Usability of GIS software

The International Organization for Standardization [13] defines system usability as the combination of three criteria: effectiveness, efficiency, and satisfaction. Komarkova, Jakoubek, and Hub [14] adopted these three criteria as their framework to evaluate GIS usability. Despite the increasing prevalence of geospatial data, Komarkova et al. [14] note that traditional GIS software tools suffer from poor usability. They are often desktop applications, requiring users to install and configure them before use. Often not designed with usability in mind [15], GIS user interfaces are usually complicated and require extensive training before they can be used. The reliance of certain GIS tools on proprietary file formats is also a concern for users who need integration with other software systems [14]. The emergence of web-based GIS tools presents an opportunity to address some of these concerns because these tools do not need to be installed and configured. However, web-based applications must overcome certain additional challenges. While it may be acceptable for desktop GIS packages to require extensive training for users to take advantage of their rich feature set, web applications must cater to users who are not tech-savvy and are often in a hurry.

In a survey of GIS usability studies, Unrau and Kray [15] pinpointed additional areas where GIS tools possess poor usability. Over half the tools surveyed were reported to give poor error messages that did not provide any indication of how to fix the problem encountered. Moreover, Unrau and Kray [15] found that many users failed to complete study tasks because the interface of the studied GIS tool provided no visual indication of how to proceed. These findings echo the results of a survey of users of desktop GIS software conducted by Davies and Medyckyj-Scott [16]. Participants in this survey also complained of “total nonsense” error messages and being “unsure what to do when they sit in front of a GIS” [16]. This latter point may result from the failure of many GIS software tools to conform to traditional norms of software interface layout, preventing users from applying general software skills to GIS systems. Davies and Medyckyj-Scott [16] also found that most GIS systems do not do enough to support novice and infrequent users. They suggested that GIS software should provide better documentation and online help because the presence of these features was associated with a higher usability rating by survey participants.

Cartogram generation tools

Existing cartogram generation tools also suffer from some of the usability challenges discussed above. Markowska and Korycka-Skorupa [9] conducted an extensive survey of cartogram generation tools. Out of the five studied tools, three supported generating contiguous cartograms. One of these tools, MAPresso, relies on obsolete Java applet technology and is no longer usable [17]. Another tool, ScapeToad was a standalone desktop application but is no longer available online [18]. The third tool, a cartogram utility for ArcGIS [19], remains working but is inaccessible to non-technical users because it is a plugin for ArcGIS, a commercial GIS software package requiring a software license and specialized training to use effectively [9]. While all the above-mentioned tools aim for a high degree of automation, the recently developed Windows application Cartogram Studio [20] focuses on manual construction of cartograms. On the one hand, this feature makes Cartogram Studio an excellent didactic tool. On the other hand, manual customization of cartograms is time-consuming [21], limiting the practical usability of Cartogram Studio outside an educational setting.

Markowska and Korycka-Skorupa [9] quantify the performance of the generation tools they studied using a numeric scale that awards points for possessing certain functionality (e.g., whether the tool supports saving generated cartograms in a vector image format). However, they do not explicitly consider usability in their evaluation. This omission, combined with the development of new tools such as go-cart.io, provides an opportunity for an updated, usability-focused assessment of currently available cartogram generation tools.

System Usability Scale

Brooke’s [22] 1996 System Usability Scale (SUS) questionnaire provides a standardized method of quantifying the usability of software and other systems. The questionnaire comprises ten statements alternating between positive and negative sentiment. Table 1 lists all the SUS items. The alternating sentiment of the questions ensures that participants carefully consider each questionnaire item [16]. Respondents indicate their agreement or disagreement with each statement on a 5-point Likert scale. Since its development, the SUS has become widely adopted and has been evaluated as one of the best performing surveys at measuring system usability [23].

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Table 1. The 10 items of the System Usabilty Scale [22].

For each item, participants were asked to indicate their agreement with the item on a 5-point Likert scale.

https://doi.org/10.1371/journal.pone.0298192.t001

Unrau and Kray [15] also pointed out that the SUS has been adopted by many studies that evaluate GIS software specifically. Therefore, we adopted the SUS as the main measure of cartogram generation tool usability in our experiment.

