# FAIR Geovisualizations: Definitions, Challenges, and the Road Ahead

Institute for Geoinformatics, University of Münster auriol.degbelo@uni-
muenster.de

###### Abstract

The availability of open data and of tools to create visualizations on top of
these open datasets have led to an ever-growing amount of geovisualizations on
the Web. There is thus an increasing need for techniques to make
geovisualizations FAIR - Findable, Accessible, Interoperable, and Reusable.
This article explores what it would mean for a geovisualization to be FAIR,
presents relevant approaches to FAIR geovisualizations and lists open research
questions on the road towards FAIR geovisualizations. The discussion is done
using three complementary perspectives: the computer, which stores
geovisualizations digitally; the analyst, who uses them for sensemaking; and
the developer, who creates them. The framework for FAIR geovisualizations
proposed, and the open questions identified are relevant to researchers
working on findable, accessible, interoperable, and reusable online
visualizations of geographic information.

###### keywords:

Geovisualization; semantic interoperability; software reuse; FAIR principles

††articletype: REVIEW ARTICLE

##  1 Introduction

The momentum gained by the open data movement both in government and research
has yielded a large amount of open (geographic) datasets available on the Web.
Statistics from Open Data Inception (https://opendatainception.io/) suggest
that there are currently more than 2600 open data portals worldwide. If one
considers that the average data portal offers thousands of datasets, this
means hundreds of thousands of open datasets to be made sense of.
Visualization of these datasets has been identified as a key user need in
previous work [39, 65, 66]. Geovisualizations indeed hold the promise of
increasing information transparency [114, 41], facilitating data-literate
citizenry [40], and catalyzing citizen participation [52, 114], ultimately
“making the world a better place” [96].

Parallel to the developments in the open data landscape, commercial and open-
source tools/libraries are increasingly available to create visualizations
(e.g. ArcGIS Online, Tableau, D3.js, R). It can thus be expected that
(geo)visualizations - next to websites and datasets - will become important
contributors to the information overload problem in the digital age. In fact,
it can be argued that they are already - witness for example the overwhelming
amount of online maps created during the COVID-19 pandemic111See examples of
these maps in [67]. For a non-exhaustive collection, see
https://www.lrg.tum.de/lfk/service/online-maps-on-corona-covid-19/ (accessed:
March 12, 2021).. Finding online maps on a specific topic is becoming
increasingly challenging, though solutions are emerging ad-hoc (primarily in
the form of online platforms) to provide entry points for them. Example of
these platforms include the D3.js Graph Gallery (https://www.d3-graph-
gallery.com/), DataUSA (https://datausa.io/), Data-smart city solutions
(http://bit.ly/2p23CrS), ArcGIS StoryMaps (https://storymaps.arcgis.com/),
Observable (https://observablehq.com/collection/@observablehq/maps), and the R
Shiny Gallery (https://shiny.rstudio.com/gallery/), to name a few222All links
were last accessed on July 08, 2021.. The time is ripe to develop techniques
making these visualizations first-class in information search processes on the
Web.

[139] identified users of open government data to include citizens,
businesses, researchers, developers, non-governmental organizations, and
journalists. All these groups can potentially benefit from techniques that
make not only websites/datasets about a topic, but also available
geovisualizations FAIR - Findable, Accessible, Interoperable, and Reusable. A
question at this point is: why care about making online geovisualizations FAIR
at all?

From the theoretical perspective, the uniqueness of web maps as geospatial
resources can be highlighted from two perspectives mentioned in [105]: map as
a tool and map as a representation of geographic space. First, as a tool, web
maps are useful to explore the spatial dimensions and interrelationships
between phenomena and activities located in space. Thus, they are helpful to
create and communicate (visual) stories about geographic phenomena. This
storytelling feature is neither present in (raw) datasets nor web services on
the spot. Second, as a form of knowledge representation, they index
information by location in a plane as opposed to using sentences as primary
units to organize knowledge. Thus, they enable the retrieval of insight hidden
in datasets in a more efficient and effective way. Whether approached from the
perspective of a tool that enables story construction, or an artifact that
stores geographic knowledge in an effective way, (web) maps are sufficiently
distinct from raw datasets and web services to deserve efforts aimed to
improve their discoverability.

From the practical perspective, a user survey conducted by [66] has reported
evidence of stakeholders’ interests in the reuse of existing online
visualizations. Finding these visualizations in the first place is thus
critical to meet these users’ needs. Yet, the findability of online
(geo)visualizations using existing search engines is currently limited.
Exemplar questions where state-of-the-art engines would fail to return
appropriate answers include: show all online maps about the latest earthquake
in Christchurch (e.g. for a geologist); show all online maps about the
demographics of Berlin, Germany (e.g. for an entrepreneur prospecting about
opening a new business), show all online maps about the kingdom of Prussia
(e.g. for a historian who wants to take advantage of map collections available
worldwide about their object of study). This suggests that more work on FAIR
geovisualizations is currently needed. This article aims to provide a start to
discussions about FAIRness issues pertaining to online geovisualizations. Four
questions are addressed:

  * •

What does it mean for a geovisualization to be FAIR?

  * •

What are approaches relevant to FAIR geovisualizations?

  * •

What are unique challenges of FAIR geovisualizations?

  * •

What are open questions for FAIR geovisualizations research?

‘Geovisualization’ is a term used with a variety of meanings as indicated by
[33]. It can denote (i) the process of creating interactive visualizations to
aid visual thinking and knowledge building during data exploration; (ii) the
artifact (e.g. plots, maps, combinations of these) used to support that
process; (iii) the artifact resulting from that process (e.g. a map
supplemented with graphical annotations that record users’ observations during
data exploration); or (iv) the academic discipline concerned with the study of
the process and the artifacts. The focus of this article is on findable,
accessible, interoperable and reusable artifacts.

The two key contributions of the paper are a framework and a research agenda
for FAIR online geovisualizations. First, while existing discussions on
FAIRness mainly take a computer-centric perspective and focus primarily on
technical challenges, a novelty in this work is a holistic discussion of
FAIRness. That is, FAIRness is also discussed from the perspective of both the
consumer and the producer of the resources to be made FAIR. The discussion of
the peculiarities of the three perspectives results into a framework that can
be used for (systematic) mapping studies of FAIR geovisualization research. A
second novelty of the work is the application of FAIR principles to a new
domain, namely that of geovisualizations, to unveil a unique set of challenges
and opportunities for GIScience research.

Relevant literature to the questions above is compiled and critically
summarized in this article. Since FAIR geovisualizations is an emerging
research topic and not a major theme of any established outlet in GIScience, a
systematic review would be premature at this stage. Instead, the article draws
on exemplar contributions pertinent to the exposition of the ideas. Section 2
summarizes the FAIR principles, and presents a scenario for FAIR
geovisualizations. Section 3 briefly reports on existing visions/research
agendas and highlights their peculiarities from a metaphor-theoretical point
of view. As this section shall clarify, FAIR geovisualizations rely on a
slightly different metaphor from prevalent ones in geovisualization research.
Section 4 considers the three roles relevant in the context of FAIR
geovisualizations (the computer, the analyst, the developer), and elaborates
on their uniqueness. Section 5 discusses unique challenges of realizing FAIR
geovisualizations. Section 6 brings forth some open research questions on the
road towards FAIR geovisualizations, Section 7 discusses limitations, and
Section 8 concludes the article.

##  2 Setting the scene

###  2.1 What FAIR is

The FAIR principles were originally proposed by [167] in the context of Open
Science to emphasize the ability of machines to automatically find and use
datasets (or digital assets more broadly), in addition to supporting their
reuse by individuals. A wide adoption followed their introduction and with
that wide adoption, a variety of interpretations of their meaning emerged.
Follow-up articles to the original Wilkinson article tried to clarify the
original intent. The following quotes are extracted from some of these.

  * •

“FAIR refers to a set of principles, focused on ensuring that research objects
are reusable, and actually will be reused, and so become as valuable as is
possible” [118].

  * •

“The Principles are aspirational, in that they do not strictly define how to
achieve a state of ‘FAIRness’, but rather they describe a continuum of
features, attributes, and behaviors that will move a digital resource closer
to that goal” [168].

  * •

“[The Principles] describe characteristics and aspirations for systems and
services to support the creation of valuable research outputs that could then
be rigorously evaluated and extensively reused, with appropriate credit, to
the benefit of both creator and user” [118].

From the foregoing, FAIR are a set of aspirational principles introduced
initially to improve the reuse of research objects. While the principles are
useful to research objects, their scope need not be limited to these. They
could indeed benefit other types of resources on the Web, namely online
geovisualizations. These online geovisualizations may be the outcome of a
research process, or not (e.g. many online geovisualizations are typically
created by data journalists, not researchers). Realizing FAIR
geovisualizations is thus about ensuring that online geovisualizations are
reusable, will be reused, and so become as valuable as possible. In essence,
the FAIR principles point at the following key considerations discussed in
[81]:

  * •

Findable: data and metadata must be assigned a globally unique and persistent
identifier in order to be found and resolved by computers. In addition,
digital resources should be well-described so that they can be accurately
discovered.

  * •

Accessible: data and metadata should be retrieved using a standardized
communication protocol. An example of standardized access protocol is the
Hypertext Transfer Protocol (HTTP).

  * •

Interoperable: data and metadata should be described using a formal language
for knowledge representation. (It follows from this definition that
interoperability discussions in this article are centered on semantic
interoperability aspects, not structural or syntactic aspects).

  * •

Reusable: data and metadata are released with a clear and accessible usage
license.

