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The data I am usually confronted with related to movement (GPS, mobile, etc.), sensors (RFID, other static sensors) and random events (such as monetary transactions). The task, and common nominator between these fiels is the development of visual and algorithmic techniques for information extraction. The challenge is to capture the spatial and temporal aspects simultaneously! <br /><br />It is challenging, it is fun and it is always colorful! ;-)<br /><b>Address: </b>IBM Research, Haifa Lab<br /><div class="js-profile-less-about u-linkUnstyled u-tcGrayDarker u-textDecorationUnderline u-displayNone">less</div></div></div><div class="suggested-academics-container"><div class="suggested-academics--header"><p class="ds2-5-body-md-bold">Related Authors</p></div><ul class="suggested-user-card-list"><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://ugent.academia.edu/GreetDeruyter"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://ugent.academia.edu/GreetDeruyter">Greet Deruyter</a><p 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data-trace="false" data-dom-id="Pill-react-component-f2c1aa7d-b0ca-4029-b4df-0e1dd0d5ad42"></div> <div id="Pill-react-component-f2c1aa7d-b0ca-4029-b4df-0e1dd0d5ad42"></div> </a></div></div><div class="external-links-container"><ul class="profile-links new-profile js-UserInfo-social"><li class="profile-profiles js-social-profiles-container"><i class="fa fa-spin fa-spinner"></i></li></ul></div></div></div><div class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Peter Bak</h3></div><div class="js-work-strip profile--work_container" data-work-id="3606210"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/3606210/A_Visual_Analytics_Approach_for_Assessing_Pedestrian_Friendliness_of_Urban_Environments"><img alt="Research paper thumbnail of A Visual Analytics Approach for Assessing Pedestrian Friendliness of Urban Environments" class="work-thumbnail" src="https://attachments.academia-assets.com/31320557/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/3606210/A_Visual_Analytics_Approach_for_Assessing_Pedestrian_Friendliness_of_Urban_Environments">A Visual Analytics Approach for Assessing Pedestrian Friendliness of Urban Environments</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://telaviv.academia.edu/YoavLerman">Yoav Lerman</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://ibm.academia.edu/PeterBak">Peter Bak</a></span></div><div class="wp-workCard_item"><span>Geographic Information Science at the Heart of Europe</span><span>, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The availability of efficient transportation facilities is vital to the function and development ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The availability of efficient transportation facilities is vital to the function and development of modern cities. Promoting walking is crucial for supporting livable communities and cities. Assessing the quality of pedestrian facilities and constructing appropriate pedestrian walking facilities are important tasks in public city planning. Additionally, walking facilities in a community affect commercial activities including private investment decisions such as those of retailers. However, analyzing what we call pedestrian friendliness in an urban environment involves multiple data perspectives, such as street networks, land use, and other multivariate observation measurements, and consequently poses significant challenges. In this study, we investigate the effect of urban environment properties on pedestrian movement in different locations in the metropolitan region of Tel Aviv. The first urban area we investigated was the inner city of the Tel Aviv metropolitan region, one of the central regions in Tel Aviv, a city that serves many non-local residents. For simplicity, we refer to this area as Tel Aviv. We also investigated Bat Yam, a small city, whose residents use many of the services of Tel Aviv. We apply an improved tool for visual analysis of the correlation between multiple independent and one dependent variable in geographical context. We use the tool to investigate the effect of functional and topological properties on the volume of pedestrian movement. The results of our study indicate that these two urban areas differ greatly. The urban area of Tel Aviv has much more orrespondence and interdependency among the functional and topological properties of the urban environment that might influence pedestrian movement. We also found that the pedestrian movements as well as the related urban environment properties in this region are distributed geographically in a more equal and organized form.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b106f20464bc5bc0f297c77882a80149" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":31320557,"asset_id":3606210,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/31320557/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="3606210"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="3606210"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 3606210; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="32819960"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/32819960/TransUccess_Investigating_Social_Equity_in_Accessing_Public_Transportation_through_Visual_Analytics"><img alt="Research paper thumbnail of TransUccess: Investigating Social Equity in Accessing Public Transportation through Visual Analytics" class="work-thumbnail" src="https://attachments.academia-assets.com/52969325/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/32819960/TransUccess_Investigating_Social_Equity_in_Accessing_Public_Transportation_through_Visual_Analytics">TransUccess: Investigating Social Equity in Accessing Public Transportation through Visual Analytics</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://bgu.academia.edu/ShakedKaufman">Shaked Kaufman</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://ibm.academia.edu/PeterBak">Peter Bak</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">TransUccess is an interactive visualization tool aiming to allow residents , transit operators an...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">TransUccess is an interactive visualization tool aiming to allow residents , transit operators and decision-makers to easily find, analyze and validate patterns of accessibility and social equity in public transit systems. The tool comprises of interactive choropleth map and parallel coordinates chart that assist users in investigating accessibility levels throughout different city zones, so called administrative zones, while making demographic properties of the population , such as mean household income, assessable. The results of our investigations show a complex interplay of demographics and accessibility.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e499f65177e25090b751a188a96eca59" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":52969325,"asset_id":32819960,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/52969325/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="32819960"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="32819960"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 32819960; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="4226320"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/4226320/Visual_Analytics_Using_Density_Equalizing_Geographic_Distortion"><img alt="Research paper thumbnail of Visual Analytics Using Density Equalizing Geographic Distortion" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/4226320/Visual_Analytics_Using_Density_Equalizing_Geographic_Distortion">Visual Analytics Using Density Equalizing Geographic Distortion</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract Visualizing large geo-demographical data sets using pixel-based techniques involves map...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract Visualizing large geo-demographical data sets using pixel-based techniques involves mapping the geo-spatial dimensions of a data point to screen coordinates and appropriately encoding its statistical value by color. Analysis of such data is a great challenge. General ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="4226320"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="4226320"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 4226320; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=4226320]").text(description); $(".js-view-count[data-work-id=4226320]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 4226320; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='4226320']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=4226320]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":4226320,"title":"Visual Analytics Using Density Equalizing Geographic Distortion","internal_url":"https://www.academia.edu/4226320/Visual_Analytics_Using_Density_Equalizing_Geographic_Distortion","owner_id":976686,"coauthors_can_edit":true,"owner":{"id":976686,"first_name":"Peter","middle_initials":null,"last_name":"Bak","page_name":"PeterBak","domain_name":"ibm","created_at":"2011-11-23T17:24:18.254-08:00","display_name":"Peter Bak","url":"https://ibm.academia.edu/PeterBak"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="4226319"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/4226319/Space_in_Time_and_Time_in_Space_Self_Organizing_Maps_for_Exploring_Spatiotemporal_Patterns"><img alt="Research paper thumbnail of Space-in-Time and Time-in-Space Self-Organizing Maps for Exploring Spatiotemporal Patterns" class="work-thumbnail" src="https://attachments.academia-assets.com/49979211/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/4226319/Space_in_Time_and_Time_in_Space_Self_Organizing_Maps_for_Exploring_Spatiotemporal_Patterns">Space-in-Time and Time-in-Space Self-Organizing Maps for Exploring Spatiotemporal Patterns</a></div><div class="wp-workCard_item"><span>Computer Graphics Forum</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Spatiotemporal data pose serious challenges to analysts in geographic and other domains. Owing to...