categorisation specifically
targeted towards the types of maps it produces. This is problematic for a number
of reasons. Firstly, users of a system that discloses these regulations may not care
for, or even be aware of the borders and interaction between these jurisdictions.
The system should therefore be able to present all applicable regulations in
a transparent fashion, on a single map. Secondly, if for instance land use in
regulations issued by adjoining municipalities is categorised differently, the land
use ‘regime’ in these municipalities cannot be compared.
Comparability of regulations is not just beneficial to individuals or businesses
that need to decide where to conduct a certain activity, it is a crucial ingredient
for the harmonisation of these regulations. Ideally, categorisations are therefore
shared among governments, even when these governments issue their respective
regulations in different languages, as is e.g. the case within the EU.
The need for shared categorisation of types of land use has been acknowledged
in a number of standardisation initiatives. For instance, the IMRO 2006 standard
was issued by the Dutch ministry of spatial planning and environment, and is
currently obligatory for all urban planning by municipalities in the Netherlands.7
IMRO specifies a strict categorisation system for all sanctioned types of land use
5
KML, the Keyhole Markup Language is Google’s variant of GML, see http://earth.
google.com/intl/en/userguide/v4/ug_kml.html. ESRI is a leading developer of
GIS software, see http://www.esri.com/library/whitepapers/pdfs/shapefile.
pdf for the technical description of ESRI Shapefiles.
6
Both WMS and WFS are standards of the Open Geospatial Consortium, see http:
//www.opengeospatial.org/.
7
IMRO: Informatiemodel Ruimtelijke Ordening (information model for spatial plan-
ning), see http://www.geonovum.nl/informatiemodellen/imro-2006/ (Dutch).
at multiple levels of government, and even prescribes the colours to be used on
compliant maps. At the European level, the INSPIRE initiative similarly aims to
standardise the spatial information published by governments across all member
states.8 Although not specifically targeted to spatial information, the GEMET
thesaurus provides a huge multilingual vocabulary for information related to the
environment.9 The 5000+ terms defined in GEMET are organised as a hierarchy,
and available in 27 languages, which makes it ideally suited for cross language
mapping of terms.
Where IMRO and INSPIRE introduce a top-down standard for direct ex-
change of information between organisations within the same domain, GEMET
functions rather as umbrella that provides a unifying framework for information
exchange between organisations that deal with different (sub) domains. For in-
stance, GEMET identifies 40 themes ranging from ‘forestry’ to ‘physics’, that are
of relevance to all levels of government. An integrated, transparent unlocking of
content tagged using such different schemes must take into account a mapping
between them. Of course, an additional challenge is that different categorisa-
tion schemes may rely on different technology. Although GEMET is defined as
a SKOS vocabulary, IMRO is specified as an XML Schema, and accompanying
UML diagrams.10
A design goal of both IMRO and INSPIRE is that maps produced at differ-
ent levels of government may be depicted as layers on a single map. This poses
interesting opportunities from a legal perspective. Quite distinct from ordinary
maps, governments issue spatial plans not to describe the existing situation on
the ground, but rather to prescribe the restrictions and rights associated with
geospatial objects. [4] identify two important characteristics of a map-based
interface to regulations. Firstly, hierarchical relations between authorities are
mirrored in spatial inclusion relationships. We can exploit knowledge of the
regulatory bodies involved to resolve normative conflicts between overlapping
regions by applying the Lex Superior priority ordering of norms, which states
that ’higher’ norms prevail over ’lower’ norms (e.g. national legislation defeats
local regulations if these conflict).
Secondly, geospatial adjacency information can be used to determine indirect
effects; rules and restrictions can be naturally grouped by area of effect, e.g. to
prevent the development of an industrial zone right next to a nature reserve. We
can use adjacency information to apply a larger number of relevant norms to an
area, than we would be able to without this information. This information may
be a valuable contribution to cross-border decision making processes.11
8
INSPIRE: Infrastructure for Spatial Information in the European Community, see
http://inspire.jrc.ec.europa.eu/.
9
GEMET: GEneral Multilingual Environmental Thesaurus, see http://www.eionet.
europa.eu/gemet.
10
SKOS: Simple Knowledge Organization System, see http://www.w3.org/TR/
skos-reference.
11
This is in fact the main focus of the FEED project.