###  2.2 FAIR Geovisualizations - A scenario

Since there is no prior discussion on FAIR online geovisualizations and no
elaborated vision of FAIR geovisualizations in the literature, a thought
experiment333According to [171], a thought experiment is a device with which
one performs an intentional, structured process of intellectual deliberation
in order to speculate about (i) potential antecedents for a designated
consequent or (ii) potential consequents for a designated antecedent. called
backcasting is used to frame the current discussion. Following [171],
backcasting “involves the description of a definite, specific future situation
and, then, moving backwards in time, step-by-step, in as many stages as are
considered necessary, from the future to the present. The goal of backcasting
is to reveal the mechanism through which the specified future could be
attained”. Backcasting takes a different approach from forecasting. The latter
uses current dominant trends to predict what futures are likely to happen.
Instead, the former examines desirable futures, and how they can be attained.
While doing so, a backcasting study may question some suppositions inherent in
prevailing perceptions, and open some new options [51, see]. Backcasting has
been often used for policy planning and analysis (see e.g. [14] for a recent
review, and [74] for a spatially-explicit backcasting analysis for land-use
planning).

As an example of desirable future444The scenario below is just one of many
possible scenarios and is brought forward to advance the discussion on FAIR
geovisualizations. Future work may come up with other equally interesting (if
not more interesting) scenarios., consider the following scenario (Figure 1).
A data journalist is preparing a new article on population growth in cities,
and is at the exploratory stage. They have no background in Cartography and no
programming skills. They go through the following steps: 1) they enter a query
in a search engine of their choice; 2) the search engine returns previews and
short descriptions of most relevant geovisualizations; 3) the data journalist
selects one for closer inspection and realizes (through the grey boxes) that
data for the years 2018 and 2019 is missing; 4) they click on the A.M.D. (Add
Missing Data) button, and the visualization looks for the missing data on the
Web; 5) the data journalist is now interested in visualizing data from another
geographic area the same way, to compare patterns of the two regions; 6) a
simple drag and drop interaction enables the portability of the current
visualization template to the new dataset. The focus of this article is on
web-based visualizations, of which the generic structure is presented in
Listing 2. For a recent review of web-based visualizations, see [120].

Figure 1: FAIR Geovisualizations at work. 1) the user enters a query in a
search engine of their choice; 2) the search engine returns previews and short
descriptions of most relevant geovisualizations; 3) the user selects one for
closer inspection and realizes (through the grey boxes) that data for the
years 2018 and 2019 is missing; 4) the user clicks on the A.M.D. (Add Missing
Data) button, and the visualization looks for the missing data on the Web; 5)
the user is curious to see if they can visualize data from another geographic
area the same way; 6) a simple drag and drop interaction enables the
portability of the current visualization template to the new dataset.

⬇

<!DOCTYPE html>

<title>Geovisualization Title</title>

<!– loading relevant libraries, e.g. Leaflet or D3.js –>

\par

Content of the geovisualization……

\par

\par

\par

Figure 2: Basic structure of a web-based geovisualization. How to make
geovisualizations in this format FAIR?

As the reader may have noticed, this scenario necessitates approaching
geovisualizations from three distinct and complementary perspectives. In step
2 of the figure, they are web documents that are being searched (computer
perspective). In steps 3 and 4, they are software components that interact
with each other (developer perspective). In steps 5 and 6, they are interfaces
that are manipulated (analyst perspective). Web-based visualizations support
the three perspectives (e.g. through the use of <meta> tags for their
description and Javascript to add behaviour). From the theoretical point of
view, the following observations (drawing on the distinctions introduced by
[107]) can be helpful to relate the three perspectives.

  * •

At a conceptual level, a geovisualization (as an artifact) is a form of
representation of geographic knowledge. [107] distinguished two ways of
representing knowledge in general: sentential (i.e. use sentences as a unit to
organize information) and diagrammatic (i.e. index information by location in
a plane).

  * •

Online geovisualizations are materialized in several, coexistent forms555The
informational and computational equivalence of these three forms are not
discussed here.:

    * –

Sentential, descriptive: this is the case when they are available as
declarative statements of facts (e.g. triples, see [160]). This form entails
considerations from the web documents/computer perspective;

    * –

Sentential, executable: this is the case when they are made available as a
software code (e.g. specified using the Vega-Lite grammar, see [92]). This
form entails considerations from the software/developer perspective;

    * –

Diagrammatic: that is, a visual output (e.g. Chart, Map), which may be
interactive or not. This form entails considerations from the
interface/analyst perspective.

The three perspectives (computer, analyst, developer) are discussed in detail
in Section 4.

##  3 Related work

Several research agendas and visions have been suggested touching upon the
future of geovisualization research. These research agendas and visions
overlap to some extent. A means of highlighting their subtle differences is
through the use of metaphor theory. As [106] indicated, “the essence of
metaphor is understanding and experiencing one kind of thing in terms of
another”.

###  3.1 Prevalent metaphors in geovisualization research

From existing work, at least three metaphors have inspired the progress of
geovisualization research. These metaphors, extracted from [112, 88] are: (i)
geovisualization as a knowledge base (or a database) storing geographical
facts; (ii) geovisualization as an interface that can support productive
information access and knowledge construction activities; and (iii)
geovisualization as a presentation medium making a specific point and/or
persuading the viewer to adopt a particular position. The knowledge base in
(i) can take the form of an image encoding geographical facts or a geometric
structure encoding geographic facts (see [125]).

Existing visions and research agendas have discussed geovisualization as an
interface primarily (i.e. metaphor (ii) above). These interfaces can be two-
dimensional (as customary with computer screens) or three-dimensional (e.g. a
globe or an immersive environment). About 20 years ago, [112] listed open
questions for geovisualization research, starting with the premise that the
map is an interface that can support productive information access and
knowledge construction activities while retaining its traditional role as a
presentation device. [135] defined cartographic interaction as the dialog
between a human and a map mediated through a computing device. He discussed
the what, why, when, who, where, and how of cartographic interaction, and
proposed open questions related to these themes. [31] collected and
synthesized community input on what experts considered as persistent
challenges in geovisualization. [138] discussed methodological aspects of
research about user interface aspects (i.e. how to do research about the
design and use of interactive maps and visualizations). They also elaborated
on research questions in interactive geovisualizations for which more user
studies are currently needed.

A few discussions have had a more narrow task focus. [68] brought forth
research opportunities related to the design of geovisualizations as
interfaces that are transferable from one context of use to another. The focus
of [5] was on how geovisualizations as interfaces can support spatial decision
making, while [4] discussed how they can support visual analysis. [132] looked
closely into geovisualization as an interface for the analysis of big spatial
data. In [42], the focus is on making geovisualizations more ‘intelligent’.
The authors brought forth and discussed requirements for intelligent
geovisualisations from the perspective of computational user interface design.
Finally, [156] focused on geovisualisations for individual pedestrians (they
use the term geographic information display) and discuss questions of how
these can facilitate navigation through a large-scale, real-world environment.

###  3.2 Another metaphor: geovisualizations as information products

One core premise of this article is that geovisualizations are information
products in their own right, in a fashion similar to datasets or websites.
From the perspective of metaphor theory, the statement ‘geovisualizations are
information products’ is a metaphor, since it involves understanding one
domain (i.e. the domain of geovisualizations) in terms of another (i.e. the
domain of information products). Metaphors have entailments, through which
they highlight and make coherent some aspects of experience.

A key entailment of the statement ‘geovisualizations are information products’
is that geovisualizations are gestalts. A gestalt is “an integrated, coherent
structure or form, a whole that is different from the sum of the parts” [164].
That is, geoviz = geodata + code + interface, and the whole is more than the
sum of its parts. The information product viewpoint implies more than what the
knowledge base metaphor (geodata), the interface metaphor (interface) and the
presentation medium metaphor (interface) have covered in isolation.
Geovisualizations thus deserve their own treatment in information search.
Accordingly, dedicated search mechanisms should be developed to make them
findable, just as open data portals (e.g. Dataverse, Data.gov, European Data
portal) or Google Dataset Search [122] have recently emerged to ease the
findability of open datasets.

A second entailment of the sentence ‘geovisualizations are information
products’ is that geovisualizations are CONTAINERS (in the metaphor
theoretical sense), or in plain language, carriers of informational
statements. This entailment is in line with recent proposals to model maps,
and more generally visualizations, as a set of statements made by someone at
some point in time [145, 37, see e.g.].

A third entailment of ‘geovisualizations are information products’ is that
geovisualizations are informational resources and hence afford the FAIR
principles from [167]. That is, geovisualizations can (or in principle should)
be found, accessed, reused, and afford interaction with other information
products on the Web. As [111] aptly indicated: “If we accept the premise that
maps … are a useful way of obtaining spatial information, we have the
obligation to facilitate their use as information sources” (Page 11, emphasis
added). The idea that geovisualizations should be made FAIR is easier to grasp
when one looks at static images (arguably one of the simplest forms of
geovisualization, and one that is already treated as a specific information
product by current search engines).

While geovisualizations afford the FAIR principles, they are yet to be
examined in the literature from that perspective. The discussion that follows
is an attempt to fill this knowledge gap.

##  4 FAIR Geovisualizations: three perspectives

The purpose of this section is to provide a conceptual framework for
investigating FAIR geovisualizations. As mentioned above, ‘geovisualization’
can denote an information product, an area of scientific investigation, or a
creative process, and the information product viewpoint is of prime interest
in this work.

A geovisualization is an artifact, physical or digital, whose visual
properties encode geographic data. [130] proposed that visualizations of
geographic information can take one of seven types: maps/cartograms, networks,
charts/graphs, tables, symbols, diagrams, and pictures. In other words,
geovisualizations subsume maps. Besides, a further distinction needs to be
made between machine-readable and non-machine readable geovisualization in the
context of information search. An example of the former is a web-based
visualization created using D3.js, while an example of the latter is a (PNG or
JPEG) image. This implies at least 14 types of geovisualizations to be made
findable when [130]’ seven types are taken into account. Current search
engines have dedicated sections for the search of images (e.g. a map in a GIF
format), and as such partly address the findability of static or animated
geovisualizations. Solutions enabling the findability of interactive web-based
geovisualizations about a given topic will be of increasing value in the
coming years.

