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Abstract

Collaborative custodianship refers to an arrangement where a number of custodians work together to produce integrated datasets for a spatial data infrastructure (SDI), e.g. local authorities contributing address or street data to a national SDI dataset. Collaborative cloud mapping allows for ubiquitous, convenient, on-demand, configured and tailor-made mapping with resources shared between various entities collaborating on a specific initiative, such as an SDI or for disaster management. This paper presents the results of a workshop in South Africa during which case studies from the Netherlands, Belgium and Austria of collaborative custodianship of address data were presented, and OpenStreetMap as a case study of collaborative cloud mapping. Subsequently, challenges and opportunities for implementing similar initiatives in the context of the South African SDI were debated in break-away sessions. The results from these sessions were analysed using the PESTEL framework.
Collaborative Custodianship through Collaborative Cloud
Mapping: Challenges and Opportunities
Serena Coetzee a, *, Jacques Du Preez b, Franz-Josef Behr c, Antony K Cooper a,d, Martijn Odijk e,
Siegfried Vanlishout f, Raf Buyle g, Markus Jobst h, Maroale Chauke i, Nicolene Fourie j, Peter
Schmitz a,k, Frikan Erwee a
a Centre for Geoinformation Science, Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria,
South Africa; serena.coetzee@up.ac.za, ferwee@gmail.com
b Western Cape Government, Department of the Premier, Cape Town, South Africa; jacques.dupreez@westerncape.gov.za
c Stuttgart University of Applied Sciences, Stuttgart, Germany; franz-josef.behr@hft-stuttgart.de
d CSIR Built Environment, Pretoria, South Africa; acooper@csir.co.za
e Policy Geo Information (National SDI), Ministry of the Interior and Kingdom Relations, Netherlands; Martijn.Odijk@minbzk.nl
f Informatie Vlaanderen, Flanders, Belgium; siegfried.vanlishout@kb.vlaanderen.be
g imec IDLab - Ghent University, Ghent, Belgium; raf.buyle@ugent.be
h Research Group Cartography, Vienna University of Technology, Vienna, Austria; markus@jobstmedia.at
i Committee for Spatial Information, Directorate: NSIF, South Africa; maroale.chauke@drdlr.gov.za
j CSIR Meraka Institute, Pretoria, South Africa; nfourie@csir.co.za
k University of South Africa, Pretoria, South Africa; schimpmu@unisa.ac.za
* Corresponding author
Abstract: Collaborative custodianship refers to an arrangement where a number of custodians work together to produce
integrated datasets for a spatial data infrastructure (SDI), e.g. local authorities contributing address or street data to a
national SDI dataset. Collaborative cloud mapping allows for ubiquitous, convenient, on-demand, configured and tailor-
made mapping with resources shared between various entities collaborating on a specific initiative, such as an SDI or for
disaster management. This paper presents the results of a workshop in South Africa during which case studies from the
Netherlands, Belgium and Austria of collaborative custodianship of address data were presented, and OpenStreetMap as
a case study of collaborative cloud mapping. Subsequently, challenges and opportunities for implementing similar
initiatives in the context of the South African SDI were debated in break-away sessions. The results from these sessions
were analysed using the PESTEL framework.
Keywords: SDI, spatial data infrastructure, custodianship, collaborative mapping, cloud
1. Introduction
Drawing on the definitions for ‘infrastructure’ in
Dictionary.com (2018) and Wiktionary (2018), a spatial
data infrastructure (SDI) can be defined as the facilities,
services, systems and installations to provide a country,
city or area with spatial data and services that are required
for the functioning of society. The Commission on SDI &
Standards (and its predecessors) of the International
Cartographic Association (ICA) used the Reference Model
for Open Distributed Processing (RM-ODP) (ISO/IEC
10746-1:1998) to develop formal models of an SDI,
describing an SDI from the Enterprise and Information
Viewpoints of RM ODP (Hjelmager et al. 2008), from the
Computational Viewpoint (Cooper et al. 2012), and
describing SDI stakeholders in detail (Cooper et al. 2011,
2013). Subsequently, the Commission examined academic
SDIs, i.e. SDIs for research and education, and how they
differ from ‘regular’ SDIs (Coetzee et al. 2017).
Collaborative custodianship refers to an arrangement
where a number of custodians collaborate to produce a
national SDI dataset, e.g. local authorities contributing
address or street data to a national SDI dataset (Coetzee et
al. 2018). Such datasets can become massive what is
often referred to as big data .
