Open Geospatial Software and Data: A Review of the Current State and A Perspective into the Future

Open Geospatial Software and Data: A Review of the Current State and A Perspective into the Future

Abstract

  • Organizations worldwide are increasingly adopting open source software and open data, including in the geospatial domain.
  • Significant advances have been made in open source geospatial software and data in recent decades.
  • The review focuses on:
    • The Open Source Geospatial Foundation (OSGeo) software ecosystem and its communities.
    • Three kinds of open geospatial data: collaboratively contributed, authoritative, and scientific.
  • Openness has changed how geospatial data are collected, processed, analyzed, and visualized.
  • Future developments suggest open source geospatial software and data will have an even more profound impact.

1. Introduction

  • Openness typically refers to:
    • Transparency.
    • Free and unrestricted access to information.
    • Inclusive consensus-based decision-making [1].
  • The architects of the digital age proclaim openness as a foundational value [2].
  • Technological foundations of openness:
    • The Internet.
    • Mobile telephony.
    • Distributed systems.
  • Reference [2] defines "openness" as:
    • A marriage of technology and ideology.
    • A fusion of technology, democracy, and entrepreneurial capitalism.
  • Openness is first and foremost applied to knowledge.
  • Open Knowledge Foundation definition [3]: "Knowledge is open if anyone is free to access, use, modify, and share it—subject, at most, to measures that preserve provenance and openness."
  • Two main components of knowledge:
    • Science: the process of building knowledge.
    • Education: the process of transferring knowledge.
  • Open knowledge principles have deeply affected both science and education.
  • Academia and scientific organizations have started incorporating openness into their activities.
  • Openness is a virtuous circle with many components (Figure 1):
    • Open source software: free and open collaborative software development.
    • Open data: freely accessible, shareable, and usable data.
    • Open hardware: physical products, machines, and systems designed and offered via publicly shared information.
    • Open standards: technology-neutral specifications for hardware, software, or data developed through an open process.
    • Open education: learning and teaching without barriers.
    • Open science: making scientific research and its dissemination accessible to all levels of society.
  • Each component benefits from the success of the others, and the circle is incomplete if one is missing.
  • The paper focuses on open source software and open data in the geospatial domain.
  • Advances and experiences related to openness in the geospatial field are significant and worth sharing.
  • The paper highlights the community aspect of open source software and open data.
    • They would not have emerged without sharing and participation.
    • Attention is given to both products and the people behind them.
  • Government and institutional attitudes toward open source geospatial software and data have varied.
    • From opposition to warm support.
  • Many countries are now considering openness, and some agencies are implementing flagship initiatives based on it.
  • This gives more visibility to the communities and raises awareness of their importance.
  • The paper reviews the current state of open source geospatial software and data.
  • It presents a perspective on future developments based on responses from professionals in key organizations.
  • The article structure:
    • Sections 2 and 3: review open source geospatial software and open geospatial data, respectively.
    • Section 4: brief review of open standards.
    • Section 5: summary of responses on future developments.
    • Section 6: discussion of synergies between open source geospatial software and open geospatial data.