The application of FAIR principles to geovisualizations requires a
clarification of perspectives to remove ambiguities. As [135] indicated,
cartographic interaction is the dialogue between a map and a human meditated
through a computing device. This suggests two key actors affecting a
geovisualization during an interaction process: the computer and the human. In
addition, the human may be either a producer of the geovisualization or a
consumer. In the context of online maps, this distinction matters because the
skills required to play these roles are different. A producer requires mastery
of a programming language and/or visualization tools (e.g. ArcGIS online,
D3.js), while the consumer need not worry about these. There are thus three
key roles to consider while investigating FAIR geovisualizations: the
computing device (called here ‘computer’), the consumer (called henceforth
‘analyst’ to borrow the terminology of [4]), and the producer (called
henceforth ‘developer’). The developer may be a prosumer (i.e. produce maps
without formal cartographic training), or a trained Cartographer as discussed
in [80]. Following the scenario (Figure 1), the analyst may be involved in
information analysis tasks and/or information search tasks. Realizing FAIR
geovisualizations implies coping with demands from a computing perspective and
addressing needs of analysts. It also puts some constraints on developers of
these geovisualizations. While the exact boundaries demarcating these roles
are challenging to formally pinpoint, the following informal distinction will
be used at this point: the computer stores FAIR geovisualizations digitally,
the analyst uses them for sensemaking, and the developer creates them.

The concurrent presence of these different roles suggests that there are many
dimensions of FAIRness, according to the perspective considered. Yet, the
literature so far has only taken a computer-centric view of FAIRness issues.
To advance our understanding of these and provide a more holistic view, the
three perspectives are considered at this point. Interpretations of FAIR
according to the role considered, relevant approaches to address the issues
arising for each role and scientific communities of interest are considered
below. The discussion is done from a knowledge representation perspective
(computer); from the perspective of user interface design (analyst); and from
the perspective of technical considerations about the publishing of online
geovisualizations (developer). Table 1 summarizes the key takeaways. Since
each of the keywords or approaches mentioned in the table would need extensive
literature reviews to cover them appropriately, the discussion only points at
examples of relevant work and recent reviews, where available.

|  |  Computer’s Demands |  Analyst’s Needs |  Developer’s Constraints
---|---|---|---|---
[HTML]FFFFC7 F | Definition |  persistent + described |  highlighted |  aliased
[HTML]FFFFC7 | Example approaches |  digital object identifiers, containerization; manual- (e.g. tagging, full description), automatic-, and semi-automatic description; |  query auto completion; relevance ranking; visual hierarchy and layout; attention-guiding visualization |  query expansion
[HTML]FFFFC7 | Relevant to |  Digital Libraries; Semantic Web |  Geographic Information Retrieval |  GIScience/Geospatial Semantic Web
[HTML]9AFF99 A | Definition |  compliant (with a standardized communication protocol) |  tailored (to abilities and expertise) |  platform- and device-agnostic
[HTML]9AFF99 | Example approaches |  https, ftps |  accessible design |  map plasticity
[HTML]9AFF99 | Relevant to |  Web Science |  Human-Computer Interaction |  Human-Computer Interaction
[HTML]CBCEFB I | Definition |  formalized |  compliant (with widespread design conventions) |  compliant (with web mapping standards)
[HTML]CBCEFB | Example approaches |  map as statements/knowledge base |  symbol stores; map-, text-, and colour brewers |  wms, wfs, wcs, sld, html5, css3, svg, rdf/owl, mapML, webGL, x3d/x3dom
[HTML]CBCEFB | Relevant to |  GIScience/Cartography & GIScience/Geospatial Semantic Web |  GIScience/Cartography |  GIScience/Cartography & GIScience/SDI; Semantic Web
[HTML]FFCCC9 R | Definition |  licensed (under an open and machine-readable licence) |  transformable |  modular + open-source + backward-compatible
[HTML]FFCCC9 | Example approaches |  JSON hashes, RDF |  template-based design |  declarative frameworks, shelf construction tools, geodocuments, blockchain
[HTML]FFCCC9 | Relevant to |  Semantic Web; Open Data |  GIScience/Geovisualization & Information Visualization |  GIScience/Geovisualization & Information Visualization
Table 1: A framework for FAIR geovisualizations. The framework features three
perspectives: demands from a computing perspective, analyst’s needs, and
constraints for developers of these geovisualizations. The table also shows
examples of approaches to realize these demands/needs/constraints. Finally,
the table lists exemplar disciplines and communities that investigate and
address the demands/needs/constraints. Figure 3: Interdependences between the
dimensions of FAIR geovisualizations. The analyst’s demands include many of
the computer’s demands, and the demands of both computer and analyst result in
constraints for the developers. Legend: licenced = licenced with a machine-
readable open licence; compliant (c) = compliant with standardized
communication protocols; compliant (d) = compliant with widespread design
conventions; compliant (m) = compliant with web mapping standards. Some
aspects are highlighted in black to indicate that there are optional to
realize interoperability and reuse from the user’s viewpoint.

###  4.1 FAIR Geovisualizations - The computer’s demands

Findable, from the computer’s perspective, means ‘persistent’ so that
resources can be located over and over again. Web evolution (e.g. API
interfaces updates, webpages redesign, server relocation) is the main cause
for non-persistence of web urls. Studies on the dynamics of the Web have
revealed that death rates for web pages are 20% after six months [93]. Death
rates for web sites are 12% after a six-month period [93], and death rates of
Linked Data documents amount to about 5% for a six-month period [87].
Persistence is currently addressed through the use of digital object
identifiers (DOIs) to uniquely identify resources, and containerization to
encapsulate computational components. Docker is currently the dominant
platform for containerization and offers an open-source specification for
defining and running multi-container applications (i.e. Docker Compose).
Applications based on Docker Compose can be easily deployed on the Web, but
the adoption of the technology has been relatively slow, perhaps due to the
learning curve required to master it. In addition, since metadata is key to
any effective search, findable from the computer’s viewpoint also connotes
‘described’. Metadata can be generated manually (e.g. place name tags or full-
text description), automatically, or semi-automatically. The question of how
to produce these metadata has been investigated for scanned maps in the
context of digital libraries [90, e.g.], and in the context of designing
geoportals [25, e.g.]. FAIR geovisualizations can learn from, and build upon
lessons learned by these and other works.

Accessible, from the computer’s viewpoint, means ‘compliant with a
standardized communication protocol’. As discussed in [81], the purpose of
this principle is to provide a predictable way for agents to access resources.
Ideally, the protocol should be open, free, and universally implementable. It
should allow for authentication and authorization when resources have access
restrictions due to ethical, legal or contractual constraints. HTTPs and FTPs
are examples of protocols underlying the Web that are compliant with these
requirements.

Interoperable, from the computer’s perspective, means encoded in a formal
language for knowledge representation (‘formalized’ for short). A relevant
approach here is the idea of map as statements made by somebody somewhere at
some time [145], also called maps as knowledge base in [160]. An early work
relying on metadata encoded in the Resource Description Framework (RDF) to
improve thematic map search was presented in [1]. More recently, [144]
illustrated how RDF can be used to describe both the properties of a
historical map (e.g. map scale) and its content (i.e. cities, buildings and
hills shown on the map). [37] showed how the Schema.org vocabulary, which is
endorsed by several search engines (e.g. Google, Microsoft, Pinterest,
Yandex), can be used to annotate web maps for subsequent search. [59] proposed
a map legend ontology, and illustrated how the ontology can help answer
questions such as “which map legends contain highway information”? or “which
maps contain both ski areas and camping areas?” Contrary to the works
mentioned above, which aimed at describing geovisualizations as finished
information products in order to improve their search, [63], and [79] focused
on describing the process of producing a geovisualization. The former partly
formalized steps of the cartographic generalization process, while the latter
formalized the rendering of geometries using ontologies and rules. Finally,
MARC [8, 109] is a format that has been used by projects digitizing paper maps
(e.g. [90]) in the context of digital libraries. It may be extended, or some
of its metadata fields (e.g. projection, type of cartographic material) reused
for the description of online web-based geovisualizations.

Reusable, from the computer’s viewpoint, means that there is a machine-
readable open license available that can be accessed (‘licensed’ for short).
Initiatives to advance work in this area come from the open data community,
and include the Open Data Commons’ License
Service666https://opendatacommons.org/ (last accessed: March 12, 2021). and
the RDF Creative Commons
Licenses777https://github.com/creativecommons/cc.licenserdf (last accessed:
March 12, 2021).. The Open Data Commons License Service provides licenses in
JSON and an API friendly form, while the RDF Creative Commons Licenses helps
describe copyright licenses in RDF.

###  4.2 FAIR Geovisualizations - The analyst’s needs

FAIRness needs from the user’s perspective are different from the demands from
the computing perspective. For instance, from the analyst’s point of view,
Findable means ‘highlighted’. The issue of finding a needle in a haystack is
thorny, precisely because the needle resembles all hays around. Highlighting
is implemented in various contexts in different ways. To cope with the issue
that all queries resemble themselves (syntactically), search engines use an
auto-completion feature to highlight queries that are likely to better
represent users’ intent, and help them avoid spelling mistakes and save time
typing. In addition, human-computer interaction and map-making rely on user
interface elements arrangement - visual hierarchy and layout [154, see] \- to
create a ranked order of visual elements so that the most important elements
have the greatest visual prominence. For example, a menu item immediately
visible on a web page is easier to find that one that needs to be accessed
only after browsing. [155] call the former menu item ‘first-class’ and the
latter ‘second-class’. Finally, relevance ranking is also a means of
highlighting items of the information space. It puts some items on top and
leaves others at the bottom, signaling to the user where their attention
should be directed in priority. [77, 78] used a regression model incorporating
nine aspects of matching between input queries and map metadata (e.g. exact or
similarity matching on title, description, and place names) to formalize
relevance for map search. Their model was applied to the semantic search of
maps in ArcGIS online. [153] proposed attention-guiding geovisualization to
help users quickly locate and easily decode relevant information through the
use of suitable graphical variables. They reported promising results about a
computational model to guide users’ visual attention to most relevant features
during the interaction with a geovisualization.