Collaborative cloud mapping allows for ubiquitous,
convenient, on-demand, configured and tailor-made
mapping with shared resources between various entities
collaborating on a specific initiative such as an SDI or for
disaster management. It is also a methodology that allows
for a more productive and precise manufacturing process
on the basis of service-oriented architectures (Döllner et al.
2018). Main drivers to apply this methodology are earth
observation data streams, data integration across thematic
domains and data quality enhancing issues. The main
concept behind such architectures is keeping spatial data
at the place of creation. Data shall not be duplicated, but
accessed. Derivative products may be redistributed from
other sources (creators of the derivative product) and
Proceedings of the International Cartographic Association, 2, 2019.
29th International Cartographic Conference (ICC 2019), 15–20 July 2019, Tokyo, Japan. This contribution underwent
single-blind peer review based on submitted abstracts. https://doi.org/10.5194/ica-proc-2-19-2019 | © Authors 2019. CC BY 4.0 License.
intensify importance of specific values. The main
requirements are FAIR (findable, accessible, interoperable
and reusable) interfaces (Wilkinson et al, 2016). At the
moment, several activities at W3C, ISO and OGC move
technologies towards spatial data on the web (OGC &
W3C, 2017).
The main advantage of collaborative cloud mapping is that
it is ubiquitous, i.e. any user anywhere with Internet access
(even in outer space) can share, access and update many
different types of data (including big datasets).
Additionally, sophisticated tools for complex geospatial
analysis are available via the cloud, generally at a lower
cost than otherwise available in an organization. Many
geospatial products and services are now cloud-based and
their capabilities are improving rapidly, e.g. ArcGIS
Online, MangoMap and CartoDB (Schmitz et al., 2019).
Collaborative cloud mapping can support small and
medium-sized local authorities, which have limited skills
and budgets, to do spatial analysis needed for their
planning, service delivery, administration and governance.
For an SDI, such as that being developed in South Africa,
the cloud provides reliable, fast and vast storage for
geospatial datasets and relevant services, without the
custodians having to worry about the quality of their own
Internet services. Further, the cloud facilitates integrating
all these datasets, without interfering with the custodians’
control over their data.
In September 2018, two Commissions of the International
Cartographic Association (ICA), namely the Commission
on SDI & Standards, and the Commission on Map
Production & Geoinformation Management, hosted a two-
day workshop at the University of Pretoria in South Africa.
The workshop introduced attendees to collaborative
custodianship and collaborative cloud mapping through
case studies from the Netherlands, Belgium and Austria.
As a very outstanding example of cloud based
collaboration with community based custodianship the
OpenStreetMap framework was presented and discussed.
Subsequently, challenges and opportunities for
implementing collaborative custodianship through
collaborative cloud mapping in countries like South Africa
were explored in break-away sessions.
In this paper, the results of the workshop are presented.
Section 2 briefly summarizes the case studies. Section 3
provides the context for the discussions with background
information about the South African spatial data
infrastructure (SASDI). The results of the break-away
discussions are presented in section 4, namely challenges
and opportunities for collaborative custodianship and
collaborative cloud mapping respectively. Section 5
concludes.
2. Case studies
2.1 Collaborative custodianship in the
Basisregistraties Adressen en Gebouwen (BAG) in the
Netherlands
The basisregistratie adressen en gebouwen (BAG)
(English: Base Register Addresses and Buildings) is a
single national dataset that contains base information on
addresses and buildings in the Netherlands. The address
and building data of the BAG is an important part of the
national SDI, because this data facilitates reliable linking
of data about people, organizations and services (Coetzee
and Bishop, 2009). The information is captured and
maintained by 380 municipalities and integrated into a
national base register by Kadaster, the Dutch Cadastre.
The goals for the BAG are: a base register that facilitates a
more effective and efficient government and an open
dataset that can be used by everybody in the society. The
BAG has been available and operational on a national scale
since 2011 and contains more than 9 million addresses.
The national dataset is supplied by Kadaster through
various products (database extracts, web services, linked
data, APIs). In 2017 the BAG was used more than 1.6
billion times directly.
There are different roles for organizations for maintaining
the quality of the BAG and for determining the
development of the BAG:
-The Ministry of the Interior and Kingdom Relations is
responsible for policies, legislation, supervision and
control.
-The Kadaster is responsible for the national provision,
functional management, IT, national quality
management and support.
-The municipalities are responsible for data entry,
maintenance and quality assurance of the local BAG.
-The suppliers (private parties) supply the necessary
software for the municipalities.
