Demand-driven improvement of government geodata is a topic that is high on the wish-list in the geodata community. With the significant increase in use of government geodata for new purposes caused by open access, high quality data is a necessity. Dataset authorities do their best to improve data quality within the limits of resources available to them, but this is a challenging task due to the government funding model combined with the fact that benefits are harvested externally from where the costs are borne.
A solution to this challenge could be a new model focused on demand driven improvement of geodata based on financing tied to the value that improved geodata brings to key end-users in connection with significant business moments, and on scalable involvement of trusted, private data maintainers conducting acquisition and integration of improved geodata based on rules defined by government dataset authorities. The model is based on the concepts of digital platforms and volunteered geographic information.
In an article in the Kart og Plan-periodical (pp 211 – 224 • 17 December 2021) three geo-business professionals (Thorben Hansen, Lennart Hansen and Bent Hulegaard Jensen) describe the underlaying model and explore how the model could be applied to improve government geodata regulated by law, and how authorized land surveyors could play a role in this.
The article is available from Universitetsforlaget: https://www.idunn.no/doi/10.18261/issn.2535-6003-2021-03-04-07 – with full access only available behind a paywall. Free access to the full content is available in an open archive version of the article here:
A model for demand-driven quality improvement of authoritative government geodata based on the concepts of VGI (volunteered geographic information), digital platforms and ecosystems, and SDI (spatial data infrastructure) is feasible (find a brief presentation of the model here: https://geoadvice.dk/demand-driven-improvement-of-spatio-legal-data/) .
In this article in the Danish periodical for Land Surveyors (Landinspektøren) the three geo-business professionals (Thorben Hansen, Lennart Hansen and Bent Hulegaard Jensen) elaborate on how the model can be used to improve the quality of data in government registers, using the danish Building and Dwelling register (BBR) as an example. Key players from government and private business contribute to the article with testimonials highlighting the potential of the model.
Data from government registers is increasingly being used digitally. Data is often used for purposes it was not originally intended, and this challenges data quality. Tightly linked to government business, land surveyors work as authorized private players with geodata that is used or created in an environment based on legislation – the so-called ‘spatio-legal data’. Authorized land surveyors could to a greater extent contribute to improve the quality and to maintain spatio-legal data. However, this does not happen due to rules and processes associated with registers hosting this data.
A model for demand-driven quality improvement of authoritative government geodata based on the concepts of VGI (volunteered geographic information), digital platform and ecosystems, and SDI (spatial data infrastructure) is feasible.
In an article in in the danish periodical for Land Surveyors (Landinspektøren) three geo-business professionals (Bent Hulegaard Jensen, Lennart Hansen and Thorben Hansen) give a brief presentation of the model, how it can be utilized to improve the quality of spatio-legal data, and how land surveyors could play a role in this.
Society is increasingly data-driven with complex decisions being made based on spatial data representing real-world objects and phenomena. Advances in technology make spatial data more affordable, timely, and accurate, and allow for new players to contribute to still more comprehensive data collections. Data from many sources is integrated and analyzed using complex algorithms as basis for decisions e.g. about land rights with great influence on society and people. Good data fit for purpose is a prerequisite for good and accepted decisions. Implementing spatial data infrastructure using a digital platform business model allows for handling of authoritative and trusted data originating from different sources.
Land rights are
included in the targets of the UN Sustainable Development goals related to
poverty, hunger and equality, and secure land rights is an important pillar in
developed economies.
Rules of tenure define how property rights to land are to be allocated
within societies. They define how access is granted to rights to use, control,
and transfer land, as well as associated responsibilities and restraints. In
simple terms, land tenure systems determine who can use what resources for how
long, and under what conditions.
Applying rules of tenure to real-world conditions require knowledge
about a multitude of real-world objects and phenomena. For practical use in a
digital environment such real-world objects and phenomena must be represented
by spatial data that supports securing the property rights to land and allows
for making informed and
transparent decisions regarding societies development.