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Spatiotemporal data pose serious challenges to analysts in geographic and other domains. Owing to the complexity of the geospatial and temporal components, this kind of data cannot be analyzed by fully automatic methods but require the involvement of the human analyst's expertise. For a comprehensive analysis, the data need to be considered from two complementary perspectives: (1) as spatial distributions (situations) changing over time and (2) as profiles of local temporal variation distributed over space. In order to support the visual analysis of spatiotemporal data, we suggest a framework based on the “Self-Organizing Map” (SOM) method combined with a set of interactive visual tools supporting both analytic perspectives. SOM can be considered as a combination of clustering and dimensionality reduction. In the first perspective, SOM is applied to the spatial situations at different time moments or intervals. In the other perspective, SOM is applied to the local temporal evolution profiles. The integrated visual analytics environment includes interactive coordinated displays enabling various transformations of spatiotemporal data and post-processing of SOM results. The SOM matrix display offers an overview of the groupings of data objects and their two-dimensional arrangement by similarity. This view is linked to a cartographic map display, a time series graph, and a periodic pattern view. The linkage of these views supports the analysis of SOM results in both the spatial and temporal contexts. The variable SOM grid coloring serves as an instrument for linking the SOM with the corresponding items in the other displays. The framework has been validated on a large dataset with real city traffic data, where expected spatiotemporal patterns have been successfully uncovered. We also describe the use of the framework for discovery of previously unknown patterns in 41-years time series of 7 crime rate attributes in the states of the USA.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="bc94f460c4cdf2846799d0cb4ec77eaa" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":49979211,"asset_id":4226319,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/49979211/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="4226319"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="4226319"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 4226319; 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Eine vollautomatische Analyse der Bewegungsmuster gestaltet sich für komplexe räumliche und zeitliche Strukturen schwierig, insbesondere wenn die Daten mit Unsicherheiten behaftet sind oder semantische Informationen fehlen. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="1603491"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/1603491/Visualization_and_Data_Analysis_2005_Proceedings_Volume_"><img alt="Research paper thumbnail of Visualization and Data Analysis 2005 (Proceedings Volume)" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/1603491/Visualization_and_Data_Analysis_2005_Proceedings_Volume_">Visualization and Data Analysis 2005 (Proceedings Volume)</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper discusses algorithmic and implementation aspects of optimally mapping a visualization ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper discusses algorithmic and implementation aspects of optimally mapping a visualization pipeline onto a linear arrangement of wide-area network nodes to minimize the total delay. The first network node typically is a data source, the last node could be a display device ranging from a personal computer to a powerwall, and each intermediate node could be a workstation or computational cluster. This mapping scheme appropriately distributes the filtering, geometry generation, rendering, and display modules of the visualization ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="1603491"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="1603491"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 1603491; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=1603491]").text(description); $(".js-view-count[data-work-id=1603491]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 1603491; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='1603491']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=1603491]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":1603491,"title":"Visualization and Data Analysis 2005 (Proceedings Volume)","internal_url":"https://www.academia.edu/1603491/Visualization_and_Data_Analysis_2005_Proceedings_Volume_","owner_id":976686,"coauthors_can_edit":true,"owner":{"id":976686,"first_name":"Peter","middle_initials":null,"last_name":"Bak","page_name":"PeterBak","domain_name":"ibm","created_at":"2011-11-23T17:24:18.254-08:00","display_name":"Peter Bak","url":"https://ibm.academia.edu/PeterBak"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="1603490"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/1603490/User_settings_of_cue_thresholds_for_binary_categorization_decisions"><img alt="Research paper thumbnail of User settings of cue thresholds for binary categorization decisions" class="work-thumbnail" src="https://attachments.academia-assets.com/30921833/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/1603490/User_settings_of_cue_thresholds_for_binary_categorization_decisions">User settings of cue thresholds for binary categorization decisions</a></div><div class="wp-workCard_item"><span>Journal of Experimental …</span><span>, Jan 1, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The output of binary cuing systems, such as alerts or alarms, depends on the threshold setting-a ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The output of binary cuing systems, such as alerts or alarms, depends on the threshold setting-a parameter that is often user-adjustable. However, it is unknown if users are able to adequately adjust thresholds and what information may help them to do so. Two experiments tested threshold settings for a binary classification task based on binary cues. During the task, participants decided whether a product was intact or faulty. Experimental conditions differed in the information participants received: all participants were informed about a product's fault probability and the payoffs associated with decision outcomes; one third also received information regarding conditional probabilities for a fault when the system indicated or did not indicate the existence of one (predictive values); and another third received information about conditional probabilities for the system indicating a fault, in the instance of the existence or lack thereof, of an actual fault (diagnostic values). Threshold settings in all experimental groups were nonoptimal, with settings closest to the optimum with predictive-values information. Results corresponded with a model describing threshold settings as a function of the conditional probabilities for the different outcomes. From a practical perspective, results indicate that predicti ve-values information best supports decisions about threshold settings. Consequently, for users to adjust thresholds, they should receive information about predictive-values, provided that such values can be computed.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2decf297adedd88727078dffbdc16da5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":30921833,"asset_id":1603490,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/30921833/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="1603490"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="1603490"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 1603490; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="1603489"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/1603489/Methods_for_Interactive_Exploration_of_Large_Scale_News_Streams"><img alt="Research paper thumbnail of Methods for Interactive Exploration of Large-Scale News Streams" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/1603489/Methods_for_Interactive_Exploration_of_Large_Scale_News_Streams">Methods for Interactive Exploration of Large-Scale News Streams</a></div><div class="wp-workCard_item"><span>Web Intelligence and …</span><span>, Jan 1, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">... 