=Paper=
{{Paper
|id=Vol-3773/paper4
|storemode=property
|title=Vertical Context of Geographic Locations: An Empirical Comparison of Three Visualization Approaches
|pdfUrl=https://ceur-ws.org/Vol-3773/paper1.pdf
|volume=Vol-3773
|authors=Prasad Madushanka,Auriol Degbelo
|dblpUrl=https://dblp.org/rec/conf/voila/MadushankaD24
}}
==Vertical Context of Geographic Locations: An Empirical Comparison of Three Visualization Approaches==
https://ceur-ws.org/Vol-3773/paper1.pdf
Vertical Context of Geographic Locations: An
Empirical Comparison of Three Visualization
Approaches
Prasad Madushanka1 , Auriol Degbelo2,∗
1
Institute for Geoinformatics, University of Münster, Germany
2
Chair of Geoinformatics, TU Dresden, Germany
Abstract
The vertical context of a geographic location encompasses all known information about that location.
Though linked data is suitable for representing the vertical context of geographic locations, there is still a
need for means to help users explore this vertical context visually and guidelines for designers of vertical
context visualizations. To address this gap, this article compared three visualization approaches: map +
table at the location of interest, map + markers at the location of interest and map + circular treemap at
the location of interest. The three approaches were tested using two datasets: DBpedia (vertical context
of places), and Umweltbundesamt data (vertical context of environmental data). While the approaches
were comparable in terms of efficiency and effectiveness for most tasks, the map + circular treemap
approach received higher ratings from participants (N=18) for enjoyment, usefulness, and satisfaction.
The findings from this empirical study are an initial step towards developing guidelines for visualizing
vertical context information extracted from geolinked data and beyond.
Keywords
linked data visualization, geolinked data, geovisualization, vertical context of geographic location
1. Introduction
Geographic locations are more than points on a map; they are only one dimension of the more
complex notion of place [1], and often act as the connecting link between many attributes.
Following [2], these attributes can be divided into two groups: those belonging to the horizontal
context and those belonging to the vertical context. The horizontal context refers to the context
established by information about surrounding locations. This could involve attributes such as
the physical proximity to other landmarks, the accessibility to transportation networks, or the
cultural and economic ties with neighbouring regions. By contrast, the vertical context pertains
to the context established by all things that are known about a location. It encapsulates various
attributes such as topography, climate, land use, historical significance, etc. and any other feature
that adds depth to the understanding of a location. The topic of this article is the visualization
of the vertical context of a geographic location. While linked data is suitable to represent
VOILA 2024: The 9th International Workshop on the Visualization and Interaction for Ontologies, Linked Data and
Knowledge Graphs co-located with the 23rd International Semantic Web Conference (ISWC 2024), Baltimore, USA,
November 11-15, 2024.
∗
Corresponding author.
Envelope-Open prasad.dream13@gmail.com (P. Madushanka); auriol.degbelo@tu-dresden.de (A. Degbelo)
Orcid 0000-0001-5087-8776 (A. Degbelo)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Table 1
Examples of approaches to inform about the vertical context of geographic locations. VerticalGeoVis
refers to the prototype built during this work with its variants.
Application User Interface Elements Windows Content Categorization
DBpedia Page [11] table-only single-window no
AWI Map [12] map+table multiple windows no
IOER Monitor [13] map+table+diagram single-window yes
TERENO [14] map+table single-window no
VerticalGeoVis (MT) map+table single-window yes
VerticalGeoVis (MM) map+markers single-window yes
VerticalGeoVis (MCT) map+circular treemap single-window yes
contextual information in general [3] and hence, the vertical context of geographic locations in
particular, there are currently few means to help users explore this vertical context visually,
and limited empirically derived guidelines for designers of vertical context visualizations. Since
the spatial dimension is important in organizing knowledge (see e.g. [4]), investigating means
to visualize the vertical context is a prerequisite to getting a holistic picture of what happens at
a location. Hence, research on visualizing the vertical context of geographic location is relevant
to work on linked geographic data [5, 6, 7], linked science [8], open (geo)data reuse [9] and
spatial data infrastructures [10], to name a few. Table 1 shows a few examples of applications
displaying all attribute information about a place, along with their strategy. While the study
presented next considers visualization approaches to answer basic questions as a first step, the
work’s long-term goal is to build visualization techniques that help users “digest” all information
available about a location, to the end of formulating spatial hypotheses about places.
This article presents an empirical study addressing the question: ‘How to effectively visualize
the vertical context of geographic locations?’. The article compared three approaches: map
+ table at the location of interest (hereafter MT), map + markers at the location of interest
(hereafter MM) and map + circular treemap at the location of interest (hereafter MCT). The
map is present in the three approaches because it is the most suitable medium to communicate
spatial knowledge [15]. Besides, in all three cases, the content is grouped according to thematic
categories (e.g. political, weather, administrative) that can be explored. The exploration of the
attribute values within the thematic categories happens through different interaction techniques
respectively: scrolling (MT), panning (MM) and zooming (MCT). The three approaches were
tested using two datasets: DBpedia [16] (vertical context of places), and Umweltbundesamt data
(vertical context of environmental data). The Umweltbundesamt is the environment agency of
the German government. DBpedia (knowledge graph data) was accessed through the DBpedia
Live Sync API1 , while the Umweltbundesamt data (structured data as JSON) was accessed
through the ‘Air Data API (UBA)’2 . The contribution of this work is an empirical investigation
informing about the respective merits of the three approaches.
1
https://www.dbpedia.org/resources/live/dbpedia-live-sync/ (accessed: July 10, 2024).
2
https://www.umweltbundesamt.de/daten/luft/luftdaten/doc (accessed: July 10, 2024).