In a state
governed by law, it is government responsibility to ensure that rules of tenure
are applied based on data that is fit for purpose. As rules of tenure define
legal rights of profound importance to people, spatial data representing the
relevant real-world objects and phenomena must be generally accepted as authoritative
and trusted.
Spatial
data infrastructure is the technology enabled framework dealing with capture,
creation, maintenance, management, and utilization of spatial data. Spatial
data infrastructure must handle and document authoritative and trusted data in
a way that supports its use for legal purposes.
Spatial data infrastructure will increasingly be implemented using a digital platform business model, i.e. as a business based on enabling value-creating interactions between external producers and consumers.
The digital platform provides an open, participative infrastructure for such interactions and sets governance conditions for them. Government must take the role as ecosystem driver for the digital platform for authoritative and trusted data and is as such accountable for the platform business model, the rules and architecture, and the technology platform enabling the digital business.
Four
separate, complementary categories of users interact with each other in the
digital platform for authoritative and trusted data business ecosystem:
authorities
accountable for datasets (government bodies only)
data
maintainers (private-sector businesses and government bodies)
application
suppliers (private-sector businesses, non-government and government bodies)
end-users
(citizens, private-sector businesses, non-government and government bodies)
Maintaining
authoritative and trusted data is done by trusted entities operating under
rules given by the authority accountable for the dataset to be maintained. Such
entities are given authorization to maintain data under professional responsibility
and liability.
The digital
platform business approach provides strong opportunities for mutually
beneficial cooperation between government and private-sector industry involving
small and medium size enterprises as authorized data maintainers and as
application suppliers offering tools utilizing data to help end-users undertake
their task.
Spatial data infrastructure (SDI) is all about forming and utilizing a virtual representation of the real world. Advances in sensor and positioning technology allow for collection of data creating still more sophisticated, timely and accurate representation of real-world objects, and advances in communication, storage and processing technology let increased volume of data be utilised, support increased variety of data types being combined, and allow for increased velocity in creating, updating and analysing data. SDI is becoming location-enabler for a fully-fledged data infrastructure and a corner stone in data-driven innovation. This development represents unprecedented opportunities for SDI – and challenges competences and the ability to collaborate and partner with players in a wide variety of ecosystems.
Spatial data collection
Improved methods and tools for data collection are changing our ability to create a virtual representation of the real world. Global navigation satellite systems (GNSS), such as GPS and Galileo, make positioning universally available and simple to use, and sensor technology is the foundation for surveying technologies such as laser scanning (LIDAR) capturing 3D point clouds, and digital imaging cameras and scanners capturing information in a broad spectrum of wavelength ranges. Using drones, airplanes and satellites as vehicles to carry equipment allow surveying tasks to be scaled to purpose, and broadband communication conveys data fast from point of capture to point of use.
Said improvements in methods and tools for spatial data collection in combination with improvements in how data is stored, processed and made available to users, drive how the virtual model of the real world evolves to best fulfil user requirements.
Spatial data content
SDI includes a large volume of spatial data in a large variety of data types, and the content expands due to technology improvements and continuous data collection. Giving a full description of SDI data content is outside the scope of this article, the following is a few highlights that illustrate important developments.
Due to cost considerations 2D models are traditionally often used for virtual representation of the real world. This obviously sets undesirable limits for how the virtual representation can be utilized. With improved methods and tools for positioning and data capture, the third dimension is now much more affordable and 3D models are increasingly used.
The bulk part of data content in SDI has traditionally been vector features using points, lines and areas to represent location of real-world objects. Although the relative significance of vector features diminishes as more spatial data types gain popularity, they are still a key element in SDI and essential when it comes to SDI serving as location-enabler for other data describing real-world objects.
Georeferenced point clouds have become important elements in SDI and are source for SDI 3D developments such as improved elevation models holding matrices of elevation values describing terrain and surface, and 3D city models adding buildings and other landscape elements belonging to urban areas.
