=Paper=
{{Paper
|id=Vol-2977/paper11
|storemode=property
|title=A SKOS Taxonomy of the UN Global Geospatial Information Management Data Themes (short paper)
|pdfUrl=https://ceur-ws.org/Vol-2977/paper11.pdf
|volume=Vol-2977
|authors=Beyza Yaman,Kevin Thompson,Rob Brennan
|dblpUrl=https://dblp.org/rec/conf/esws/Yaman0B21
}}
==A SKOS Taxonomy of the UN Global Geospatial Information Management Data Themes (short paper)==
https://ceur-ws.org/Vol-2977/paper11.pdf
A SKOS Taxonomy of the UN Global Geospatial
Information Management Data Themes?
Beyza Yaman,1 Kevin Thompson2 and Rob Brennan1
1
ADAPT Centre, Dublin City University, Dublin, Ireland
2
Ordnance Survey Ireland, Dublin, Ireland
{beyza.yaman,rob.brennan}@adaptcentre.ie, kevin.thompson@osi.ie
Abstract. Complex data domains increase the difficulty of structuring,
sharing, discovering and governing information. For the geospatial do-
main common models such as INSPIRE have been established in the
European Union. The United Nations initiative on Global Geospatial
Information Management (UN-GGIM) draws together national and re-
gional capacities. Interoperability is the main principle behind these ini-
tiatives. Nonetheless there is a lack of published research to date on map-
ping agency geospatial linked data leveraging the UN-GGIM taxonomy
of information management data themes. Thus, we have identified use
cases and defined a Simple Knowledge Organization System (SKOS)[3]
taxonomy expressing the UN-GGIM data themes for national spatial in-
frastructure. This has been applied in a metadata generation and report-
ing tool for Ordnance Survey Ireland (OSi) which underpinned improved
governance and reporting infrastructure in OSi. This demonstrated the
contribution of Semantic Web technology to spatial data governance as
well as its importance for data publishing. This paper presents a doc-
umented open license SKOS taxonomy for the UN-GGIM data themes
that follows Linked Data best practices. It provides a set of three use
cases, an overview of UN-GGIM theme definitions and an example ap-
plication of the taxonomy for deployment in OSi for DCAT metadata
generation and data publishing pipeline reporting.
1 Introduction
Geospatial data is essential in part due to its importance in social, economic,
and environmental policy formation and decision making. In the geospatial data
domain, large organizations -especially, ones working at a National level- have
to confront high data heterogeneity due to the need to collect, analyze and share
information within national, regional and global policy frameworks. Interopera-
ble aggregation and reporting of this geospatial statistical information is key to
avoid data quality problems and to streamline data transfers and management.
Interoperability requires common vocabularies, models, meta-data and interfaces
for creating, reporting on and curating geospatial statistical data.
?
Copyright © 2021 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
2 B. Yaman et al.
In these circumstances, developing standards and norms becomes crucial to
providing a coherent service infrastructure and to create meaningful relations
among data integrated from multiple organizations. In order to boost countries’
geospatial data activities, the United Nations has developed a framework called
the Global Geospatial Information Management (UN-GGIM) Data Themes [4].
These themes are a minimum set of concepts to be used to label datasets in
order to enable interoperability between national mapping agencies. They form
a foundation to support global geospatial information management and an in-
tegrated geospatial information framework to strengthen geospatial information
sharing among other global initiatives[4]. They are similar in concept to the EU
INSPIRE data themes but with global scope. However, to date it was seen that
no paper was published establishing a geo Linked Data approach to supporting
UN-GGIM metadata and no documented UN-GGIM data themes use cases for
Semantic Web technology.
Thus this paper explores the research question of to what extent a geo Linked
Data approach could increase the interoperability of geospatial data by modelling
UN-GGIM concepts and using standardized tools to report on national geospa-
tial data production in terms of UN-GGIM data themes in an understandable
manner. In order to solve this problem, we created a taxonomy for the UN-
GGIM Data Themes to classify the data and provide a meaningful relationship
among the data. The contribution of this paper is a new SKOS vocabulary [3]
that can be used to enhance geospatial datasets with the UN-GGIM data themes
concepts to increase the interpretability of the data.
