Geovisualizations are powerful tools for communicating spatial information, but are inaccessible to screen-reader users. To address this limitation, we present GeoVisA11y, an LLM-based question-answering system that makes geovisualizations accessible through natural language interaction. The system supports map reading, analysis, interpretation and navigation by handling analytical, geospatial, visual, and contextual queries. Through user studies with six screen-reader users and six sighted participants, we demonstrate that GeoVisA11y effectively bridges accessibility gaps while revealing distinct interaction patterns between user groups. We contribute: (1) an open-source, accessible geovisualization system, (2) empirical findings on query and navigation differences, and (3) a dataset of geospatial queries to inform future research on accessible data visualization.

Figure 1: We introduce GeoQA 3 , a novel accessible AI-based question-answering system for geovisualizations designed for screen-reader users.(A) Through a custom query pipeline, we combine geo-statistical analysis with an LLM to balance accuracy and performance.(B) Users can navigate the map through natural language commands or keyboard controls and (C) zoom in to view county-level data.The AI Chat system is context-aware, taking into account user interactions.See video for demonstration.

Enhancing visualization creators’ knowledge and understanding of the accessibility of data visualizations remains a critical step toward reducing the digital divide screen-reader users experience. Recently, Sharif et al. shed light on the challenges visualization creators face with making data visualizations accessible to screen-reader users, identifying four technological interventions and one educational intervention (i.e., workshops) to minimize these challenges. Although they implemented the technological intervention and provided guidelines to conduct an effective workshop, they did not implement a workshop for creators. I extend their work by conducting a workshop for visualization creators based on their findings. My results show that the workshop improved the creators’ accessibility knowledge by 39%, prioritization of implementing accessibility by 15%, perceived importance of accessibility by 4%, challenges with making visualizations accessible by 16%, and desired frequency of conducting studies with screen-reader users by 157%.

We sought to understand and reduce the challenges creators face with making their data visualizations accessible. Specifically, we administered a formative survey of 57 creators to comprehend their challenges, perceived importance, knowledge, and prioritization of data visualization accessibility. Participants identified five interventions to minimize their challenges: Workshops, Emulators, Evaluators, Feedback Collectors, and Multi-Modal Automated Tools. Additionally, we report specifications and recommendations from 12 visualization creators for effective versions of each intervention, gathered via semi-structured interviews. Utilizing our findings, such as a “mini-survey” format that is effective for collecting accessibility-related feedback from screen-reader users, we implemented and integrated these interventions into VoxLens (Sharif et al., 2022). We assessed our enhancements through a task-based user study with 10 visualization creators, finding 44%, 17%, and 12% improvements in their understanding of screen-reader users’ challenges with data visualizations, knowledge of visualization accessibility, and perceived usefulness of the enhanced VoxLens, respectively.

Touchpads are used widely to interact with computers, yet they provide minimal utility for screen-reader users. We explore the utility of touchpads as input devices for screen-reader users through the development and preliminary evaluation of Touchpad Mapper. This system maps digital content (i.e., images and videos) to the physical coordinates of a touchpad. We examined two usage scenarios: (1) identification of objects and their relative positioning in an image and (2) controlling a video seek bar and slider with rewinding and fast-forwarding features. We conducted task-based semi-structured interviews with two screen-reader users to assess our system’s performance. The participants reported positive experiences, highlighting that they extracted information faster using our system than the conventional keyboard-only interaction.

Geovisualizations are powerful tools for exploratory spatial analysis, enabling sighted users to discern patterns, trends, and relationships within geographic data. However, these visual tools have remained largely inaccessible to screen-reader users. We introduce AltGeoViz, a new interactive geovisualization approach that dynamically generates alt-text descriptions based on the user’s current map view, providing voiceover summaries of spatial patterns and descriptive statistics. In a remote user study with five screen-reader users, we found that participants were able to interact with spatial data in previously infeasible ways, demonstrated a clear understanding of data summaries and their location context, and could synthesize spatial understandings of their explorations. Moreover, we identified key areas for improvement, such as the addition of spatial navigation controls and comparative analysis features.