Imagery adds image sensor information to the virtual representation of the real world. With improved methods and tools to collect, store and process imagery, image data is becoming an increasingly popular part of SDI. Imagery can be both ‘map’ rectified images and sensor model images that can be used, for example, for precision measurement or to construct 3D models of ground structures. Examples of image types in the SDI are orthoimages, oblique images and panoramic images.
Spatial data handling
Spatial data comes from many sources and is used within many domains. A goal for SDI is that spatial data is stored, made available and maintained at the most appropriate level and that it is possible to combine spatial data from different sources and share them between several users and applications.
To reach this goal spatial data in SDI is provided as a service on demand to the user regardless of geographic or organizational separation of provider and consumer, i.e. in a network of connected players.
Data and the corresponding services must fulfil user requirements. This can only be achieved via strong governance combined with sound business incentives and close cooperation between the players in the SDI ecosystem.
Seamless sharing of data requires robust technology standards. Open Geospatial Consortium (OGC) [1] plays an important role in defining and continuously develop the standards used in SDI.
To align these standards with mainstream ICT standards, OGC and the World Wide Web Consortium (W3C) collaborates about ‘spatial data on the web best practices’ targeting ‘those who publish spatial data through SDI and want to better integrate that data within a wider web ecosystem’ [2]. OGC is modernising its web service standards for retrieving, creating, modifying and querying spatial data on the web to adhere to these best practices.
Cloud computing is shared pools of configurable computer system resources and higher-level services that can be rapidly provisioned with minimal management effort, often over the internet. Cloud computing relies on sharing of resources to achieve coherence and economies of scale. Cloud computing is traditionally categorised in three models: Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). Cloud computing based on these models is part of many SDI components.
Data as a Service (DaaS) is an emerging cloud computing model. With service-oriented architecture (SOA) and the widespread use of application programming interface (API) the actual platform on which the data resides doesn’t matter. With DaaS, any business process can access data for use and/or modification wherever it resides. The cloud computing DaaS model supports the SDI data sharing approach and represents a strong model to be used in SDI implementations.
SDI in the bigger picture
SDI is location-enabler for data in general, both with respect to geocoding objects that can be linked to georeferenced objects, and when it comes to provide the spatial dimension in a data space that can be used to analyse spatially related scenarios.
As such SDI is part of a common data space that forms the foundation for the data-driven society based on a seamless digital data infrastructure that enables the development of new products and services based on data.
Utilizing data to their full potential means that data can be embedded in the generation and use of huge volumes of data – commonly referred to as ‘big data’. According to Organisationfor Economic Co-operation and Development (OECD), big data is a core asset for data-driven innovation, and data-driven innovation forms a key pillar in 21st century sources of growth [3].
Big data refers to a process that is used when traditional data mining and handling techniques cannot uncover the insights and meaning of the underlying data. Data that is unstructured or time sensitive or simply very large cannot be processed by relational database engines. This type of data requires a different processing approach called big data, which uses massive parallelism on readily-available hardware.
Big data is often described via three defining properties or dimensions: volume, variety and velocity (the three Vs). Volume refers to the amount of data, variety refers to the number of types of data and velocity refers to the increased pace of incoming data. The three Vs together describe a set of data and a set of analysis conditions that define the concept of big data and label the data space in which SDI must fit.
Big data is used by organisations to optimise planning and operation. Big data utilizes spatial data and services from SDI and combine these with other data and services, potentially including streamed data coming from internet of things (IoT) devices. Big data analysis takes advantage of artificial intelligence (AI) such as machine learning and can examine large amounts of data to uncover hidden patterns, correlations and other insights.
SDI in digital transformation
Digital transformation is the transformation of business and organizational activities, processes, competencies and models to leverage the changes and opportunities of digital technologies and their impact across society.