Maps thus provide intuitive handles for evaluating the normative content
of spatial plans. However, although even the most rudimentary geoservers (e.g.
MySQL with spatial extensions12 ) provide some form of spatial reasoning, no
such functionality exists for the normative aspects of spatial plans. The Legal
Information Server (LIS) of [5] was the first attempt to provide normative reason-
ing as a service. Its main task was to assess whether some situation description
is allowed or disallowed given a set of norms [6]. Where the LIS implementation
depended on a custom-built representation in Prolog, the more recent imple-
mentation of [7] represent regulations using OWL 2 DL [8].13
Arguably, the use of a restricted language such as OWL 2 DL poses some
problems for the representation of regulations. Firstly, certain aspects of legal
reasoning may be hard or impossible to represent using a language that depends
on monotonic reasoning. Especially considering the commonly held claim that
legal reasoning is in principle defeasible [9]: reasoning follows a dialectic process
of argumentation by which contrary positions are continuously attacked and re-
vised. The use of a defeasible logic serves a practical purpose in that it enables
us to circumvent situations where normative conflicts give rise to logical contra-
dictions: considered separately, each defeasible state is consistent, and without
taking their defeasibility into account, the whole would be inconsistent. Mono-
tonic approaches consider each state separately, depending on knowledge-base
updates for moving from one state to another. Given the limited task at hand –
normative assessment – and a user interface where users may ‘play around’ with
possible input, the restricted expressiveness of DL is instrumental to our pur-
poses in that it offers guarantees to response times [10]. Furthermore, the work
of [7] has shown that a significant portion of exceptions between norms, such as
lex specialis, can be dealt with without resorting to defeasible representations.
A second potential problem for using OWL 2 DL concerns not the complexity
of legal reasoning, but rather the complexity of the world that is governed by law
[11]. To retain decidability, OWL 2 DL is restricted to models that have the ‘tree
property’: situations that describe complex configurations of multiple objects can
be only approximately defined in DL [12, 11]. In the case of spatial information,
this may pose problems in the description of relations between areas. Consider
for instance the definition of a body of water, e.g. a river R, that separates two
areas of land, L1 and L2 . In OWL 2 DL, and other decidable description logics,
R cannot be defined as it is impossible to exclude in its definition a situation
where L1 and L2 overlap.
However, the domain itself is otherwise at a relatively high level of abstraction
that excludes the complex structures found in domains such as biology and
engineering. Not only can spatial reasoning be delegated to a geoserver; the land
use categories defined in IMRO and INSPIRE provide a standard intermediary
for communicating normative content (see Section 4).
12
See http://www.mysql.com.
13
OWL 2 is an extension of the OWL Web Ontology Language, currently in Proposed
Recommendation stage of the W3C standardisation procedure, see http://www.w3.
org/2007/OWL.
la:Region
metalex:region geo:id
la:land_use
metalex:ASource geoserver:Polygon
la:Land_Use
Fig. 1. Relations between maps, metadata and texts in Legal Atlas.
A final requirement for the system described here, is that it should cater for
not just heterogeneity in content, but also in location. We have argued that the
regulations issued by different authorities are to be made visible on the map
to improve transparency and comparability. However, these regulations are also
maintained by these authorities, and are continuously subject to change. It is
important therefore that the spatial and semantic representation of regulations
should likewise be maintained by their respective owners. The system should
therefore be able to cope with distributed content and semantics: a prime use
case for Semantic Web technology [13].
3 Maps, Metadata and Texts in a Prototype System
We represent the relation between texts, maps, and metadata as depicted in Fig-
ure 1. Texts are annotated using metalex:region properties that point to a region.
The la:Region class describes all individual regions in a spatial plan. Regions are
related to polygons stored in a GeoServer instance via the geo:id property, and
to an individual of type la:Land Use via the la:land use property. Contrary to the
initial approach [3], these individuals are described as SKOS concepts, i.e. they
are simultaneously of type skos:Concept and of type la:Land Use. Consequently,
land use categorisations such as captured by IMRO and GEMET are represented
as separate skos:Schema hierarchies of skos:Concepts. E.g. a model that allows
only the use of land use categories from GEMET and IMRO defines the class
la:Land Use as those individuals of type skos:Concept that have a skos:inScheme
relation with either gemet:gemet or imro:imro2006. See also Figure 2.