The remainder of this paper is structured as follows: Section 2 discusses
Related Work and Section 3 describes the Use Case for Ordnance Survey Ire-
land (OSi) deployment. Section 4 introduces the UN-GGIM Data Themes and
Section 5 demonstrates the UN-GGIM taxonomy development and data theme
solution for the OSi National Map dataset (Prime2). Finally, the paper provides
conclusions in Section 6.
2 Related Work
The INSPIRE Data Themes3 are the first set of classification themes imple-
mented for geospatial data within the scope of the INSPIRE Directive in the
EU. An RDF model version has been developed for the description of the data
themes and provided to end-users such as national mapping agencies so that ap-
propriate descriptive metadata could be generated for the datasets. The model
consists of 34 INSPIRE data themes which makes the model more detailed than
UN-GGIM themes.
The UN-GGIM Secretariat has provided an interactive web interface im-
plemented by the ArcGIS tool employing the UN-GGIM data themes4 . The
interface demonstrates the use case examples for statistical reporting based on
UN-GGIM data themes and how they can be used in the context. The use cases
3
https://inspire.ec.europa.eu/theme
4
https://www.arcgis.com/apps/Cascade/index.html?appid=4741ad51ff7a463d833d18cbcec29fff
UN-GGIM Data Themes 3
include population distribution, transport network maps integrating the stan-
dardised, fundamental data from national mapping agencies, statistical offices
and other institutions.
The W3C’s data catalog vocabulary standard, DCAT [1], allows the descrip-
tion of data themes and they are proposed as recommended properties by the
DCAT application profile (DCAT-AP) for describing a resource (dataset, data
service or dataset distribution). The DCAT vocabulary provides two different
properties for describing data theme information: the dcat:themeTaxonomy
property for the schema to be used for data themes, and the dcat:theme prop-
erty for the specific “theme/category” from the available theme options5 . Thus
a DCAT record can be used to describe a dataset and associate it with one or
more data themes in one or more data theme taxonomies.
3 Use Cases
Ordnance Survey Ireland (OSi) is the national mapping agency of Ireland that
produces geospatial data. The data is first captured through surveying the land
using aeroplanes and then adjusted by surveyors if necessary. This digital data
is typically in image or point cloud formats and later converted and stored
in an Oracle Spatial and Graph database using the Prime2 model. Prime2 is
the object-oriented spatial model of over 50 million spatial objects tracked in
time followed by conversion for printing as cartographic products or data sales
and distribution by OSi. After going through transformation phases the data is
provided in different formats either with open access or with a cost to private
end-users, stakeholders, or other governmental institutions.
Acting as a source of spatial data, OSi has to comply with several statisti-
cal regulations such as National, European Directives like INSPIRE and global
statistical reporting like UN agencies. OSi reporting use cases must take into
account three different aspects: i) Classification of the data: The end-users need
a common understanding of key characteristics of the data they are consum-
ing. For instance, an insurance company is interested in subdatasets related to
flood risk management, or a post office is interested in addresses. ii) Report-
ing to stakeholders: Institutions like the United Nations and the Organisation
for Economic Co-operation and Development (OECD) demand the standardiza-
tion of geospatial statistical data according to the field of geospatial information
management. It is also mandatory for INSPIRE Directive states, to share the
country spatial data and metadata created for spatial data sets and services
corresponding to the themes through interoperable infrastructures. iii) Dataset
metadata generation: Internally the departments need to catalog their data and
provide information about suitability for intended uses. This allows high level
monitoring of the data including quality scores or provenance. The lack of pro-
viding the data and metadata in an interoperable and standardized way can
result in failures in the system, reduced co-operation with partners and fines or
other penalties for non-compliance.
5
https://www.w3.org/TR/vocab-dcat-2/#Property:catalog themes
4 B. Yaman et al.
4 UN-GGIM Data Themes
The UN-GGIM created strategic pathways6 proposing implementation and cus-
todianship guidelines for best practices in collection and management of inte-
grated geospatial information to establish a global geospatial data framework.
This way UN-GGIM supports and guides the geospatial data infrastructure of
the member countries through the approach, content, rationale, options and con-
siderations, principles that align with actions, and sample outcomes for compar-
ison [4]. UN-GGIM Data Themes are the set of prioritized national data themes,
aligned to the globally endorsed fundamental geospatial data themes. 14 Global
Fundamental Geospatial Data Themes are proposed to be used by the strategic
pathways to classify geospatial data. The 14 data themes from UN-GGIM are
as follows [UN-GGIM]: Global Geodetic Reference Frame (GGRF), Addresses,
Buildings and Settlements, Elevation and Depth, Functional Areas, Geograph-
ical Names, Geology and Soils, Land Cover and Land Use, Land Parcels, Or-
thoimagery, Physical Infrastructure, Population, Distribution, Transport Net-
works, and Water. Theme descriptions can be found in the Global Fundamental
Geospatial Data Themes document[2].
