The Experts below are selected from a list of 35658 Experts worldwide ranked by ideXlab platform
Jeffrey Heer - One of the best experts on this subject based on the ideXlab platform.
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Surprise! Bayesian Weighting for De-Biasing Thematic Maps
IEEE Transactions on Visualization and Computer Graphics, 2017Co-Authors: Michael Correll, Jeffrey HeerAbstract:The promise of "smart" homes, workplaces, schools, and other environments has long been championed. Unattractive, however, has been the cost to run wires and install sensors. More critically, raw sensor data tends not to align with the types of questions humans wish to ask, e.g., do I need to restock my pantry? Although techniques like computer vision can answer some of these questions, it requires significant effort to build and train appropriate classifiers. Even then, these systems are often brittle, with limited ability to handle new or unexpected situations, including being repositioned and environmental changes (e.g., lighting, furniture, seasons). We propose Zensors, a new sensing approach that fuses real-time human intelligence from online crowd workers with automatic approaches to provide robust, adaptive, and readily deployable intelligent sensors. With Zensors, users can go from question to live sensor feed in less than 60 seconds. Through our API, Zensors can enable a variety of rich end-user applications and moves us closer to the vision of responsive, intelligent environments.
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Surprise! Bayesian Weighting for De-Biasing Thematic Maps
IEEE Transactions on Visualization and Computer Graphics, 2017Co-Authors: Michael Correll, Jeffrey HeerAbstract:Thematic Maps are commonly used for visualizing the density of events in spatial data. However, these Maps can mislead by giving visual prominence to known base rates (such as population densities) or to artifacts of sample size and normalization (such as outliers arising from smaller, and thus more variable, samples). In this work, we adapt Bayesian surprise to generate Maps that counter these biases. Bayesian surprise, which has shown promise for modeling human visual attention, weights information with respect to how it updates beliefs over a space of models. We introduce Surprise Maps, a visualization technique that weights event data relative to a set of spatia-temporal models. Unexpected events (those that induce large changes in belief over the model space) are visualized more prominently than those that follow expected patterns. Using both synthetic and real-world datasets, we demonstrate how Surprise Maps overcome some limitations of traditional event Maps.
Michael Correll - One of the best experts on this subject based on the ideXlab platform.
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Surprise! Bayesian Weighting for De-Biasing Thematic Maps
IEEE Transactions on Visualization and Computer Graphics, 2017Co-Authors: Michael Correll, Jeffrey HeerAbstract:The promise of "smart" homes, workplaces, schools, and other environments has long been championed. Unattractive, however, has been the cost to run wires and install sensors. More critically, raw sensor data tends not to align with the types of questions humans wish to ask, e.g., do I need to restock my pantry? Although techniques like computer vision can answer some of these questions, it requires significant effort to build and train appropriate classifiers. Even then, these systems are often brittle, with limited ability to handle new or unexpected situations, including being repositioned and environmental changes (e.g., lighting, furniture, seasons). We propose Zensors, a new sensing approach that fuses real-time human intelligence from online crowd workers with automatic approaches to provide robust, adaptive, and readily deployable intelligent sensors. With Zensors, users can go from question to live sensor feed in less than 60 seconds. Through our API, Zensors can enable a variety of rich end-user applications and moves us closer to the vision of responsive, intelligent environments.
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Surprise! Bayesian Weighting for De-Biasing Thematic Maps
IEEE Transactions on Visualization and Computer Graphics, 2017Co-Authors: Michael Correll, Jeffrey HeerAbstract:Thematic Maps are commonly used for visualizing the density of events in spatial data. However, these Maps can mislead by giving visual prominence to known base rates (such as population densities) or to artifacts of sample size and normalization (such as outliers arising from smaller, and thus more variable, samples). In this work, we adapt Bayesian surprise to generate Maps that counter these biases. Bayesian surprise, which has shown promise for modeling human visual attention, weights information with respect to how it updates beliefs over a space of models. We introduce Surprise Maps, a visualization technique that weights event data relative to a set of spatia-temporal models. Unexpected events (those that induce large changes in belief over the model space) are visualized more prominently than those that follow expected patterns. Using both synthetic and real-world datasets, we demonstrate how Surprise Maps overcome some limitations of traditional event Maps.
Raymond W Kulhavy - One of the best experts on this subject based on the ideXlab platform.
