We compared the ability of two legend designs on a soil-landscape map to efficiently and effectively support map reading tasks with the goal of better understanding how the design choices affect user performance.Devel...We compared the ability of two legend designs on a soil-landscape map to efficiently and effectively support map reading tasks with the goal of better understanding how the design choices affect user performance.Developing such knowledge is essential to design effective interfaces for digital earth systems.One of the two legends contained an alphabetical ordering of categories,while the other used a perceptual grouping based on the Munsell color space.We tested the two legends for 4 tasks with 20 experts(in geography-related domains).We analyzed traditional usability metrics and participants’eye movements to identify the possible reasons behind their success and failure in the experimental tasks.Surprisingly,an overwhelming majority of the participants failed to arrive at the correct responses for two of the four tasks,irrespective of the legend design.Furthermore,participants’prior knowledge of soils and map interpretation abilities led to interesting performance differences between the two legend types.We discuss how participant background might have played a role in performance and why some tasks were particularly hard to solve despite participants’relatively high levels of experience in map reading.Based on our observations,we caution soil cartographers to be aware of the perceptual complexity of soil-landscape maps.展开更多
This special issue is the Part II of a two-volume special issue comprising of a series of papers documenting the state-of-the-art research in geovisual analytics.Part I consisted of four papers and focused on design a...This special issue is the Part II of a two-volume special issue comprising of a series of papers documenting the state-of-the-art research in geovisual analytics.Part I consisted of four papers and focused on design and implementation issues in geovisual analytics.Here,with an additional four papers focused on the human factors in geovisual analytics,we present Part II.展开更多
With the explosion of digital data,the need for advanced visual analytics,including coordinated multiple views(CMV),is rapidly increasing.CMV enable users to discover patterns and examine relationships across multiple...With the explosion of digital data,the need for advanced visual analytics,including coordinated multiple views(CMV),is rapidly increasing.CMV enable users to discover patterns and examine relationships across multiple visualizations of one or multiple datasets.CMV have been implemented in a web-based environment through the Australian Urban Research Infrastructure Network(AURIN)project.AURIN offers a platform providing seamless and secure access to an extensive range of distributed urban datasets across Australia.Visual exploration of these datasets is essential to support research endeavors.This paper focuses on the challenges in dealing with complexity and multidimensionality of datasets used in CMV.We rely on the concept of multidimensional data cubes as the theoretical framework for coordination across visualizations.Using the concept of data cubes and hierarchical dimensions,we present strategies to automatically build render groups.This provides an implicit coordination based on cube structures and a framework to establish links between a dataset with its aggregates in a one-to-many fashion.The CMV approach is demonstrated using aggregate-level data,which is provided through federated data services.The paper discusses the issues around our CMV implementation and concludes by reflecting on the challenges in supporting spatio-temporal urban data exploration.展开更多
This special issue presents a series of papers that document the state-of-the-art research on the topic of geovisual analytics.The special issue is presented as two parts.This first issue,comprising of four papers,is ...This special issue presents a series of papers that document the state-of-the-art research on the topic of geovisual analytics.The special issue is presented as two parts.This first issue,comprising of four papers,is focused on design and implementation considerations in geovisual analytics(Part I).Part II will follow,with an additional four papers,which is focused on the human factors in geovisual analytics.展开更多
文摘We compared the ability of two legend designs on a soil-landscape map to efficiently and effectively support map reading tasks with the goal of better understanding how the design choices affect user performance.Developing such knowledge is essential to design effective interfaces for digital earth systems.One of the two legends contained an alphabetical ordering of categories,while the other used a perceptual grouping based on the Munsell color space.We tested the two legends for 4 tasks with 20 experts(in geography-related domains).We analyzed traditional usability metrics and participants’eye movements to identify the possible reasons behind their success and failure in the experimental tasks.Surprisingly,an overwhelming majority of the participants failed to arrive at the correct responses for two of the four tasks,irrespective of the legend design.Furthermore,participants’prior knowledge of soils and map interpretation abilities led to interesting performance differences between the two legend types.We discuss how participant background might have played a role in performance and why some tasks were particularly hard to solve despite participants’relatively high levels of experience in map reading.Based on our observations,we caution soil cartographers to be aware of the perceptual complexity of soil-landscape maps.
文摘This special issue is the Part II of a two-volume special issue comprising of a series of papers documenting the state-of-the-art research in geovisual analytics.Part I consisted of four papers and focused on design and implementation issues in geovisual analytics.Here,with an additional four papers focused on the human factors in geovisual analytics,we present Part II.
文摘With the explosion of digital data,the need for advanced visual analytics,including coordinated multiple views(CMV),is rapidly increasing.CMV enable users to discover patterns and examine relationships across multiple visualizations of one or multiple datasets.CMV have been implemented in a web-based environment through the Australian Urban Research Infrastructure Network(AURIN)project.AURIN offers a platform providing seamless and secure access to an extensive range of distributed urban datasets across Australia.Visual exploration of these datasets is essential to support research endeavors.This paper focuses on the challenges in dealing with complexity and multidimensionality of datasets used in CMV.We rely on the concept of multidimensional data cubes as the theoretical framework for coordination across visualizations.Using the concept of data cubes and hierarchical dimensions,we present strategies to automatically build render groups.This provides an implicit coordination based on cube structures and a framework to establish links between a dataset with its aggregates in a one-to-many fashion.The CMV approach is demonstrated using aggregate-level data,which is provided through federated data services.The paper discusses the issues around our CMV implementation and concludes by reflecting on the challenges in supporting spatio-temporal urban data exploration.
文摘This special issue presents a series of papers that document the state-of-the-art research on the topic of geovisual analytics.The special issue is presented as two parts.This first issue,comprising of four papers,is focused on design and implementation considerations in geovisual analytics(Part I).Part II will follow,with an additional four papers,which is focused on the human factors in geovisual analytics.