Data practitioners can follow these strategic plans and data themes to design,
develop, maintain a high standards, high quality and sustainable geospatial data
infrastructure. On the other hand, the data themes also allow the data to be
analysed and provide statistical results w.r.t. the specific data theme such as
population distribution in a specific area, e.g. for policy development. They
are specifically important for organizing a country’s geospatial, statistical and
other information. Fundamental data themes (e.g. transportation) are required
for a broad range of decision-making applications, or application data themes
(e.g. flood models) required for specific studies; and socio-economic data themes
that provide demographic information, such as census and population data 7 .
Integrating data themes information into a data catalogue allows end-users to
decide the suitability of the dataset for their purpose.
5 Taxonomy Development
This section describes the implementation of the taxonomy for the OSi use case.
UN-GGIM Data Themes Taxonomy was designed and implemented as a Linked
Data controlled vocabulary to provide common and standardized definitions for
managing the data. SKOS concept taxonomy was selected as the most appro-
priate modelling language for this task and it corresponds to the DCAT data
theme property requirements. A 3 step methodology was followed to create this
vocabulary. First, UN-GGIM Data Themes were encoded to RDF to generate
the taxonomy in Linked Data format. Second, error checking process was per-
formed to validate the data. Third, the generated vocabulary was published on
the web.
6
https://ggim.un.org/IGIF/part2.cshtml
7
https://ggim.un.org/IGIF/documents/SP4-Data 10Jan2020 GLOBAL CONSULTATION.pdf
UN-GGIM Data Themes 5
unggim−dt : B u i l d i n g s −S e t t l e m e n t s
a s k o s : Concept ;
skos : broader unggim−dt : DataTheme ;
skos : prefLabel ” B u i l d i n g s and S e t t l e m e n t s ”@en ;
s k o s : n o t e ”A B u i l d i n g r e f e r s t o any r o o f e d s t r u c t u r e p e r m a n e n t l y
c o n s t r u c t e d o r e r e c t e d on i t s s i t e , f o r t h e p r o t e c t i o n o f humans , a n i m a l s
, things , or the pr od uc ti on o f economic goods . S e t t l e m e n t s ar e c o l l e c t i o n s
o f b u i l d i n g s and a s s o c i a t e d f e a t u r e s where a community c a r r i e s o u t
s o c i o −e c o n o m i c a c t i v i t i e s . ” .
Listing 1.1. Example Data Theme Snippet
Each proposed theme in the UN-GGIM data themes was defined as a SKOS
concept class. All the classes in the data theme vocabulary were collected under
a generic Data Theme class which was defined as a top concept of the scheme
and the themes were described as the narrower concepts of this concept. The
created vocabulary is available online and it can be seen partially in Listing 1.1.
Since there is no direct relation between concepts they have been classified under
the top concept of data theme8 .
OSi have identified the UN-GGIM data themes as an important framework
for reporting to their stakeholders while using Prime2 dataset. A key issue was
how to map or present the contents of the 50 million spatial objects captured
in Prime2 as data themes. This would enable adding additional meaning that
not only humans but also machines could use and interpret the data. In turn
this would enable Prime2 data quality reports to be generated for a specific
data theme or group of data themes. At first glance it seems that Prime2 (as a
universal model) spans all UN-GGIM data themes. However within Prime2 there
are different types of spatial classes totaling to 34, e.g. Building and Locale
datasets are spatial object classes in the Prime2 dataset so each class can be
considered to form a sub-dataset in Prime2. Our approach has been to create a
mapping between Prime2 spatial objects and UN-GGIM data themes. Then a
DCAT record can be created for each sub-dataset and this in turn enables it to
be assocated with one or more data themes. This enhanced data catalog for OSi
data assets enables us to write queries for parts of Prime2 that correspond to
specific data themes and to associate the quality data for those spatial objects
with the data theme.
It was important to take into consideration what type of processes will be
performed upon these datasets and decide the relevant themes for each class.