2. Open Source Geospatial Software

2.1. Introduction
  • Open source software originated from scientific collaboration in early computing.
    • Software was shared, and programmers added to existing knowledge [4].
  • It evolved into a software development and licensing approach ensuring transparency.
    • Access to the source code.
    • Collaboration through rights protecting the copyright to the source code.
  • Free redistribution allows creating software products based on others' work [5].
  • Open source software development involves a community of developers collaborating [5].
    • Historically without legal agreement or financial remuneration.
    • Today, many developers contribute as part of their job.
  • Removes barriers like software licensing costs [6].
  • Transparent development process, including bug reporting, encourages healthy competition.
  • Modern technologies and globalization have accelerated open source software advancement.
  • Open source geospatial software includes a broad range of:
    • Libraries.
    • Tools.
    • Applications.
    • Platforms.
  • These are developed and released under different Open Source Initiative (OSI) licenses [7].
  • The focus is on the Open Source Geospatial Foundation’s (OSGeo) software ecosystem.
    • It provides core libraries and vetted software packages.
  • Also highlights relevant projects and trends outside the OSGeo umbrella.
    • Projects within data science languages.
    • Institutional and government initiatives.
2.2. Open Source Geospatial Software Roots
  • The roots of open source geospatial software go back to the early 1980s [7, 8].
  • Many open source software packages emerged, making evaluation and navigation difficult.
  • A key event occurred in February 2006.
    • Leading teams of free and open source geospatial software projects created the Open Source Geospatial Foundation (OSGeo).
    • (www.osgeo.org, accessed on 19 October 2019).
  • OSGeo is a not-for-profit organization.
    • Mission: support the collaborative development of open geospatial technologies, data, and education.
    • Promote their widespread use.
  • Founding driven by the need to organize and navigate the growing field of open source geospatial projects.
    • It highlighted the maturity achieved by software projects.
    • Reflected the need for coordination and synergy.
  • Interoperability became a high priority.
  • The open source software development model became a leading approach.
  • Geospatial data and its applications moved to the mainstream.
  • The Internet presented new requirements and opportunities.
  • Main benchmarks for OSGeo projects:
    • Interoperability.
    • Choice of Open Source Initiative (OSI) certified licenses.
  • This increased interoperability and allowed code integration and exchange.
  • Richard Stallman argues:
    • "The free software movement campaigns for freedom for the users of computing; it is a movement for freedom and justice."
    • "By contrast, the open source idea values mainly practical advantage and does not campaign for principles” [9].
  • In OSGeo, the practical open source approach and the principled free software view coexist.
  • OSGeo respects projects that choose to embrace “free software” ideals.
  • The foundation recognizes this level of commitment and offers full support to “free software” projects [10].
  • OSGeo is open, self-organizing, and global.
  • Participation in the Foundation is free.
  • Volunteer-driven and built with partnerships.
    • Open approach to software.
    • Standards.
    • Data.
    • Education.
  • Since its founding, OSGeo has provided organizational, legal, and financial support.
    • For open source geospatial software projects.
    • Related educational initiatives.
  • Support available through the OSGeo portal and project websites:
    • Forums.
    • Wikis.
    • Mailing lists.
    • Blogs.
    • Web seminars.
    • Tutorials.
    • Notebooks.
    • Open courses offered by GeoForAll (the OSGeo Education Initiative).
2.3. OSGeo Software Ecosystem
  • OSGeo serves as an umbrella organization for projects.
    • Foundation of the open source geospatial software ecosystem.
    • Provide core functionality for many proprietary geospatial software products and services as well.
  • Quality and sustainability addressed through an incubation process.
    • Examines licenses, software development process, and management.
  • Incubation outlines conditions for open source software sustainability.
    • Open source software means more than access to source code.
  • The projects must function openly and publicly.
    • Open source license(s).
    • Open communication channels.
    • Open decision-making process.
  • An active and vigorous community is essential for sustainability.
    • Developers and users who actively collaborate and support each other.
    • Collaboration on project activities such as testing, creating documentation, and training material.
  • The community is diverse in terms of expertise.
    • More capacity for addressing requests from the external world.
  • Anyone is welcome and can find their role within the community.
    • User contributions e.g. documentation are very well appreciated.
  • Long-term viability demonstrated by participation and direction from multiple developers.