This approach leaves freedom to specify restrictions on combinations of land
use independently from either IMRO or GEMET, which benefits maintainability
of our models. The next sections present the architecture of a prototype imple-
mentation, and describe how the representation of regions and land use has been
incorporated in the specification of spatial norms.
The current version of Legal Atlas is a prototype system that allows users
to query for potential conflicts.14 Given a set of land use categories, the system
14
See http://feed.leibnizcenter.org.
Plan Description
Designations Region
land use regions with use
(OWL Classes) (OWL Classes)
e rdf
:typ :typ
rdf e
GeoServer
IMRO 2006 la:land_use Region
geo:id
(SKOS Vocabulary) (OWL Instances)
rdf: type
Normative Descriptions
allowed and disallowed situations
(OWL Classes)
Plan Regulations
asserted type mapping to database usage in OWL restriction
inferred type asserted property
Fig. 2. Relations between knowledge sources in the new Legal Atlas.
shows all regions that conflict with an overlapping region that has one of the cat-
egories from that set. It relies on two web services: a SPARQL endpoint, allowing
the querying of our Sesame RDF repository, and a WFS service (Web Feature
Service), that allows us to retrieve geospatial information from a GeoServer in-
stallation.15 Maps in the RDF repository are straightforwardly represented by
means of the feature identifiers (i.e. through the geo:id property, see Figure 1)
used in GeoServer. GeoServer is designed to store and reason on polygons, and
supports the determination of certain spatial relations between features, allowing
us to represent relations such as e.g. la:overlaps, la:next to, and la:within 500 m
as queries on the WFS service. The result of these queries can be added as
relations to the RDF repository. Reasoning services are provided through the
SwiftOWLIM inference layer on the Sesame repository.16
4 Representing Spatial Norms
We distinguish the possible violation of land use from a conflict between spatial
norms:
– A region stands in some relation to another region that is incompatible with
their respective types of land use. For instance, an industrial area overlaps
with a nature reserve.
15
See http://geoserver.org
16
SwiftOWLIM provides a limited subset of OWL DL inferences. See http://www.
ontotext.com/owlim/.
la:Region la:Land_Use skos:Concept
la:land_use some ex:Industry
ex:Industry
la:land_use some ex:Nature
ex:IndustryRegion rdf:type
rdfs:isDefinedBy
rdf:type
ex:NatureRegion rdf:type
ex:Nature
rdf:type
rdf:type
rdfs:isDefinedBy
rdf:type
rdf:type
ex:region_industry la:land_use imro:industry
overlaps
ex:region_nature la:land_use imro:nature
rdf:type
ex:IndustryRegion and la:overlaps some ex:NatureRegion
rdf:type
norm:ConflictRegion norm:NoOverlapIN
Fig. 3. Representation of norms in spatial regulations. Ovals indicate classes, solid
boxes instances, dashed boxes restrictions, solid arrows explicit relations, dashed arrows
inferred relations.
– Two types of land use are deemed compatible by one regulatory body,
whereas they are seen as incompatible by another.
In this paper, we focus on the first issue.17
The normative content of spatial plans is represented by specifying OWL
descriptions of those situations, e.g. regions, that are allowed or disallowed by
a spatial plan. Indeed, spatial plans usually only specify the areas where a par-
ticular type of land use is allowed, rather than the exclusion relations between
types of land use. The latter is typically delegated to the land use categorisa-
tion schema. For instance, IMRO prescribes that no piece of land can have more
than one type of land use, with the exception of a fixed set of allowed ‘double
designations’.
Figure 3 illustrates the representation of spatial norms and land use in Legal
Atlas. It depicts two types of land use: ex:Industry and ex:Nature, both of which
are subclasses of the general class of la:Land Use:
ex:Industry v la:Land Use
ex:Nature v la:Land Use
17
For a more general approach for dealing with conflicts between norms using standard
OWL 2 DL reasoning, we refer to [7].