641.5Kb). Visual analysis of news streams with article threads. krstajic_visual.pdf (583.5Kb)...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">... 641.5Kb). Visual analysis of news streams with article threads. krstajic_visual.pdf (583.5Kb). Krstajic, Milos; Bertini, Enrico ... Keim_2009_VisualAnalyticsChallenges.pdf (5.420Mb). Keim, Daniel A.; Kohlhammer, Jörn; Santucci, Giuseppe; Mansmann, Florian; Wanner, Franz; Schäfer ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="1603489"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="1603489"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 1603489; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=1603489]").text(description); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="1603486"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/1603486/2009_Index_IEEE_Transactions_on_Visualization_and_Computer_Graphics_Vol_16"><img alt="Research paper thumbnail of 2009 Index IEEE Transactions on Visualization and Computer Graphics Vol. 16" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/1603486/2009_Index_IEEE_Transactions_on_Visualization_and_Computer_Graphics_Vol_16">2009 Index IEEE Transactions on Visualization and Computer Graphics Vol. 16</a></div><div class="wp-workCard_item"><span>IEEE TRANSACTIONS …</span><span>, Jan 1, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">... de Oliveira-Kumar, Jeremy, see Crampes, Michel, T-VCG Nov-Dec 2009 985-992 de Verdiére, Guill...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">... de Oliveira-Kumar, Jeremy, see Crampes, Michel, T-VCG Nov-Dec 2009 985-992 de Verdiére, Guillaume Colin, see Marchesin, Stéphane, T-VCG Nov-Dec 2009 1611-1618 DeFanti, Thomas A., see Kooima, Robert, T-VCG Sept-Oct 2009 719-733 Deladisma, Adeline, see ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="1603486"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="1603486"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 1603486; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=1603486]").text(description); $(".js-view-count[data-work-id=1603486]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 1603486; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='1603486']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=1603486]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":1603486,"title":"2009 Index IEEE Transactions on Visualization and Computer Graphics Vol. 16","internal_url":"https://www.academia.edu/1603486/2009_Index_IEEE_Transactions_on_Visualization_and_Computer_Graphics_Vol_16","owner_id":976686,"coauthors_can_edit":true,"owner":{"id":976686,"first_name":"Peter","middle_initials":null,"last_name":"Bak","page_name":"PeterBak","domain_name":"ibm","created_at":"2011-11-23T17:24:18.254-08:00","display_name":"Peter Bak","url":"https://ibm.academia.edu/PeterBak"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="1603485"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/1603485/Exploration_through_enrichment_a_visual_analytics_approach_for_animal_movement"><img alt="Research paper thumbnail of Exploration through enrichment: a visual analytics approach for animal movement" class="work-thumbnail" src="https://attachments.academia-assets.com/15389751/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/1603485/Exploration_through_enrichment_a_visual_analytics_approach_for_animal_movement">Exploration through enrichment: a visual analytics approach for animal movement</a></div><div class="wp-workCard_item"><span>Proceedings of the 19th …</span><span>, Jan 1, 2011</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The analysis of trajectories has become an important field in geographic visualization, as cheap ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The analysis of trajectories has become an important field in geographic visualization, as cheap GPS sensors have become commonplace and, in many cases, valuable information can be derived either from the data themselves or their metadata if processed and visualized in the right way. However, showing the "right" information to highlight dependencies or correlations between different measurements remains a challenge, because the technical intricacies of applying a combination of automatic and visual analysis methods prevents the majority of domain experts from analyzing and exploring the full wealth of their movement data. This paper presents an exploration through enrichment approach, which enables iterative generation of metadata based on exploratory findings and is aimed at enabling domain experts to explore their data beyond traditional means.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7b1142ca8fffa6ebc4e7dbae0e613906" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":15389751,"asset_id":1603485,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/15389751/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="1603485"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="1603485"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 1603485; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="1603484"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/1603484/Applied_Visual_Exploration_on_Real_Time_News_Feeds_using_Polarity_and_Geo_Spatial_Analysis"><img alt="Research paper thumbnail of Applied Visual Exploration on Real-Time News Feeds using Polarity and Geo-Spatial Analysis" class="work-thumbnail" src="https://attachments.academia-assets.com/15389752/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/1603484/Applied_Visual_Exploration_on_Real_Time_News_Feeds_using_Polarity_and_Geo_Spatial_Analysis">Applied Visual Exploration on Real-Time News Feeds using Polarity and Geo-Spatial Analysis</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper presents a visual analytics approach to explore large news article collections in the ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper presents a visual analytics approach to explore large news article collections in the domains of polarity and spatial analysis. The exploration is performed on the data collected with Europe Media Monitor (EMM), a system which monitors over 2500 online sources and processes 90,000 articles per day. By analyzing the news feeds, we want to find out which topics are important in different countries and what is the general polarity of the articles within these topics. To assess the polarity of a news article, automatic techniques for polarity analysis are employed and the results are represented using Literature Fingerprinting for visualization. In the spatial description of the news feeds, every article can be represented by two geographic attributes, the news origin and the location of the event itself. In order to assess these spatial properties of news articles, we conducted our geo-analysis, which is able to cope with the size and spatial distribution of the data. Within this application framework, we show opportunities how real-time news feed data can be analyzed efficiently.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f9ead76668c8ca8a154b6ba2cb91317e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":15389752,"asset_id":1603484,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/15389752/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="1603484"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="1603484"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 1603484; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=1603484]").text(description); $(".js-view-count[data-work-id=1603484]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 1603484; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='1603484']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="1603483"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/1603483/Where_Would_You_Go_on_Your_Next_Vacation"><img alt="Research paper thumbnail of Where Would You Go on Your Next Vacation" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/1603483/Where_Would_You_Go_on_Your_Next_Vacation">Where Would You Go on Your Next Vacation</a></div><div class="wp-workCard_item"><span>A Framework for Visual …</span><span>, Jan 1, 2010</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="1603483"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="1603483"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 1603483; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=1603483]").text(description); $(".js-view-count[data-work-id=1603483]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 1603483; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='1603483']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=1603483]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":1603483,"title":"Where Would You Go on Your Next Vacation","internal_url":"https://www.academia.edu/1603483/Where_Would_You_Go_on_Your_Next_Vacation","owner_id":976686,"coauthors_can_edit":true,"owner":{"id":976686,"first_name":"Peter","middle_initials":null,"last_name":"Bak","page_name":"PeterBak","domain_name":"ibm","created_at":"2011-11-23T17:24:18.254-08:00","display_name":"Peter Bak","url":"https://ibm.academia.edu/PeterBak"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="1603482"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/1603482/Advanced_visual_analytics_interfaces"><img alt="Research paper thumbnail of Advanced visual analytics interfaces" class="work-thumbnail" src="https://attachments.academia-assets.com/15389749/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/1603482/Advanced_visual_analytics_interfaces">Advanced visual analytics interfaces</a></div><div class="wp-workCard_item"><span>Proceedings of the …</span><span>, Jan 1, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Advanced visual interfaces, like the ones found in information visualization, intend to offer a v...