Accessible, from the analyst’s viewpoint, means ‘tailored to the user’s
abilities and expertise’. It is strongly related to [151]’s idea of ‘universal
usability’: break technology barriers, bridge knowledge gaps, and accommodate
user diversity. Example approaches to broaden the accessibility of
cartographic products include: the use of multi-layered interfaces to bridge
the knowledge gap between novice and experts [135, see]; and the use of multi-
modal interaction to engage a multiplicity of user capabilities in the
computer-human communication process [123, see]. Challenges of accessible maps
were recently discussed in [55], and recommendations to make web maps more
accessible were suggested in [75]. From the theoretical point of view, [68]
presented a model for operationalizing map use contexts. The model has four
key elements: the user (e.g. individual differences, capabilities), the
environment (e.g. the setting of the map use), the activity (e.g. purpose for
using the map, actions undertaken while using the map), and the map (e.g.
representation design, nature of data, display device). Accessible design, as
discussed here, primarily addresses the diversity of user abilities.

Interoperable, from the analyst’s viewpoint, means that the geovisualization
‘uses widespread design conventions’. Following [137], a user interacts
primarily with four aspects of map features: content, geometries (i.e points,
lines, polygons), symbols, and labels. Widespread adoption of conventions is
particularly relevant for symbols (map symbology) and labels (map typography).
As regards symbology for topographic maps, organizations such as the
OrdnanceSurvey888See
https://www.ordnancesurvey.co.uk/mapzone/assets/doc/Explorer-25k-Legend-en.pdf
(last accessed: March 12, 2021)., the National Park Service999See
https://github.com/nationalparkservice/symbol-library/ (last accessed: March
12, 2021)., the USGS101010See
https://pubs.usgs.gov/gip/TopographicMapSymbols/topomapsymbols.pdf (last
accessed: March 12, 2021)., and communities such as OpenStreetMap111111See
https://wiki.openstreetmap.org/wiki/SymbolsTab (last accessed: March 12,
2021). have proposed catalogs of symbols that can be used for application
domains such as tourist and leisure information, as well as general-purpose
mapping. Reusing symbols from these catalogs can increase interoperability
from the user’s point of view, since this increases their chances of
interacting with ‘familiar’ symbols. Besides, previous work (e.g. [133]) has
brought forth the concept of a symbol store to promote the sharing of map
symbols in a specialized domain (e.g. emergency management). Next to
topographic maps, symbology for thematic maps needs to specify design
conventions for the use of [13]’s visual variables during the encoding of
geographic data. Approaches to democratize available knowledge regarding such
design conventions include (i) recommendations from the literature (e.g.
scientific papers, textbooks), and (ii) the use of tools encapsulating
(cartography) expertise. As to (i), recommendations for quantitative geodata
transformations and effective visualization are available for example in [97,
98]. Guidance for labeling and text placement are available online121212E.g.
https://www.arcgis.com/apps/Cascade/index.html?appid=e739c503a1f04d38839834a0fe4ca6d4
(last accessed: Marc 12, 2021). and in the literature [69, e.g.]. As to (ii),
Colorbrewer [73], the Map Symbol Brewer [147], and the TypeBrewer131313At the
moment of this writing, TypeBrewer is undergoing an update, and is temporarily
offline. are examples of tools suggested to help (web) map makers
select/create pertinent color schemes, map symbols, and typographic palettes
respectively. A brewer is a tool that uses mapping principles to offer users a
selection of choices for a cartographic representation challenge [22]. ArcGIS
and QGIS also provide options to select color/map/typographic palettes during
the creation of digital maps. Again, the availability of these tools does not
guarantee interoperability per se. Interoperability is truly realized if the
symbols provided by all these catalogs are more or less similar, to minimize
the element of surprise on the user’s side. Collecting empirical evidence on
(i) the extent to which the available symbols/palettes are used, and (ii) the
extent to which they differ across providers is an open question for research
(see Section 6.3).

Reusable, from the analyst’s viewpoint, means ‘transformable’ (or
controllable). With respect to the theoretical model of map use contexts from
[68], reuse from the user’s point of view - as discussed here - primarily
addresses the diversity of user activities. A relevant idea from [68] is the
notion of design transferability, that is, the exploitation of one or more
aspects of an existing design for a new application or map use situation. A
way of realizing design transferability from a user’s perspective is through
the use of templates (a.k.a. meaningful defaults). These templates are usually
available as a feature of a toolkit, and several toolkits have been proposed
in the past, for example, Tableau Public141414Tableau Public is sometimes
classified as a shelf-configuration user interface (UI) rather than a template
editor UI [64, 142, see e.g.]. Here, Tableau Public is mentioned as an example
of template-based design software in reference to the “Show Me” feature that
suggests meaningful charts during the visualization creation process., IBM
ManyEyes [161] launched in 2007 and closed in 2015, the GaV Toolkit [158], the
Geoviz toolkit [70], the Geospatial Visual Analytics Toolkit for movement data
[6], and AdaptiveMaps [43] for the visualization of open geographic data.

###  4.3 FAIR Geovisualizations - The developer’s constraints

The demands from the computing and user perspective bring about constraints
for a developer of FAIR geovisualizations. For example, a constraint imposed
by Findable visualizations on the developer is that they should be ‘aliased’
(i.e. items are being referred to in more than one way). [58] found that the
probability of two people spontaneously picking the same term to refer to an
entity is less than 20%, and called this issue the ‘vocabulary problem’.
Recent studies have found equally low rates. [46] found that the probability
of two providers naming categories for open data search in a similar way is
less than 30 % for European open data catalogs; [54] reported a probability of
less than 7% regarding developers picking similar variable and function names
in their study. As a cure to the vocabulary problem, [58] suggested that
developers use an unlimited number of aliases. Query expansion is the main
technique used to mitigate the vocabulary problem. It can be done using (i)
terminological associations learned from users’ past queries, or (ii) a
knowledge base. The former approach has been called ‘adaptive indices’ (see
e.g. [57]), and the latter typically uses a geographic ontology (e.g. [27,
113]), a domain ontology (e.g. [84]), or both (e.g. [56, 77]).

A constraint imposed by Accessible geovisualizations on developers is that
they should be ‘platform- and device-agnostic’. Users can now interact with
geovisualizations on different devices (e.g. desktop computers, smartphones,
and large-scale displays), using different interaction modalities (touch, eye-
movements, sound, freehand gestures, speech). The challenge for the developer
is to provide geovisualizations that preserve functionalities and user
experience across all these. The idea of interface plasticity [155] and its
adaptation to the context of geographic information map plasticity [99] were
proposed to mitigate this issue. Plasticity, as discussed here, primarily
addresses the diversity of user environments [68]. Designing for plasticity,
also called ‘model-based design’ [115], advocates a separation of concerns, so
that an interactive system can withstand variations due to the physical
characteristics of devices and properties of the software environments. An
abstract model describes the content of a geovisualization on an abstract
level, independent of how it is ultimately rendered to the user. This abstract
model is then translated into one or many concrete geovisualizations, which
respectively fit the constraints imposed by the user’s platform (e.g. Android
vs iOS) and interaction device (e.g. a tablet or mobile phone). In the context
of user interface design, the abstract model has been typically encoded in a
machine-readable format, for instance, XML (see [104]), JSON (see [9]) or the
UsiXML (USer Interface eXtensible Markup Language)151515See
http://www.usixml.org/ (last accessed: March 12, 2021).. [9] presented
BusinessVis, a plastic visualization of the Yelp academic dataset. BusinessVis
is web-based and has three views connected through brush-and-link interaction:
a map view of businesses, a category treemap, and a rating view showing a list
of companies and their user ratings; it supports automated rendering of a
JSON-based abstract geovisualization model on both a large display and
tablets/smartphones.

A constraint imposed by Interoperable geovisualizations on developers is that
they should be ‘compliant with (open) web mapping standards’. (Standards here
includes both de facto and de jure standards.) In the context of web-based
geovisualizations, two types of standards are particularly relevant, namely
those from the OGC (Open Geospatial Consortium), and those from the W3C (World
Wide Web Consortium). Examples of OGC standards relevant to geovisualizations
include the Web Map Service ([WMS] for retrieval of map images), the Web
Feature Service ([WFS] for retrieval of geographical features), the Web
Coverage Service ([WCS] for retrieval of raster data) and the Styled Layer
Descriptor ([SLD] for symbolization and coloring of feature and coverage
data). Examples of web standards include HTML5 (for web content structuring
and presentation), CSS3 (for styling), and the Scalable Vector Graphics (for
resolution-independent graphics). Work is ongoing to bridge the two worlds. At
the institutional level, the OGC and the W3C have partnered between 2015 and
2017 to develop a joint note on best practices for the publishing of spatial
data on the Web161616See the announcement about the collaboration at
https://www.w3.org/2015/01/spatial.html.en, and the note at
https://www.w3.org/TR/sdw-bp/ (both last accessed: March 12, 2021).. At the
conceptual level, [82] proposed a semantic enablement layer for spatial data
infrastructures to support the discovery of geospatial content. At the
technical level, different adapters were proposed to bridge between the two
worlds. These adapters typically support the translation of WFS requests into
SPARQL queries (e.g. [162]), the reverse translation of SPARQL queries into
WFS requests (e.g. [173]), or both (e.g. [86]). Next to the bridging between
different open standards, interoperability between different geovisualization
environments is also an important matter of concern. To this end, [150]
proposed an information model to enable interoperability between
geovisualizations in geographic, virtual reality (VR), and augmented reality
environments (AR). The information model extends HTML with custom element tags
to support the rendering of 2D/3D and VR/AR geo-applications in the browser.
Another line of work is developing the Map Markup Language (MapML), a text
format for encoding map information for the World Wide Web171717See
https://maps4html.org/ (last accessed: March 12, 2021).. The key objective of
MapML is to “identify the Web map processing that is currently performed by
JavaScript libraries which should instead be defined as elements and
attributes supported by CSS”181818https://github.com/Maps4HTML/MapML-Proposal
(last accessed: March 12, 2021)..