The created vocabulary is used to organize Prime2 dataset. The datasets (spatial
classes) were enriched by one or more than one label using the UN-GGIM themes.
The preliminary version of the assignments are performed as in Table 1. There
are 3 options for a relationship between a Prime 2 spatial object class and a data
theme category: partial match, full match (yes in table), no match (no in table).
A full match means all the instances of a class are relevant to the data theme. A
partial match means some instances of the class are relevant to the data theme.
No match means it does not have any instances related to that data theme.
8
https://linkeddataops.adaptcentre.ie/vocabularies/unggim-data-themes
6 B. Yaman et al.
Table 1. Example OSi Prime2 Sub-Datasets to UN-GGIM Data Theme Mapping
Buildings and Geographical Transport
Addresses Water
Settlements Names Networks
Boundary Area partial partial yes no no
Building yes yes partial no no
Locale yes yes yes no no
Site partial yes yes no no
Water no no partial yes yes
Way yes no partial yes no
A DCAT catalog description (Listing 1.2) was created for each datasets -
including subdatasets- in the pipeline. Listing 1.2 presents this description sam-
ple of Building dataset which was enhanced by Buildings-Settlements
theme relation. This enabled us using a standard model to describe the metadata
features from multiple vocabularies and having different values throughout the
time. This description was used as a metadata repository to enable effective data
governance controls to store and track the data practically. Beside the advan-
tages adding to the data practitioners, the themes add value to the data in the
international level. For instance, the data with transport network theme allows
to track the transportation ways around Europe and between the continents.
The dashboard (Fig. 1) presents the reporting features based on the OSi
data catalog which includes data theme, provenance and data quality metadata
about each dataset and subdataset. This is a part of the LinkedDataOps project
which aims to manage the data in a useful way [5]. The dashboard page has
several filters on the left side of the page which allows users to click interactively.
Users can pose various queries with different views by clicking on the filters
and visualize the dataset relations semantically. The description of the data
themes allowed an easy classification and exploration process of the datasets in
the pipeline. Thus, while the classification of subdatasets helps OSi to better
understand, classify and provide the data, it also helps the end users to consume
the parts of data they need. This approach brings convenience not only to the
data producer but also to the end user of the data.
a dcat : Dataset ;
a prov : E n t i t y ;
dc : t i t l e ” B u i l d i n g ”@en ;
dc : d e s c r i p t i o n ” B u i l d i n g i s a permanent r o o f e d c o n s t r u c t i o n ,
c u r r e n t l y o r f o r m e r l y u s e d o r i n t e n d e d f o r s h e l t e r . The c o n s t r u c t i o n
must have permanent f o u n d a t i o n s . A work under c o n s t r u c t i o n , i s i n c l u d e d
a s a B u i l d i n g , i f i t i s a p p a r e n t t h a t , on c o m p l e t i o n , i t w i l l meet t h e
d e f i n i t i o n f o r B u i l d i n g . A s t r u c t u r e that i s i d e n t i f i a b l e as having once
been a B u i l d i n g but which no l o n g e r has a r o o f , i s i n c l u d e d a s a
B u i l d i n g . ”@en ;
d c t : c r e a t e d ”2019−01−09” ˆˆ xsd : d a t e ;
d c t : m o d i f i e d ”2020−01−09” ˆˆ xsd : d a t e ;
d c a t : theme unggim−dt : A d d r e s s e s , unggim−dt : B u i l d i n g s −S e t t l e m e n t s .
Listing 1.2. Part of OSi Data Catalog
UN-GGIM Data Themes 7
Fig. 1. OSi Dasboard for Data Catalog
6 Conclusions/Future Work
A UN-GGIM data theme taxonomy was implemented in order to increase inter-
operability, facilitate the integration of non-centralized data, and improve search
facilities with specific data themes. It is anticipated that using this taxonomy
will be used as a means of integrating geospatial data in the national and in-
ternational level and will address global challenges and help build international
awareness. As a future work, we would like to map the UN-GGIM data themes
to INSPIRE data themes in order to improve the interoperability further.
Acknowledgement This research received funding from the European Union’s
Horizon 2020 research and innovation programme under Marie Sklodowska-Curie
grant agreement No. 801522, by Science Foundation Ireland and co-funded by the
European Regional Development Fund through the ADAPT Centre for Digital
Content Technology [grant number 13/RC/2106] and Ordnance Survey Ireland.
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