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Advanced visual interfaces, like the ones found in information visualization, intend to offer a view on abstract data spaces to enable users to make sense of them. By mapping data to visual representations and providing interactive tools to explore and navigate, it is possible to get an understanding of the data and possibly discover new knowledge. With the advent of modern data collection and analysis technologies, the direct visualization of data starts to show its limitations due to limited scalability in terms of volumes and to the complexity of required analytical reasoning. Many analytical problems we encounter today require approaches that go beyond pure analytics or pure visualization. Visual analytics provides an answer to this problems by advocating a tight integration between automatic computation and interactive visualization, proposing a more holistic approach. In this paper, we argue for Advanced Visual Analytics Interfaces (AVAIs), visual interfaces in which neither the analytics nor the visualization needs to be advanced in itself but where the synergy between automation and visualization is in fact advanced. We offer a detailed argumentation around the needs and challenges of AVAIs and provide several examples of this type of interfaces.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1fec5aa17e9cf2eb68b80495ea3649d6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":15389749,"asset_id":1603482,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/15389749/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="1603482"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="1603482"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 1603482; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=1603482]").text(description); $(".js-view-count[data-work-id=1603482]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 1603482; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='1603482']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "1fec5aa17e9cf2eb68b80495ea3649d6" } } $('.js-work-strip[data-work-id=1603482]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":1603482,"title":"Advanced visual analytics interfaces","internal_url":"https://www.academia.edu/1603482/Advanced_visual_analytics_interfaces","owner_id":976686,"coauthors_can_edit":true,"owner":{"id":976686,"first_name":"Peter","middle_initials":null,"last_name":"Bak","page_name":"PeterBak","domain_name":"ibm","created_at":"2011-11-23T17:24:18.254-08:00","display_name":"Peter Bak","url":"https://ibm.academia.edu/PeterBak"},"attachments":[{"id":15389749,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/15389749/thumbnails/1.jpg","file_name":"10.1.1.173.1572.pdf","download_url":"https://www.academia.edu/attachments/15389749/download_file","bulk_download_file_name":"Advanced_visual_analytics_interfaces.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/15389749/10.1.1.173.1572-libre.pdf?1390864160=\u0026response-content-disposition=attachment%3B+filename%3DAdvanced_visual_analytics_interfaces.pdf\u0026Expires=1740547228\u0026Signature=EM4XGoXks8-brCsEFBB15bkDhz8nU~gPn5euyMNWkN8e42HmHDgqjGoZGfeS18TSiFBVZyYauYwLnN1O3qQITiLOlpoDyKSVG9iOO0ZEM-OXo9DZg0gqN5DsigPi0NDIdhRx1iLsRM6tOj5GfL3FQ6P9y1jtqS1z5lmZSUT9xfyMuRai-jq8Dv630s2bsX1vyLbkny43Mzg1fSmAgDtfYJOVVebKvLp15C3gCId0RspvaDByprztozFso1-iLtuskff-0ShGn5XUteV1AsHc8Awa9GpLyqj1ybZ1qCiBXRvoxBuBDBkd82bIKAtS8G1s5jegoFrCcRdXwIJinPWwnQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="1603481"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/1603481/Visual_quality_metrics_and_human_perception_an_initial_study_on_2D_projections_of_large_multidimensional_data"><img alt="Research paper thumbnail of Visual quality metrics and human perception: an initial study on 2D projections of large multidimensional data" class="work-thumbnail" src="https://attachments.academia-assets.com/15389750/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/1603481/Visual_quality_metrics_and_human_perception_an_initial_study_on_2D_projections_of_large_multidimensional_data">Visual quality metrics and human perception: an initial study on 2D projections of large multidimensional data</a></div><div class="wp-workCard_item"><span>Proceedings of the …</span><span>, Jan 1, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Visual quality metrics have been recently devised to automatically extract interesting visual pro...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Visual quality metrics have been recently devised to automatically extract interesting visual projections out of a large number of available candidates in the exploration of highdimensional databases. The metrics permit for instance to search within a large set of scatter plots (e.g., in a scatter plot matrix) and select the views that contain the best separation among clusters. The rationale behind these techniques is that automatic selection of "best" views is not only useful but also necessary when the number of potential projections exceeds the limit of human interpretation. While useful as a concept in general, such metrics received so far limited validation in terms of human perception. In this paper we present a perceptual study investigating the relationship between human interpretation of clusters in 2D scatter plots and the measures automatically extracted out of them. Specifically we compare a series of selected metrics and analyze how they predict human detection of clusters. A thorough discussion of results follows with reflections on their impact and directions for future research.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="28e974722c69acbe6efcbb7ce904aa16" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":15389750,"asset_id":1603481,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/15389750/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="1603481"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="1603481"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 1603481; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="1603480"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/1603480/Age_populations_and_biometrics_in_eelgrass_Zostera_marina_L"><img alt="Research paper thumbnail of Age populations and biometrics in eelgrass, Zostera marina L" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/1603480/Age_populations_and_biometrics_in_eelgrass_Zostera_marina_L">Age populations and biometrics in eelgrass, Zostera marina L</a></div><div class="wp-workCard_item"><span>Age</span><span>, Jan 1, 1980</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract Differences in turion and leaf size of eelgrass, Zostera marina L., were examined in 26 ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract Differences in turion and leaf size of eelgrass, Zostera marina L., were examined in 26 populations in the Limfjord, Denmark. The number of flowering turions reached a peak in late June with 24.4% of turions being reproductive. The sheath constituted about 25% of ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="1603480"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="1603480"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 1603480; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=1603480]").text(description); $(".js-view-count[data-work-id=1603480]").attr('title', description).tooltip(); 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=1603480]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":1603480,"title":"Age populations and biometrics in eelgrass, Zostera marina L","internal_url":"https://www.academia.edu/1603480/Age_populations_and_biometrics_in_eelgrass_Zostera_marina_L","owner_id":976686,"coauthors_can_edit":true,"owner":{"id":976686,"first_name":"Peter","middle_initials":null,"last_name":"Bak","page_name":"PeterBak","domain_name":"ibm","created_at":"2011-11-23T17:24:18.254-08:00","display_name":"Peter Bak","url":"https://ibm.academia.edu/PeterBak"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="139205" id="papers"><div class="js-work-strip profile--work_container" data-work-id="3606210"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/3606210/A_Visual_Analytics_Approach_for_Assessing_Pedestrian_Friendliness_of_Urban_Environments"><img alt="Research paper thumbnail of A Visual Analytics Approach