A constraint imposed by Reusable geovisualizations on developers is that they
should be ‘modular and open-source’. Modularity is well covered by several web
mapping libraries (e.g. R, Leaflet, D3.js). A key idea to facilitate reuse
from the developer’s perspective is that of declarative frameworks.
Declarative programming languages specify what the results of a computation
should be instead of how the results should be computed. They target
programmers for what [17] called ‘code as Cartography’. Besides, some
visualization authoring tools provide developers less conversant in
programming with decent expressiveness during the visualization creation
process. The Information Visualization literature [64, 142] distinguishes
between shelf construction tools (which enable authors to map data fields to
visual variables) and visual builders (where authors make use of fine-grained
user interface elements - marks, glyphs, coordinate systems, and layouts - for
more expressiveness during the visualization creation process). Examples of
declarative languages for (geo)visualization design include Protovis [18], its
successors D3 [19] and Vega-lite [143]. Examples of shelf construction tools
include Tableau Public ([[, which evolved from Polaris:]]Stolte2002) and
Voyager [170]. Examples of visual builders include research prototypes such as
Lyra [141] and iVizDesigner [129]. Furthermore, another important aspect of
reuse from the developer’s viewpoint is licensing, and at least two useful
ideas can be found in existing work: the use of geo-documents, and the use of
blockchains. [49] proposed to create geovisualizations as geo-documents that
embed digital rights about their components. A geo-document viewer is then in
charge of rendering those components of a geovisualization that a user should
see (e.g. interaction or animation elements), and hide those they are not
allowed to see. [172] proposed a decentralized approach to digital rights
management for spatial datasets, and implemented the approach using the open-
source blockchain Ethereum. Key features of their approach include: support
for digital rights registration, querying, as well as transactions where a
software agent asks and explicitly obtains permission before using a spatial
dataset; support for web technologies; and support for both raster and vector
datasets. Finally, since web mapping libraries used to build online
geovisualizations often undergo changes, backward-compatibility of components
is an important aspect of reuse from the developers’ viewpoint.

###  4.4 Summary: what does it mean for a geovisualization to be FAIR?

This section has tried to specify what FAIR means for geovisualizations and
reported briefly on existing approaches relevant to making geovisualizations
FAIR. Two lessons can be learned from the previous sections and Table 1.
First, ‘FAIR’ in the context of geovisualization can mean different things
depending on the perspective adopted. The twelve senses proposed in Table 1
can thus be used by researchers to disambiguate and clarify their standpoint.
Second, FAIR geovisualizations provide great potential for interdisciplinary
research as their contributions can be relevant to several disciplines and
communities.

Though the aspects of FAIRness have been discussed separately to highlight
their unique demands, they are interdependent. Figure 3 captures the
interdependences. The figure illustrates two key points. First, the concepts
of FAIRness involved are multidimensional and the number of dimensions is
perspective-dependent as said just above. Second, many of the analyst’s
demands include the computer’s demands to capture the fact that FAIRness from
the user’s point of view necessitates a solid technological base. The
computer’s demands and the analyst’s needs bring about constraints for the
developer.

As to the usefulness of the framework from Table 1, it can be used to
systematically map research on FAIR geovisualizations as it evolves. To
illustrate the idea, articles on FAIR geovisualization were downloaded using
Taylor & Francis Online191919https://www.tandfonline.com/ and the Wiley Online
Library202020https://onlinelibrary.wiley.com/. These two online catalogs were
used because they index the majority of GIScience journals (see the list of
journals in the supplementary material). 202 articles were returned from a
search done in January 2021 using the keywords: ‘findable map OR findable
geovisualization’; ‘accessible map or accessible geovisualization’;
‘interoperable map or interoperable geovisualization’; and ‘reusable map or
reusable geovisualization’. After removing duplicates,
review/editorial/encyclopedia articles, 40 articles were found relevant to the
issues of knowledge representation, user interface design, or technical
considerations of publishing geovisualizations discussed above. The
distribution of the topics of these articles is shown in Figure 4. As the
figure suggests, the coverage of current research is uneven at the moment.
Research on interoperability is well-represented in the sample, and reflects
the fact that GIScience work has focused on ontology/knowledge base
development, development of empirically-derived guidelines for map design, and
development of visualizations based on existing standards (e.g. WMS and WFS).
Perhaps unsurprisingly, research on accessibility from the computer
perspective is not represented in the sample, as this does not fall within the
core of GIScience work. However, research proposing solutions on machine-
readable licenses, advancing aliasing to improve semantic search, or
highlighting platform/device-independence of geovisualizations has been
relatively scarce so far, and could perhaps receive more attention. Including
articles from conference proceedings may alter the picture painted here. Thus,
these comments on the current coverage of FAIR geovisualization research are
tentative. They serve an illustrative purpose for the value of the framework
from Table 1 only. The data for the figure is available as a supplementary
material (Section Supplementary material).

Figure 4: Coverage of research on FAIR geovisualization as of January 2021.

##  5 Unique challenges

Having specified how FAIR principles can be applied to geovisualizations, I
now turn to the challenges of realizing FAIR geovisualizations. As we shall
see, FAIRness applied to web-based geovisualizations presents unique issues
for GIScience research. As discussed in Section 4, a geovisualization can come
in several formats, for example, raster or web documents. Geovisualizations in
raster format typically appear in the context of scanned (historical) maps and
some challenges they pose have been discussed extensively in previous work
[29, 28, e.g.]. The focus of this section is on web-based visualizations. Web
visualizations sometimes contain SVG elements, and [11] have shown that their
types (e.g. bar chart, pie chart, map) can be identified with reasonable
accuracy (86%). The challenges of making online web-based geovisualizations
FAIR are summarized in Table 2.

|  Computer |  Analyst |  Developer
---|---|---|---
[HTML]FFFFC7 F |  persistence is not standalone, but dependent on multiple components (data and libraries); provenance description involves both aspects of data and aspects of processing (e.g. projection, mapping libraries); geovisualization descriptions need to comply with requirements that come with legislation; geovisualizations are composed of many data views |  relevance should integrate unique human factors (e.g. perceptual effectiveness, readability); geovisualizations present multiple perspectives on the data; geovisualizations have multiple usages on the Web |  aliasing, applied to geovisualization content, has no exact limits
[HTML]9AFF99 A |  none |  users’ abilities and expertise are unknown a priori; ability and expertise are multifaced concepts |  device properties (e.g. size, input modalities) are limiting factors to consistent cross-device functionality development
[HTML]CBCEFB I |  content modelling as statements has no exact limits, because a geovisualization can communicate many messages at a time; geovisualizations inherently use spatial scale as an organizing dimension for content |  empirically-derived guidelines are lacking; not all guidelines are amenable to brewers |  there is a large number of standards to master; protocols to establish mutual understanding during content exchange are still lacking
[HTML]FFCCC9 R |  geovisualization licenses are inherited, and licensing policies of datasets/libraries may differ |  the design space of geovisualizations is complex (e.g. it involves data attributes, data dimensions, and visual variables) |  defining/anticipating reuse units is not straightforward; empirical evidence about reuse needs of geovisualization developers is lacking
Table 2: Unique challenges of FAIR geovisualizations from the perspective of
knowledge representation (computer), user interface design (analyst) and
technical considerations about the publishing of online geovisualizations
(developer).

###  5.1 The computer’s demands

As discussed in the previous section, findability necessitates persistence.
Given that web-based geovisualizations are composite products, their
persistence is dependent on the persistence of their components, notably
datasets and libraries. It follows that a persistence strategy for
geovisualizations needs an explicit account of these dependencies. In
addition, findability of geovisualizations necessitates their description.
Description touches primarily on provenance. Aspects of provenance for web
pages may include their author, creation date, currency, contact information,
and topic. Likewise, aspects of provenance for datasets could include author,
creation date, spatial and temporal coverage. A unique feature of
geovisualization is that describing them includes data & web page provenance
aspects, but needs to go beyond them to include aspects of processing (e.g.
map projection used, and web mapping libraries involved in the development of
the geovisualization). Furthermore, geovisualizations, qua online products,
need descriptions compliant with requirements that come with legislation, e.g.
the Americans with Disabilities
Act212121https://www.ada.gov/pcatoolkit/chap5toolkit.htm (last accessed: July
23, 2021)., or the EU’s Web Accessibility Directive (which in turn references
the Web Content Accessibility Guidelines)222222https://eur-
lex.europa.eu/eli/dir/2016/2102/oj (last accessed: July 23, 2021).. At last,
geovisualizations package several views on the data. These views take several
forms discussed in [38]: map, data table, bar chart, pie chart, histogram, Q-Q
plots, stacked bars, adjacency diagrams, tree-maps. Rich, fine-granular
descriptions need to account for all of these.

Interoperability requires the use of machine-readable formats. Following [145]
who distinguished between context (e.g. map scale, spatial extent) and
content-based aspects of a geovisualization (i.e. statements that users can
extract from the visualization by looking at it), machine-readability can
touch on both aspects. Simple examples of describing context and content
aspects were presented in [145]. Yet, the key challenge, when it comes to
representing content formally lies in the fact that a geovisualization may
provide multiple perspectives (e.g. a bar chart view or a map view) on a given
dataset. To this multiplicity of perspectives should be added the different
types of meanings conveyed by geovisualizations. As [111] pointed out, the
meaning of a map can be approached from two angles: denotative and
connotative. Denotative meaning is either precisely specified in a map legend
or assumed to be part of the normal reader’s general map schema; connotative
meaning is implicitly conveyed by the map (e.g. connotation of veracity and
connotation of integrity). It remains unclear at the moment how these
different types of meanings can be represented and linked formally in a
geographic knowledge base, for the purpose of (semantic) search. At last, as
discussed in [38], statements about the content of geovisualizations are
granularity-dependent (e.g. some facts pertain only to a given zoom level on a
map). This dependence of statements on spatial granularity and the inherent
use of spatial scale as an organizing dimension is a distinguishing feature of
geovisualizations from other online web documents. Capturing this granularity-
dependence in knowledge graphs also presents a unique challenge for semantic
interoperability research.

As to reuse, geovisualization licenses are inherited from those of their
components. Since licensing policies of datasets/libraries may differ, a
general statement about a geovisualization’s reuse policy becomes challenging.
Tools to automatically reason about copyright policies of the sub-components
and inform about the copyright of the final geovisualization will become
increasingly valuable.