2. Background
As discussed in [17], there are at least 16 linked data visualization use cases. Visualizing the
vertical context can be seen as a case of visualizing the information related to a specific instance
[17], when the specific instance is a location of interest. The necessity of communicating the
spatial context of the data instances is an additional requirement for tools visualizing the vertical
context of geographic locations, next to the visualization of all attributes available for that
location (the attributes may be provided as linked data or not). Ideally, these tools should also
support the follow-your-nose principle, which adds another dimension of complexity. As a first
step, all attributes retrieved for a location were treated as RDF-literals in this work, i.e. available
links were removed for simplicity.
The existing literature offers several reviews about tools and approaches to visualize linked
data (e.g. [18, 19, 20, 21]), but ‘vertical context exploration’ as a use case is mostly absent
from these reviews. Tools visualizing geo-linked data exist, but they often have a different
focus than user interfaces’ impact on the exploration of all things known about a location. For
instance, Mai et al. [22] proposed a multi-view interface (i.e. table view + graph view + map
view) to enable the exploration of scientific geographic data sources. Here, the focus was on the
discovery of detailed information about an entity, relationships between entities and between
entity types, and the spatial distribution of entities. In [23], different configurations can be
loaded (and a plugin that interprets the point geometry for locations), to display information
about DBpedia cities in the Phuzzy.link browser. Attribute information is displayed as a table
(similar to DBpedia Page), and hence the Phuzzy.link interface would qualify as a vertical context
visualization. Nonetheless, vertical context exploration was not the focus of the work, but
rather the concept of an adaptable interface to explore SPARQL endpoints from the browser.
At last, Potnis and Durbha [24] illustrate how to show country information on a 3D globe, but
their focus was on the display of geographic relationships (e.g. “hasBorderWith”) between
geographical entities.
The study presented next is a user-based evaluation of three strategies to retrieve information
about the vertical context of a location (MT, MM and MCT). We consider three information-
seeking questions: elementary-level questions (questions about specific values of a property
at a given location), intermediate-level questions (questions about a specific category and its
subelements at the location of interest) and global-level questions (questions about all categories
of topics at the location of interest).
3. Method
We compared the merits of the three approaches through a controlled experiment. The following
variables were considered during the experiment:
• Independent variables: the three visualization approaches (map+table, map+markers,
map+circular treemap), the type of questions asked (specific attribute values, overview
[count of attributes per category], overview [attribute categories]), and the six attribute
levels (60, 90, 105, 140, 180, 200).
• Dependent variables: efficiency (task completion time), effectiveness (accuracy of tasks),
perceived enjoyment, perceived usefulness, perceived satisfaction and perceived ease of
use regarding the visualization approaches.
• Controlled variables: number of screens used (N=1) and screen size (all between 13 inches
and 27 inches).
• Subject variables: age, gender, educational background, English proficiency, computer
literacy and prior experience with visualization tools (i.e. web maps, Leaflet.js markers
and D3.js zoomable circle packing).
We anticipated before the study that the magnitude of attributes displayed might influence
the performance of the techniques assessed and introduced the notion of ‘attribute level’ to
capture this potential effect. ‘Attribute level X’ stands for ‘number of attribute values in the
order of X’ or ‘number of attribute values roughly equal to X’. The initial plan was to start
with attribute level 60 and increase it incrementally by 30 attributes for each subsequent level.
However, due to attributes actually offered by the datasets, we adjusted the final attribute levels
slightly, i.e. 60, 90, 105 (instead of 120), 140 (instead of 150), 180, and 200 (instead of 210).
3.1. Datasets
Two datasets were used: DBpedia and the Umweltbundesamt data (hereafter UBA). We used
each to collect information about the vertical context of 10 cities in Germany (Table 2). While
both provide information about the vertical context of places, a difference between the two is
that DBpedia provides mostly information in the form of text, while Umweltbundesamt offers
primarily numerical information, e.g., for attributes such as Fine dust, Carbon monoxide (CO),
Sulphur dioxide (SO2), Ozone (O3) and Nitrogen dioxide (NO2). The DBpedia Live Sync API was
used by inputting the city names as query parameters and the data was obtained in N-triples
format. Relevant data from Umweltbundesamt was obtained in JSON format from the “Air Data
API (UBA) 3.0.0”. Raw data obtained from the two APIs underwent cleaning and post-processing
before rendering through the visualization approaches.
3.2. Prototype
The application was built using Web technologies (HTML, CSS, JavaScript), a web mapping
library (Leaflet) and a visualization library (D3.js). We used Leaflet (version 1.9.4) for the base
maps and location markers, D3.js (Version 4) for the circular treemap, and Bootstrap (version
5.3.2) as the CSS framework. The application features search for locations, the choice of datasets,
the choice of a visualisation approach, as well as short answers to frequently asked questions.
The approaches were renamed into “Visualization Approach 1”, “Visualization Approach 2”
and “Visualization Approach 3” during the experiment to avoid potential participant bias. A
demo of the application is available at https://youtu.be/zLCy3shSOoU. Figures 1, 2 and 3 show
screenshots of the three visualization approaches on the two datasets respectively.
• Map+table: The information is presented in a table format, grouped by the main attribute
category (Figure 1a). This arrangement enables users to efficiently search for specific
data by scrolling through the table. Categorizing the data facilitates swift navigation
Table 2
Datasets used during the experiment, along with the exact number of attributes extracted for a location
and the attribute level for which they were used during the experiment.