for Assessing Pedestrian Friendliness of Urban Environments" class="work-thumbnail" src="https://attachments.academia-assets.com/31320557/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/3606210/A_Visual_Analytics_Approach_for_Assessing_Pedestrian_Friendliness_of_Urban_Environments">A Visual Analytics Approach for Assessing Pedestrian Friendliness of Urban Environments</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://telaviv.academia.edu/YoavLerman">Yoav Lerman</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://ibm.academia.edu/PeterBak">Peter Bak</a></span></div><div class="wp-workCard_item"><span>Geographic Information Science at the Heart of Europe</span><span>, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The availability of efficient transportation facilities is vital to the function and development ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The availability of efficient transportation facilities is vital to the function and development of modern cities. Promoting walking is crucial for supporting livable communities and cities. Assessing the quality of pedestrian facilities and constructing appropriate pedestrian walking facilities are important tasks in public city planning. Additionally, walking facilities in a community affect commercial activities including private investment decisions such as those of retailers. However, analyzing what we call pedestrian friendliness in an urban environment involves multiple data perspectives, such as street networks, land use, and other multivariate observation measurements, and consequently poses significant challenges. In this study, we investigate the effect of urban environment properties on pedestrian movement in different locations in the metropolitan region of Tel Aviv. The first urban area we investigated was the inner city of the Tel Aviv metropolitan region, one of the central regions in Tel Aviv, a city that serves many non-local residents. For simplicity, we refer to this area as Tel Aviv. We also investigated Bat Yam, a small city, whose residents use many of the services of Tel Aviv. We apply an improved tool for visual analysis of the correlation between multiple independent and one dependent variable in geographical context. We use the tool to investigate the effect of functional and topological properties on the volume of pedestrian movement. The results of our study indicate that these two urban areas differ greatly. The urban area of Tel Aviv has much more orrespondence and interdependency among the functional and topological properties of the urban environment that might influence pedestrian movement. We also found that the pedestrian movements as well as the related urban environment properties in this region are distributed geographically in a more equal and organized form.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b106f20464bc5bc0f297c77882a80149" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":31320557,"asset_id":3606210,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/31320557/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="3606210"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="3606210"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 3606210; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="32819960"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/32819960/TransUccess_Investigating_Social_Equity_in_Accessing_Public_Transportation_through_Visual_Analytics"><img alt="Research paper thumbnail of TransUccess: Investigating Social Equity in Accessing Public Transportation through Visual Analytics" class="work-thumbnail" src="https://attachments.academia-assets.com/52969325/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/32819960/TransUccess_Investigating_Social_Equity_in_Accessing_Public_Transportation_through_Visual_Analytics">TransUccess: Investigating Social Equity in Accessing Public Transportation through Visual Analytics</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://bgu.academia.edu/ShakedKaufman">Shaked Kaufman</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://ibm.academia.edu/PeterBak">Peter Bak</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">TransUccess is an interactive visualization tool aiming to allow residents , transit operators an...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">TransUccess is an interactive visualization tool aiming to allow residents , transit operators and decision-makers to easily find, analyze and validate patterns of accessibility and social equity in public transit systems. The tool comprises of interactive choropleth map and parallel coordinates chart that assist users in investigating accessibility levels throughout different city zones, so called administrative zones, while making demographic properties of the population , such as mean household income, assessable. The results of our investigations show a complex interplay of demographics and accessibility.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e499f65177e25090b751a188a96eca59" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":52969325,"asset_id":32819960,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/52969325/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="32819960"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="32819960"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 32819960; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="4226320"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/4226320/Visual_Analytics_Using_Density_Equalizing_Geographic_Distortion"><img alt="Research paper thumbnail of Visual Analytics Using Density Equalizing Geographic Distortion" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/4226320/Visual_Analytics_Using_Density_Equalizing_Geographic_Distortion">Visual Analytics Using Density Equalizing Geographic Distortion</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract Visualizing large geo-demographical data sets using pixel-based techniques involves map...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract Visualizing large geo-demographical data sets using pixel-based techniques involves mapping the geo-spatial dimensions of a data point to screen coordinates and appropriately encoding its statistical value by color. Analysis of such data is a great challenge. General ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="4226320"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="4226320"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 4226320; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=4226320]").text(description); $(".js-view-count[data-work-id=4226320]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 4226320; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='4226320']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=4226320]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":4226320,"title":"Visual Analytics Using Density Equalizing Geographic Distortion","internal_url":"https://www.academia.edu/4226320/Visual_Analytics_Using_Density_Equalizing_Geographic_Distortion","owner_id":976686,"coauthors_can_edit":true,"owner":{"id":976686,"first_name":"Peter","middle_initials":null,"last_name":"Bak","page_name":"PeterBak","domain_name":"ibm","created_at":"2011-11-23T17:24:18.254-08:00","display_name":"Peter Bak","url":"https://ibm.academia.edu/PeterBak"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="4226319"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/4226319/Space_in_Time_and_Time_in_Space_Self_Organizing_Maps_for_Exploring_Spatiotemporal_Patterns"><img alt="Research paper thumbnail of Space-in-Time and Time-in-Space Self-Organizing Maps for Exploring Spatiotemporal Patterns" class="work-thumbnail" src="https://attachments.academia-assets.com/49979211/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/4226319/Space_in_Time_and_Time_in_Space_Self_Organizing_Maps_for_Exploring_Spatiotemporal_Patterns">Space-in-Time and Time-in-Space Self-Organizing Maps for Exploring Spatiotemporal Patterns</a></div><div class="wp-workCard_item"><span>Computer Graphics Forum</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Spatiotemporal data pose serious challenges to analysts in geographic and other domains. Owing to...