As spatial data moves from a relatively closed environment into mainstream ICT and serves as location-enabler for a wider web ecosystem, collaboration needs to take place on a much wider scale. Where collaboration used to be limited to geo-savvy players, moving forward collaboration includes mainstream ICT players, and with the role of SDI in big data, collaboration also extends to environments around emerging IoT and AI technologies.
Collaboration is not only between people, but also between organisations in a wider web ecosystem. In digital business, organisations must partner in order to complete digital offerings and jointly fulfil customer needs. This contrasts traditional business that is supported by vendors that are often detached from their customers’ business outcomes.
Changes are profound and challenges the competencies available: new technologies need to be understood and mastered and business models change from a relatively narrow customer focus to a broader ecosystem focus. Substantial investments must be made in developing the human skills needed to drive the changes.
The changes and trends mentioned are drivers for one of the most significant changes happening in digital business: the digital platform revolution, where technology enabled business is developing around so-called digital platforms, disrupting traditional ways of doing business. [4]
Data infrastructure can benefit from the digital platform business approach [5] and SDI has an important role to play as location-enabler for a fully-fledged data infrastructure and a corner stone in data-driven innovation [6]. Such digital business transformation can help release the potential of the described digital technology transformation.
[4] Platform Revolution: How Networked Markets Are Transforming the Economy—and How to Make Them Work for You. Parker, G., Van Alstyne, M.,& Choudary, S. (2016)
Spatial data infrastructure (SDI) exists today as a useful framework for sharing spatial data. However, there is still a long way to go before SDI can fill the role as location enabler for a fully-fledged open data infrastructure serving data-driven innovation. Focus on governance, business value and scalability are key elements to be addressed to facilitate such development. Digital transformation supported by digital platforms are key enablers that can drive development towards filling this role.
Data-driven innovation
According to Organisation for Economic Co-operation and Development (OECD), ‘data-driven innovation forms a key pillar in 21st century sources of growth. The confluence of several trends, including the increasing migration of socio-economic activities to the Internet and the decline in the cost of data collection, storage and processing, are leading to the generation and use of huge volumes of data – commonly referred to as “big data”. These large data sets are becoming a core asset in the economy, fostering new industries, processes and products and creating significant competitive advantages.’ [1]
Establishing the right framework for the data economy is crucial for releasing the potential for data-driven innovation in data-intensive industries. Government on national as well as supranational level must play a leading role in this.
In EU building a European data economy is part of the Digital Single Market strategy. In this initiative the Commission intends to unlock the re-use potential of different types of data and its free flow across borders to achieve a European digital single market. In support of this the Commission proposes a package of measures as a key step ‘towards a common data space in the EU – a seamless digital area with the scale that will enable the development of new products and services based on data’. In the communication about the package it is stated that the Commission will continue to support the deployment of a fully-fledged open data infrastructure. [2]
SDI as location enabler – and current challenges
Spatial data infrastructure (SDI) is an element in the open data infrastructure. The goal for SDI is to achieve interoperability between all spatial data, for which there is an interest in data sharing, and to make SDI the location enabler in the data infrastructure. This goal supports a common data space, and the quest for a common data space in the EU will be an additional driver for developing SDI.
National SDIs have been under development for some years, and good progress is made, especially regarding sharing of basic data, i.e. data describing essential objects that is used in common across multiple domains (e.g. land parcels). However, unlocking the re-use potential and the free flow of the vast amount of data that exists in different domains is still far from being achieved. Doing this within an acceptable timeframe and with an acceptable economy will take an innovative approach involving focus on governance, business value and the ability to scale.
Main challenges encountered when developing national SDI today are:
Demand for sharing of data come uncoordinated from consumers of data (organisations and individuals). This makes it difficult to establish a consolidated view of requirements, straining the resources of those coordinating the development of national SDI – and it makes it difficult to establish solid business cases for extensions and assign accountability for realising the benefits.
Dataset authorities typically produce data to solve specific tasks within their core business. Sharing data with other organisations is typically not part of their core business, making it difficult for them to prioritize resources to do that.
