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 eficiency and efectiveness 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. linked data visualization, geolinked data, geovisualization, vertical context of geographic location 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, ∗Corresponding author.
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contextual information in general [
This article presents an empirical study addressing the question: ‘How to efectively 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 diferent 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. 1https://www.dbpedia.org/resources/live/dbpedia-live-sync/ (accessed: July 10, 2024). 2https://www.umweltbundesamt.de/daten/luft/luftdaten/doc (accessed: July 10, 2024).
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 ofers 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 diferent
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], diferent 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 [
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 informationseeking 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).
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: eficiency (task completion time), efectiveness (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 efect. ‘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 ofered 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).
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 diference between the two is that DBpedia provides mostly information in the form of text, while Umweltbundesamt ofers 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.
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 eficiently search for specific
data by scrolling through the table. Categorizing the data facilitates swift navigation
City
Berlin
Hamburg
Munich
Cologne
Frankfurt am Main
Stuttgart
Düsseldorf
Leipzig
Dortmund
Essen
to sections of interest, enhancing the overall eficiency 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’ [
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?
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)”?
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.
The experiment was held online (Google Meet) and was conducted each time with one
participant 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
proceeding 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
questionnaire was completed, the participants performed three tasks using a diferent 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 [
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.
We now present the experiment’s results, starting with the outcomes of the quantitative analysis, before proceeding with the qualitative feedback.
• Eficiency [elementary-level]: while the data did not suggest any significant diference 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). • Eficiency [intermediate-level]: the three approaches are comparable overall, but Map+Table 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). • Eficiency [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).
• Efectiveness [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). • Efectiveness [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). • Efectiveness [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).
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 eficiently. • 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 eficiently move through the content and explore diferent attributes without delays.
• 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 dificult to identify attribute counts and discern the attribute categories easily.
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. eficiency, efectiveness) 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 diferent 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. efectiveness, 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 [
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 diferent 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 efective location emphasis techniques to highlight the current location unambiguously but is worth further investigation in 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 efective
navigation through the wealth of attributes and their values (R2), and enable the traversal of
paths between diferent 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 [
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.
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).
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/). [11] DBpedia, About:Baltimore, 2024. URL: https://dbpedia.org/page/Baltimore, accessed: July 10, 2024. [12] Alfred Wegener Institute, GIS Viewer - Arctic Coastal Dynamics, 2024. URL: https://maps.
awi.de/awimaps/projects/public/?cu=arctic_coastal_dynamics, accessed: July 10, 2024. [13] Leibniz-Institut für ökologische Raumentwicklung (IÖR), Monitor der Siedlungs und Freiraumentwicklung, 2024. URL: https://monitor.ioer.de/?ind=N01EG&time= 2023&baselayer=topplus&opacity=0.8&raeumliche_gliederung=gebiete&zoom=8&lat= 50.93073802371819&lng=9.755859375000002&glaettung=0&raumgl=bld&klassenanzahl= 7&klassifizierung=haeufigkeit&darstellung=auto&ags_array=&, accessed: July 10, 2024. [14] TERENO, TERENO - Data Discovery Portal, 2024. URL: https://ddp.tereno.net/ddp/ dispatch?searchparams=freetext-Free%20Text%20Search, accessed: July 10, 2024. [15] A. Degbelo, J. Wissing, T. Kauppinen, A comparison of geovisualizations and data tables for transparency enablement in the open government data landscape, International Journal of Electronic Government Research 14 (2018) 39–64. doi:10.4018/IJEGR.2018100104. [16] J. Lehmann, R. Isele, M. Jakob, A. Jentzsch, D. Kontokostas, P. N. Mendes, S. Hellmann, M. Morsey, P. Van Kleef, S. Auer, C. Bizer, DBpedia – A large-scale, multilingual knowledge base extracted from Wikipedia, Semantic Web 6 (2015) 167–195. doi:10.3233/SW- 140134. [17] F. Desimoni, N. Bikakis, L. Po, G. Papastefanatos, A comparative study of state-of-theart linked data visualization tools, in: V. Ivanova, P. Lambrix, C. Pesquita, V. Wiens (Eds.), Proceedings of the Fifth International Workshop on Visualization and Interaction for Ontologies and Linked Data co-located with the 19th International Semantic Web Conference (ISWC 2020), volume 2778 of CEUR workshop proceedings, CEUR-WS.org, Virtual Event, 2020, pp. 1–13. [18] E. Bernasconi, M. Ceriani, D. D. Di Pierro, S. Ferilli, D. Redavid, Linked Data Interfaces: A
Survey, Information 14 (2023) 483. doi:10.3390/info14090483. [19] A.-S. Dadzie, E. Pietriga, Visualisation of Linked Data - Reprise, Semantic Web 8 (2017) 1–21. doi:10.3233/SW- 160249. [20] J. Klímek, P. Škoda, M. Nečaský, Survey of tools for Linked Data consumption, Semantic
Web 10 (2019) 665–720. doi:10.3233/SW- 180316. [21] M. Aguiar, S. Nunes, B. Giesteirad, A survey on user interaction with linked data, in: P. Lambrix, C. Pesquita, V. Wiens (Eds.), Proceedings of the Sixth International Workshop on the Visualization and Interaction for Ontologies and Linked Data co-located with the 20th International Semantic Web Conference (ISWC 2021), volume 3023 of CEUR workshop proceedings, CEUR-WS.org, Virtual Event, 2021, pp. 13–28. [22] G. Mai, K. Janowicz, Y. Hu, G. McKenzie, A linked data driven visual interface for the multi-perspective exploration of data across repositories, in: V. Ivanova, P. Lambrix, S. Lohmann, C. Pesquita (Eds.), Proceedings of the Second International Workshop on Visualization and Interaction for Ontologies and Linked Data co-located with the 15th International Semantic Web Conference (VOILA@ISWC 2016), volume 1704 of CEUR workshop proceedings, CEUR-WS.org, Kobe, Japan, 2016, pp. 93–101. [23] B. Regalia, K. Janowicz, G. Mai, phuzzy.link: A SPARQL-powered client-sided extensible semantic web browser, in: V. Ivanova, P. Lambrix, S. Lohmann, C. Pesquita (Eds.), Proceedings of the Third International Workshop on Visualization and Interaction for Ontologies and Linked Data co-located with the 16th International Semantic Web Conference (ISWC
This section presents detailed results about the pairwise comparison of the diferent approaches
across all dependent variables. The R package bootES [