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Spatiotemporal data pose serious challenges to analysts in geographic and other domains. Owing to the complexity of the geospatial and temporal components, this kind of data cannot be analyzed by fully automatic methods but require the involvement of the human analyst's expertise. For a comprehensive analysis, the data need to be considered from two complementary perspectives: (1) as spatial distributions (situations) changing over time and (2) as profiles of local temporal variation distributed over space. In order to support the visual analysis of spatiotemporal data, we suggest a framework based on the “Self-Organizing Map” (SOM) method combined with a set of interactive visual tools supporting both analytic perspectives. SOM can be considered as a combination of clustering and dimensionality reduction. In the first perspective, SOM is applied to the spatial situations at different time moments or intervals. In the other perspective, SOM is applied to the local temporal evolution profiles. The integrated visual analytics environment includes interactive coordinated displays enabling various transformations of spatiotemporal data and post-processing of SOM results. The SOM matrix display offers an overview of the groupings of data objects and their two-dimensional arrangement by similarity. This view is linked to a cartographic map display, a time series graph, and a periodic pattern view. The linkage of these views supports the analysis of SOM results in both the spatial and temporal contexts. The variable SOM grid coloring serves as an instrument for linking the SOM with the corresponding items in the other displays. The framework has been validated on a large dataset with real city traffic data, where expected spatiotemporal patterns have been successfully uncovered. We also describe the use of the framework for discovery of previously unknown patterns in 41-years time series of 7 crime rate attributes in the states of the USA.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="bc94f460c4cdf2846799d0cb4ec77eaa" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":49979211,"asset_id":4226319,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/49979211/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="4226319"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="4226319"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 4226319; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="1603495"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/1603495/Visuelle_Bewegungsanalyse_in_Video_und_Geodaten"><img alt="Research paper thumbnail of Visuelle Bewegungsanalyse in Video-und Geodaten" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/1603495/Visuelle_Bewegungsanalyse_in_Video_und_Geodaten">Visuelle Bewegungsanalyse in Video-und Geodaten</a></div><div class="wp-workCard_item"><span>Informatik-Spektrum</span><span>, Jan 1, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Zusammenfassung Bewegungsdaten von Personen, Fahrzeugen und anderen Objekten liegen durch die Fo...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Zusammenfassung Bewegungsdaten von Personen, Fahrzeugen und anderen Objekten liegen durch die Fortschritte bei Sensoren zur Positionsbestimmung und durch deren weite Verbreitung in immer größer werdendem Umfang vor. Eine vollautomatische Analyse der Bewegungsmuster gestaltet sich für komplexe räumliche und zeitliche Strukturen schwierig, insbesondere wenn die Daten mit Unsicherheiten behaftet sind oder semantische Informationen fehlen. Diese Probleme lassen sich durch eine geeignete Kombination</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="1603495"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="1603495"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 1603495; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=1603495]").text(description); $(".js-view-count[data-work-id=1603495]").attr('title', description).tooltip(); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="1603492"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/1603492/The_effect_of_user_characteristics_on_the_efficiency_of_visual_querying"><img alt="Research paper thumbnail of The effect of user characteristics on the efficiency of visual querying" class="work-thumbnail" src="https://attachments.academia-assets.com/50909081/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/1603492/The_effect_of_user_characteristics_on_the_efficiency_of_visual_querying">The effect of user characteristics on the efficiency of visual querying</a></div><div class="wp-workCard_item"><span>Behaviour & Information Technology</span><span>, Jan 1, 2011</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="87946b2aec290e81f0c5997a3f42ddb6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":50909081,"asset_id":1603492,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/50909081/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="1603492"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="1603492"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 1603492; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=1603492]").text(description); $(".js-view-count[data-work-id=1603492]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 1603492; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='1603492']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="1603491"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/1603491/Visualization_and_Data_Analysis_2005_Proceedings_Volume_"><img alt="Research paper thumbnail of Visualization and Data Analysis 2005 (Proceedings Volume)" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/1603491/Visualization_and_Data_Analysis_2005_Proceedings_Volume_">Visualization and Data Analysis 2005 (Proceedings Volume)</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper discusses algorithmic and implementation aspects of optimally mapping a visualization ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper discusses algorithmic and implementation aspects of optimally mapping a visualization pipeline onto a linear arrangement of wide-area network nodes to minimize the total delay. The first network node typically is a data source, the last node could be a display device ranging from a personal computer to a powerwall, and each intermediate node could be a workstation or computational cluster. This mapping scheme appropriately distributes the filtering, geometry generation, rendering, and display modules of the visualization ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="1603491"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="1603491"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 1603491; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=1603491]").text(description); $(".js-view-count[data-work-id=1603491]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 1603491; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='1603491']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=1603491]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":1603491,"title":"Visualization and Data Analysis 2005 (Proceedings Volume)","internal_url":"https://www.academia.edu/1603491/Visualization_and_Data_Analysis_2005_Proceedings_Volume_","owner_id":976686,"coauthors_can_edit":true,"owner":{"id":976686,"first_name":"Peter","middle_initials":null,"last_name":"Bak","page_name":"PeterBak","domain_name":"ibm","created_at":"2011-11-23T17:24:18.254-08:00","display_name":"Peter Bak","url":"https://ibm.academia.edu/PeterBak"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="1603490"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/1603490/User_settings_of_cue_thresholds_for_binary_categorization_decisions"><img alt="Research paper thumbnail of User settings of cue thresholds for binary categorization decisions" class="work-thumbnail" src="https://attachments.academia-assets.com/30921833/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/1603490/User_settings_of_cue_thresholds_for_binary_categorization_decisions">User settings of cue thresholds for binary categorization decisions</a></div><div class="wp-workCard_item"><span>Journal of Experimental …</span><span>, Jan 1, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The output of binary cuing systems, such as alerts or alarms, depends on the threshold setting-a ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The output of binary cuing systems, such as alerts or alarms, depends on the threshold setting-a parameter that is often user-adjustable. However, it is unknown if users are able to adequately adjust thresholds and what information may help them to do so. Two experiments tested threshold settings for a binary classification task based on binary cues. During the task, participants decided whether a product was intact or faulty. Experimental conditions differed in the information participants received: all participants were informed about a product's fault probability and the payoffs associated with decision outcomes; one third also received information regarding conditional probabilities for a fault when the system indicated or did not indicate the existence of one (predictive values); and another third received information about conditional probabilities for the system indicating a fault, in the instance of the existence or lack thereof, of an actual fault (diagnostic values). Threshold settings in all experimental groups were nonoptimal, with settings closest to the optimum with predictive-values information. Results corresponded with a model describing threshold settings as a function of the conditional probabilities for the different outcomes. From a practical perspective, results indicate that predicti ve-values information best supports decisions about threshold settings. 