There is also user involvement organized for the BAG at
three levels:
-The BAG BAO (BAG custodians and users committee)
is a strategic steering committee that can give advice to
the minister of the Interior and Kingdom Relations. The
committee forms a board of municipalities, VNG
(Cooperation Agency of the Association of Netherlands
Municipalities), mandatory government users,
Kadaster and the Ministry;
-The Agendaoverleg BAG BAO (BAG agenda
committee) is a tactical steering committee that
prepares the strategic committee. The same parties as in
BAG BAO are represented.
-The BAG user council gives operational feedback and
advice, which may lead to requests to the BAG BAO. In
the BAG user council, municipalities, government users
and private parties represented.
Kadaster, the municipalities, the Ministry of the Interior
and Kingdom Relations and the VNG have the largest
relative influence on developments of the BAG (Coetzee et
al., 2018).
For building up and maintaining the national dataset,
different instruments are used, besides the governance
framework described above. Legislation and financial
resources and a system of quality assurance on a national
and local scale are needed. While building up the BAG, a
four-stage approach was used with a mix approach of ‘the
carrot (compliance) and the stick (non-compliance)’. In
2009, after the BAG legislation came into force, there was
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Proceedings of the International Cartographic Association, 2, 2019.
29th International Cartographic Conference (ICC 2019), 15–20 July 2019, Tokyo, Japan. This contribution underwent
single-blind peer review based on submitted abstracts. https://doi.org/10.5194/ica-proc-2-19-2019 | © Authors 2019. CC BY 4.0 License.
little encouragement for municipalities to contribute data
to the BAG, apart from the BAG legislation itself. The
Ministry realized that interventions were needed.
Therefore, it conducted a dedicated three-year campaign to
assist municipalities with their implementations of the
BAG. A team of account managers paid regular visits to
municipalities who were in the process of implementing
the BAG. They offered advice and guidance, and also built
up pressure by signing contracts and monitoring
administrative meetings. The campaign led to compliance
regarding data contributions to the national BAG dataset
by all municipalities at the end of the campaign in 2011
(Coetzee et al., 2018).
2.2 Collaborative custodianship in the Centraal
Referentieadressenbestand (CRAB) in Flanders
Governments in Flanders provide well over a thousand
different public services
1
to citizens, businesses and
organisations. To provide public services, such as
environmental permits, government administrations
manage large amounts of data using different information
systems and data definitions. Reuse of already obtained
information is limited and citizens and businesses are
repeatedly requested for the same information (Krimmer,
2017).
The e-government decree (Belgisch Staatsblad, 2018)
makes it compulsory for all Flemish government
administrations to reuse information from authentic
sources.
The Flanders Information Agency focuses on the
development of base registries. These registries form a
coherent system of interconnected authentic data sources
and facilitate re-use of information in the public and
private sector. The base registry Centraal
ReferentieadressenBestand (CRAB)’ (Central Reference
Address File) is a digital authoritative address dataset for
Flanders, one of three regions in Belgium. CRAB contains
well over 4 million addresses (Belgisch Staatsblad, 2009).
Each address has a geographical position and a ‘locator’ to
distinguish it from neighbouring addresses. In the CRAB
data standard, an address is defined as information
constructed from a combination of address components
(e.g. the municipality, postal code, street name, house
number and box number). The address points to an
addressable object, such as a building, building unit or land
parcel.
CRAB was managed centrally by the Flemish government
until 2011 when the CRAB decree came into effect
(Belgisch Staatsblad, 2011). It provides the technical,
legislative and organisational framework for an authentic
geographic data source for addresses in the Flemish region.
The CRAB decree appoints municipalities as initiators of
address data in the CRAB, while the Flemish government
has ownership of this authoritative dataset of addresses
(Belgisch Staatsblad, 2009). A set of web services and a
web application are available to municipalities to register
their address data in the registry. The data in the address
registry are made available via download, through a
1
https://productencatalogus.vlaanderen.be/search/products
number of web services and via various platforms,
including the Flemish geoportal (https://geopunt.be/).
At the national level, the inter-federal memorandum of
understanding on ‘Belgian Streets and Addresses’ (BeSt
Add) (Belgisch Staatsblad, 2016), agreed between the
federal government and the three regions, aims to establish
the organisational framework and data model for address
data maintenance according to a common standard so that
address data can be exchanged across the country.
The BeSt Add cooperation agreement makes the
collaborative custodianship of address data in Belgium
complete. All administrative levels in Belgium have their
own role and responsibilities in contributing to the address
registries:
-Municipalities are responsible for maintaining the
address data for their territory autonomously in the
address registries.
-The regional governments host the central address
registries.
Government administrations from all levels (local,
regional and federal) are obliged to only use address data
derived from the regional address registries and to report
identified errors.