###  5.2 The analyst’s needs

The primitive spatial query232323There are many more dimensions of spatial
querying not discussed here, e.g. objective (e.g. identify, compare), query
formulation format (unstructured, formal, visual) and type of user interface
feedback (lookup, filtering), see [7]. in the context of geographic
information retrieval has the form ‘what, relationship, where’ [56, 27] or
‘$<$theme$><$relationship$><$location$>$’ [128]. In the context of geodata
search, the primitive query takes the form ‘dataset about, what, where, when’
or ‘dataset about $<$space$><$time$><$theme$>$’ [44]. The key difference
between geographic information retrieval and geodata search is that the former
takes unstructured text as input, while the latter takes a structured
description of data items (e.g. in a JSON format) as input. Since web-based
geovisualizations are based on HTML that is structured, finding them shares
some similarities with dataset search. The primitive query here takes the form
‘geovisualization about, what, where, when’, or ‘geovisualization about
$<$space$><$time$><$theme$>$’. Nonetheless, modeling the notion of relevance
in this context poses fundamentally new challenges. First and foremost,
relevance modeling should integrate perceptual effectiveness, i.e. the ability
of a person to retrieve data presented in a chart by decoding a visual
representation. Graphical perception studies were conducted to assess, among
other things, the effectiveness of bar charts and pie charts [30], treemaps
and wrapped bars [121], line charts and scatterplots [140], small multiples
for time series [83], the differences between interactive tables and
interactive geovisualizations [47], and alternative layouts for online map
design [32]. These have revealed a strong task-dependency effectiveness for
geovisualizations, and general guidelines for visualizations’ perceptive
effectiveness have not yet emerged. Next to perceptual effectiveness, visual
map complexity and map readability are important to the interaction with
geovisualizations. The literature has come up with preliminary measures for
visual map complexity [148] and map readability [72]. How these could factor
in relevance remains an open question. At last, a relevance ranking scheme for
online geovisualizations needs to deal with the fact that they have multiple
usages. For instance, online geovisualizations can also be used as spatial
dialogue platforms (e.g. ‘Dialog Map’, see [52]), platforms for spatial
collaboration and data collection (e.g. Ethermap, see [53]), tools to persuade
(see [119]), or media to display and refine search results. Retrieving online
geovisualizations (e.g. thematic maps) that communicate facts about topics is
the main purpose of steps 1-2 of the scenario (Figure 1). Finding means to
model and distinguish purposes of online geovisualizations is also a unique
research challenge.

Accessibility implies adaptation to the user’s abilities and expertise. The
key challenge here is that “for maps presented on the Web, the audience is
very much unknown” [92]. [55] listed two high-level challenges for accessible
maps: (i) putting accessibility information in existing maps, and (ii) making
existing maps more accessible. As to (i), the major impediment is the lack of
data about accessibility in general. As to (ii), a key challenge is to model
the user’s abilities. And the impediment is that ‘ability’ is a multifaceted
concept. For instance, [55] distinguish between sensory abilities (e.g.
vision, hearing), physical abilities (e.g. dexterity, mobility), and cognitive
abilities (e.g. memory, concentration, language). ‘Expertise’ too is a
multifaceted concept. There may be at least four aspects to consider: (i)
definition of user expertise, (ii) topics of user expertise, (iii) dimensions
of user expertise, and (iv) proxy measures for user expertise. Following [50],
an expert can be defined as someone who possesses a certain amount of
principled knowledge about a topic. In the case of geovisualization, knowledge
is necessary about at least two topics: geographic space as well as the
operative principles of digital systems. Dimensions of expertise may include
strategies for problem-solving, the ability to recognize mistakes, and the
ability to discriminate between surface (i.e. irrelevant) features and deep
(i.e. important) features pertaining to a topic. Finally, proxy measures may
include experience, performance on specialized tasks, and users’ self-
assessments. In general, GIScience still lacks means of formally documenting
user expertise across evaluation studies of maps and geovisualizations.

Interoperability implies the use of widespread design conventions, that is,
the use of design conventions that can be easily recognized by a broad range
of users. There are three key challenges here. First, there is evidence that
users do not recognize all map symbols to the same extent, but the causes for
these discrepancies are not straightforward to pin down. For instance, [76]
conducted a study where they investigated the extent to which users associate
map symbols used by map providers (e.g. Google Maps, Open Street Maps, Bing
Maps, ArcGIS Maps) to their intended function (e.g. search, geolocation,
change of layers). Their results suggested that (i) no one provider achieved
high recognition rates on all functions tested, and (ii) recognition rates do
not necessarily correlate with the frequency of usage of the services. Second,
and as mentioned in [135, 100], there is still a lack of (empirically-derived)
guidelines specific to cartographic interfaces. The third key challenge is
that not all design conventions can be encapsulated into tools (à la
ColorBrewer) to facilitate their democratization. Examples here include the
use (or not use) of north arrows, recommendations about contrast use and
symbol grouping to effectively communicate visual hierarchies [154], or
effects of the use of visual variables during map icon design [12].

Reuse implies giving users the possibility to transform the visualizations as
mentioned above, and necessitates some level of adaptivity on the side of the
geovisualization. While adaptivity is a truly desirable feature, realizing it
in practice proves challenging because the design space of geovisualizations
is complex. It involves at least: the display medium (e.g. mobile phone, large
display, desktop computer, tablet computer), interaction modalities (e.g.
gaze, speech, touch, mid-air gestures, keyboard, mouse, pen), data attributes
(which may be few or many depending on the use case), the measurement scales
of the data attributes (e.g. Steven’s four levels of measurements), visual
variables (up to 12, see [136]), and tasks (using [20]’s typology, the lower
bound for these is 242).

###  5.3 The developer’s constraints

Findability necessitates the use of aliases to mitigate the vocabulary
problem. Previous work reported that ‘more is not always better for query
expansion’ [45]. Thus, the key challenge here is to find the optimal aliasing
strategy, i.e. the one that will maximize user relevance ratings. In the
context of data search, aliasing is often performed on the description of the
item at hand (e.g. description metadata field about a given dataset). However,
in the case of geovisualizations, content is more than mere topical
description. Content also covers the statements that a user can extract from a
geovisualization by looking at it [145]. Developing a proper aliasing strategy
for geovisualization search is thus an uncharted research area.

Realizing accessibility from the developer’s viewpoint necessitates platform-
and device independence (in addition to coping with the diversity of users).
Strong constraints are imposed by the device properties (e.g. size, input
modalities). In addition, and from the perspective of programming, there is
still a need for languages that support plasticity of GI interfaces, so that
developers can ‘build once, deploy anywhere’. These languages should support
both declarative (e.g. a map has two layers), and imperative statements (e.g.
re-scale map canvas if device size decreases/increases).

In line with [15], two geovisualizations Geoviz1 and Geoviz2 are said to be
interoperable if Geoviz1 can send requests for services R to Geoviz2, which
then returns responses S to Geoviz1, based on a mutual understanding M. The
exchange of messages is thus key to realize interoperable geovisualizations.
Unique challenges here include a large number of standards for developers to
master, and the specification of communication protocols that automatically
assess ‘mutual understanding’ of content to the exchange. As [23] indicated,
two types of messages are important in the context of interoperability:
control messages informing about how the entities interoperate (e.g. error
messages, data requests) and content messages containing actual subject
matter. HTTP provides a good starting point for the exchange of control
messages on the Web (e.g. through the use of HTTP headers and HTTP content
negotiation). [91] suggested a vision (and preliminary implementation) of
using web technologies to exchange rich content messages for web media. Their
approach relies on the use of HTTP 303 See Other (for redirection to semantic
descriptions of media), and the use of RDF as a content description language.
Nonetheless, there is still a lack of infrastructure where software agents can
autonomously establish the existence of a ‘mutual understanding’ (e.g. our
concepts of ‘River’ overlap or are equivalent).

Code reuse is an active research topic in the field of software engineering
(see e.g. [101, 26]), and can happen opportunistically (i.e. copy-pasting
snippets from existing programs into new programs) or systematically, with the
latter more challenging to realize in practice (see [146]). As discussed in
[116], the emergence of Internet-based developer forums (e.g. Stack Overflow)
and open-source software repositories (e.g. GitHub) has catalyzed
opportunistic reuse. Nonetheless, the key challenge here is to
define/anticipate reuse units, in order to encapsulate them as modules. As
[21] indicated: “Web page developers rarely want to reuse existing pages
completely, but they may want to reuse existing page components”. This likely
holds for geovisualizations. Thus, understanding why developers of
geovisualizations engage in reuse enterprises, what they expect (i.e. at which
granularity reuse happens more often), and issues they face, is a challenge of
geovisualization research. This understanding will help design best practices
(relevant for instance to the GIScience teaching curriculum) about ‘designing
for reuse’.

###  5.4 Summary: what are unique challenges of FAIR geovisualizations?

This section has looked closely into the unique challenges of realizing FAIR
geovisualizations. As the section indicated, realizing FAIR geovisualizations
demands more of current research regarding provenance description, relevance
modelling, user modelling, model-driven development, semantic
interoperability, persistence management, licensing policies management and
software reuse. As mentioned in Section 2.1, FAIRness is not an absolute
state, but a continuum of behaviours (see also, [36]). That is, “absolute
FAIRness” is not achievable, but online geovisualizations can be made more
FAIR, gradually and continually. Also, the challenges listed in this section
do not exhibit the same benefit-cost ratio at the time of writing. Addressing
the findability of online geovisualizations (steps 1 and 2 of the scenario)
seems to offer low-hanging fruits at the moment: the number of online
geovisualizations is increasing rapidly and building tools to produce metadata
about these so that web-crawlers can semi-automate their retrieval is a task
within reach242424See [105] for a recent example of work reporting promising
results on semantic metadata generation for online maps.. If in addition, the
metadata is formalized and include machine-readable license information, the
computer’s demands (Table 1) can already be satisfied to some extent.
Accessibility, Interoperability and Reuse challenges from the analyst and
developer’s viewpoint would need more sustained effort for substantial
progress to become visible.