DBpedia dataset UBA dataset
City
Attribute count Attribute level Attribute count Level of attributes
Berlin 103 90 192 180
Hamburg 110 105 204 200
Munich 113 105 168 140
Cologne 112 105 204 200
Frankfurt am Main 114 105 204 200
Stuttgart 113 105 144 140
Düsseldorf 115 105 60 60
Leipzig 108 105 108 105
Dortmund 64 60 60 60
Essen 90 90 120 105
to sections of interest, enhancing the overall efficiency of data retrieval. Additionally,
attribute indexing improves the ease of counting the attributes relevant to each group or
attribute within the dataset.
• Map+markers: Instead of a traditional table format, the data is represented by leaflet
markers arranged in a spiral pattern, i.e. the makers are ‘spiderfied’ [25]. The spiderify
method, described in [25], was originally suggested to tackle the problem of visualizing
multiple markers at the same location. Thus, we reused it in this context to convey the
idea that all attributes are attached to the same location. The spiderify method takes the
markers placed in the same position and arranges them in a spiral. The advantage of
the spiral pattern is its compactness, which enables the display of numerous attributes.
Each marker represents different attributes, making it possible for users to identify and
differentiate between them. When users click on a marker, a pop-up message appears,
showing the name of the attribute category and its corresponding values. Colour hue is
used to highlight markers belonging to the same category (Figure 2a).
• Map+circular treemap: The visualization organizes information into circles representing
distinct attribute categories for a city (Figure 3a). Upon a user’s click on a category, the
visualization zooms in to display the attribute-value pair information available in that
specific circle (Figure 3b). We opted for a circular treemap over a traditional treemap
because, for the two hierarchy levels required to display the datasets in this work, the
circular treemap reveals hierarchical structures and facilitates interaction between the
levels more effectively.
3.3. Tasks
We generated six tasks for the experiment. A task has three dimensions: a city, an attribute
level for the vertical context, and a dataset. The six tasks were: Dortmund-60-DBpedia (T1),
Düsseldorf-105-DBpedia (T2), Essen-90-DBpedia (T3), Hamburg-200-UBA (T4), Berlin-180-UBA
(a) DBpedia Data (b) UBA Data
Figure 1: Visualization of the vertical context for Dortmund, using the map+table approach.
(a) DBpedia Data (b) UBA Data
Figure 2: Visualization of the vertical context for Dortmund, using the map+markers approach. (a)
overview of available attributes for the DBpedia data; (b) Zoom on Ozone information from the UBA
data.
(T5), and Munich-140-UBA (T6). Each task features three types of questions: q1-q3 all deal with
retrieving specific attribute values, q4 is about counting the number of attributes belonging
to a category, and q5 is about listing all thematic categories of attributes available for a city.
Adapting Bertin [26]’s distinction to the current context, we can distinguish questions about a
single element (elementary level of reading), questions about a group of elements (intermediate
level of reading), and questions about the whole set of visual elements (overall or global level
of reading). q1-q3 correspond to the elementary level of reading, q4 touches the intermediate
level of reading, and q5 addresses the global level of reading of the dataset at hand. The order
of the questions (q1-q5) remained identical for all participants during the study. We provide
two examples of tasks below (T1, T4). A description of the remaining tasks (T2, T3, T5, T6) can
be found in the supplementary material, Section 7.
(a) DBpedia Data (b) UBA Data
Figure 3: Visualization of the vertical context for Dortmund, using the map+circulartreemap approach:
(a) overview of available attributes for the DBpedia data; (b) Zoom on the Fine-dust value for the
Dortmund-Evin station in June (UBA Data).
Task (T1) is described as follows (Dortmund, 60 attributes, DBpedia).
specific attribute values
• q1: What is the lowest temperature in Dortmund for September?
• q2: What is the recorded highest temperature in Dortmund for May?
• q3: Who is the leader of Dortmund (Leader Name)?
overview (count of attributes per category)
• q4: How many attributes belong to the ‘Weather’ category?
overview (attribute categories)
• q5: What are the attribute categories for Dortmund that you can access through this
approach?
Task (T4) is described as follows (Hamburg, 200 attributes, UBA).
specific attribute values
• q1: What is the monthly maximum (μg/m³) of “Ozone (O3)” recorded in the “Hamburg
Sternschanze” station for April?
• q2: What is the monthly maximum (μg/m³) of “Nitrogen dioxide (NO2)” recorded in the
“Hamburg Max-Brauer-Allee II (Straße)” station for October?.
• q3: What is the monthly average (μg/m³) of “Fine dust (PM10)” recorded in the “Hamburg
Habichtstrasse” station for December?
overview (count of attributes per category)
• q4: How many data records (vertical attributes) are available for the air pollutant “Fine
dust (PM10)”?
Figure 4: Steps followed by the participants during the experiment.
overview (attribute categories)
• q5: What are the categories of air pollutants that exist in Hamburg city?
The experiment followed a within-group design. The order of the visualization approaches
and of the tasks was counterbalanced. The randomization and distribution of the subjects across
conditions helped collect 18 data points per visualization approach and 9 data points per attribute
level. Additional details about the randomization strategy are shown in the supplementary
material, Section 7. Figure 4 shows all experiment steps, including the tasks.
