Thematic maps are widely used to communicate spatial patterns to non-expert audiences. Although uncertainty is inherent in thematic map data, it is rarely visualized, raising questions about how its inclusion affects trust. Prior work offers mixed perspectives: some argue that uncertainty fosters trust through transparency, while others suggest it may reduce trust by introducing confusion. Yet few empirical studies explicitly measure trust in thematic maps. We conducted a between-subjects experiment (N = 161) to evaluate how visualizing uncertainty at varying levels (low, medium, high) influences trust. We find that uncertainty visualization generally reduces trust, with greater reductions observed as uncertainty levels increase. However, maps dominated by low uncertainty do not significantly differ in trust from those with no uncertainty. Moreover, while uncertainty visualization tends to make readers question the accuracy of the data, it appears to have a weaker influence on perceptions of the mapmaker’s integrity.

Maps are crucial in conveying geospatial data in diverse contexts such as news and scientific reports. This research, utilizing thematic maps, probes deeper into the underexplored intersection of text framing and map types in influencing map interpretation. In this work, we conducted experiments to evaluate how textual detail and semantic content variations affect the quality of insights derived from map examination. We also explored the influence of explanatory annotations across different map types (e.g., choropleth, hexbin, isarithmic), base map details, and changing levels of spatial autocorrelation in the data. From two online experiments with N = 103 participants, we found that annotations, their specific attributes, and map type used to present the data significantly shape the quality of takeaways. Notably, we found that the effectiveness of annotations hinges on their contextual integration. These findings offer valuable guidance to the visualization community for crafting impactful thematic geospatial representations.

Visualization linting is a proven effective tool in assisting users to follow established visualization guidelines. Despite its success, visualization linting for choropleth maps, one of the most popular visualizations on the internet, has yet to be investigated. In this paper, we present GeoLinter, a linting framework for choropleth maps that assists in creating accurate and robust maps. Based on a set of design guidelines and metrics drawing upon a collection of best practices from the cartographic literature, GeoLinter detects potentially suboptimal design decisions and provides further recommendations on design improvement with explanations at each step of the design process. We perform a validation study to evaluate the proposed framework's functionality with respect to identifying and fixing errors and apply its results to improve the robustness of GeoLinter. Finally, we demonstrate the effectiveness of the GeoLinter - validated through empirical studies - by applying it to a series of case studies using real-world datasets.

Deep learning can discover intricate patterns hidden in big data, and has much better scalability than traditional machine learning when the volume of data increases dramatically. Thus, deep learning has gained many successes in various domains and applications such as image classification, text classification, and machine translation. In this paper, we use deep learning to classify geographical features (e.g., mountains, rivers, landmarks, and cities) from text, using geolocated Wikipedia entries as the case study application. We employ one of the most commonly used deep learning architectures, convolutional neural networks (CNNs) and its integration with active learning (creating what we call active CNNs), to train the geographical feature classifiers on the Wikipedia text data set obtained from GeoNames (which provides the feature type for each geolocated entity). We evaluate the performance of CNNs and active CNNs with multiple metrics (i.e., accuracy, F1 score, and confusion matrix). Our experiment results demonstrated that CNNs and active CNNs can effectively classify geo-referenced text entities into predefined geographical features. In addition, our experiment results show that active CNNs outperform CNNs for hard to distinguish classes. In our experiment, we also compared results for hierarchical multi-class classification and flat multiclass classification, and the results show that hierarchical multiclass classification significantly outperforms flat multi-class classification for the data set we used.

Characterizing event attendees’ travel patterns is key to understanding the dynamics of social events in cities. However, the scientific investigation of event travel patterns has been hindered by the difficulty in gathering travel diaries of participants. Geotagged microblogs provide new opportunities for studying event travel patterns by offering rich locational and semantic information of attendees. Here, we develop, implement, and apply a workflow to characterize travel behaviors of event attendees with geotagged Twitter data, using college football events as a case study. The workflow includes five steps: 1) filtering event attendees using real-time geotagged tweets, 2) identifying origins of the event attendees using historical timeline tweets, 3) identifying past sports-related activities at travel destinations using topic modeling, 4) computing user movement features using origin-destination travel flows, and 5) identifying atypical travel patterns to characterize event attendees. The travel patterns uncovered in the study offer insights into user interests and travel behaviors related to sporting event attendance. The findings demonstrate that our method holds promise in revealing long-term event travel patterns (not limited to sporting events) through the use of geotagged microblogs.