Incorporating uncertainty in data visualizations is critical for users to interpret and reliably draw informed conclusions from the underlying data. However, visualization creators conventionally convey the information regarding uncertainty in data visualizations using visual techniques (e.g., error bars), which disenfranchises screen-reader users, who may be blind or have low vision. In this preliminary exploration, we investigated ways to convey uncertainty in data visualizations to screen-reader users. Specifically, we conducted semi-structured interviews, finding that these users prefer to obtain statistical information on uncertainty expressed in plain language, conveyed holistically with avenues to explore the data further in a drilled-down manner. To support screen-reader users in extracting information about uncertainty in online data visualizations, we utilized our findings to enhance VoxLens—an open-source JavaScript plug-in that makes online data visualizations accessible to screen-reader users.

Inaccessible urban infrastructure creates and reinforces systemic exclusion of people with disabilities and impacts public health, physical activity, and quality of life for all. To improve the design of our cities and to enable more equitable policies and location-centric technology designs, we need new data collection techniques, data standards, and accessibility-infused analytic tools and interactive maps focused on the quality, safety, and accessibility of pathways, transit ecosystems, and buildings. In this workshop, we bring together leading experts in human mobility, urban design, disability, and accessible computing to discuss pressing urban access challenges across the world and brainstorm solutions. We invite contributions from practitioners, transit officials, disability advocates, and researchers.

The usage of identity- (e.g., “disabled people”) versus person-first language (e.g., “people with disabilities”) to refer to disabled people has been an active and ongoing discussion. However, it remains unclear which semantic language should be used, especially for different disability categories within the overall demographics of disabled people. To gather and examine the language preferences of disabled people, we surveyed 519 disabled people from 23 countries. Our results show that 49% of disabled people preferred identity-first language whereas 33% preferred person-first language and 18% had no preference. Additionally, we explore the intra-sectionality and intersectionality of disability categories, gender identifications, age groups, and countries on language preferences, finding that language preferences vary within and across each of these factors. Our qualitative assessment of the survey responses shows that disabled people may have multiple or no preferences. To make our survey data publicly available, we created an interactive and accessible live web platform, enabling users to perform intersectional exploration of language preferences. In a secondary investigation, using part-of-speech (POS) tagging, we analyzed the abstracts of 11,536 publications at ACM ASSETS (N=1,564) and ACM CHI (N=9,972), assessing their adoption of identity- and person-first language. We present the results from our analysis and offer recommendations for authors and researchers in choosing the appropriate language to refer to disabled people.

ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) is considered one of the premium forums for research on accessibility. Recently, Mack et al. shed light on the demographics, goals, research methodologies, and evolution of accessibility research over time. We extend their work by exploring the frequencies and trends of disability categories and computer science research domains in publications at ASSETS (N=1,678). Our results show that disability categories and research domains varied significantly across the publication years. We found that in the past 10 years, publications targeting Mental-Health-Related disabilities and the research domain of AR/VR show an increasing trend. In opposition, Gaming, Input Methods/Interaction Techniques, and User Interfaces domains portray a decreasing trend. Additionally, our results show that the majority of the publications utilize the AI/ML/CV/NLP domain (19%) and focus on people with visual disabilities (42%). We share our preliminary exploration results and identify avenues for future work.

Current web-based maps do not provide visibility into real-time elevator outages at urban rail transit stations, disenfranchising commuters (e.g., wheelchair users) who rely on functioning elevators at transit stations. In this paper, we demonstrate UnlockedMaps, an open-source and open-data web-based map that visualizes the real-time accessibility of urban rail transit stations in six North American cities, assisting users in making informed decisions regarding their commute. Specifically, UnlockedMaps uses a map to display transit stations, prominently highlighting their real-time accessibility status (accessible with functioning elevators, accessible but experiencing at least one elevator outage, or not-accessible) and surrounding accessible restaurants and restrooms. UnlockedMaps is the first system to collect elevator outage data from 2,336 transit stations over 23 months and make it publicly available via an API. We report on results from our pilot user studies with five stakeholder groups: (1) people with mobility disabilities; (2) pregnant people; (3) cyclists/stroller users/commuters with heavy equipment; (4) members of disability advocacy groups; and (5) civic hackers.

Sonification is a commonly used technique to make online data visualizations accessible to screen-reader users through auditory means. While current sonification solutions provide plausible utility (usefulness) to screen-reader users in exploring data visualizations, they are limited in exploring the quality (usability) of the sonified responses. In this preliminary exploration, we investigated the usability and user-friendliness of data visualization sonification for screen-reader users. Specifically, we evaluated the Pleasantness, Clarity, Confidence, and Overall Score of discrete and continuous sonified responses generated using various oscillator waveforms and synthesizers through user studies with 10 screen-reader users. Additionally, we examined these factors using both simple and complex trends. Our results show that screen-reader users preferred distinct non-continuous responses generated using oscillators with square waveforms. We utilized our findings to extend the functionality of Sonifier—an open-source JavaScript library that enables developers to sonify online data visualizations. Our follow-up interviews with screen-reader users identified the need to personalize the sonified responses per their individualized preferences.