3.4. Procedure
The experiment was held online (Google Meet) and was conducted each time with one partici-
pant and one examiner (the first author). The procedure started with a brief explanation of the
experiment’s goals. Then the participants were asked to provide a video consent before proceed-
ing with the experimental tasks. Afterwards, participants filled in a background questionnaire
about personal details, computer literacy, familiarity with web maps, Leaflet.js marker patterns,
and the D3.js zoomable circle-packing visualization approach. Once the background question-
naire was completed, the participants performed three tasks using a different visualization
approach each time to find information about one of the ten cities (Figure 4). The examiner only
observed the entire process. Upon completion of each task, participants were asked to answer
one question related to the enjoyment [27] of the visualization approach and three questions
selected from the USE questionnaire [28, 29] to measure usefulness, satisfaction and ease of
use on a 7-point Likert scale. The three questions selected were: “X makes the things I want to
accomplish easier to get done” (Question UU5, Usefulness); “I don’t notice any inconsistencies
as I use X” (Question UE8, Ease of Use); and “I am satisfied with X” (Question US1, Satisfaction),
where X refers to the visualization approach, to find out the extent to which X helps users
answer the questions considered (UU5), smoothly navigate the attribute values (UE8) and brings
about satisfaction in the process (US1). At the end of the session, the participants were asked to
answer three questions: ‘Considering the three visualization approaches you’ve interacted with,
could you please rank them in order of preference based on which one you found most effective
in helping you answer the questions?’, ‘Could you please provide reasons for ranking them?’
And ‘Do you have any suggestions regarding further improvements for the Web Application?’.
All answers to questions were recorded through the LimeSurvey platform, which was also
used to record the time participants took to answer specific questions. The experiment was
pilot-tested with two participants and approved by the institutional ethics board.
3.5. Participants
The study involved 18 participants (6F, 12M), which were recruited through word-of-mouth.
Participation was voluntary and the participants were not compensated for their participation.
Their age distribution varied: (12/18) participants fell within the age range of 21 to 30 years,
while (4/18) participants were between 31 and 40. Additionally, there was (1/18) participant in
the age groups of 41-50 and 51-60 respectively. Regarding computer literacy, the participants
reported varying levels of proficiency, with (2/18) participants self-identifying as beginners,
(7/18) as intermediate, and (9/18) as advanced users. The familiarity with Leaflet.js markers
and D3 zoomable circle packing also exhibited a range of expertise among the participants.
Specifically, (1/18) participants were highly familiar with Leaflet.js marker patterns, while (8/18)
participants reported moderate familiarity and (9/18) participants indicated no familiarity. The
familiarity distribution was similar for D3.js zoomable circle packing, indicating a diverse range
of expertise levels within the participant pool. All participants used their own laptops with
one screen to complete the study and confirmed that their screen size was between 13 and 27
inches, to keep the experimental setup somewhat similar.
4. Results
We now present the experiment’s results, starting with the outcomes of the quantitative analysis,
before proceeding with the qualitative feedback.
4.1. Results of the pairwise comparison of the three visualization approaches
Figure 5 shows the similarities and differences observed across the visualization approaches
and the attribute levels. The Map+Circular Treemap approach yielded the best outcomes in
most cases (it was better than at least one of the other two in 32 of all 70 comparison scenarios),
followed by the Map+Markers approach (better 9 times) and the Map+Table approach at last
(favourable 8 times). We found no significant advantage for any visualization approach in 27
of the 70 comparison scenarios (Figure 5). A detailed presentation of the results is shown in
Appendix A (Tables 4 to 13).
• Efficiency [elementary-level]: while the data did not suggest any significant difference
cross-attribute-levels, the Map+Table approach recommends itself for the attribute levels
90 and 105, while the Map+Markers approach seems best for the attribute levels 180 and
200 (see also Table 4).
• Efficiency [intermediate-level]: the three approaches are comparable overall, but Map+Ta-
ble may be preferable at attribute level 105, while Map+Makers or Map+Circular Treemap
may be advantageous at attribute level 60 (see also Table 5).
• Efficiency [global-level]: Map+Circular Treemap is preferable at attribute level 60,
Map+Table at attribute level 105, and Map+Makers may be a good option for attribute
levels 105 and 180 (see also Table 6).
Figure 5: Summary of the differences observed per condition regarding the dependent variables. Two
colours in a cell should be read “AND”, e.g. for efficiency at attribute level 60 [question type: intermediate-
level], map+markers was better than the other two techniques at least once, and map+circular treemap
was better than the other two techniques at least once. That is, map+table was worse than the other
two techniques for efficiency at attribute level 60.
• Effectiveness [elementary-level]: the approaches may be deemed comparable overall, but
Map+Markers provided higher accuracy values at attribute level 60, while Map+Circular
Treemap increased users’ accuracy during question answering at attribute level 200 (see
also Table 7).
• Effectiveness [intermediate-level]: the Map+Circular Treemap approach recommends
itself here overall, and at attribute levels 105 and 180. The Map+Markers approach seems
best at attribute level 90 (see also Table 8).
• Effectiveness [global-level]: the Map+Circular Treemap approach seems best at levels
60, 140 and 180, and the Map+Markers approach helped users increase their answers’
accuracy at level 90 (see also Table 9).
• Perceived enjoyment: there seems to be a clear user preference for the Map+Circular
Treemap approach (see also Table 10).
• Perceived usefulness: there seems to be a clear user preference for the Map+Circular
Treemap approach both overall and across all attribute levels (see also Table 11).
• Perceived satisfaction: here also, the Map+Circular Treemap approach seems to have
provided more satisfaction to participants during the question-answering tasks (see also
Table 12).
• Perceived ease of use: overall, the easiest approaches to use according to the participants
were either the Map+Circular Treemap or the Map+Table approach. The Map+Makers
approach was perceived as slightly easier to use by participants at attribute level 60 only
(see also Table 13).
4.2. Participants’ subjective preference
Here, the Map+Circular Treemap approach emerged as the most favoured by participants,
garnering 15 out of 18 votes as Rank 1 (Table 3). In contrast, the Map+Markers approach
received the least preference with 16 out of 18 votes as Rank 3. As for the reasons for their
ranking provided by the users, the key advantages mentioned were:
• Map+Circular Treemap: Users can zoom in for a detailed inspection of specific attributes
and easily zoom out to transition to other attributes. The attributes are organized into
clusters, grouping similar ones in close proximity. This systematic arrangement enhances
navigation, making it easy to locate specific attributes efficiently.