Cartogram software

When evaluating the usability of a software system, Lewis [23] recommends performing norms-based evaluations, where software system usability is evaluated against standards for usability generally applicable to software systems, and competitive evaluations. Norms-based evaluations are insufficient by themselves because they may be unduly harsh or lenient based on the category of software being considered. To conduct a competitive evaluation of go-cart.io’s usability, we performed a survey of similar cartogram generation tools. At the time of the experiment, the fBlog Online Cartogram Tool was the only such tool that is web-based and does not require users to download any software program or browser plugin.

Fig 3 shows a screenshot of the fBlog user interface, which has a more linear layout than go-cart.io. To generate a cartogram, users must:

1. Select whether they would like to generate a cartogram of the United States or Europe.
2. Input numeric data and colors for each region of the selected geography in the text boxes on the page. Alternatively, users may choose from a few preset numeric data sets, including population and gross domestic product, by clicking the appropriate button at the bottom-right of the numeric input section.
3. Fill in the captcha and click “Create Map”. The cartogram image may be previewed in the browser and downloaded in Portable Network Graphics (PNG) format; however, no interactive analysis tools are provided.

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Fig 3. Screenshot of the fBlog Online Cartogram Tool user interface.

Important interface elements are highlighted according to their functionality. Numbers in red circles indicate the sequence of steps required to create a cartogram (see main text for details). Reprinted from [10] under a CC BY license, with permission from Korneel van den Broek, original copyright 2012.

https://doi.org/10.1371/journal.pone.0298192.g003

Tasks

The experiment comprised cartogram generation and analysis tasks. During the generation tasks, participants were instructed to generate a cartogram of a provided data set using one of the two generation tools (i.e., go-cart.io or fBlog). Data sets provided for both tools were for the United States because this is one of two maps available on both generation tools. A different data set was used for each generation tool to avoid a learning effect upon subsequent generation and analysis tasks. Both data sets involve agricultural data by state. To ensure similar difficulty of the analysis tasks, both datasets had a similar order of magnitude (between 10⁸ and 10¹⁰ USD) and were strongly correlated (r = 0.947). During the go-cart.io generation task, participants generated a cartogram of 2018 agricultural output by state [1], while for the fBlog generation task participants generated a cartogram of 2017 crop sales by state [28]. The usage of similar data sets helped equalize the difficulty of the subsequent analysis tasks. Additionally, we anticipated that most participants would be unfamiliar with these data sets, thereby reducing the likelihood of them relying on their own knowledge of the data sets to complete the analysis tasks.

Participants could not ask the experiment supervisor for help during generation tasks, but they were allowed to reference a written tutorial provided by each generation tool on its website. While the fBlog tutorial focuses on how to choose good data and colors for a cartogram, the go-cart.io tutorial provides step-by-step instructions for generating a cartogram once you already have a data set. Screenshots of both tutorials are available as online supplemental material on the publisher’s website. If participants could not complete a generation task, they were allowed to skip it and proceed to the tasks for the next generation tool.

Upon completing a generation task, participants were presented with the cartogram they generated and a correct reference cartogram. Participants were asked to compare the two by eye. If they found the two were not identical, participants could reattempt the generation task to correct their mistakes. Otherwise, they proceeded to a set of analysis tasks.

Analysis tasks were designed to simulate how cartograms are used as a visual aid in reports and presentations. For each analysis task, a static figure was generated from the participant’s cartogram that resembled a figure in a report. Fig 4 depicts an example cartogram analysis task and generated figure. Using the figure or any interactive analysis features provided by the generation tool, participants answered one multiple choice question about the data set. The questions in the analysis tasks were loosely inspired by the cartogram task taxonomy adopted by Nusrat, Alam, and Kobourov [2]. All analysis tasks given to participants during the experiment are available in the supplemental online material to this article.

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Fig 4. Example cartogram analysis task for go-cart.io.

The example task contains a figure created from a correctly completed generation task. The figure, question, and answer options appeared on the same screen.

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Participants

We recruited 35 participants to complete the experiment from August 11th to December 18th, 2020. All participants were university students and staff. A departmental ethics review committee at Yale-NUS College approved this research (case number 00016352), and the Institutional Review Board at the National University of Singapore issued a statement of concurrence for the study. Participants were recruited to the study through the college’s online research participation system. All participants provided informed consent in written form. The majority of participants reported being at least somewhat familiar with computer graphics (20 participants), spreadsheet software (28 participants), and cartograms (20 participants). Half of the participants (17) also indicated that they would at least generally look up unfamiliar locations on a map. The participants’ age ranged from 18 to 28 (mean 20.8; standard deviation 1.86), and their gender was evenly distributed (17 female; 16 male; 2 other). All participants received the equivalent of US$7.40 in local currency or one hour of research credit for a college course as compensation for participation.