    • Developers come from multiple organizations (at least two).
    • Resilient enough to sustain loss of a developer or a supporting organization.
  • Decisions about software development and future directions are made openly.
    • Empowers all developers to take ownership of the project.
    • Facilitates knowledge spreading from long-term to new team members.
  • The principles of the OSGeo projects (“OSGeo Way”):
    • Consensus/inclusiveness: participation from all people is welcomed.
    • Fostering: projects encourage and recognize participation of volunteers.
    • Openness: projects adopt open standards and collaborate with other OSGeo projects.
    • Responsibility: projects check their code integrity with respect to open source basics.
  • There is at least one mature open source software product for every geo-technology area.
    • Data collection in the field, crowdsourcing.
    • Data processing, analysis, modeling and simulations.
    • Spatial extensions to database management systems.
    • Visualization, web mapping.
  • Can be integrated within software stacks (Figures 2–4).
  • Together, they can be used to create sophisticated free and open Web and cloud-based systems [11, 12].
  • Projects under the OSGeo umbrella are categorized as either “OSGeo” or “Community” projects.
  • “OSGeo” projects are mature, sustainable projects that passed the incubation process.
    • "Certified" by the OSGeo Incubation Committee.
    • Fulfill the requirements of:
      • Being geospatial.
      • Having an open source license.
      • Accepting contributions.
      • Accepting the code of conduct of OSGeo.
  • OSGeo projects must have an open community with transparent communication.
    • Include users and developers who collaborate constructively.
  • Members supporting the projects must belong to several organizations.
    • Ensure long-term project viability.
  • The development leadership (Project Steering Committee) must:
    • Offer transparent decision-making.
    • Offer opportunities for new members to participate.
  • Technical side:
    • Code development must be supported by version control and an issue tracker.
    • User and developer documentation must be available.
    • Well-defined procedures for release and testing of the software must be available.
  • Described in more detail in an incubation graduation checklist document [18].
  • Currently, there are 25 such projects of five different types (Table 1).
  • Several of the desktop systems are also available on web or mobile platforms.
    • Ecology of the QGIS System [19].
    • gvSIG mobile [20] and gvSIG Online [21].
  • Or are used as geospatial processing back-end for web-based or cloud-based applications.
    • GRASS GIS (Figure 2).
  • Community projects are at the entrance door to the OSGeo project family.
    • Proposers get acquainted with the rules and conventions of OSGeo.
    • Get more opportunities to interact with mature projects and communities.
    • Increase their visibility.
    • Often leads to attracting new developers and users.
  • Of the 16 Community projects, six are currently in incubation (Table 2).
  • To support innovation, several OSGeo projects provide infrastructure for contributing add-ons or plugins outside the core code base.
    • Lower barriers for contributions.
    • Developers are fully responsible for maintaining and updating their code.
  • The rigor of the incubation process or even the lower requirements for community projects may be challenging for individual developers.
    • May chose to focus on the development outside the OSGeo ecosystem.
    • Often within well-established data science communities.
  • The open source software “market” plays an important role in selecting.
    • Which tools survive.
    • Which are quickly replaced.
    • Or never broadly adopted.
  • Vibrant, loose development of geospatial software tools creates a broad base from which sustainable long term projects can emerge.
2.4. Open Source Geospatial Software Development Community
  • Every year an international Free and Open Source Software for Geospatial (FOSS4G) conference is held.
    • Reaches beyond the OSGeo community.
    • Represents a larger array of collaboratively developed open source geospatial projects.
  • The event is a good opportunity to get updated on the latest projects, applications, and tools.
  • Apart from presentations, the conferences host:
    • Workshops in computer labs.
    • “Installfests”.
    • “Birds of a Feather” meetings.
    • Hackers’ Code Sprints.
  • Much effort goes into providing the presentations remotely.
  • For example, the videos of the last conference in Bucharest, August 2019, [22] were already available online in September [23].
  • In addition to the global conference, many regional conferences are organized by local communities or individual projects.
    • Highlighting the need for face-to-face communication and interaction.
    • For sustainable software development, user feedback, and contribution.
  • Education and training are critical for the future of open source geospatial software.
  • OSGeo has transformed its education initiative into a global network of open source geospatial laboratories (GeoForAll).