• Map+Markers: the participants did not mention any specific advantage.
• Map+Table: Users can navigate through attributes by employing a rapid scrolling feature,
allowing them to efficiently move through the content and explore different attributes
without delays.
The key disadvantages mentioned were:
• Map+Circular Treemap: the participants did not mention any specific disadvantage.
• Map+Markers: To locate a specific attribute, users need to click on multiple markers, as it
can be challenging to distinguish the desired attribute from others in the same category.
• Map+Table: The list view arrangement makes it difficult to identify attribute counts and
discern the attribute categories easily.
Table 3
Participants’ feedback about their preferred approach.
Visualization Approach Rank 1 Rank 2 Rank 3
Map+Table 3 13 2
Map+Markers 0 2 16
Map+Circular Treemap 15 3 0
5. Discussion
5.1. Implications
The display of attribute values as a table is currently the most common - and straightforward -
way of presenting vertical context information (e.g. DBpedia Page, Table 1). The preliminary
observations from this study suggest that there are usually better alternatives, both in terms
of maximizing utilitarian (e.g. efficiency, effectiveness) and hedonic objectives (e.g. perceived
user satisfaction). Another consistent observation of the study is that both attribute level and
question types matter when comparing the merits of the different visualisation approaches.
Since this study is a first of this kind, it is still unclear at this point whether the variability
observed can be truly attributed to the performance of an approach at an attribute level, or to
the current (limited) sample of participants. Still, the study has highlighted that attribute level
may be a confounding variable for similar experiments in the future, and this suggests that this
dimension should be explicitly controlled for. Moving forward, there are two possible ways of
reusing the results from Figure 5: reading a column to find out the most suitable visualization
at an attribute level or reading a line to find the best suitable row to maximize the outcomes
for a dependent variable (e.g. effectiveness, perceived ease of use). These two ways of reading
can be used by visualization designers in the future to formulate hypotheses, as they use any
of the three visualization approaches considered in the current work as a baseline. Finally, as
mentioned in [30], spatial data collection, processing and sharing is a common thread of various
disciplines within the Earth System Sciences. Hence, the observations made here about vertical
context visualization are relevant to ongoing efforts to establish (national) infrastructures for
the Earth System Sciences.
5.2. Limitations
The prototype was designed exclusively for desktop-size screens (e.g. personal computers) and
is not yet optimized for use on mobile devices such as phones and tablets. Hence, no claim
can be made as to the generalizability of the results to these devices. Another limitation of the
study was the relatively small number of participants and their homogeneous backgrounds.
This was necessary because of the exploratory nature of the study, but a larger-scale study
would be needed, with a more diversified user base, in follow-up work to learn about the
applicability of the results to different user groups. At last, all three techniques overlay the
vertical context information on top of the location selected. While this has the advantage that - in
the context of the navigation - the location for which the vertical context is currently visualized
is unambiguous, this comes at the cost of the occlusion of the map. An alternative worth
considering would be the juxtaposition of the vertical context display and the map (i.e. placing
the vertical context display alongside the map). This would come at the extra cost of designing
effective location emphasis techniques to highlight the current location unambiguously but is
worth further investigation in future work.
5.3. Future work
We have mentioned three key requirements of visualizations of the vertical context of geographic
locations in Section 2: communicate the spatial context (R1), enable intuitive and effective
navigation through the wealth of attributes and their values (R2), and enable the traversal of
paths between different datasets, and paths between properties of the same dataset (R3, a.k.a.
follow-your-nose principle). The three techniques in this work addressed R1 (through the
map) and R2 (through either the table, the marker, or the circular treemap). Hence, an open
question is how to address R3 through existing or new visualization approaches. Given the
number of attributes to display, the categorization of content comes in handy. Hence, this work
tested circular treemaps, which are only one way of visualizing hierarchical data, to display
the attribute values. It is an open question whether or not alternative ways of visualizing
hierarchical data (for examples, see [31, 32]) would be equally effective in this context, if not
more effective. Furthermore, the three approaches have in common that attribute information
is displayed in a popup after clicking on the map. Given the necessity for at least two views
for the display of vertical context information, geodashboards (i.e. multiple-view systems of
geographic data, arranged on a single screen so that the information can be perceived at a
glance [33]) may be considered to fulfil the three requirements as well. Besides, Figure 5 has
shown that attribute levels matter, hence scalability across several attribute levels needs more
scrutiny. In addition, the work only tested the visualization approaches for up to 200 attribute
values. The extent to which the observations hold for even greater levels of attribute values
also needs to be investigated in future work. At last, we mentioned in Section 1 that, beyond
simple question-answering about attribute values at a location, one higher-level goal of vertical
context exploration is the formulation of spatial hypotheses. The extent to which existing and
novel visualization techniques support that goal would need systematic testing in future work.
6. Conclusion
There are two types of contextual information related to a geographical location: the vertical
context (all things known about a location) and the horizontal context (all things known about
surrounding locations to a location). While linked data is suitable to represent contextual
information in general and the vertical context of geographic locations in particular, we still
lack means to help users explore this vertical context visually, and guidelines for designers
of vertical context visualizations. This work has provided an empirical comparison of three
approaches (map+table, map+marker, map+circular treemap) to address this gap. The merits
of the approaches were compared for question-answering tasks involving 60 to 200 attribute
values. The map+circular treemap approach yielded the best outcomes in most cases, followed
by the map+markers approach, and both can be used as a starting point in further investigations
of visualizations that help users explore all that is known about a place.