In this article, we present the GeoCorpora corpus building framework and software tools as well as a geo-annotated Twitter corpus built with these tools to foster research and development in the areas of microblog/Twitter geoparsing and geographic information retrieval. The developed framework employs crowdsourcing and geovisual analytics to support the construction of large corpora of text in which the mentioned location entities are identified and geolocated to toponyms in existing geographical gazetteers. We describe how the approach has been applied to build a corpus of geo-annotated tweets that will be made freely available to the research community alongside this article to support the evaluation, comparison and training of geoparsers. Additionally, we report lessons learned related to corpus construction for geoparsing as well as insights about the notions of place and natural spatial language that we derive from application of the framework to building this corpus.

This paper presents two linked empirical studies focused on uncertainty visualization. The experiments are framed from two conceptual perspectives. First, a typology of uncertainty is used to delineate kinds of uncertainty matched with space, time, and attribute components of data. Second, concepts from visual semiotics are applied to characterize the kind of visual signification that is appropriate for representing those different categories of uncertainty. This framework guided the two experiments reported here. The first addresses representation intuitiveness, considering both visual variables and iconicity of representation. The second addresses relative performance of the most intuitive abstract and iconic representations of uncertainty on a map reading task. Combined results suggest initial guidelines for representing uncertainty and discussion focuses on practical applicability of results.

Automatic and accurate extraction of destinations in human-generated route descriptions facilitates visualizing text route descriptions on digital maps. Such information further supports research aiming at understanding human cognition of geospatial information. However, as reproted in previous work, the recognition of destinations is not satisfactory. In this paper, we show our approach and achievements in improving the accuracy of destination name recognition. We identified and evaluated multiple features for classifying a named entity to be either "destination" or "non-destination"; after that, we use a simple yet effective post-processing algorithm to improve classification accuracy. Comprehensive experiments confirm the effectiveness of our approach.

A dendrogram that visualizes a clustering hierarchy is often integrated with a reorderable matrix for pattern identification. The method is widely used in many research fields including biology, geography, statistics, and data mining. However, most dendrograms do not scale up well, particularly with respect to problems of graphical and cognitive information overload. This research proposes a strategy that links an overview dendrogram and a detail-view dendrogram, each integrated with a reorderable matrix. The overview displays only a user-controlled, limited number of nodes that represent the ""skeleton" of a hierarchy. The detail view displays the sub-tree represented by a selected meta-node in the overview. The research presented here focuses on constructing a concise overview dendrogram and its coordination with a detail view. The proposed method has the following benefits: dramatic alleviation of information overload, enhanced scalability and data abstraction quality on the dendrogram, and the support of data exploration at arbitrary levels of detail. The contribution of the paper includes a new metric to measure the "importance" of nodes in a dendrogram; the method to construct the concise overview dendrogram from the dynamically-identified, important nodes; and measure for evaluating the data abstraction quality for dendrograms. We evaluate and compare the proposed method to some related existing methods, and demonstrating how the proposed method can help users find interesting patterns through a case study on county-level U.S. cervical cancer mortality and demographic data.

A fundamental challenge that must be met to achieve a usable conversational interface to geographic information system (GIS) is how to enable a more natural interaction between the user and the system. This paper presents a design of an agent-based computational model, PlanGraph, and implementation of this model in a software agent, GeoDialogue, as a dialogue manager for a conversational GIS. This dialogue agent enables intelligent collaborative human-GIS dialogues. The capabilities of this agent are demonstrated through its performance in a conversational GIS, Dave_G.