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=1603486]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":1603486,"title":"2009 Index IEEE Transactions on Visualization and Computer Graphics Vol. 16","internal_url":"https://www.academia.edu/1603486/2009_Index_IEEE_Transactions_on_Visualization_and_Computer_Graphics_Vol_16","owner_id":976686,"coauthors_can_edit":true,"owner":{"id":976686,"first_name":"Peter","middle_initials":null,"last_name":"Bak","page_name":"PeterBak","domain_name":"ibm","created_at":"2011-11-23T17:24:18.254-08:00","display_name":"Peter Bak","url":"https://ibm.academia.edu/PeterBak"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="1603485"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/1603485/Exploration_through_enrichment_a_visual_analytics_approach_for_animal_movement"><img alt="Research paper thumbnail of Exploration through enrichment: a visual analytics approach for animal movement" class="work-thumbnail" src="https://attachments.academia-assets.com/15389751/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/1603485/Exploration_through_enrichment_a_visual_analytics_approach_for_animal_movement">Exploration through enrichment: a visual analytics approach for animal movement</a></div><div class="wp-workCard_item"><span>Proceedings of the 19th …</span><span>, Jan 1, 2011</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The analysis of trajectories has become an important field in geographic visualization, as cheap ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The analysis of trajectories has become an important field in geographic visualization, as cheap GPS sensors have become commonplace and, in many cases, valuable information can be derived either from the data themselves or their metadata if processed and visualized in the right way. However, showing the "right" information to highlight dependencies or correlations between different measurements remains a challenge, because the technical intricacies of applying a combination of automatic and visual analysis methods prevents the majority of domain experts from analyzing and exploring the full wealth of their movement data. This paper presents an exploration through enrichment approach, which enables iterative generation of metadata based on exploratory findings and is aimed at enabling domain experts to explore their data beyond traditional means.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7b1142ca8fffa6ebc4e7dbae0e613906" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":15389751,"asset_id":1603485,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/15389751/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="1603485"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="1603485"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 1603485; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=1603485]").text(description); $(".js-view-count[data-work-id=1603485]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 1603485; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='1603485']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "7b1142ca8fffa6ebc4e7dbae0e613906" } } $('.js-work-strip[data-work-id=1603485]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":1603485,"title":"Exploration through enrichment: a visual analytics approach for animal movement","internal_url":"https://www.academia.edu/1603485/Exploration_through_enrichment_a_visual_analytics_approach_for_animal_movement","owner_id":976686,"coauthors_can_edit":true,"owner":{"id":976686,"first_name":"Peter","middle_initials":null,"last_name":"Bak","page_name":"PeterBak","domain_name":"ibm","created_at":"2011-11-23T17:24:18.254-08:00","display_name":"Peter Bak","url":"https://ibm.academia.edu/PeterBak"},"attachments":[{"id":15389751,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/15389751/thumbnails/1.jpg","file_name":"355.pdf","download_url":"https://www.academia.edu/attachments/15389751/download_file","bulk_download_file_name":"Exploration_through_enrichment_a_visual.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/15389751/355-libre.pdf?1390864150=\u0026response-content-disposition=attachment%3B+filename%3DExploration_through_enrichment_a_visual.pdf\u0026Expires=1740547228\u0026Signature=NPqXcvPDQtVeuJrHDFVCryAOGq9KfNXlG3jgkm-gBHKFZGIJwX9wSxYY5s1chE~bw7iJUpWldxahCECGZFb6rL7pbaxbpEX56tjhb50JEZWm2islickzIruqeJqqqqg9HCsfitp8O0IweB7-fsotQJQBpYvaLf6Q4Yo8fffrO1Q~JYL3r~IVTnk6dFN03nVDAv7XN-kM0yoiscbLWZdLQhnjuXl0SXnT0MHHhvVqoK713-vgL0DOsuqMjRMHs62a2aNw5nMsoqJcsWY-eUKstw9Ps9bDKEa~OB4NdznhCzz6pPMSL3p6SOFKyAKo4DhCj5Trdx8XWYkKisIdMbI-3g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="1603484"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/1603484/Applied_Visual_Exploration_on_Real_Time_News_Feeds_using_Polarity_and_Geo_Spatial_Analysis"><img alt="Research paper thumbnail of Applied Visual Exploration on Real-Time News Feeds using Polarity and Geo-Spatial Analysis" class="work-thumbnail" src="https://attachments.academia-assets.com/15389752/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/1603484/Applied_Visual_Exploration_on_Real_Time_News_Feeds_using_Polarity_and_Geo_Spatial_Analysis">Applied Visual Exploration on Real-Time News Feeds using Polarity and Geo-Spatial Analysis</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper presents a visual analytics approach to explore large news article collections in the ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This paper presents a visual analytics approach to explore large news article collections in the domains of polarity and spatial analysis. The exploration is performed on the data collected with Europe Media Monitor (EMM), a system which monitors over 2500 online sources and processes 90,000 articles per day. By analyzing the news feeds, we want to find out which topics are important in different countries and what is the general polarity of the articles within these topics. To assess the polarity of a news article, automatic techniques for polarity analysis are employed and the results are represented using Literature Fingerprinting for visualization. In the spatial description of the news feeds, every article can be represented by two geographic attributes, the news origin and the location of the event itself. In order to assess these spatial properties of news articles, we conducted our geo-analysis, which is able to cope with the size and spatial distribution of the data. Within this application framework, we show opportunities how real-time news feed data can be analyzed efficiently.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f9ead76668c8ca8a154b6ba2cb91317e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":15389752,"asset_id":1603484,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/15389752/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="1603484"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="1603484"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 1603484; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=1603484]").text(description); $(".js-view-count[data-work-id=1603484]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 1603484; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='1603484']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f9ead76668c8ca8a154b6ba2cb91317e" } } $('.js-work-strip[data-work-id=1603484]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":1603484,"title":"Applied Visual Exploration on Real-Time News Feeds using Polarity and Geo-Spatial Analysis","internal_url":"https://www.academia.edu/1603484/Applied_Visual_Exploration_on_Real_Time_News_Feeds_using_Polarity_and_Geo_Spatial_Analysis","owner_id":976686,"coauthors_can_edit":true,"owner":{"id":976686,"first_name":"Peter","middle_initials":null,"last_name":"Bak","page_name":"PeterBak","domain_name":"ibm","created_at":"2011-11-23T17:24:18.254-08:00","display_name":"Peter Bak","url":"https://ibm.academia.edu/PeterBak"},"attachments":[{"id":15389752,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/15389752/thumbnails/1.jpg","file_name":"webist2010.pdf","download_url":"https://www.academia.edu/attachments/15389752/download_file","bulk_download_file_name":"Applied_Visual_Exploration_on_Real_Time.