7. Supplementary material
The supplementary material showing the randomization approach during the experiment, all
tasks completed by the participants, as well as the data collected during the experiment, is
available at https://doi.org/10.6084/m9.figshare.26264594. The code of the prototype is available
on GitHub (https://github.com/Prasadmadhusanka/VerticalGeoVis-prototype).
Acknowledgments
The work has been partly funded by the European Commission through the Erasmus
Mundus Master in Geospatial Technologies (Erasmus+/Erasmus Mundus program, project
no. 101049796, http://mastergeotech.info/) and the German Research Foundation through the
project NFDI4Earth (DFG project no. 460036893, https://www.nfdi4earth.de/) within the German
National Research Data Infrastructure (NFDI, https://www.nfdi.de/).
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A. Appendix - Detailed results
This section presents detailed results about the pairwise comparison of the different approaches
across all dependent variables. The R package bootES [34] was used for the analysis. The
following applies to all tables. The first two columns of the tables represent the two visualization
approaches being compared. The third column represents the mean value difference between
the two groups: 𝑀𝑒𝑎𝑛(𝐺𝑟𝑜𝑢𝑝1) - 𝑀𝑒𝑎𝑛(𝐺𝑟𝑜𝑢𝑝2) . Positive values for both the lower and upper
confidence interval bounds (𝐶𝐼𝐿𝑜𝑤 and 𝐶𝐼𝐻 𝑖𝑔ℎ values) suggest that the visualization in the first
column produced significantly higher values than the one in the second column. Conversely,
negative values for both 𝐶𝐼𝐿𝑜𝑤 and 𝐶𝐼𝐻 𝑖𝑔ℎ indicate that the visualization in the second column
resulted in significantly higher values than the one in the first column. A statistically significant
difference between the two groups is implied when the confidence interval of the mean difference
does not enclose zero. This is highlighted through a light yellow coloured background in the
tables. The bias is the difference between the mean of the resamples and the mean of the original
sample. The SE (standard error) is the standard deviation of the resampled means [34]. The
number of resamples used in the analysis was N=5000.
Table 4
Efficiency results for the elementary-level questions (q1,q2,q3).
Approach A Approach B Attribute Level Mean Difference (Seconds) CI Low CI High Bias SE
60 -43.310 -134.190 76.153 0.431 54.013
90 -11.140 -69.007 26.860 -0.828 24.726
105 -101.840 -237.563 -23.733 1.342 53.167
Map+Table Map+Markers 140 -35.120 -124.582 23.467 0.595 38.373
180 98.113 -38.647 262.097 -0.069 76.213
200 -169.897 -295.583 11.613 -1.066 78.929
Overall -43.866 -110.265 25.693 0.192 34.299
60 20.437 -11.610 69.793 0.235 20.829
90 -72.140 -186.220 -6.927 0.652 43.801
105 -126.763 -155.253 -90.330 0.297 16.494
Map+Table Map+Circular Treemap 140 -57.557 -150.340 29.053 -0.108 45.853
180 146.840 13.633 310.823 0.819 76.095
200 44.660 -30.920 86.480 0.511 28.071
Overall -7.421 -52.244 53.610 -0.425 26.507
60 63.747 -58.157 135.720 0.069 50.418
90 -61.000 -194.047 15.123 0.699 48.904
105 -24.923 -98.250 97.203 -0.156 49.740
Map+Markers Map+Circular Treemap 140 -22.437 -99.923 59.220 -0.928 41.653
180 48.727 -32.127 118.797 -0.350 40.575
200 214.557 21.670 320.337 0.099 74.771
Overall 36.445 -17.974 104.815 -0.026 31.268
Table 5
Efficiency results for the intermediate level/counting questions (q4).
Approach A Approach B Attribute Level Mean Difference (Seconds) CI Low CI High Bias SE
60 61.117 14.913 98.223 -0.470 22.017
90 -1.493 -33.227 34.497 0.033 18.500
105 -90.450 -138.650 -11.513 0.345 33.096
Map+Table Map+Markers 140 13.160 -77.970 104.290 -0.367 47.891
180 2.197 -48.913 56.037 -0.144 26.311
200 -65.287 -177.020 4.870 -0.285 47.113
Overall -13.459 -49.128 16.653 0.214 16.627
60 66.107 20.547 101.897 0.315 21.473
90 -23.620 -53.177 5.937 0.252 16.035
105 -59.510 -126.890 -21.377 -0.319 27.364
Map+Table Map+Circular Treemap 140 30.657 -33.353 125.637 -0.630 40.885
180 4.833 -53.397 58.270 -0.110 29.273
200 0.397 -15.270 21.097 -0.019 9.475
Overall 3.144 -19.454 30.724 -0.102 12.979
60 4.990 -13.180 17.063 -0.075 7.144
90 -22.127 -62.433 3.173 0.121 15.967
105 30.940 -78.987 102.610 -0.254 43.603
Map+Markers Map+Circular Treemap 140 17.497 -26.160 79.467 0.551 27.002
180 2.637 -63.330 47.120 -0.328 27.482
200 65.683 -11.977 175.110 -0.022 47.627
Overall 16.603 -9.914 50.504 -0.130 15.334
Table 6
Efficiency results for the global level/category overview questions (q5).