Prior work has studied the interaction experiences of screen-reader users with simple online data visualizations (e.g., bar charts, line graphs, scatter plots), highlighting the disenfranchisement of screen-reader users in accessing information from these visualizations. However, the interactions of screen-reader users with online geospatial data visualizations, commonly used by visualization creators to represent geospatial data (e.g., COVID-19 cases per US state), remain unexplored. In this work, we study the interactions of and information extraction by screen-reader users from online geospatial data visualizations. Specifically, we conducted a user study with 12 screen-reader users to understand the information they seek from online geospatial data visualizations and the questions they ask to extract that information. We utilized our findings to generate a taxonomy of information sought from our participants’ interactions. Additionally, we extended the functionalities of VoxLens—an open-source multi-modal solution that improves data visualization accessibility—to enable screen-reader users to extract information from online geospatial data visualizations.

JavaScript visualization libraries are widely used to create online data visualizations but provide limited access to their information for screen-reader users. Building on prior findings about the experiences of screen-reader users with online data visualizations, we present VoxLens, an open-source JavaScript plug-in that—with a single line of code—improves the accessibility of online data visualizations for screen-reader users using a multi-modal approach. Specifically, VoxLens enables screen-reader users to obtain a holistic summary of presented information, play sonified versions of the data, and interact with visualizations in a “drill-down” manner using voice-activated commands. Through task-based experiments with 21 screen-reader users, we show that VoxLens improves the accuracy of information extraction and interaction time by 122% and 36%, respectively, over existing conventional interaction with online data visualizations. Our interviews with screen-reader users suggest that VoxLens is a “game-changer” in making online data visualizations accessible to screen-reader users, saving them time and effort.

Respectfully and adequately referring to people with various disabilities is difficult due to societal norms and constantly evolving languages. In this work, we address the question of how expert researchers in the field of accessibility are referring to people with disabilities and whether this terminology corresponds to how people with disabilities prefer to be addressed. By conducting a systematic literature review of the past three ASSETS proceeding, we summarize how accessibility researchers are currently referring to people with disabilities in English. A survey of 63 people with disabilities further revealed that while researchers from ASSETS are using terms that are mostly aligned with participants’ expectations, the same terminologies can be perceived both respectful and disrespectful by varying participants. Through this preliminary work, we pave the path for researchers to further explore respectful terminology and encourage researchers to improve the inclusivity and diversity of language use in our community.

Online data visualizations are widely used to communicate information from simple statistics to complex phenomena, supporting people in gaining important insights from data. However, due to the defining visual nature of data visualizations, extracting information from visualizations can be difficult or impossible for screen-reader users. To assess screen-reader users’ challenges with online data visualizations, we conducted two empirical studies: (1) A qualitative study with nine screen-reader users, and (2) a quantitative study with 36 screen-reader and 36 non-screen-reader users. Our results show that due to the inaccessibility of online data visualizations, screen-reader users extract information 61.48% less accurately and spend 210.96% more time interacting with online data visualizations compared to non-screen-reader users. Additionally, our findings show that online data visualizations are commonly indiscoverable to screen readers. In visualizations that are discoverable and comprehensible, screen-reader users suggested tabular and textual representation of data as techniques to improve the accessibility of online visualizations. Taken together, our results provide empirical evidence of the inequalities screen-readers users face in their interaction with online data visualizations.

How do sidewalks change over time? Are there geographic or socioeconomic patterns to this change? These questions are important but difficult to address with current GIS tools and techniques. In this demo paper, we introduce three preliminary crowd+AI (Artificial Intelligence) prototypes to track changes in street intersection accessibility over time—specifically, curb ramps—and report on results from a pilot usability study.