2.3 Collaborative cloud mapping for federal address
and street network datasets in Austria
In Austria the legal framework for collaborative cloud
mapping is diverse. The national “GeoDIG” act (RIS,
2018a) and the nine legal acts of federal provinces
determine the implementation of the INSPIRE Directive
(European Union, 2007) and its coordination board, but
not a national coordination structure for collaboration in
geoinformation. Instead collaborations for selected spatial
core datasets (UN-GGIM 2018), like orthoimagery or
addresses, are done individually as described below.
The production of orthoimagery is a collaboration of two
ministries and the nine federal provinces of Austria, which
commission a three-year cycle for the area of Austria. The
challenge of this collaborative production was the
resulting licensing agreement, which does not restrict
participants in their dissemination process. This means that
one institution can sell the dataset, whereas others may
offer the content as open data.
The addresses data theme goes beyond a financial and
license agreement. Its collaborative approach is
determined in the act “Adressregisterverordnung” (RIS,
2018b), which regulates the production procedure,
georeferencing for addresses, the central register
provision, as well as the flat data model. In Austria, about
2500 municipalities are responsible for the recording and
management of addresses (ADR Register, 2018).
Georeferencing is done in the form of a spatial data service
by the National Mapping and Cadastral Agency (NMCA).
The National Statistical Agency (Statistik Austria) embeds
the register for data-integration in other registers and
statistical services. The NMCA provides a web shop for
the register dissemination.
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Proceedings of the International Cartographic Association, 2, 2019.
29th International Cartographic Conference (ICC 2019), 15–20 July 2019, Tokyo, Japan. This contribution underwent
single-blind peer review based on submitted abstracts. https://doi.org/10.5194/ica-proc-2-19-2019 | © Authors 2019. CC BY 4.0 License.
The high grade of data-integration leads to quality
challenges for addresses in Austria. The different layers of
addresses, the address layer and the buildings layer, have
to be checked for their plausibility. For example, an
address point is related to the position and naming of its
cadastral parcel. These plausibility checks are solved with
a system of various spatial data services in the background
during the procedure of georeferencing. Dependencies for
a cross-availability of these services are reduced in a way
that georeferencing is still possible when one service is
unavailable.
In 2016, the project GeoGIP focused on enhancing the
quality for addresses in Austria with a collaborative cloud
mapping approach. The reason for this activity was the
observation that neither addresses nor transport network
graphs could support the routing on authority level and
therefore for emergency units. Transport network graphs
missed the nodes at the entrance to the cadastral parcel;
address points were located as centroid on the cadastral
parcel in the best case. The connecting key of the “street
code”, which is also stored at the address point, allowed to
establish spatial data services, which move the address
point to the corresponding street graph, one meter within
the parcel. For most of the cases this position is the
entrance to the address. For the others the local familiar
contributor can change the position with a graphical user
interface and the orthoimagery in the background. The
dropped perpendicular foot on the street graph is stored in
the transport network graph as routing entry point to the
parcel´s address. Spatial data services in the transport
network register, as well as other spatial data services in
the address register, calculate and store the information.
Operation agreements with all contributing parties assure
quality of services and therefore functionality of the
overall system.
2.4 OpenStreetMap as an example of a volunteered
collaborative framework
Volunteered geographic information (VGI) has grown
expansively as a source of data in the last decade.
Organising collaboration between volunteers will always
be a problem that needs solving. OpenStreetMap is an
example of a real-life and evolving solution to this
problem. As a framework, OpenStreetMap and its data are
aimed to be open and accessible by all, as stated in the core
values of the OpenStreetMap Foundation's mission
statement (OpenStreetMap Foundation 2018). Since its
beginning in 2004, the quality of the data and the value of
the information derived from it has been the focus of many
research studies (see Arsanjani at al., 2015).
Because OpenStreetMap does not define what is allowed
to be mapped on the platform, the community sharing
similar interests has over the years organised itself into
subgroups aimed at specific use cases and requirements,
implementing some kind of custodianship. One such group
is the Humanitarian OpenStreetMap Team (HOT), aimed
at organising volunteers in response to disasters or other
humanitarian aid projects. The response of the global
community to the earthquake that struck Haiti on January
12, 2010 is a testament to the valued impact of volunteered
geographic information (Soden and Palen, 2014).
OpenStreetMap allowed remotely located volunteers to
participate in the relief effort by mapping affected areas.
This provided ground teams with rich geographic
information, facilitating better resource management and
decision making.