Approach A Approach B Attribute Level Mean Difference (Seconds) CI Low CI High Bias SE
60 -4.390 -41.030 37.610 -0.080 21.318
90 19.683 -16.667 66.177 0.021 20.717
105 -0.810 -19.057 15.877 -0.009 8.942
Map+Table Map+Markers 140 19.260 -22.960 68.283 0.180 23.447
180 30.757 9.897 64.733 -0.289 14.284
200 -30.750 -67.843 14.970 0.151 21.372
Overall 5.625 -12.597 24.235 0.064 9.331
60 41.932 13.573 72.240 0.274 15.334
90 32.127 -8.717 78.620 -0.196 22.393
105 -40.610 -68.783 -25.167 -0.150 11.062
Map+Table Map+Circular Treemap 140 -4.283 -45.590 42.461 0.151 22.760
180 10.447 -18.457 44.800 -0.057 16.285
200 -3.440 -39.480 30.550 -0.211 18.799
Overall 6.032 -11.024 23.421 0.212 8.727
60 46.313 8.387 71.33 0.560 16.061
90 12.443 -14.767 31.110 0.108 11.671
105 -39.800 -72.600 -17.243 0.058 14.161
Map+Markers Map+Circular Treemap 140 -23.543 -62.590 15.503 0.180 20.150
180 -20.280 -39.203 -4.370 0.010 9.249
200 27.310 -31.107 67.507 0.020 25.442
Overall 0.407 -16.586 18.957 -0.144 9.106
Table 7
Effectiveness results for the elementary-level questions (q1,q2,q3). ***** indicates identical values in the
two conditions, so no possibility of computing a confidence interval.
Approach A Approach B Attribute Level Mean Difference CI Low CI High Bias SE
60 -22.222 -33.333 -11.111 0.187 8.921
90 11.111 -22.222 44.444 -0.291 20.036
105 22.222 -22.222 44.444 0.160 18.078
Map+Table Map+Markers 140 11.111 0.000 22.222 -0.007 8.969
180 11.111 0.000 22.222 0.207 9.280
200 0.000 -33.333 11.111 0.249 12.722
Overall 5.556 -7.407 16.667 -0.245 6.402
60 *****
90 22.222 0.000 33.333 0.151 9.021
105 0.000 -33.333 11.111 0.044 13.014
Map+Table Map+Circular Treemap 140 11.111 0.000 22.222 -0.071 9.044
180 11.111 0.000 22.222 0.136 9.036
200 -11.111 -33.333 -11.111 -0.031 9.049
Overall 0.000 -12.963 9.259 -0.224 5.277
60 -11.111 -33.333 0.000 0.164 9.140
90 11.111 -33.333 11.111 0.000 17.718
105 -22.222 -55.556 11.111 0.124 18.294
Map+Markers Map+Circular Treemap 140 0.000 -33.333 11.111 -0.027 12.689
180 0.000 -33.333 11.111 0.109 12.843
200 -11.111 -33.333 0.000 -0.022 9.096
Overall -5.556 -22.222 3.704 -0.013 6.443
Table 8
Effectiveness results for the intermediate level/ counting questions (q4).
Approach A Approach B Attribute Level Mean Difference CI Low CI High Bias SE
60 -33.333 -100.000 33.333 -0.693 38.079
90 -66.667 -100.000 -33.333 -0.127 27.119
105 66.667 0.000 100.00 -0.533 27.241
Map+Table Map+Markers 140 *****
180 -33.333 -100.000 33.333 -0.267 38.416
200 *****
Overall 22.222 -16.667 50.000 -0.029 16.266
60 -33.333 -100.000 33.333 0.033 38.668
90 *****
105 0.000 -100.000 33.333 0.380 38.493
Map+Table Map+Circular Treemap 140 *****
180 -66.667 -100.000 -33.333 0.220 27.061
200 *****
Overall -16.667 -50.000 11.111 -0.150 15.851
60 0.000 -100.000 33.333 0.353 38.034
90 -33.333 -100.000 0.000 -0.493 27.445
105 -66.667 -100.000 -33.333 -0.293 27.114
Map+Markers Map+Circular Treemap 140 *****
180 -33.333 -100.000 0.000 0.387 26.998
200 *****
Overall -38.889 -72.222 -11.111 -0.333 15.302
Table 9
Effectiveness results for the global level/category overview questions (q5).
Approach A Approach B Attribute Level Mean Difference CI Low CI High Bias SE
60 -33.333 -100.000 0.000 -0.500 38.062
90 -66.667 -100.000 -33.333 -0.407 26.919
105 33.333 0.000 66.667 -0.073 27.348
Map+Table Map+Markers 140 66.667 0.000 100.000 0.293 27.114
180 33.333 0.000 66.667 -0.667 27.222
200 -33.333 -100.000 0.000 -0.060 27.526
Overall 0.000 -38.889 27.778 0.237 16.107
60 -66.667 -100.000 -33.333 -0.387 27.039
90 -33.333 -100.000 33.333 -0.327 38.302
105 33.333 0.000 66.667 0.353 27.289
Map+Table Map+Circular Treemap 140 *****
180 -66.667 -100.000 -33.333 -0.387 27.219
200 33.333 -66.667 66.667 0.380 38.591
Overall -16.667 -50.000 11.111 0.293 14.985
60 -33.333 -100.000 0.000 -0.153 27.429
90 33.333 0.000 66.667 0.127 27.291
105 0.000 -100.000 33.333 0.467 38.960
Map+Markers Map+Circular Treemap 140 -66.667 -100.000 -33.333 -0.233 27.225
180 *****
200 66.667 0.000 100.000 0.393 27.207
Overall -16.667 -50.000 11.111 -0.124 14.805
Table 10
Pairwise comparison of the perceived enjoyment for the visualization approaches.