    • Participants contribute to enabling geospatial education, data, and capabilities accessible to everybody.
  • Many of these laboratories develop and contribute open source software.
    • Offering more than just “plain” software user training.
  • The OSGeo community puts huge effort into participating in related community initiatives which expose younger generations to open source.
  • Since 2007, almost 200 students have participated in the Google Summer of Code under the mentorship of OSGeo volunteers.
  • In the last few years, OSGeo also participated in:
    • The Google Code-in.
    • The Google Season of Docs.
  • Open source communities have created self-organized models of collaborative software development.
    • Code sprints or community sprints.
  • These events bring together developers from multiple projects.
    • Address development issues through face-to-face interaction, discussion, and hands-on coding.
  • The sprints are frequently associated with FOSS4G conferences or as separate events focused on core functionality within or between projects.
2.5. Beyond the OSGeo Software Ecosystem
  • Geospatial data and tools are becoming ubiquitous.
    • Across scientific disciplines, industries, governments, and communities.
  • There are rapidly growing geospatial software development projects associated with:
    • Open source data science languages.
    • Modeling and simulation platforms.
    • Virtual reality engines.
    • Web applications (Table 3).
  • R [24] has recently emerged as one of the leading open source data science languages in remote sensing and geospatial science.
    • Builds upon its well-established support for processing of georeferenced data.
    • Extensive set of tools for spatial analysis [25, 26].
  • The raster package [27] facilitates development of tools for efficient analysis of large gridded and imagery data sets.
  • Numerous packages support handling and analyzing spatio-temporal data [27].
  • R packages are developed by contributors from all over the world.
  • R packages are distributed through Comprehensive R Archive Network (CRAN).
    • Must follow CRAN repository policy.
    • Ensuring that methods and code are of scientific publication quality.
  • R and OSGeo communities closely collaborate to support interoperability.
  • Python is the leading scripting and programming language for both proprietary and open source geospatial software.
    • Including several OSGeo projects (GeoPython [28]).
  • There is also a rapidly growing number of independent geospatial projects based on Python.
    • GeoPandas [29].
    • Spatial statistics package PySAL [30].
    • Landscape simulation libraries landlab [31].
  • Python is also used to create applications in sophisticated 3D rendering engines.
    • Blender (recently added support for geospatial data [32]).
  • Relatively new libraries and platforms support 3D mapping and modeling.
    • Point cloud data processing with PDAL [34].
    • Related on-line point cloud visualization plas.io.
    • Drone data processing with OpenDroneMap [35].
  • WebODM [36], is a cloud-based platform that integrates several open source geospatial software tools.
    • Process and analyze drone imagery and derived 3D models.
  • OpenEO is an example of “open application programming interface (API) to connect R, python, javascript and other clients to big Earth observation cloud back-ends in a simple and unified way” [37]
  • SpatioTemporal Asset Catalog (STAC) is a community-driven catalog initiative based on JSON [38].
  • Geoscience computing is supported in a data science language Julia using JuliaGeo projects [39].M
  • Many open source agent based models have geospatial components (CoMSES Network) [40].
  • Although independent from OSGeo, many of these projects have close partnerships with OSGeo.
    • They use some of the OSGeo libraries for core functionality.
    • GDAL or PROJ.
  • Currently the most comprehensive resource for open source geospatial software is OSGeo Live (Table 4).
    • Self-contained bootable DVD, USB thumb drive or Virtual Machine based on Lubuntu operating system.
    • Allows users to try a wide variety of open source geospatial software without installing anything.
  • In addition to the OSGeo projects and OSGeo Community projects, it also includes:
    • Well-established and emerging geospatial software that is not part of the OSGeo software stack but uses OSGeo libraries (such as GDAL).
    • Cesium (Figure 5).
  • Getting started tutorials and sample data accompany the software packages.
    • Facilitating the use of OSGeo Live for workshops and other educational opportunities.
  • Several commercially focused open source software projects (released under Apache license or similar and strictly vetted) have formed a LocationTech working group within the Eclipse foundation.
    • GeoMesa, Spatial4j, GeoWave, GeoTrellis are examples of projects in this community.
  • Several of these projects rely on OSGeo libraries and tools.
  • Some of the LocationTech libraries (JTS) and platforms (uDig) are included in OSGeo Live.
  • Additionally, some geospatial open source software tools and applications are dependent on proprietary software.
    • MapWindow runs on Microsoft Windows only.
2.6. Institutional and Government Supported Open Source Geospatial Initiatives