We administered the Ishihara color blindness test to all participants because completing the analysis tasks required participants to distinguish between map regions by color. Three participants made at least one error during the test. However, the responses of these participants did not differ significantly from the others; thus, we included them in the data analysis.

We acknowledge that the exclusive participation of university students and staff may primarily represent a demographic of digital natives with higher education, potentially introducing bias to the results. However, the tasks in our study do not require specialized computer skills and are accessible to adolescents and adults with normal vision, including those who wear eyeglasses or contact lenses. Therefore, we believe that our results are broadly applicable to the typical user group of cartogram generation software.

Procedure

Participants completed the experiment remotely over Zoom using a single screen. Each participant’s screen was recorded during the session so that their interactions with the generation tools could be analyzed later in more detail. We used Qualtrics XM to display the experiment tasks and collect participants’ answers. The experiment comprised four parts:

1. Introduction: Participants first watched a short introductory video giving a brief overview of cartograms and a description of experiment tasks. They could ask the experiment supervisor for clarification at any time. The video is included in the supplemental online material to this article.
2. Preliminary questions: Participants answered demographic questions about age, gender, and level of education. Then, they indicated their familiarity with computer graphics, maps, cartograms, and spreadsheet software on a 5-point Likert scale. Finally, participants completed the Ishihara color blindness test.
3. Cartogram tasks: Participants completed one cartogram generation task and six analysis tasks for each generation tool. After each set of tasks, participants indicated the extent to which they relied on the tutorial provided by the generation tool. If they did indicate that they relied on this tutorial, they were also required to indicate the helpfulness of the tutorial on a 5-point Likert scale. Finally, participants indicated how much they relied on the following sources when completing the analysis tasks:
   • The numbers in the data set table.
   • The cartogram they generated.
   • The interactive analysis tools provided by the generation tool.
   • Their own knowledge of the data set.
4. Usability survey: Participants completed an SUS questionnaire for go-cart.io and fBlog. They then left written, free-form feedback about their experience with both web tools.

Design

We adopted a within-subject experiment design. Participants completed one trial for each generation tool. The order in which participants encountered the tools was treated as a blocking factor: 18 participants completed the go-cart.io trial before the fBlog trial, and 17 participants completed the fBlog trial before the go-cart.io trial. Each trial consisted of one generation task followed by six analysis tasks.

Hypotheses

Prior to the experiment, we anticipated that features of go-cart.io and fBlog would impact how participants rated their relative usability. Our hypotheses were as follows:

Data analysis

For analyzing generation task times, we excluded all attempts that resulted in incorrect cartograms (i.e., at least one area or one color differed from the desired output). Because the generation task times, conditioned on generating correct cartograms, were not normally distributed, we used the non-parametric paired Wilcoxon ranked-sign test to compare generation task times between generation tools. For this and other tests, we considered p-values as significant if they are less than 0.05, and we also computed confidence intervals. We adopted the method developed by Bauer [30] to estimate the 95% confidence interval of the pseudomedian difference in generation task times between the two tools.

For rating participants’ accuracy on the generation tasks, we analyzed mistakes made by entering the wrong color and wrong numeric data for each region separately as well as the total number of errors of all types made by each participant. Similarly, for the analysis tasks, we analyzed the error rates for each task as well as the number of analysis tasks performed correctly for each generation tool. We considered an analysis task to be performed correctly only if all parts of the task were completed correctly. A permutation test with 10000 random simulations was used to determine the effect of generation task accuracy on the number of correct analysis tasks for each generation tool.

For the SUS, we computed the score for each participant using the standard methodology presented by Brooke [22]. We used Cronbach’s alpha with an acceptability range of 0.7 < α < 0.95, as recommended by Lewis [23] to evaluate the internal consistency of the SUS questions as applied to this experiment. Because SUS scores were approximately normally distributed, we used a paired t-test to evaluate the effect of generation tool on mean SUS score. We also used Welch’s unequal variances t-test to evaluate the effect of generation task completion on mean SUS score for each tool.