Approach A Approach B Attribute Level Mean Difference CI Low CI High Bias SE
60 1.333 0.000 2.667 0.014 1.099
90 0.667 -1.333 2.333 -0.003 0.978
105 -1.000 -4.000 2.000 0.001 1.553
Map+Table Map+Markers 140 -0.667 -3.000 1.000 0.008 1.004
180 2.667 0.333 5.000 -0.027 1.195
200 -1.667 -4.667 2.000 -0.068 1.708
Overall 0.222 -1.056 1.389 0.005 0.646
60 0.667 0.000 1.333 0.555 0.543
90 -1.333 -2.000 -1.000 0.002 0.273
105 -2.667 -4.667 -1.000 -0.008 0.968
Map+Table Map+Circular Treemap 140 -1.667 -3.333 -0.333 0.006 0.766
180 -0.667 -2.333 1.000 -0.018 0.905
200 -3.000 -5.333 -0.667 -0.005 1.203
Overall -1.444 -2.556 -0.667 0.011 0.478
60 -0.667 -4.000 0.667 0.013 1.218
90 -2.000 -4.000 -0.667 -0.003 0.928
105 -1.667 -5.000 0.000 0.004 1.261
Map+Markers Map+Circular Treemap 140 -1.000 -2.667 0.333 0.002 0.779
180 -3.333 -5.333 -1.333 -0.003 1.092
200 -1.333 -5.333 1.000 -0.009 1.557
Overall -1.667 -2.833 -0.722 0.005 0.529
Table 11
Pairwise comparison of the perceived usefulness of the visualization approaches.
Approach A Approach B Attribute Level Mean Difference CI Low CI High Bias SE
60 0.000 -1.000 0.333 -0.003 0.388
90 2.000 0.000 3.000 -0.006 0.772
105 0.333 -1.333 2.667 -0.006 1.021
Map+Table Map+Markers 140 1.667 -0.951 2.667 0.007 0.850
180 2.333 -0.333 4.000 0.003 1.091
200 1.333 -4.000 1.333 0.002 1.391
Overall 0.833 -0.333 1.889 -0.005 0.576
60 0.667 -1.000 -0.333 -0.003 0.276
90 -0.333 -1.000 0.333 0.000 0.389
105 -2.333 -3.000 -2.000 0.001 0.272
Map+Table Map+Circular Treemap 140 -1.333 -3.667 -0.333 -0.009 0.869
180 -2.000 -3.667 -0.333 0.001 0.896
200 -3.333 -5.333 -1.667 0.008 0.900
Overall -1.667 -2.667 -0.944 -0.004 0.434
60 -0.667 -1.000 -0.333 0.001 0.270
90 -2.333 -3.667 -1.000 -0.005 0.771
105 -2.667 -5.000 -1.333 0.011 0.979
Map+Markers Map+Circular Treemap 140 -3.000 -3.667 -2.333 -0.005 0.385
180 -4.333 -6.000 -2.667 -0.027 0.974
200 -2.000 -5.333 -0.333 -0.009 1.315
Overall -2.500 -3.500 -1.611 0.001 0.483
Table 12
Pairwise comparison of the perceived satisfaction of the visualization approaches.
Approach A Approach B Attribute Level Mean Difference CI Low CI High Bias SE
60 0.667 -0.667 1.667 0.006 0.546
90 1.333 -0.333 2.667 -0.002 0.851
105 1.333 -1.333 3.333 -0.002 1.205
Map+Table Map+Markers 140 0.333 -1.000 1.000 -0.001 0.545
180 3.667 1.667 5.000 0.005 0.861
200 -2.000 -4.333 0.333 0.011 1.250
Overall 0.889 -0.444 1.889 0.002 0.588
60 0.000 -1.000 0.333 0.003 0.381
90 -0.333 -1.667 0.667 -0.009 0.547
105 -1.333 -2.000 -1.000 -0.005 0.271
Map+Table Map+Circular Treemap 140 -1.000 -2.667 -0.333 0.000 0.599
180 -0.667 -2.000 0.000 0.006 0.553
200 -3.667 -5.667 -1.667 0.010 1.078
Overall -1.167 -2.333 -0.500 0.010 0.453
60 -0.667 -2.000 0.000 0.012 0.550
90 -1.667 -3.333 -0.333 -0.006 0.773
105 -2.667 -5.000 -1.000 0.015 1.183
Map+Markers Map+Circular Treemap 140 -1.333 -2.000 -1.333 0.001 0.270
180 -4.333 -5.667 -3.000 -0.008 0.774
200 -1.667 -4.000 0.333 -0.020 1.184
Overall -2.056 -2.944 -1.222 -0.001 0.452
Table 13
Pairwise comparison of the perceived ease of use of the visualization approaches.
Approach A Approach B Attribute Level Mean Difference CI Low CI High Bias SE
60 -0.333 -1.000 -0.333 0.003 0.270
90 3.667 3.000 4.000 -0.004 0.274
105 1.333 -0.333 3.667 -0.013 1.016
Map+Table Map+Markers 140 1.667 -0.333 3.000 0.009 0.862
180 2.667 -0.667 4.667 -0.005 1.269
200 -0.333 -4.667 2.333 0.023 1.728
Overall 1.444 0.167 2.389 -0.003 0.550
60 -0.667 -1.000 -0.333 -0.006 0.268
90 0.333 -0.667 1.333 0.002 0.613
105 -1.333 -2.000 -1.333 0.003 0.272
Map+Table Map+Circular Treemap 140 0.000 -1.667 2.000 -0.009 0.950
180 0.000 -1.667 1.667 0.014 1.126
200 -2.667 -5.667 -0.667 0.013 1.213
Overall -0.722 -1.722 0.000 0.007 0.420
60 -0.333 -1.000 0.333 0.004 0.388
90 -3.333 -4.000 -2.667 0.007 0.548
105 -2.667 -5.000 -1.333 0.009 0.983
Map+Markers Map+Circular Treemap 140 -1.667 -3.667 0.333 -0.012 1.093
180 -2.667 -5.667 0.333 -0.034 1.650
200 -2.333 -4.333 0.667 -0.019 1.258
Overall -2.167 -3.111 -1.111 -0.005 0.517