Correspondingly we define two regions, ex:IndustryRegion and ex:NatureRegion,
both of which are subclasses of la:Region:
ex:IndustryRegion ≡ (la:land use some ex:Industry) u la:Region
ex:NatureRegion ≡ (la:land use some ex:Nature) u la:Region
We map the corresponding land use categorisations from IMRO by assigning
them as individuals belonging to the respective classes. This representation has
a number of benefits, the most important being that our representation of land
use is independent of the categorisation scheme adopted. For instance, we can
map gemet:industry to ex:Industry in the same way, allowing us to specify norms
on both IMRO and GEMET encoded maps.
norm:NoOverlapIN is a norm that states that an overlap between these two
types of regions is not allowed. In simplification of the approach presented in
[7], where norms are kept disjunct from the domain being governed, this norm is
represented as a subclass of ex:IndustryRegion with the restriction that the region
overlaps with a region of type ex:NatureRegion. This norm is simultaneously
defined as a subclass of norm:ConflictRegion:
norm:NoOverlapIN ≡ ex:IndustryRegion u la:overlaps some ex:NatureRegion
v norm:ConflictRegion
As an illustration of the representation, consider the following spatial plan, spec-
ifying a single region with land use imro:nature:
ex:region nature ∈ la:Region
la:land use(ex:region nature, imro:nature)
A user draws a region ex:region industry and specifies its land use to be imro:industry.
The system queries the GIS server to find all regions that ex:region industry
overlaps with. Suppose, in this case we find ex:region nature, we then update the
repository with the assertion:
la:overlaps(ex:region industry, ex:region nature)
The third step is to perform OWL 2 DL realization on the knowledge base.
This will infer that ex:region industry ∈ ex:IndustryRegion and ex:region nature ∈
ex:NatureRegion (the dotted lines in 3). However, ex:region industry also meets
the requirements of norm:NoOverlapIN, and we can therefore infer ex:region industry ∈
norm:NoOverlapIN.
Finally, the system will gather all individuals of the class norm:ConflictRegion
using a simple SPARQL query:18
SELECT ?region
WHERE { ?region rdf:type norm:ConflictRegion .}
Because norm:NoOverlapIN is a subclass of norm:ConflictRegion, ex:region industry
will be bound to the ?region variable. The system will bring this fact to the
attention of the user by highlighting his region on the map.
18
http://www.w3.org/TR/rdf-sparql-query/
5 Conclusions and Discussion
This paper presents an approach for specifying spatial norms using Semantic
Web technology that enables an intuitive way of visualising their effects: map
based legal case assessment. Users can see what is allowed and what not in
specific areas on the map, they can represent a (simple) case by selecting or
drawing an area on the map. Given a designation for that area, they can have the
system assess whether this is allowed or not. The same solution also enables the
comparison of two or more sets of spatial norms that govern the same region, e.g.
coming from a municipality and the province it is part of. We have demonstrated
a practical use of the case assessment method specified in [7] using OWL 2 DL,
and presented a prototype system that provides a partial implementation of the
approach.
The simple assessment of cases or the detection of conflicts does not consti-
tute full legal reasoning, but Legal Atlas gives users the opportunity to experi-
ment with their plans and prevailing norms. By using various queries they can
find out where their plans might be allowed and where they will meet resistance.
A next step will be explaining or justifying the results. Partly this can be done
by referring to the original sources of law through the links to the MetaLex rep-
resentation (see Figure 1). For explaining the OWL 2 DL reasoning we may be
able to use built in functionality of e.g. the Pellet reasoner (cf. [14]).
A drawback of the current representation is that since conflicts are repre-
sented at the class level, they cannot be queried at the instance level. For in-
stance, this means that we currently cannot query the system for all regions that
have a land use which excludes that of a hypothetical new region: the exclusion
relations do not hold between the la:Land Use individuals directly, but are only
inferred on the fly, for concrete situations. Ideally, one would therefore like the
exclusions between types of land use specified at the class level, to propagate to
all instances of these classes. One way to achieve this is by introducing a com-
plex combination of OWL 2 DL role chains, self restrictions and the universal
property [8, 15]. A similar approach has been described in [11] in the context of
processes and actions, and we are currently investigating its use for the problems
described here.
Future research will be directed towards more advanced spatial and numerical
reasoning and the use of dynamic data to cater for the planning (compensate for
the loss of x square meters of nature by creating the same area somewhere else)
and evaluation scenarios specified in Section 2. A promising development is the
recent addition of a spatial reasoning module to the SWI-Prolog engine in the
POSEIDON project.19 Together with the Semantic Web library of this Prolog
implementation, the reasoning services required by the application sketched here
may be combined in one service.
19
See http://www.swi-prolog.org and http://www.esi.nl/short/poseidon/ re-
spectively
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