There are also cases where local government and decision
makers used OpenStreetMap as a platform to provide
volunteered geographic information in making decisions
(Vaz and Arsanjani, 2015, Haklay et al., 2014). For
example, in Tanzania a coalition named Data Zetu has
mapped access to sexual health services. This lead to
understanding how communities utilise these services and
insight into the impact of development decisions impact on
communities (Data Zetu, 2019). Not only was it cost
effective, it also provided the most current data in
combination with traditional surveying methods which led
to improved planning and governance.
OpenStreetMap and its ecosystem of tools support a wide
spectrum of communities, each with diverse requirements
for geographic information, with the opportunity to solve
their problems with the help of a global community even
without officially appointed stakeholders and clear
custodianships.
3. Context: South African SDI (SASDI)
The Constitution of the Republic of South Africa (1996),
the supreme law of the land, sets out basic values and
principles of cooperative government and
intergovernmental relations, which promote coordination,
collaboration and cooperation amongst organs of state.
The Spatial Data Infrastructure (SDI) Act, No. 54 of 2003
(section 16) echoes the same principles, encouraging
organs of state who are appointed as data custodians to
exchange spatial information in terms of collaborative
agreements and to support each towards achieving
synchronised updates of spatial datasets.
The SDI Act establishes three main components:
1) SASDI as a national technical, institutional and policy
framework to coordinate the collection and
management of spatial information. The objective of
the SASDI is to promote the sharing and use of spatial
information, and to provide for the avoidance of
duplication of spatial data capture.
2) The Electronic Metadata Catalogue (EMC) as a
clearinghouse to promote the capturing and publishing
of metadata.
3) The Committee for Spatial Information (CSI)
comprises of members from a pre-defined list of
institutions appointed by the Minister with clear powers
and functions to oversee the implementation of SASDI
and the EMC, and to also advise the Minister, the
Director-General or an organ of state dealing with
spatial information on any matter the CSI considers
necessary or expedient for achieving the objectives of
the SASDI.
The implementation of the SASDI was not immune to
challenges. There was a lull period, between 2004 to 2009,
leading to a delay in the implementation of SASDI.
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According to Clarke (2011), the passing of the SDI Act
was not immediately followed by the development of the
SASDI as had been anticipated by the community.
The situation improved in 2010 with the appointment of
the CSI. The CSI, echoing the same principles of
coordination and collaboration in letter and in spirit, put
forward the Base Dataset Custodianship (BDSC) Policy
(CSI, 2015a) and the Policy on the Pricing of Spatial
Information Products and Services (CSI, 2015b).
The BDSC policy makes provision for the CSI to appoint
base dataset custodians and to hold them accountable for
the spatial data they are entrusted with. To date, custodians
have been identified for the following datasets:
administrative boundaries (Chief Surveyor General and
Municipal Demarcation Board); satellite imagery (South
African National Space Agency); aerial photography, land
cover and geodesy (National Geospatial Information);
transport (National Department of Transport); Hydrology
(Department of Water and Sanitation); Conservation
(Department of Environmental Affairs) and cadastre
(Chief Surveyor General). The policy explicitly embraces
the concept of collaborative custodianship as it promotes
cooperative relationships among base dataset custodians
and other entities or organisations to ensure access to, and
availability of, relevant base datasets.
Through the same policy and in support of the United
Nations Committee of Experts on Global Geospatial
Information Management (UN-GGIM) Fundamental
Dataset Framework (UN-GGIM n.d.), the CSI identified
and appointed base dataset coordinators for ten themes.
Figure 1. BDSC/Custodian Governance Model (Fourie 2018)
To achieve optimal collaboration amongst coordinators
and custodians, the CSI adopted the Base Dataset
Coordinator/Custodian Governance Model illustrated in
Figure 1. The model illustrates the respective roles of the
coordinator and custodians in the creation and
maintenance of a base dataset. The emphasis is on the co-
creation of policies, standards and specification by all
parties involved. Partnership and teamwork are
encouraged without elevating the coordinator into a
superior role or undermining the role of the contributing
custodians, hence base dataset governance is at the centre
of the model. The model also acknowledges the role of
shared custodianship in circumstances where more than
one organisation is appointed as custodian for a single base
dataset. For example, one organisation could be the
custodian for the spatial data and another organisation for
the attribute data.
As the CSI is determined to ensure full implementation of
this model, more work still needs to be done to address
challenges, particularly the lack of funding and skills, and
seize opportunities, such as sharing data, presented by the
implementation of the collaborative custodianship model.
The deep-rooted silo approach supported by the system of
fixed, conservative mandates remains a challenge.
Different organizations continue to collect the same data
resulting in wasteful and fruitless expenditure.