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learning and remembering from Thematic Maps of familiar regions
Educational Technology Research and Development, 1998Co-Authors: Kent A Rittschof, Raymond W KulhavyAbstract:To examine how four methods of symbolizing data affect learning from Thematic Maps of familiar regions, two experiments were conducted. In Experiment 1, 86 college students viewed one of three types of Thematic map or a control table, then read a map-related text. Recall of regions with their associated theme information was greater for those who studied a map than for those who studied a table. In Experiment 2, 83 college students viewed one of two types of Thematic map for either 1 or 3 min, followed by a map-related text. Shaded-region, or choropleth Maps were associated with greater recall of theme information, but longer exposure time was not. In both experiments, map-related text information was recalled more than map-unrelated text information. Choropleth Maps and proportional symbol Maps were associated with higher reported use of metacognitive strategies. Instructional and theoretical implications of these findings are discussed.
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cartographic experience and thinking aloud about Thematic Maps
Cartographica: The International Journal for Geographic Information and Geovisualization, 1992Co-Authors: Raymond W Kulhavy, Doris R Pridemore, William A StockAbstract:High school students and college geography majors studied a Thematic map titled the Ancient Mayan World. During the 15-minute study period subjects produced 'think aloud' data by describing the processes they used to encode the map. An analysis of the verbal protocols yielded no important differences due to sex of subject or instructions to learn the map. Cartographically experienced college subjects tended to emphasize theme information rather than geography, displayed wider scanning ranges during initial map viewing, and produced more inference and context information during a map reconstruction task. These results suggest that cartographic experience predicts the type of information learned from a complex map. Des etudiants des niveaux secondaire et collegial, avec specialite en geographie, ont etudie une carte thematique intitulee L'ancien monde Maya. Au cours de la periode d'etude de quinze minutes, les sujets ont emis des commentaires verbaux en decrivant les processus utilises pour encoder la carte...
Hung Bui Quang - One of the best experts on this subject based on the ideXlab platform.
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KSE - TMACT: A Thematic map automatically creating tool for maintaining WebGIS systems
2017 9th International Conference on Knowledge and Systems Engineering (KSE), 2017Co-Authors: Thang Luu Quang, Thanh Nguyen Thi Nhat, Thuy Nguyen Thanh, Hung Bui QuangAbstract:In maintaining a WebGIS system, the update and creation of Thematic Maps are time consuming, human resource comsuming and then cost consuming. This paper concentrates on developing TMACT tool for automatically creating Thematic Maps. There are two main functions of TMACT. The first one is that TMACT automatically creates Thematic-map-services from an Excel file which stored geodata collected by end-users. The second one is that TMACT provides APIs to make WebGIS systems work with TMACT to get Thematic-map-services and generate Thematic Maps. TMACT supports an Interactive Web User Interface for normal users and a command line interface for developers. We then demonstrate the application of TMACT to a Intelligent Traffic System (ITS) system. It costs 15 minutes to create a new Thematic map for ITS system by a technician. When we applied TMACT to ITS, it costs only two minutes to create a new Thematic map without a technician.
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TMACT: A Thematic map automatically creating tool for maintaining WebGIS systems
2017 9th International Conference on Knowledge and Systems Engineering (KSE), 2017Co-Authors: Thang Luu Quang, Thanh Nguyen Thi Nhat, Thuy Nguyen Thanh, Hung Bui QuangAbstract:In maintaining a WebGIS system, the update and creation of Thematic Maps are time consuming, human resource comsuming and then cost consuming. This paper concentrates on developing TMACT tool for automatically creating Thematic Maps. There are two main functions of TMACT. The first one is that TMACT automatically creates Thematic-map-services from an Excel file which stored geodata collected by end-users. The second one is that TMACT provides APIs to make WebGIS systems work with TMACT to get Thematic-map-services and generate Thematic Maps. TMACT supports an Interactive Web User Interface for normal users and a command line interface for developers. We then demonstrate the application of TMACT to a Intelligent Traffic System (ITS) system. It costs 15 minutes to create a new Thematic map for ITS system by a technician. When we applied TMACT to ITS, it costs only two minutes to create a new Thematic map without a technician.
P Vandeusen - One of the best experts on this subject based on the ideXlab platform.
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correcting bias in change estimates from Thematic Maps
Remote Sensing of Environment, 1994Co-Authors: P VandeusenAbstract:Abstract Statistical procedures are presented for estimating change from Thematic Maps available for the same region at two times. Estimators for the joint distribution of the true proportions of classes at both times are given for a simple random sampling scheme and a stratified sampling scheme. Estimators are derived for the true marginal distributions of the proportions of each class at time 1, time 2, and the difference between times 1 and 2 along with variance estimates.