Finally, we analyzed whether the hypotheses we presented in the previous section are supported. For H1, we used Spearman’s ρ, which evaluates how well a monotonic function describes the relationship between two numeric variables, to detect if there is a positive correlation between participants’ indicated familiarity with spreadsheet software and SUS score for go-cart.io. We adopted the bootstrapping method presented by Bishara and Hittner [31] to compute 95% confidence intervals for ρ. For H2, we also used Spearman’s ρ to see if there was a positive correlation between participants’ indicated reliance on go-cart.io’s interface to complete the go-cart.io analysis tasks and SUS score for go-cart.io. For H3, we considered participants’ reliance on the tutorial provided by the generation tool during the tool’s generation task as an indication that the tool’s interface is unclear. We used a permutation test with 10000 simulations to determine if reliance on the tutorial was significantly higher for go-cart.io than for fBlog.

Generation tasks

Duration.

Median generation task duration for fBlog, conditioned on generating the correct cartogram, was 17.2 minutes versus 10.2 minutes for go-cart.io (95% confidence interval for pseudomedian difference in minutes: [0.442, 8.77]). Fig 5 shows the distribution of generation task duration for both tools. The distribution of duration for fBlog was approximately uniform, with a minimum time of 7.62 minutes and maximum time of 28.12 minutes. By contrast, the distribution of duration for go-cart.io was right-skewed, with a minimum time of 3.70 minutes and maximum time of 34.45 minutes. The difference in pseudomedian duration between the two tools was statistically significant (p = 0.038).

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Fig 5. Generation task duration by generation tool.

Distribution of duration in minutes taken by participants to complete both generation tasks.

https://doi.org/10.1371/journal.pone.0298192.g005

Accuracy.

Most participants who finished the generation tasks completed these tasks accurately. 69.2% of the participants who completed the go-cart.io generation task completed it with perfect accuracy, while this number was 50% for the fBlog generation task. Table 3 provides a breakdown of the accuracy and error rates for both fBlog and go-cart.io generation tasks. Overall, participants’ accuracy in entering state areas was high across both tasks. 92.3% of the participants completing the go-cart.io generation task entered the areas of all 48 contiguous U.S. states and Washington D.C. correctly, while 81.2% of the participants completing the fBlog generation task entered all depicted region areas correctly. For both tasks, accuracy of region colors was lower. 69.2% of the participants who completed the go-cart.io generation task entered all region colors correctly, while only 56.3% did so for the fBlog generation task.

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Table 3. Generation task accuracy by generation tool.

Accuracy and error rates for region areas and colors in the (a) fBlog and (b) go-cart.io generation tasks. “With Error” refers to the proportion of participants making at least one error in entering the area or color of a map region.

https://doi.org/10.1371/journal.pone.0298192.t003

Among participants who made errors during the fBlog generation task, the number of errors was usually small. Fig 6 shows the distribution of the number of area and color errors for both generation tasks. For the fBlog generation task, 5 out of the 6 participants who made an area error made only one such error. Similarly, 10 out of the 14 participants who made a color error on the fBlog task made at most two such errors.

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Fig 6. Generation task errors by generation tool.

Distribution of the number of area and color errors made by participants during the (a) fBlog generation task and (b) go-cart.io generation task.

https://doi.org/10.1371/journal.pone.0298192.g006

However, the opposite is true for the go-cart.io generation task. Both participants who made an area error made at least three such errors, and 7 out of the 8 participants who made a color error made at least three such errors.

Reliance on and helpfulness of tutorial.

Participants indicated lower reliance on the tutorial during the fBlog generation task than during the go-cart.io generation task. Fig 7 provides an overview of participants’ indicated reliance on and helpfulness of the tutorials during both generation tasks. Considering participants’ responses on an interval scale from 0 (“Not at all”) to 4 (“Very frequently”), mean reliance on the tutorial was found to be 1.03 points higher for go-cart.io than for fBlog (95% confidence interval [0.491, 1.58]). Mean reliance on the tutorial for fBlog was 2.19, while it was 1.16 for fBlog. This difference is significant (p = 0.002).

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Fig 7. Self-reported reliance and helpfulness of generation tool tutorials.