Implementing the collaborative custodianship model
presents positive prospects as data collection will be
coordinated and efficiencies will be realised.
4. Results
The challenges and opportunities of the two break-away
sessions are presented in section in 4.1 and 4.2 according
to the Political, Economic, Social, Technological,
Environmental and Legal (PESTEL) framework, useful
for analysing macro-environmental factors while starting a
new initiative or business endeavor (Morrison, 2012,
Dcosta, 2018). Political factors include the national,
provincial, or local politics, governing bodies that have an
influence on business, as well as internal politics of the
organisation. Economic factors that may have an impact
on the initiative include cost, inflation, interest rates and
unemployment. Understanding the individuals in the
market and the aspects influencing the demographics are
social factors, such as education levels, distribution of
wealth, and lifestyles. Technological factors include new
relevant discoveries and innovations, as well as
obsolescence. Environmental factors look at the physical
surroundings and the influence on or from them (e. g., the
built, as well as the natural environment). Legal factors
refer to laws, regulations and policies that may influence
the initiative.
4.1 Collaborative custodianship: Challenges and
Opportunities
4.1.1 Challenges
Politically, the organisational goals and silos,
accompanied by perceived individual goals, objectives and
“hidden agendas” are challenges that may hamper progress
in terms of collaborative custodianship. The governance,
in terms of structure, to support collaboration is lacking in
many instances. Maturity regarding accountability,
ownership, and shared responsibility is questionable.
Clearly defined roles, responsibilities and duties regarding
collaboration are lacking.
Economically, the lack of funding to support collaboration
and the cost implications of practically putting measures in
place are inhibiting. Hosting, maintenance, and upskilling
of staff across the three spheres of government, especially
in smaller organs of state, may be untenable, especially in
an austere environment.
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Socially, mistrust in other parties’ data, a lack of common
understanding, an unwillingness to share, and apathy are
reasons to be apprehensive about collaboration. The fear
of sharing due to mis-interpretation, confidence in own
data quality, or uncertainty about mandates, further
complicate matters. There is also a sense that skills and HR
capacity may be a challenge.
Technologically, the challenges relate to non-standardised
settlement and service delivery topology, lack of common
data standards and common identifiers, a perceived lack of
appropriate hardware and tools, lack of broad network
connectivity and a gap in guidance for business and
technology units to implement solutions.
Environmentally, the public sector is often perceived to be
bureaucratic and dragging its feet. In the current milieu,
the role of the private sector is indeterminable, sometimes
seen as duplicating efforts and using public data without
adding value or improving on the data quality.
Legislatively, there is a myriad of challenges. These
include uncertainty on mandates to collect specific themed
data due to conflicting legislation such as the SDI Act No.
54 of 2003 and the Statistics Act No. 6 of 1999 regarding
geospatial statistics; a lack of formal agreements or
mechanisms to share; current inability for identified
custodians to comply with regulations, privacy and
confidentiality issues; and questions related to the
Protection of Personal Information Act (Act 4 of 2013) and
the European Union’s General Data Privacy Regulations
(2016b); questions about intellectual property rights;
SITA
2
constraints, such as procurement red tape and lack
of resources; and the lack of a national SDI strategy 15
years after the ratification of the Act.
4.1.2 Opportunities
Politically, the governance of collaboration can be clearly
defined through guidelines and toolkits, the strategic intent
could produce a “golden thread” from Sustainable
Development Goals down to institutional goals, and the
advantages and greater chance of sustainability where
collaboration exists could be advocated.
Economically, the cost saving in terms of collaboration,
avoiding duplication in data management as a whole, and
speeding up decision-making and processes is achievable.
Add to this, the derived value of shared datasets, such as
unlocking hidden potential and value to a wider audience,
and it starts making sense economically.
Socially, with exposure of the data to more people it
increases the chance of improving the quality of the data,
improved data quality leads to better data and information
and subsequently better decisions, and wider access and
use among organs of state. Including crowdsourcing would
expand the known data landscape, while skills and
expertise would be shared naturally, and with more eyes
on the data to check and report on quality, trust will
improve. Forums could help with discussions on
collaboration, learning from one another, documenting
solutions, and training and e-learning, by tapping into
2
State Information Technology Agency as established by the
SITA Act (Act 88 of 1998)
existing resources and agreeing to shared responsibilities.
A stakeholder and data landscape analysis could identify
who is using the data, what data they need, at which level,
and for what purpose. Benefits and value can be
demonstrated by getting buy-in using use cases that are
relevant to each audience and taking a “carrot approach”.