  • Institutions have also been playing a central role in the development of open source geospatial software.
  • Examples:
    • GRASS: developed since 1982 with the effort of federal US agencies and universities.
      • Evolution and integration managed by the U.S. Army—Construction Engineering Research Laboratory (USA-CERL).
    • MapServer: originally developed (1994) at the University of Minnesota with support from NASA.
    • Worldwind: curated by NASA since 2002.
  • In 2016, the United States implemented a new federal source code policy.
    • At least 20% of custom-developed code by or for any federal agency must be released as open source software.
    • All source code has to be shared between agencies [47].
  • Tony Scott commented:
    • “This is, after all, the People’s code. Explore it. Learn from it. Improve it. Use it to propel America’s next breakthrough in innovation” [48].
  • The United States Chief Information Officer declared that openness boosts innovation.
  • In the last decade, Europe has moved rapidly toward openness.
  • In November 2019, a workshop on the future of Open Source Software and Open Source Hardware was jointly organized by:
    • The European Commission Directorate-General Communications Networks, Content and Technology (DG CONNECT).
    • The Directorate-General Informatics (DIGIT) [49].
  • Topics discussed:
    • The role of open source as an innovation enabler.
    • How to nurture open source communities.
    • Based on results and inputs from the Commission’s Free and Open Source Software Auditing (EU-FOSSA) project [50] and the Commission’s Open Source Observatory and Repository (OSOR) [51].
  • In the geospatial domain, European agencies (ESA and the Copernicus Programme) have taken important initiatives.
    • The Sentinel Hub [52] is a web service.
      • Allows users to create Web Mapping Services (WMS) instances of Sentinel data.
      • Readable by a QGIS plugin [53] and available in a user-friendly GIS environment.
    • The Sentinel Toolboxes [54] and the Sentinel Application Platform full code [55] are freely available in Github under the GNU GPL license.
    • STEP [56] is the community platform for accessing software and documentation, communicating with the developers, promoting results, and providing tutorials and material for training users.
  • These products are meant for the exploitation of the huge amount of available open satellite data.
    • They use some OSGeo software, like GDAL, GeoTools, and Orfeo Toolbox.
    • At the same time, contributions to the code are elicited through the developer wiki [57] and the forum.
  • The ESA Thematic Exploitation Platforms (TEP), developed for various applications, are based on the same philosophy.
    • They are open source platform environments.
    • Allows users to integrate, test, run, and manage applications without building their own infrastructure.
  • The European Copernicus Programme has completely embraced the open source software logic [59].
    • Emphasizing the importance that OSGeo has played in this field.
    • Calling for collaboration and the sharing of new code.
  • Digital Earth Australia [60] is the Australian government’s implementation of the open-source analysis platform developed as part of the Open Data Cube (ODC) initiative [61].
    • ODC is an initiative to increase the value and use of satellite data.
    • Providing users access to free and open data management technologies, based on a set of Python libraries and the PostgreSQL database [62, 63].
    • ODC will always be 100% open source software [64].
  • The added value of these new solutions:
    • The possibility for advanced users to query and access data and do analyses.
  • A collection of Jupiter notebooks.
    • Shared in the GitHub repository.
    • Everyone is encouraged to publish their new algorithms and applications.
  • The open source nature of the ODC was an important factor in this tool being selected by many other countries [65].
  • Through the CEOS Data Cube (CDC) Initiative, CEOS organization was established in 2017 to reach operational Data Cubes in 20 countries by 2022 under the leadership of NASA’s CEOS Systems Engineering Office (SEO) [66].
  • The Swiss Data Cube (SDC) [67] is an initiative supported by:
    • The Federal Office for the Environment (FOEN).
    • Developed, implemented, and operated by the United Environment Program (UNEP)/GRID-Geneva.
    • In partnership with the University of Geneva (UNIGE).
    • The University of Zurich (UZH).
    • The Swiss Federal Institute for Forest, Snow and Landscape Research (WSL).
  • The Colombia Data Cube has been developed by the IDEAM and the University of Andes.
  • The Africa Regional Data Cube (ARDC) was launched in May 2018 to support five countries.
  • The Mexican Geospatial Data Cube (MGDC) is being developed at the National Institute of Statistics and Geography of Mexico (INEGI).
  • Many other projects [68] are in development or under review under the CEOS umbrella initiative.
  • These initiatives demonstrate there is much more than a rustle of open source software in the public sector.
  • OSGeo is the most relevant and structured geospatial community.
    • Others are emerging as a result of the efforts and initiatives of some institutions highlighted above.