Keyboard-based emoji entry can be challenging for people with visual impairments: users have to sequentially navigate emoji lists using screen readers to find their desired emojis, which is a slow and tedious process. In this work, we explore the design and benefits of emoji entry with speech input, a popular text entry method among people with visual impairments. After conducting interviews to understand blind or low vision (BLV) users’ current emoji input experiences, we developed Voicemoji, which (1) outputs relevant emojis in response to voice commands, and (2) provides context-sensitive emoji suggestions through speech output. We also conducted a multi-stage evaluation study with six BLV participants from the United States and six BLV participants from China, finding that Voicemoji significantly reduced entry time by 91.2% and was preferred by all participants over the Apple iOS keyboard. Based on our findings, we present Voicemoji as a feasible solution for voice-based emoji entry.

Computing students with disabilities face a variety of difficulties in computing education and careers including inaccessible technology, difficulty arranging accommodations, attitudinal barriers, and a lack of mentors. This panel of computing students and recent graduates with disabilities will describe their experiences both in and out of the classroom. The goal is to provide the audience with an opportunity to hear first-hand how their educational needs were met as non-traditional computing students. In addition to the panelists' short presentations, the moderator will facilitate a dialog between the members of the audience and the panelists.

In graduate school, people with disabilities use disability accommodations to learn, network, and do research. However, these accommodations, often scheduled ahead of time, may not work in many situations due to uncertainty and spontaneity of the graduate experience. Through a three-person autoethnography, we present a longitudinal account of our graduate school experiences as people with disabilities, highlighting nuances and tensions of situations when our requested accommodations did not work and the use of alternative coping strategies. We use retrospective journals and field notes to reveal the impact of our self-image, relationships, technologies, and infrastructure on our disabled experience. Using post-hoc reflection on our experiences, we then close with discussing personal and situated ways in which peers, faculty members, universities, and technology designers could improve the graduate school experiences of people with disabilities.

For over six decades, Fitts’s law (1954) has been utilized by researchers to quantify human pointing performance in terms of “throughput,” a combined speed-accuracy measure of aimed movement efficiency. Throughput measurements are commonly used to evaluate pointing techniques and devices, helping to inform software and hardware developments. Although Fitts’s law has been used extensively in HCI and beyond, its test-retest reliability, both in terms of throughput and model fit, from one session to the next, is still unexplored. Additionally, despite the fact that prior work has shown that Fitts’s law provides good model fits, with Pearson correlation coefficients commonly at r=.90 or above, the model fitness of Fitts’s law has not been thoroughly investigated for people who exhibit limited fine motor function in their dominant hand. To fill these gaps, we conducted a study with 21 participants with limited fine motor function and 34 participants without such limitations. Each participant performed a classic reciprocal pointing task comprising vertical ribbons in a 1-D layout in two sessions, which were at least four hours and at most 48 hours apart. Our findings indicate that the throughput values between the two sessions were statistically significantly different, both for people with and without limited fine motor function, suggesting that Fitts’s law provides low test-retest reliability. Importantly, the test-retest reliability of Fitts’s throughput metric was 4.7% lower for people with limited fine motor function. Additionally, we found that the model fitness of Fitts’s law as measured by Pearson correlation coefficient, r, was .89 (SD=0.08) for people without limited fine motor function, and .81 (SD=0.09) for people with limited fine motor function. Taken together, these results indicate that Fitts’s law should be used with caution and, if possible, over multiple sessions, especially when used in assistive technology evaluations.

Graphs are considered as one of the most important tools to represent information, and as such, they are widely used across the Internet in various formats to visualize statistics and data. However, for the visually impaired individuals, who can presently rely only on the ALT attribute of images, receiving and interpreting information displayed in graphs is a highly challenging task. This paper presents a novel solution to this problem, called evoGraphs, which is a jQuery plugin that allows users to create fully dynamic, customizable graphs that are easily read by screen readers. In contrast to traditional images, the proposed approach relies on HTML, CSS and jQuery components in order to create graphs while reducing page load time and making the graphs readable by voiceover software applications and screen readers. This paper presents the design and customization of evo-Graphs. Examples are provided to highlight the advantages of the proposed methodology by comparing page load times of traditional image-based graphs with those produced by evoGraphs.

Cloud services are widely used across the globe to store and analyze Big Data. These days it seems the news is full of stories about security breaches to these services, resulting in the exposure of huge amounts of private data. This paper studies the current security threats to Cloud Services, Big Data, and Hadoop. The paper analyzes a newly proposed Big Data security system based on the EnCoRe system which uses sticky policies, and the existing security architectures of Verizon and Twilio, presenting the preventive measures taken by these firms to minimize security concerns.