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/). References [1] W. Kuhn, E. Hamzei, M. Tomko, S. Winter, H. Li, The semantics of place-related questions, Journal of Spatial Information Science (2021) 157–168. doi:10.5311/JOSIS.2021.23.161 . [2] M. F. Goodchild, H. Guo, A. Annoni, L. Bian, K. de Bie, F. Campbell, M. Craglia, M. Ehlers, J. van Genderen, D. Jackson, A. J. Lewis, M. Pesaresi, G. Remetey-Fulopp, R. Simpson, A. Skidmore, C. Wang, P. Woodgate, Next-generation digital earth, Proceedings of the National Academy of Sciences 109 (2012) 11088–11094. doi:10.1073/pnas.1202383109 . [3] S. Scheider, A. 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Gerlanc, BootES: An R package for bootstrap confidence intervals on effect sizes, Behavior Research Methods 45 (2013) 905–927. doi:10.3758/s13428- 013- 0330- 5 . 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