Technologically, the hardware and software are available,
and solutions exist. Improved data quality will happen
through collaboration on solutions and infrastructure, joint
responsibilities, and holding each other to account.
Creating a Single Point (version) of Truth (SPOT
3
), also
referred to as the ‘once-only principle’ in Flanders, will
avoid duplication (unless the context requires it).
Environmentally, the public sector should be providing
their authoritative datasets, including derived data where
the original source has constraints, such as confidentiality.
Focus should start at the regional level to establish a data
catalogue, before progressing to national.
Legislatively, government should move from a compliance
driven (“stick approach”) to a benefit or value driven
(“carrot approach”). Legislation should recognise or
reward parties that comply or show good practice. Tender
clauses should force contractors to provide data in
appropriate formats; the contractor may retain authorship,
but the tenderer retains intellectual property. National
Treasury should issue supporting directives, e.g. no
funding of spatial data collection without approval of CSI.
The metadata standard should also apply at object level,
while processes related to quality, standards and best
practice should be advocated.
4.2 Collaborative cloud computing: Challenges and
Opportunities
4.2.1 Challenges
Politically, challenges include ignorance and apathy;
losing a perceived mandate or control; differing policies
and practices between governments; viability due to power
struggles; lack of accountability; and change in political
focus. The ignorance relates to the technology of cloud
computing and the perceived risks (real, imagined or
unanticipated) associated therewith. Governments on all
levels need to work with one another, but their policies and
practices can be incompatible, such as classifying
geospatial data differently (e.g. land use, land cover or
transportation networks), working to different spatial
resolutions or using different quality or metadata
standards. This can be exacerbated when municipalities
are merged (South Africa reduced the number of
municipalities from 278 to 257 in 2016) or move from one
province to another.
Economically, the austere environment, scarce skills and
capacity, ill-defined business cases and requirements, and
previous failures hamper buy-in. There is a lack of focus
on the value it will add to the organisation and the greater
public good.
3
https://data.gov.au/dataset/single-point-of-truth-spot-
metholodogy
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Socially, the lack of a common lexicon and terms causes
confusion and distrust. Trust is vital when anyone can edit
a map. Clarity is required to know who would have access
to and the rights to edit maps. People perceive it as a risk
if anyone can edit a map without due diligence. Objectives
are not aligned to common benefits and there is a lack of a
“working together” culture. People are in a comfort zone
and are afraid to change. Open technology is feared,
whether that be due to poor understanding or irrational
thought.
Technologically, if data is captured that is not of survey
quality, it may be questioned or be inconsistent. Imagery
displacement may be off-putting, but could easily be
addressed through training. People may still wonder
whether the data was approved and by whom. If there is a
need to comply with laws and regulations, these may be a
challenge. The question of how to “ring-fence” sensitive
data also remains. The updating of data off-line has some
perceived difficulties. Finally, current challenges, such as
municipal boundaries that change over time, will not
disappear when the data is moved into the cloud.
Environmentally, no challenges were recorded.
Legislatively, the legal implications, whether perceived or
not, relate to storage and access of data in the cloud,
especially across borders. The Protection of Personal
Information Act No. 4 of 2013, which relates to privacy,
confidentiality and security, is in the back of many
people’s minds.
4.2.2 Opportunities
Politically, the opportunity is there to gain support for
“doing the right thing” and to take the lead in driving this
approach for the benefit of all concerned; the public good.
Economically, savings could be realised through sharing
resources, and free hosting opportunities. At a local level,
authorities without sufficient funds for their own GIS
divisions could capture data through cloud platforms. It
may also assist in linking spatial datasets from alternate
contexts, either thematically or spatially. This reduces the
need for servers. Some additional economic benefit can be
derived from available data by providing economic
opportunities for companies to add value to data. The
custodians or providers of authoritative data could then
provide free access, while gaining benefits in return, such
as additional fields, interoperability, and improved quality.
Socially, the use of relevant use cases could lead to a
common lexicon and understanding, and would help with
the definition of the “most relevant common denominator”
and spatial context. A stakeholder analysis would assist in
understanding what people want to see on an
“OpenStreetMap” solution, which base datasets should be
there, and what level of access to the individual datasets
should be assigned. Principally, access should be open to
all and promoting the sharing of data is essential.
Accessibility should be advocated in all public access
facilities, e.g. schools and libraries. OpenStreetMap is user
friendly and easy to use and could be considered as a
common sharing platform, like in the activities of the
4
https://www.etymonline.com/word/synergy
Humanitarian OpenStreetMap Team (HOT). Educating
the public, students, learners and citizens to assist in
capturing their own neighbourhood, recreation, point of
interest, etc. can assist populating the datasets and
establish joint responsibility and ownership. It is perceived
that youth and younger workers have the necessary skills
required. This data democratisation is a form of
crowdsourcing where everyone owns, updates, shares, and
updates the content. The provision and focus on service
delivery is bottom-up, and possibly less buy-in is required.