##  6 The road ahead

Some of the features mentioned in the scenario above (Figure 1) are partly
addressed by existing tools. In particular, [77] and [113]’s work on semantic
search for ArcGIS online can provide valuable insights as one moves from
search of geovisualizations in one geoportal to their search on the Web as a
whole. Yet, they are many open questions raised by the scenario. They are
summarized in Table 3.

|  Questions | C | A | D
---|---|---|---|---
[HTML]FFFFC7 F |  |  |  |
[HTML]FFFFC7 |  How to generate large-scale semantic annotations for web-based geovisualizations? | x | x |
[HTML]FFFFC7 |  How to enable fine-grained annotation of geovisualization components? |  | x |
[HTML]FFFFC7 |  How to record and share geographic insights across interaction sessions? | x | x |
[HTML]FFFFC7 |  How to model relevance and incorporate relevance feedback into geovisualization search? |  | x | x
[HTML]FFFFC7 |  How to realize effective aliasing for geovisualization search? |  |  | x
[HTML]FFFFC7 |  How to design mobile search for geovisualizations? |  | x | x
[HTML]FFFFC7 |  What are developer needs and issues for container-based geovisualization development? |  |  | x
[HTML]9AFF99 A |  |  |  |
[HTML]9AFF99 |  How to collect and integrate accessibility information in existing geovisualizations? | x |  | x
[HTML]9AFF99 |  How to model user abilities and experience, and include these aspects into relevance rankings? |  | x | x
[HTML]9AFF99 |  How to automatically verify the accessibility (level) of web-based geovisualizations? |  |  | x
[HTML]9AFF99 |  How can accessibility information be gathered on-the-fly, and integrated into relevance ratings? |  | x | x
[HTML]9AFF99 |  How to realize plastic geovisualizations? |  | x | x
[HTML]9AFF99 |  How to encode accessibility information about web-based geovisualizations in a machine readable format? | x |  | x
[HTML]CBCEFB I |  |  |  |
[HTML]CBCEFB |  To which extent are given symbols/palettes intuitive for users and to which extent do they differ across providers? |  | x |
[HTML]CBCEFB |  How to formalize content of geovisualizations as machine-readable statements? | x |  | x
[HTML]CBCEFB |  How to formalize user insights as machine-readable statements? | x |  | x
[HTML]CBCEFB |  How to capture connotations of geovisualizations in a machine-readable format? | x |  | x
[HTML]CBCEFB |  How to make map design conventions machine-accessible? | x |  | x
[HTML]CBCEFB |  How to model data gaps formally? | x |  | x
[HTML]CBCEFB |  How to design interactive guidelines for geovisualization design? |  | x | x
[HTML]CBCEFB |  How to establish mutual understanding between web agents during geodata exchange tasks? | x |  | x
[HTML]FFCCC9 R |  |  |  |
[HTML]FFCCC9 |  How to automatically infer a geovisualization licensing policy from the licensing policies of its components? | x |  | x
[HTML]FFCCC9 |  How to manage licensing policies of geovisualizations over time? |  |  | x
[HTML]FFCCC9 |  How do geovisualization reuse approaches perform for different groups of users, and how to describe these approaches formally? |  | x |
[HTML]FFCCC9 |  How to estimate transferability of geovisualization components a priori? |  |  | x
[HTML]FFCCC9 |  What are developer needs and issues for reuse of geovisualization components? |  |  | x
[HTML]FFCCC9 |  How to realize digital rights management and smart contracting for geovisualizations at a Web scale? |  |  | x
Table 3: Examples of open questions on the road towards FAIR
geovisualizations. A cross indicates the perspectives for which the question
are mostly relevant: C stands for computer and indicates knowledge
representation issues; A stands for analyst and suggests user interface design
issues; and D stands for developer and indicates issues related to technical
considerations of publishing online geovisualizations.

###  6.1 Findable geovisualizations

Realizing step 1 of the scenario needs work on ‘description’, i.e. semantic
annotation of existing online geovisualizations. Linked Data [103] has been
the de facto technique for semantic annotation of georeferenced data in recent
years. Semantic annotation research needs mechanisms to produce large-scale
annotations for existing geovisualizations on the Web. These annotations need
to cope with the peculiarities of geovisualizations, i.e. annotations can be
graphical, or textual or a mix of both (see [159]). Also, these annotations
need to be fine-grained, that is, capture specifics of the data views (e.g.
map, data table, bar chart) composing a geovisualization. Using form-based
interaction to produce annotations could be daunting, and we could explore
techniques that take advantage of recent progress in speech-based interaction
and named entity recognition to enable users to accurately describe
cartographic products on the Web. The descriptions should not only touch on
missing metadata (e.g. author, creation date), but also inform about content-
related aspects of the geovisualizations (e.g. what a user U learned from a
geovisualization G during an interaction session I). Work in this area could
capitalize on the descriptions of the sub-components (e.g. datasets or
software modules/functions/libraries used to produce the geovisualization), if
available. Besides, these annotation mechanisms should not be disconnected
from existing platforms (e.g. ESRI Storytelling, DataUSA, or the European Open
Data Portal), but available ‘where the action is’, that is, as easy-to-use
add-ons within these platforms. Finally, techniques will be needed to maintain
these crowdsourced annotations over time, and preserving the multiplicity of
views while ruling out mischief.

‘Persistence’ is mostly relevant to step 2 of the scenario. A user does not
want to click on a geovisualization that points to a “HTTP 404 not found”
error. Several platforms (e.g. Zenodo, Spatial Data Hub,
PURL252525www.purl.org) make it now possible to assign persistent URLs
(Uniform Resource Locators) to documents on the Web. In the context of Linked
Data, the Web community has proposed PingtheSemanticWeb and the “Web of Data –
Link Maintenance Protocol” [163] as preliminary ideas for protocols to manage
the evolution of links on the Web. Traditional web-geovisualization
development produces HTML/CSS/JavaScript code deposited on a server, and then
accessed from a client that possesses a rendering engine (i.e. the browser).
Evolving from this status-quo will necessitate an increasing adoption of a
container-based approach to geovisualization development (coupled perhaps with
continuous deployment). In addition, application programming interfaces (API)
will play a key role, particularly when datasets are too big to be stored
locally. For these cases, a persistent way of retrieving datasets at the API
level would need to emerge. That is, a DOI-based approach to dataset retrieval
is key to accessible geovisualizations. Platforms such as
Dataverse262626https://dataverse.org/ and DataCite272727https://datacite.org/
allow the assignment of DOIs to datasets. DataCite enables also retrieval of
these datasets according to their DOIs at the API level, and is an example
implementation of the DOI-based approach to dataset retrieval. Thus, the
challenges of persistent geovisualization ahead are more of social nature
(i.e. how to ensure widespread adoption) than technical. Further
collaborations between relevant institutions (e.g. OGC, the W3C) will be key
in overcoming them.

‘Relevance’ is also a notion that needs to be put under closer scrutiny. In
particular, geographic information retrieval research needs to explore how the
ranking of geovisualizations can be implemented to produce cognitively
plausible results (e.g. how do factors affecting relevance ranking for
geovisualizations differ from those used for ranking websites or datasets?).
Finally, most graphical perception studies so far have only considered static
geovisualizations, but interactivity should receive due attention on the road
towards sensible rankings of findable geovisualizations.

Example questions:

  * •

How to generate large-scale semantic annotations for web-based
geovisualizations?

  * •

How to enable fine-grained annotation of geovisualization components (e.g.
data views, datasets, projection, mapping libraries)?

  * •

How to record and share geographic insights across interaction sessions?

  * •

How to model the relevance of online geovisualizations for the purpose of
semantic search?

  * •

How to realize effective aliasing for geovisualization search?

###  6.2 Accessible geovisualizations

As mentioned above, modeling user abilities/expertise, and realizing plastic
geovisualization presents opportunities for further research on accessible
geovisualization, i.e. tailoring to abilities and expertise, platform- and
device-independence. There is also a need for tools translate accessibility
information into machine readable format, wherever possible, so that these can
be used during the implementation of geovisualizations. At the moment, there
is still a disconnect between work on empirically derived recommendations
regarding the accessibility of web maps (e.g. [75]) and work on technical
means to document accessibility requirements for web-pages (e.g. [71, 124]).

Example questions:

  * •

How to collect and integrate accessibility information in existing
geovisualizations?

  * •

How to model user abilities and experience, and include these aspects into
relevance rankings?

  * •

How to automatically verify the accessibility (level) of web-based
geovisualizations?

  * •

How can accessibility information be gathered on-the-fly, and integrated into
relevance ratings?

  * •

How to realize plastic geovisualizations?

  * •

How to encode accessibility information about web-based geovisualizations in a
machine readable format?

###  6.3 Interoperable geovisualizations

Interoperability of geovisualizations (i.e. formalization of statements,
compliance with widespread design conventions) is relevant to steps 2, 3 and 4
of the scenario. There is a need for user testing to evaluate actual user
interpretation of map symbols, potential misunderstandings, and the reasons
for these. This research will be mostly valuable for step 2. Despite much
research on the interpretation of map symbols (e.g. [94, 3, 89, 149, 95]),
formal rules regarding map symbolization are still not consolidated, perhaps
due to the diversity of map context usages available and studied. Initiatives
such as the GIS&T Body of Knowledge (Section: Cartography & Visualization,
https://gistbok.ucgis.org/), which synthesize insights from the academic
literature and communicate them in a language accessible to non-experts will
thus play an important role for awareness-raising, the adoption of best
practices, and the realization of more interoperable geovisualizations
(analyst’s perspective). In addition, advances on semantic interoperability
research (e.g. formal specification of geodatasets’ content and
geovisualization components) is needed to realize steps 3 and 4 of the
scenario. So far, semantic interoperability research in GIScience has invested
heavily in ontology building. These ontologies have been used to generate
theories of geoinformation (e.g. [34, 16, 2]), for query disambiguation and
expansion (e.g. [85, 110, 127]), and for the (semi-automatic) composition of
geoprocessing services (e.g. [108]). Ontologies are also key to the Sensor Web
Enablement initiative of the OGC (see [24]). While these works offer valuable
insight, demos of agents that use these ontologies to automatically exchange
things during a communication process have been less frequent. Ergo, GIScience
research needs to go beyond ontology building to embrace semantic agent
building, where the agents select ontologies relevant to a given scenario, and
exchange information with other relevant agents in an autonomous way, to solve
some specific tasks. This is a major gap in current research. Identifying the
tasks that cover scenarios of value in GIScience, and following up with agents
that solve these tasks, will provide a much clearer measure of progress for
semantic interoperability research than ontology provision alone. As to coping
with missing data, formalizing and providing solutions to the issue has been
relatively rare in geovisualization research. Notable exceptions are [131]’s
typology of strategies to draw users’ attention to the presence of gaps in the
data, and [10]’s context frame approach to generates visual cues about objects
not shown on the map in order to minimize the need for zooming operations
during an interaction session.