Panels (a) and (b) show how often participants indicated that they relied on the written tutorials provided by fBlog and go-cart.io, respectively, during each generation task. For participants who indicated that they relied on the tutorial for a generation tool at least “Rarely”, panels (c) and (d) show how helpful participants rated the respective tutorials for fBlog and go-cart.io.

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Among participants who relied on a tutorial, the mean helpfulness of the go-cart.io tutorial was 1.48 points higher than the mean helpfulness of the fBlog tutorial (95% confidence interval [0.962, 1.98]), on an interval scale from 0 (“Not at all”) to 4 (“Very helpful”). Mean helpfulness was 1.35 for the fBlog tutorial and 2.83 for the go-cart.io tutorial. This difference is also significant (p < 0.001).

Analysis tasks

Error rates.

Participants found most analysis tasks for both fBlog and go-cart.io to have moderate difficulty, with some tasks being unexpectedly very difficult. Fig 8 provides an overview of participants’ performance on the analysis tasks. The first analysis task for both generation tools had the highest error rates (0.452 for fBlog and 0.385 for go-cart.io). This may have been the partial result of the first analysis task having two parts for both generation tools (participants had to name the largest and second-largest region), while all other analysis tasks had only one part.

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Fig 8. Participants’ performance on analysis tasks.

Panels (a) and (b) show error rates by analysis task question for fBlog and go-cart.io, respectively. Panels (c) and (d) show the distribution of the number of correct tasks for fBlog and go-cart.io, respectively.

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Participants’ indicated methodology.

Most participants relied on the figure created from their cartogram during the generation tasks for both generation tools. Fig 9 provides an overview of the sources of information participants indicated they relied on while completing the analysis tasks. 80.6% of the participants who completed the fBlog analysis tasks reported relying on the generated cartogram figure frequently or very frequently, while 84.6% who completed the go-cart.io analysis tasks indicated the same.

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Fig 9. Participants’ indicated methodology for completing analysis tasks.

Participants’ indicated reliance on the data table [panels (a) and (b)], generated figure with cartogram [panels(c) and (d)], software interface [panels (e) and (f)], and their own knowledge [panels (g) and (h)] while completing the analysis tasks for fBlog and go-cart.io.

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A minority relied on the numbers in the data table or their own knowledge to complete the analysis tasks. Only 35.5% of those completing the fBlog analysis tasks reported relying on numbers in the data table more than rarely, and 22.6% relied on their own knowledge more than rarely. Similarly, 26.9% of the participants completing the go-cart.io analysis tasks indicated that they relied on the numbers in the data table more than rarely, and 23.1% relied on their own knowledge more than rarely.

Reported reliance on the generation tool interface, including interactive analysis tools, was also low. Only 24.4% of those completing the fBlog analysis tasks reported relying on the fBlog interface, while 19.9% indicated the same for go-cart.io.

System Usability Scale score

The SUS is a highly reliable measure of perceived usability for both fBlog (α = 0.860) and go-cart.io (α = 0.923).

The mean SUS score was 18.2 points higher for go-cart.io than for fBlog (95% confidence interval [6.78, 29.6]). While the mean SUS score for fBlog was 47.1 (standard deviation 19.4), the mean SUS score for go-cart.io was 65.3 (standard deviation 23.0). The difference in mean SUS score for fBlog and go-cart.io was significant (p = 0.003).

Completion of the generation task for fBlog and go-cart.io was associated with a higher mean SUS score. The mean fBlog SUS score for participants failing to complete the fBlog generation task was 30; however, for those who completed this task, the mean SUS score was 48.0 (95% confidence interval of difference in means: [7.93, 28.1]). Similarly, while the mean go-cart.io SUS score was 38.6 for participants who did not complete the go-cart.io generation task, the mean SUS score was 72.5 for those who did complete this task (95% confidence interval of difference in means [16.7, 51.1]). For both fBlog (p = 0.003) and go-cart.io (p = 0.001), completion of generation task had a significant effect on mean SUS score.

Participants’ indicated familiarity with spreadsheet software was not significantly correlated with the SUS score for fBlog (ρ = 0.318, 95% confidence interval [−0.03, 0.596]) or go-cart.io (ρ = 0.014, 95% confidence interval [−0.331, 0.355]. Reliance on the generation tool interface, including any interactive analysis tool, during the analysis tasks was not significantly correlated with SUS score for fBlog (ρ = 0.190, 95% confidence interval [−0.176, 0.510]) or go-cart.io (ρ = 0.236, 95% confidence interval [−0.166, 0.571]) either.