Government business value is created around real-world topics that need to be addressed (such as road transportation management, water resource management, healthcare management, etc.). Doing this typically involves many organisations, each having their own reason for being. The environment around said value creation – hereafter referred to as a government business domain – is typically not set up to govern cross-organisational issues about elements like a common (spatial) data infrastructure.
Digital transformation, digital platforms and SDI
An underlaying problem in the traditional environment is a general absence of a holistic view at government business domains that also includes the data and technology framework enabling the business. Digital transformation supported by digital platforms is a trend that deals with this issue.
Digital transformation is the integration of digital technology into all areas of a business, resulting in fundamental changes to how businesses operate and how they deliver value to customers. The trend applies equally to private and public business.
A digital platform is a technology enabled business that offers new ways for organisations to collaborate in an ecosystem for value creation. Value creation is not restricted to monetary value but can also be the value of a public service. Digital platforms support digital transformation.
Digital platforms are expected to become the dominant business model for business based on digital transactions. Each digital platform has an ecosystem consisting of an owner, who controls business model, rules and architecture for the platform, plus participants, who perform value creating interactions.
Digital platforms define a new paradigm for how to model business ecosystems and have characteristics that can be exploited to develop more expedient types of cooperation. This includes some important merits that can be applied to spatial data infrastructure.
It makes good sense to develop government business domains around digital platforms, each with its own digital platform in the centre. The digital platform owner builds an ecosystem by bringing the relevant stakeholders together and acts as intermediary, setting up the business model, rules for the value creating interactions, and architecture for the supporting digital technology platform. The platform owner’s task is to orchestrate transactions being exchanged via the platform and make sure that the platform is attractive for the entire ecosystem around the platform.
The digital government business domain platform is where requirements for shared data resources will emerge and the platform owner is responsible for collecting and acting on such requirements. In support of data-driven innovation, shared data resources should be made available, not only to the stakeholders in the platform ecosystem identifying the need, but as a shared resource in a fully-fledged open data infrastructure, allowing others to benefit from the data. For spatial data this should be achieved via the national SDI, i.e. by adhering to the business model, rules and architecture for the SDI.
In this development the national SDI itself is developing into an ecosystem thriving around a digital platform handling the business of sharing spatial data, and with dataset authorities, application suppliers and end-users as the groups of users exchanging value via the platform. The SDI platform owner is handling business model, rules and architecture for the platform, and is working closely with the users of the platform, and with the owners of the digital government business domain platforms that it serves, to develop the SDI. [3]
SDI funding considerations
In the traditional business environment, funding has turned out to be one of the most difficult issues to handle, when building an open spatial data infrastructure. Digital platforms offer a framework that lends itself well to address this issue from a more comprehensive business perspective.
The digital platform is where business considerations come together for the ecosystem that the platform serves. Driving the demand for the core interactions of the digital platform sometimes means that resources in the digital platform must be subsidized (or free).
In the case of data sharing the benefit lies with the end-users and application suppliers using the data, whereas the cost of data lies with the dataset authorities. Anchoring the business case for a specific business makes the digital platform the obvious place to handle funding and licensing issues.
The guiding principle should be that the cost of data sharing must be recovered from the business that benefits the most from data sharing – and that the data shared must become part of an open data infrastructure.
The funding model for the digital SDI platform could for instance be based on the following guidelines:
SDI basic data is data describing essential objects that is used in common across multiple domains. This data is best handled as a separate topic with a separate business case. The demand for the data is high and exists across all business domains. The need for an open spatial data infrastructure with this data seems to be increasingly accepted, and with separate funding available in many nations. The digital platform owner for the national SDI should take ownership of the business case and funding for basic data.
For data sharing founded in a government business domain that represents the primary interest in the data sharing, funding should be provided from the corresponding digital government business domain platform. For non-basic data, where funding cannot be justified by the need in one government business domain, the platform owners for the digital government business domain platforms in need, should figure out how to share the funding.