This kind of collaboration leads to synergy, where the
whole is greater than the sum of its parts
4
.
Technologically, data gaps can be addressed through
collaborative mapping, the data may be the best and most
complete version at the time, interoperability would be
possible due to open standards, the latest technology
would always be taken advantage of (e.g. block chain),
hard drive issues and space are a thing of the past as
“cloud” is scalable, no funds for software licensing are
required as the OpenStreetMap platform is freely
available, easy-to-use tools enable map editing in
OpenStreetMap, and JOSM
5
is an extendible editor that
allows advanced users to develop their own back-drops.
Environmentally, no opportunities were recorded.
Legislatively, the Constitution (Act 106 of 1996) and
White Paper on Public Service Transformation (DPSA,
1997) provide for the fundamental rights of access to
information, transparency, and open government. The SDI
Act and Regulations also stipulate defined processes to
deal with incorporate authoritative data (base datasets),
where any updates to the data need to be communicated to
the Committee for Spatial Information.
A great opportunity exists for collaboration on an actual
requirement of Section 37 of the Public Service
Regulations (DPSA, 2016), which states that all organs of
state need to annually publish a list of service delivery
points. A pilot collaboration project among primary role
players, who already understand each other and already
work together, could be formalised and implemented to
ensure there is a master list of service delivery points per
organ of state that is maintained using a standard and
sustainable process.
5. Discussion and conclusion
This paper has presented case studies from a workshop of
collaborative custodianship of address data and
collaborative cloud computing. Specifically, these are the
Base Register Addresses and Buildings in the Netherlands
(Basisregistraties Adressen en Gebouwen (BAG)); the
Central Reference Address File in Flanders in Belgium
(Centraal Referentieadressenbestand (CRAB)); the
collaborative cloud mapping for federal address and street
network datasets in Austria (GeoGIP); a volunteered
collaborative framework, OpenStreetMap; and the South
African SDI (SASDI).
In the Dutch case, collaborative custodianship is
implemented through legislation, a governance
5
https://josm.openstreetmap.de/
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framework, and support and technical services by the
Ministry and Kadaster. It reveals the different kinds of
roles and responsibilities required in a collaborative
custodianship agreement. The Flemish and Belgian cases
show that legislation and collaboration agreements are
required to implement collaborative custodianship at
different levels of government, especially if some of the
collaborating agencies are autonomous.
For all different cases of collaborative cloud mapping in
Austria, there is one main lesson to be learned: the
collaborative approach was successful and sustainable
when all collaborating partners benefited from its results.
For example, each of the address recording municipalities
receives recognition in the form of a small payment, IT
service provision or data exchange. It is not about
producing revenue, but about receiving recognition for the
most important part of the geoinformation management
process.
The OpenStreetMap case is interesting because the
collaboration is coordinated by volunteers with varying
levels of geospatial knowledge and skills, and it shows that
even in the case of VGI, some form of governance with
rules and guidelines is required to achieve collaboration on
a dataset. The OpenStreetMap framework includes a set of
tools that are useful for cloud-based collaborative mapping
approaches.
Break-away sessions at the workshop identified and
discussed challenges and opportunities for implementing
collaborative custodianship of base datasets and
collaborative cloud computing in a country such as South
Africa. These were analysed using the PESTEL
framework: political, economic, social, technological,
environmental and legal factors. Next, we plan to
experiment with collaborative custodianship through
collaborative cloud mapping by designing and evaluating
solutions for the opportunities and challenges identified in
this paper.
6. Acknowledgements
The authors are grateful for the input, feedback and
thoughts of the participants of the Joint ICA Commission
Workshop, organized by the ICA Commission on SDI &
Standards, the ICA Commission on Map Production &
Geoinformation Management and the South African
Committee for Spatial Information Subcommittee on
Education & Training, 14 - 15 September 2018, at the
Centre for Geoinformation Science, University of Pretoria
in South Africa. The workshop received financial and
other support from the Knowledge, Interchange and
Collaboration (KIC) and ICSU-South Africa Scientific
Events/ Travel Grants of the National Research
Foundation (NRF) of South Africa, the Committee for
Spatial Information (CSI), the University of Pretoria,
AfriGIS, the CSIR and the International Cartographic
Association (ICA).
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