3. Open Geospatial Data

3.1. Introduction
  • Similar to open source software, many open datasets emerged from the need to collaboratively collect data.
  • Involving volunteers with local knowledge in geospatial data collection is an effective crowdsourcing mechanism [69].
  • Globalization and modern technologies have led to global initiatives that do not rely on local knowledge only.
    • The Internet, smartphones, the Internet of Things (IoT), and satellite imagery.
    • A global community of data collectors contributes to a wide range of open datasets, many with global coverage [1].
  • Tweets and social media posts are another source of contributed geospatial data, albeit collected passively.
  • The scope is limited to actively contributed open data only.
  • Another kind of open data is rooted in the principle that some information should be shared.
    • Focus is on sharing data collected by authorities.
  • In the spirit of the freedom of access to information, open spatial data infrastructures (SDI) have emerged [70].
  • Generally, transparency and collaboration are well aligned with the principles of democratic governments and the Charter of the United Nations [3].
  • Finally, there is open scientific data where research results are shared.
  • There are similarities between collaboratively contributed open data, authoritative open data, and open scientific data.
  • This section reviews the current state of these three kinds of open geospatial data.
    • Data contributed by volunteers.
    • Authoritative data collected and published by public administrations.
    • Open scientific geospatial data.
3.2. Collaboratively Contributed Open Geospatial Data

  • Various terms are used to distinguish between the different ways in which geospatial data are collected collaboratively, however, the terms are not mutually exclusive.

  • For example, user generated content refers to material that is contributed by the public to a website.

  • Crowdsourcing refers to the enlisting of a large number of people to collect information via the Internet.

  • In citizen science, data about the natural world is collected by the general public for analysis by professional scientists [71].

  • In community science, the community takes a more active role [1].

  • Depending on how data were collected, one or more of these terms could apply.

  • Several applications allow collaborative geospatial data acquisition.

    • Google Maps.
    • Wikimapia.
    • OpenStreetMap (OSM).

  • OSM started in the UK in 2004.

    • The most widely known example of a global open geospatial dataset.
    • Maintained and expanded through a global community of contributors.

  • OSM was inspired by restrictions on the use and availability of geospatial data.

  • Its growth was facilitated by Web 2.0 capabilities, inexpensive portable satellite navigation devices, and freely available satellite imagery.

  • Today, OSM has a global community of 5.5 million registered users and between 4000 and 5000 daily active members [72].

  • OSM is maintained through an ecosystem of software, servers, tools, users, and contributors.

  • Mapathons are a popular way of contributing data to OSM [73].

  • OSM data are open, and users are free to create, share, and adapt the data.

    • As long as they keep this new data open.
    • Attribute the original source.
    • Share it under the Open Data Commons Open Database License [74].

  • OSM’s coverage increases steadily.

  • The data has been integrated into many applications, ranging from routing applications to mobile games.

  • OSM data are accessible through web services.

  • Derivative datasets have emerged.

    • Wheelmap.
    • OpenSeaMap.
    • OpenSnowMap.org.

  • Some OSM data are donated by authorities.

  • Mostly it is collected by amateur enthusiasts with variable geoscientifc knowledge and skills.

  • As a result, the data may vary in quality over different regions (Figure 7).

  • Without a thorough evaluation of the quality, OSM data are unreliable for complex spatial analysis and modeling [75–77].

  • Nevertheless, OSM data are still useful as a map backdrop or for positional applications.

  • The generation of large-scale 3D city models at low cost is increasing [43, 78] (Figure 8).

  • Without active contributors, a geospatial dataset will quickly degenerate.

  • Active and constant use of geospatial open data in a specific region can trigger the inception of a contributing community and further on, help to consolidate the community so that data quality can be improved [77].

  • Corsar, et al. [80] report that there is growing recognition that the focus must shift from publication of data to coverage and quality.

  • Several suggestions have been proposed for increasing the reliability of OSM data.