Digital platforms driving SDI development
A business environment based on collaborating digital platforms as described above could provide the driver for developing a fully-fledged SDI in support of data-driven innovation within an acceptable timeframe and with an acceptable economy.
The development happens around a digital SDI platform, where:
business model, rules and architecture are clearly anchored at a digital SDI platform owner,
funding is tied to business value within the relevant digital platforms, and
scalability is addressed by involving government business domains, orchestrated by their platform owners.
The concept of digital platforms is still in the phase of evolving as the dominant business model for government business based on digital transactions. This makes it a good time to clarify the digital SDI platform business model and how it interacts with the digital platforms that contribute to developing the SDI, and to communicate how SDI influences the digital government business domain platforms as they evolve.
Using digital platforms to facilitate digital transformation is a recognized development directionin digital government. Data infrastructure can benefit from the digital platform business approach. Existing developments already point in this direction and can profit from and provide a shortcut to an open digital data infrastructure platform.
Digital platforms will become the preferred and dominant business model for digital government in the future. Digital platforms offer citizens and businesses the ability to connect to government and other service providers as an integrated part of their day-to-day activities.
Digital government is data-driven. The technology, policies, standards, and human resources necessary to acquire, process, store, distribute, and improve utilization of data constitute the underlaying data infrastructure necessary to provide the reliable data that is crucial for making informed and transparent decisions.
Data in the data infrastructure comes from many sources and is used within many domains. An efficient use of government resources requires that data is stored, made available and maintained at the most appropriate level, and that it is possible to combine data from different sources and share them between several users and applications.
A data infrastructure is based on a framework that allows a community of resource providers and end-users to exchange data sets and data services with one another. A digital platform is a business based on enabling value-creating interactions between external producers and consumers. Data infrastructure and digital platforms fit well together, and the core of a data infrastructure can be developed as a digital platform.
In the geospatial domain spatial data infrastructure (SDI) has been developed since the mid-nineties with national initiatives creating national spatial data infrastructures (NSDIs), and with a regional initiative (INSPIRE) developing a pan-European spatial data infrastructure based on NSDIs.
Aligning SDI concepts and developments to digital platform concepts and developments will be of mutual benefit – and a necessity for the future of SDI. At the same time, data infrastructure – as required by digital government – can benefit substantially from the experience already available in the geospatial domain.
An important driver for a digital platform is the so-called network effects that refer to the impact that the number of users of a platform has on the value created for each user. A digital data infrastructure platform offers significant positive network effects and only few – if any – negative network effects.
Users of a digital platform can be categorized in multiple, separate, complementary classes of users interacting with each other in a multi-sided network. Network effects can be better understood by looking at the impact that the number of users in one class have on users in the same class (same-side effects) and on users in other classes (cross-side effects).
A suitable classification of users in a digital data infrastructure platform is:
End-users utilizing data to solve their task
Dataset authority protecting, taking care of, and maintaining data
Application suppliers offering tools utilizing data to help end-users solve their task
The most important network effects of the platform are cross-side network effects and can be summarized as follows: more end-users attract more dataset authorities and vice versa, more end-users attract more application suppliers and vice versa, and more dataset authorities attract more application suppliers and vice versa.
The digital data infrastructure platform must provide an open, participative infrastructure for the value-creating interactions and must set governance conditions for them.
Building a successful data infrastructure based on a digital platform requires a platform owner that appreciates the nature of the digital platform and its network effects, that manages the platform framework with its governance and its technology platform, and that ensures relevant measures for the quality of the value-creating interactions exchanged between the users of the digital platform.
With exchange of authenticated government data sets and data services for consumption in digital government as key feature, accountability for the role as digital data infrastructure platform owner should reside close to digital government leadership on national and supranational level.
Question is: How do we come to a mutual understanding of how to combine best practices from digital platforms with the experience available from existing (spatial) data infrastructure developments, and how do we facilitate that forces are joined in support of further development of a digital data infrastructure platform?