Written participant feedback

Hypotheses

Recommendations

Data entry.

Entering the numeric data and color for each map region during the go-cart.io generation task proved to be one of the most difficult tasks for participants during the experiment. A variety of factors are responsible for making this task more difficult than anticipated.

First, while go-cart.io only accepts CSV-format spreadsheets for upload, several participants were unaware of the difference between spreadsheet formats and erroneously assumed they could upload Excel-format spreadsheets.

• Recommendation 1: Due to the format’s popularity, web-based cartogram generation tools should accept Excel-format spreadsheet files in addition to CSV-format spreadsheet files. Support for additional file formats, such as Open Document Format Spreadsheet (ODS) and JavaScript Object Notation (JSON), is also desirable.

Secondly, go-cart.io requires the numeric and color data for each region in the uploaded spreadsheet to be organized in a very particular way. Fig 10 shows a spreadsheet template for the “Conterminous United States” map that participants had to edit, save, and reupload to generate their cartogram using the spreadsheet upload feature. In order for go-cart.io to recognize their data, participants were required to delete the third and fourth columns and replace them with the numeric and color data, respectively, given during the generation task. If participants replaced the “Population” column instead, or inserted another column before or after the “Colour” column, their data were either silently ignored, or a nondescript error message “There was a problem reading your CSV file” was displayed. No participant was able to successfully complete the go-cart.io task using the spreadsheet upload feature without referencing the tutorial, and most made several attempts while referencing the tutorial before they were successful.

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Fig 10. Excerpt of go-cart.io CSV spreadsheet template for the “Conterminous United States” map.

This template must be edited and uploaded by users to generate a cartogram of their own data set. The “Population” column is only for reference, while users must overwrite the third and fourth columns with their numeric and color data, respectively, for each region.

https://doi.org/10.1371/journal.pone.0298192.g010

• Recommendation 2: Web-based cartogram generation tools should attempt to automatically determine which columns in the uploaded spreadsheet contain the numeric information for target areas (e.g., population) and other visual variables (e.g., color) for each region.
• Recommendation 3: If the cartogram tool is unable to parse the uploaded spreadsheet, it should provide a descriptive error message with hints about possible fixes (e.g., “It looks like you have uploaded a spreadsheet for Austria, even though you have selected a map of the United States.”).

Finally, although the spreadsheet upload feature is the preferred data-entry method for go-cart.io, the pop-up editing interface [shown in panel (b) of Fig 2] remains an important alternative data-entry method. Several participants used this interface after they were unable to successfully use the spreadsheet upload feature. However, the pop-up editing interface suffers from several usability challenges. While it is styled to look like a spreadsheet, the interface does not support basic spreadsheet data-entry methods. Entire columns cannot be copied and pasted, only individual cells. Additionally, the implementation of the color column using the HTML color input element means that color codes cannot be pasted at all on some browsers.

• Recommendation 4: Web-based cartogram generation tools should include a pop-up editing interface as an alternative to uploading a spreadsheet. The interface should support copying and pasting entire columns to and from the clipboard.

User interface layout.

While participants complimented the aesthetics of go-cart.io’s user interface, many complained that the generation tool interface was unintuitive. Indeed, participants’ reliance on the tutorial during the generation tasks was much higher for go-cart.io than for fBlog. Having good written documentation has been shown to improve the perceived usability of a software system [16], and most participants rated the go-cart.io tutorial as helpful or very helpful. However, as a web-based tool, go-cart.io cannot expect users to read an extensive written tutorial before using the tool. If a user gets an initial impression that a web-based tool is difficult to use or unintuitive, they are likely to abandon it quickly.

• Recommendation 6: Web-based cartogram generation tools should implement a tutorial overlay that guides users through the cartogram generation process without requiring users to reference a separate written tutorial. Fig 11 depicts a proposed design for this overlay on go-cart.io.

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Fig 11. Proposed appearance of tutorial overlay.

Screenshot of the go-cart.io user interface with a proposed tutorial overlay (circled in red) to improve usability.

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