Moving to the point were semantic agents can seamlessly cooperate, needs
advances along three axes: agents, protocols, and messages. The exact role of
agents remains to be specified, but at least four roles can be envisioned:
discovery (i.e. find out other visualizations on the Web that provide
different perspectives on the data at hand); negotiation (e.g. establish with
other visualizations on the Web whether or not data transfer would be
possible); retrieval (i.e. get pieces of data from other visualizations); and
integration (i.e. close data gaps of Geoviz1 with data items from Geoviz 2,
see Figure 1). Next to agent-oriented work, protocol-oriented work is needed
to provide ways of exchanging messaging, building for example on state-of-the-
art web technologies. Finally, message-oriented work can provide a uniform way
of formally representing the content of datasets underlying the
geovisualizations (e.g. their topic & typical spatio-temporal attributes such
as extent, temporal coverage, accuracy, and resolution), and of the
geovisualizations themselves. [102]’s core concepts of spatial information may
provide a starting point for a vocabulary for data content annotation; [134]’s
taxonomy of interaction primitives for interactive geovisualization may also
be useful for the annotation of geovisualizations. It remains to be seen
whether these existing vocabularies/taxonomies are sufficient, or whether they
should be extended (and in which way) to enable message exchanging for
interoperable geovisualizations. Finally, a measure of more social nature on
the roads towards interoperable geovisualizations could be the organization of
(yearly) contests, ending up with demos showing geovisualizations exchanging
some data autonomously (or deciding not to exchange data, based on a mutual
understanding). [126] reported positive experiences from annual competitions
for the development of new visualization systems in the Information
Visualization community.

Example questions:

  * •

To which extent are given symbols/palettes intuitive for users and to which
extent do they differ across providers?

  * •

How to formalize the content of geovisualizations as machine-readable
statements?

  * •

How to formalize user insights as machine-readable statements?

  * •

How to capture connotations of geovisualizations in a machine-readable format?

  * •

How to model data gaps formally?

  * •

How to design interactive guidelines282828Interactive guidelines walk users
through problem-solution patterns in an interactive manner, to transfer
insights from the academic literature to non-experts [157]. for
geovisualization design?

  * •

How to establish mutual understanding between web agents during geodata
exchange tasks?

###  6.4 Reusable geovisualizations

Reuse (i.e. licensing, transformability, and modularity) is primarily relevant
to steps 5 and 6 of the scenario. Concepts such as ‘toolkits’, ‘map use
contexts’, ‘intelligent geovisualization’, and ‘map plasticity’ have provided
a good start to pinpointing the many dimensions of geovisualization reuse.
Yet, we still need a clearer picture of reusability approaches available to
geovisualizations creators and users, and most importantly, of the needs of
users within each of the approaches. Put differently, a theoretical
description of reusability approaches and a comparison of their merits is a
challenging, yet much need contribution necessary to advance the current
state-of-the-art on reusable geovisualization. As mentioned in Section 4.2,
template-based designs realize reuse across topics effectively: the same
visualization template (e.g. choropleth, graduated symbol, cartogram) can be
used across different topical domains (e.g. politics, demographics, real
estate). The idea of template reuse in one-click (e.g. drag and drop) is
appealing, but may be challenging to fully realize given the complexity of the
design space of geovisualizations. Yet, it could be valuable during the
comparison of geovisualization reuse approaches to assess research progress.
For example, the number of clicks needed to create a visualization can be used
along with established measures such as expressiveness (i.e. the number of
geovisualization types that can be created), during the evaluation of tools
and toolkits facilitating geovisualization reuse. An early example of a drag-
and-drop approach to visualize datasets was proposed in [169]. The approach
focused on detecting structural (as opposed to semantical) aspects of
datasets, relies on the grammar of graphics, and was illustrated primarily on
non-geographic datasets. Extending it to account at least for semantical and
spatial aspects of datasets would be needed to fully realize steps 5 and 6 of
the scenario. Finally, a more social aspect of geovisualization reuse touches
upon the licensing policy. By default, nothing is reusable unless permission
is explicitly granted. This principle does not adequately reflect the state of
matters on the Web. To give an example, the source code of web-based
geovisualizations can be accessed through browser inspection tools. If the
visualization has been developed with a library based on Web standards such as
HTML, CSS (e.g. D3.js), data items powering the visualization can also be
accessed. That is, licensing of web-based geovisualization may consider moving
to a new motto: ‘everything is reusable unless reuse is explicitly restricted’
to reflect openness of resources on the Web.

Example questions:

  * •

How to automatically infer a geovisualization’s licensing policy from the
licensing policies of its components?

  * •

How to manage licensing policies of geovisualizations over time?

  * •

How do geovisualization reuse approaches perform for different groups of
users, and how to describe these approaches formally?

  * •

How to estimate the transferability of geovisualization components a priori?

  * •

What are developer needs and issues for reuse of geovisualization components?

###  6.5 Summary: what are open questions for FAIR geovisualizations research?

Revisiting the scenario introduced at the beginning of the article, this
section has contributed open questions on the road towards FAIR
geovisualizations. The questions listed are by no means exhaustive, but
illustrate that new types of problems arise to realize findability, access,
interoperability, and reuse of web-based geovisualizations. Immediate,
practical steps to make progress on steps 1-3 of the scenario could include
the use of knowledge harvesting techniques [165] and of semantic annotation to
generate knowledge graphs from geovisualizations stored as HTML-documents, the
development of techniques to search for relevant geovisualizations over these
knowledge graphs, and techniques to maintain online geovisualizations and
their respective knowledge graphs synchronized (Figure 5). Using
visualizations of the Observable gallery as a testbed could be helpful to
start, as these are already annotated using the Open Graph
Protocol292929Thanks to one anonymous reviewer for pointing at Observable..

Figure 5: An example of practical step towards more FAIR geovisualizations.
Top: current online visualizations are treated as html documents for search;
Bottom: exploring how knowledge harvesting techniques and semantic annotation
can help build knowledge graphs for advanced search of geovisualizations at a
Web scale.

##  7 Limitations

This article should be thought of as an initial exploration of FAIR
geovisualizations, not the final word. There are some limitations that can be
noted.

First, the work has argued that geovisualizations should be considered as a
unit in their own right, for geographic information search and reuse on the
Web. Getting there necessitates more work on making geovisualizations fair (in
the literal sense) to the datasets they depict. There are indeed numerous
factors that affect visual encodings that may impede the fairness of
visualization with respect to the data they portray. These include, among
others, graphical inference (are the patterns we see really there?, see
[166]), Tufte lie factor (is the size of the effect shown in the graphic
directly proportional to the size of the effect in the data), perceptual
effectiveness (is the visual symbol to encode differences appropriately
chosen?), and imperfections of data classification [117]. These issues, though
acknowledged, are not discussed in detail here. Accounting for errors
occurring in data-to-visuals transformation processes, and more broadly of
uncertainties inherent to data exploration processes and their visual
communication, can be examined once prototypical applications emerge that
realize the steps presented in Figure 1.

Second, as to the formulation of the open questions, GIScience has often used
forecasting to articulate research agendas. For example, forecasting has been
used to predict the evolution of spatial data infrastructures in [48],
anticipate the development of the digital earth in [62, 35], and conjecture
developments of the field as a whole in [60, 61]. We’ve learned that
forecasting may miss important developments (for an anecdote, see [60]), and
backcasting too may miss important developments of the years to come. Finally,
similar to forecasting, backcasting is ‘value-driven’ [51], that is, the ideas
are inevitably biased towards the author’s research.

##  8 Conclusion

This work has applied the FAIR principles to a new domain (i.e.
geovisualization), and taken a holistic approach to FAIRness to uncover some
challenges and opportunities for GIScience research. This article contributes
to geovisualization research in several ways. First, the work highlighted that
geovisualizations are information products increasingly available, but
sufficiently distinct from websites and raw datasets to deserve sustained
effort aiming at making them FAIR (Findable, Accessible, Interoperable and
Reusable). Second, the work discussed three complementary perspectives on FAIR
geovisualizations (the computer, the analyst, and the developer), and
clarified their respective demands. The framework resulting from the
discussion can help researchers involved in realizing FAIR geovisualization to
disambiguate their stance. It can also be used to systematically map research
on FAIR geovisualizations as it evolves. Third, relevant approaches from the
literature to realize FAIRness and some unique challenges of FAIR
geovisualizations were brought forth and discussed. At last, the article
sketched open questions towards FAIR geovisualizations. These questions
arguably present huge challenges for current research but challenges worth
attending to. The future of geovisualization research and practice is FAIR.

## Acknowledgements

I thank several anonymous reviewers for their detailed and very insightful
comments on earlier versions of this article. The icons in Figures 1 and 5
were taken from www.flaticon.com (designers: ‘Freepik’, ‘Kiranshastry’, ‘Nhor
Phai’, ‘Pixel perfect’, and ‘srip’).

## Supplementary material

The supplementary material is available at
https://doi.org/10.6084/m9.figshare.13635563.

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