This article summarizes the results of the workshop on Visualization, Analytics & Spatial Decision Support, which took place at the GIScience conference in September 2006. The discussions at the workshop and analysis of the state of the art have revealed a need in concerted cross‐disciplinary efforts to achieve substantial progress in supporting space‐related decision making. The size and complexity of real‐life problems together with their ill‐defined nature call for a true synergy between the power of computational techniques and the human capabilities to analyze, envision, reason, and deliberate. Existing methods and tools are yet far from enabling this synergy. Appropriate methods can only appear as a result of a focused research based on the achievements in the fields of geovisualization and information visualization, human‐computer interaction, geographic information science, operations research, data mining and machine learning, decision science, cognitive science, and other disciplines. The name ‘Geovisual Analytics for Spatial Decision Support’ suggested for this new research direction emphasizes the importance of visualization and interactive visual interfaces and the link with the emerging research discipline of Visual Analytics. This article, as well as the whole special issue, is meant to attract the attention of scientists with relevant expertise and interests to the major challenges requiring multidisciplinary efforts and to promote the establishment of a dedicated research community where an appropriate range of competences is combined with an appropriate breadth of thinking.

This paper addresses a key problem in the development of visual‐analytical collaborative tools, how to design map‐based displays to enable productive group work. We introduce a group communication theory, the Collective Information Sharing (CIS) bias, and discuss how it relates to communicative goals that need to be considered when designing collaborative, visually enabled, spatial‐decision‐support tools. The CIS bias framework suggests that key goals for developing such tools should be: (a) the harnessing of a group's collective knowledge emerging from collaborative discussions and (b) reducing the repeat of information that has already been shared within the group. We propose that web‐accessible, map annotation tools are ideally suited to advancing these goals and outline how the CIS bias framework informs how geospatial annotation tools can maximize the potential of collaborative efforts. We offer design recommendations for annotation tools that function to: (a) facilitate access to and recall of geographically referenced discussion contributions, (b) document ideas for private as well as public discussion spaces, and (c) elicit all group members to contribute information in a given collaborative effort.

The use of new representation forms and interactive means to visualize geospatial data requires an understanding of the impact of the visual tools used for data exploration and knowledge construction. Use and usability assessment of implemented methods and tools is an important part of our efforts to build this understanding. Based on an approach to combine visual and computational methods for knowledge discovery in large geospatial data, an integrated visualization‐geocomputation environment has been developed based on the Self‐Organizing Map (SOM), the map and the parallel coordinate plot. This environment allows patterns and attribute relationships to be explored. A use and usability assessment is conducted to evaluate the ability of each of these visual representations to meet user performance and satisfaction goals. In the test, different representations are compared while exploring a socio‐demographic dataset.

The research reported here integrates computational, visual, and cartographic methods to develop a geovisual analytic approach for exploring and understanding spatio-temporal and multivariate patterns. The developed methodology and tools can help analysts investigate complex patterns across multivariate, spatial, and temporal dimensions via clustering, sorting, and visualization. Specifically, the approach involves a self-organizing map, a parallel coordinate plot, several forms of reorderable matrices (including several ordering methods), a geographic small multiple display, and a 2-dimensional cartographic color design method. The coupling among these methods leverages their independent strengths and facilitates a visual exploration of patterns that are difficult to discover otherwise. The visualization system we developed supports overview of complex patterns and, through a variety of interactions, enables users to focus on specific patterns and examine detailed views. We demonstrate the system with an application to the IEEE InfoVis 2005 Contest data set, which contains time-varying, geographically referenced, and multivariate data for technology companies in the US.

Current computing systems do not support human work effectively. They restrict human–computer interaction to one mode at a time and are designed with an assumption that use will be by individuals (rather than groups), directing (rather than interacting with) the system. To support the ways in which humans work and interact, a new paradigm for computing is required that is multimodal, rather than unimodal, collaborative, rather than personal, and dialogue‐enabled, rather than unidirectional. To address this challenge, we have developed an approach for designing natural, multimodal, multiuser dialogue‐enabled interfaces to geographic information systems that make use of large‐screen displays and integrated speech–gesture interaction. After outlining our goals and providing a brief overview of relevant literature, we introduce the Dialogue‐Assisted Visual Environment for Geoinformation (DAVE_G). DAVE_G is being developed using a human‐centred systems approach that contextualizes development and assessment in the current practice of potential users. In keeping with this human‐centred approach, we outline a user task analysis and associated scenario development that implementation is designed to support (grounded in the context of emergency response), review our own precursors to the current prototype system and discuss how the current prototype extends upon past work, provide a detailed description of the architecture that underlies the current system, and introduce the approach implemented for enabling mixed‐initiative human–computer dialogue. We conclude with a discussion of goals for future research.