    • Contributors should provide metadata.
    • Engagement strategies became just as important as the resulting data [81].
    • Engagement strategies involving voting [82], quality assessments [83] and identifying “good” contributors [84] have been proposed.
3.3. Authoritative Open Geospatial Data
  • Geospatial vector data are typically collected and maintained by governments.
    • Administrative boundaries, place names, building footprints, street centerlines, and addresses.
  • Such authoritative data are increasingly published with an open data license.
    • In the spirit of freedom of access to information and for efficiency reasons.
  • Satellite imagery has also been made available with open licenses.
    • Copernicus Open Access Hub [85].
    • USGS Earth Explorer [86].
  • The re-use and sharing of data among government organizations is expected to realize efficiencies.
  • Open and shared authoritative data have the potential to:
    • Reduce duplication and redundancy.
    • Lead to more efficient and effective government decision-making.
  • Fostering user feedback may lead to improved quality of open authoritative data [87].
  • Since the expansion of the Web, open data and the need for these have been increasingly referenced by the public sector.
  • The European Amended Public Sector Information (PSI) Reuse Directive aims to:
    • Make all suitable public government data available for reuse.
    • With as few legal restrictions as possible.
    • Through open and machine-readable formats together with their metadata.
  • In principle, charges should not exceed marginal dissemination costs [88].
  • Providing public data through open data portals supports the promise to deliver transparent governance.
  • The gateway to public government data following the PSI Directive is the European Data Portal [89].
  • Similar examples can be found world-wide data portals [90, 91, 92].
  • The most recent data portal in the United States supports sharing of data, services and apps in GeoPlatform [93] and through data.gov [91].
  • Tools such as The Open Data Barometer [94] and Global Open Data Index [95], track the state of openness and support governments with publication of open data.
  • A 2010 Danish study [96] reveals the socio-economic benefits of freely available authoritative address data.
  • A 2013 study in Europe found that the release of public sector data as open data has a practical, direct impact on increased entrepreneurial activity, and that open data are a potential catalyst for innovation [97].
  • These success stories are countered by concerns about the sustainable financing of open data [88], and proposals have been made for transitioning government-funded SDI into self-sustaining operations [98].
  • Sometimes authoritative data are augmented with crowdsourced contributions from the general public that are moderated before they are accepted into the dataset.
  • Such a process is not straightforward and there is an increasing need in the geospatial community for defining a best practice for this.
  • The OGC’s Data Quality and Citizen Science Domain Working Groups discussed the use of non-authoritative data [99].
    • An ad-hoc group on non-authoritative data was established in 2018
  • The mission of this group is to clarify and formalize best practices.
  • A spatial data infrastructure (SDI) facilitates and coordinates the exchange and sharing of spatial data.
  • Early SDIs emerged as top-down government funded initiatives [101].
  • Since then, SDIs have changed and evolved in response to crowdsourcing and mobile technologies [102].
  • With technological advancements and the paradigm shift toward open data, SDI data are increasingly published as open data [103].
  • According to [70] an open SDI is not only about making spatial data available to the public as open data, but also about organizing and governing the infrastructure in an open manner.
  • The entry point to SDI data is typically through a geoportal.
    • Such portals should improve accessibility and usability of data.
    • Move away from a single dataset centric view.
    • Improve management and usability of metadata.
    • Decrease costs and work required to publish data.
    • Introduce revision history.
    • Treat geospatial data as a first class data type [104].
  • Geoportals are often known in geoinformation communities only [105].
  • One of the drivers toward open geospatial data served through SDIs is the recognition of a citizen’s right to access information held by the government [106].
3.4. Open Scientific Geospatial Data
  • The requirement for open scientific data dates back 50+ years.
    • During preparations for the 1957–1958 International Geophysical Year [107].
  • Open scientific data allow others to verify, confirm, or reject scientific claims [108].
  • FAIR refers to the four foundational principles:
    • Findable.
    • Accessible.
    • Interoperable.
    • Reusable [109].
  • Regardless of their public availability, FAIR principles apply to digital resources [110].
  • However, as indicated in open science, FAIR and open should be considered complementary by data practitioners [110].
  • Several scientific journals promote the paradigm of science as an open enterprise and confirm the new norm of producing open scientific data [111].
  • In addition to journals and libraries, government organizations are creating infrastructure for scientific data sharing [113].
  • Several organizations and communities promote FAIR practices.

4. The