A novel interface system for accessing geospatial data (GeoMIP) has been developed that realizes a user-centered multimodal speech/gesture interface for addressing some of the critical needs in crisis management. In this system we primarily developed vision sensing algorithms, speech integration, multimodality fusion, and rule-based mapping of multimodal user input to GIS database queries. A demo system of this interface has been developed for the Port Authority NJ/NY and is explained here.

We demonstrate a same-time different-place collaboration system for managing crisis situations using geospatial information. Our system enables distributed spatial decision-making by providing a multimodal interface to team members. Decision makers in front of large screen displays and/or desktop computers, and emergency responders in the field with tablet PCs can engage in collaborative activities for situation assessment and emergency response.

Most work with geospatial data, whether for scientific analysis, urban and environmental planning, or business decision making is carried out by groups. In contrast, geographic information technologies have been built and assessed only for use by individuals. In this paper we argue that, to support collaboration with geospatial information, specific attention must be given to tools that mediate understanding and support negotiation among participants. In addition, we contend that visual representations have a particularly important role to play as mediators of geocollaborative activities. With these contentions as a starting point, we present a framework for study of visually-enabled collaboration with geospatial information and for development, implementation, and assessment of geoinformation technologies that support that collaboration. The paper concludes with a brief description of two prototype geocollaborative environments that illustrate the use of the framework developed and provide the basis for discussing goals for futher research.

Emergency response requires strategic assessment of risks, decisions, and communications that are time critical while requiring teams of individuals to have fast access to large volumes of complex information and technologies that enable tightly coordinated work. The access to this information by crisis management teams in emergency operations centers can be facilitated through various human-computer interfaces. Unfortunately, these interfaces are hard to use, require extensive training, and often impede rather than support teamwork. Dialogue-enabled devices, based on natural, multimodal interfaces, have the potential of making a variety of information technology tools accessible during crisis management. This paper establishes the importance of multimodal interfaces in various aspects of crisis management and explores many issues in realizing successful speech-gesture driven, dialogue-enabled interfaces for crisis management. This paper is organized in five parts. The first part discusses the needs of crisis management that can be potentially met by the development of appropriate interfaces. The second part discusses the issues related to the design and development of multimodal interfaces in the context of crisis management. The third part discusses the state of the art in both the theories and practices involving these human-computer interfaces. In particular, it describes the evolution and implementation details of two representative systems, Crisis Management (XISM) and Dialog Assisted Visual Environment for Geoinformation (DAVE/spl I.bar/G). The fourth part speculates on the short-term and long-term research directions that will help addressing the outstanding challenges in interfaces that support dialogue and collaboration. Finally, the fifth part concludes the paper.

(1999). Visualization for exploration of spatial data. International Journal of Geographical Information Science: Vol. 13, No. 4, pp. 285-287.

We present an approach to the process of constructing knowledge through structured exploration of large spatiotemporal data sets. First, we introduce our problem context and define both Geographic Visualization (GVis) and Knowledge Discovery in Databases (KDD), the source domains for methods being integrated. Next, we review and compare recent GVis and KDD developments and consider the potential for their integration, emphasizing that an iterative process with user interaction is a central focus for uncovering interest and meaningful patterns through each. We then introduce an approach to design of an integrated GVis-KDD environment directed to exploration and discovery in the context of spatiotemporal environmental data. The approach emphasizes a matching of GVis and KDD meta-operations. Following description of the GVis and KDD methods that are linked in our prototype system, we present a demonstration of the prototype applied to a typical spatiotemporal dataset. We conclude by outlining, briefly, research goals directed toward more complete integration of GVis and KDD methods and their connection to temporal GIS.