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/15389752/webist2010-libre.pdf?1390864169=\u0026response-content-disposition=attachment%3B+filename%3DApplied_Visual_Exploration_on_Real_Time.pdf\u0026Expires=1740547228\u0026Signature=gAXoJVGidVQcEtkFaNhfgboZO3ysCzvqHXiB1rTycC7ebibGu5c4om19i5nlzM8F9YmXGiZAdjvWQAIkmTRpUQKXEBtH2I5uN-jR3zl9Y7EkXbcQekL9iLOuzuFwTZQ~IzkUiP3zH99Qbe1kTn6VkSGn1ZLGPeRRqnDuIAwqEvv9p6SuvJCqOriQAW9hYf2fCtte~o7N2lbMxLqPmXJm7sbej5FlHZbkoUbcwYm7so7V4gGHccnUEuXDTmdVHxFD2qOAuEfH~661y6IOnOYfSRZilYlQpL2-fvJrdO9gRkTIIB1NeWz~j5I7kz~qrC5BZ~7FoK3DS3kVoSqw86TPIQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="1603483"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/1603483/Where_Would_You_Go_on_Your_Next_Vacation"><img alt="Research paper thumbnail of Where Would You Go on Your Next Vacation" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/1603483/Where_Would_You_Go_on_Your_Next_Vacation">Where Would You Go on Your Next Vacation</a></div><div class="wp-workCard_item"><span>A Framework for Visual …</span><span>, Jan 1, 2010</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="1603483"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="1603483"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 1603483; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=1603483]").text(description); $(".js-view-count[data-work-id=1603483]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 1603483; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='1603483']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=1603483]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":1603483,"title":"Where Would You Go on Your Next Vacation","internal_url":"https://www.academia.edu/1603483/Where_Would_You_Go_on_Your_Next_Vacation","owner_id":976686,"coauthors_can_edit":true,"owner":{"id":976686,"first_name":"Peter","middle_initials":null,"last_name":"Bak","page_name":"PeterBak","domain_name":"ibm","created_at":"2011-11-23T17:24:18.254-08:00","display_name":"Peter Bak","url":"https://ibm.academia.edu/PeterBak"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="1603482"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/1603482/Advanced_visual_analytics_interfaces"><img alt="Research paper thumbnail of Advanced visual analytics interfaces" class="work-thumbnail" src="https://attachments.academia-assets.com/15389749/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/1603482/Advanced_visual_analytics_interfaces">Advanced visual analytics interfaces</a></div><div class="wp-workCard_item"><span>Proceedings of the …</span><span>, Jan 1, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Advanced visual interfaces, like the ones found in information visualization, intend to offer a v...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Advanced visual interfaces, like the ones found in information visualization, intend to offer a view on abstract data spaces to enable users to make sense of them. By mapping data to visual representations and providing interactive tools to explore and navigate, it is possible to get an understanding of the data and possibly discover new knowledge. With the advent of modern data collection and analysis technologies, the direct visualization of data starts to show its limitations due to limited scalability in terms of volumes and to the complexity of required analytical reasoning. Many analytical problems we encounter today require approaches that go beyond pure analytics or pure visualization. Visual analytics provides an answer to this problems by advocating a tight integration between automatic computation and interactive visualization, proposing a more holistic approach. In this paper, we argue for Advanced Visual Analytics Interfaces (AVAIs), visual interfaces in which neither the analytics nor the visualization needs to be advanced in itself but where the synergy between automation and visualization is in fact advanced. We offer a detailed argumentation around the needs and challenges of AVAIs and provide several examples of this type of interfaces.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="1fec5aa17e9cf2eb68b80495ea3649d6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":15389749,"asset_id":1603482,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/15389749/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="1603482"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="1603482"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 1603482; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=1603482]").text(description); $(".js-view-count[data-work-id=1603482]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 1603482; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='1603482']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "1fec5aa17e9cf2eb68b80495ea3649d6" } } $('.js-work-strip[data-work-id=1603482]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":1603482,"title":"Advanced visual analytics interfaces","internal_url":"https://www.academia.edu/1603482/Advanced_visual_analytics_interfaces","owner_id":976686,"coauthors_can_edit":true,"owner":{"id":976686,"first_name":"Peter","middle_initials":null,"last_name":"Bak","page_name":"PeterBak","domain_name":"ibm","created_at":"2011-11-23T17:24:18.254-08:00","display_name":"Peter Bak","url":"https://ibm.academia.edu/PeterBak"},"attachments":[{"id":15389749,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/15389749/thumbnails/1.jpg","file_name":"10.1.1.173.1572.pdf","download_url":"https://www.academia.edu/attachments/15389749/download_file","bulk_download_file_name":"Advanced_visual_analytics_interfaces.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/15389749/10.1.1.173.1572-libre.pdf?1390864160=\u0026response-content-disposition=attachment%3B+filename%3DAdvanced_visual_analytics_interfaces.pdf\u0026Expires=1740547228\u0026Signature=EM4XGoXks8-brCsEFBB15bkDhz8nU~gPn5euyMNWkN8e42HmHDgqjGoZGfeS18TSiFBVZyYauYwLnN1O3qQITiLOlpoDyKSVG9iOO0ZEM-OXo9DZg0gqN5DsigPi0NDIdhRx1iLsRM6tOj5GfL3FQ6P9y1jtqS1z5lmZSUT9xfyMuRai-jq8Dv630s2bsX1vyLbkny43Mzg1fSmAgDtfYJOVVebKvLp15C3gCId0RspvaDByprztozFso1-iLtuskff-0ShGn5XUteV1AsHc8Awa9GpLyqj1ybZ1qCiBXRvoxBuBDBkd82bIKAtS8G1s5jegoFrCcRdXwIJinPWwnQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="1603481"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/1603481/Visual_quality_metrics_and_human_perception_an_initial_study_on_2D_projections_of_large_multidimensional_data"><img alt="Research paper thumbnail of Visual quality metrics and human perception: an initial study on 2D projections of large multidimensional data" class="work-thumbnail" src="https://attachments.academia-assets.com/15389750/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/1603481/Visual_quality_metrics_and_human_perception_an_initial_study_on_2D_projections_of_large_multidimensional_data">Visual quality metrics and human perception: an initial study on 2D projections of large multidimensional data</a></div><div class="wp-workCard_item"><span>Proceedings of the …</span><span>, Jan 1, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Visual quality metrics have been recently devised to automatically extract interesting visual pro...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Visual quality metrics have been recently devised to automatically extract interesting visual projections out of a large number of available candidates in the exploration of highdimensional databases. The metrics permit for instance to search within a large set of scatter plots (e.g., in a scatter plot matrix) and select the views that contain the best separation among clusters. The rationale behind these techniques is that automatic selection of "best" views is not only useful but also necessary when the number of potential projections exceeds the limit of human interpretation. While useful as a concept in general, such metrics received so far limited validation in terms of human perception. In this paper we present a perceptual study investigating the relationship between human interpretation of clusters in 2D scatter plots and the measures automatically extracted out of them. Specifically we compare a series of selected metrics and analyze how they predict human detection of clusters. A thorough discussion of results follows with reflections on their impact and directions for future research.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="28e974722c69acbe6efcbb7ce904aa16" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":15389750,"asset_id":1603481,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/15389750/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="1603481"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="1603481"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 1603481; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=1603481]").text(description); $(".js-view-count[data-work-id=1603481]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 1603481; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='1603481']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "28e974722c69acbe6efcbb7ce904aa16" } } $('.js-work-strip[data-work-id=1603481]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":1603481,"title":"Visual quality metrics and human perception: an initial study on 2D projections of large multidimensional data","internal_url":"https://www.academia.edu/1603481/Visual_quality_metrics_and_human_perception_an_initial_study_on_2D_projections_of_large_multidimensional_data","owner_id":976686,"coauthors_can_edit":true,"owner":{"id":976686,"first_name":"Peter","middle_initials":null,"last_name":"Bak","page_name":"PeterBak","domain_name":"ibm","created_at":"2011-11-23T17:24:18.254-08:00","display_name":"Peter Bak","url":"https://ibm.academia.edu/PeterBak"},"attachments":[{"id":15389750,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/15389750/thumbnails/1.jpg","file_name":"279.pdf","download_url":"https://www.academia.edu/attachments/15389750/download_file","bulk_download_file_name":"Visual_quality_metrics_and_human_percept.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/15389750/279-libre.pdf?1390864166=\u0026response-content-disposition=attachment%3B+filename%3DVisual_quality_metrics_and_human_percept.pdf\u0026Expires=1740547228\u0026Signature=PFg-Ee~DTDESWK6y3JLz-rygg7~ePN6dnUUYBVvJy5JJBm6E7gXggGpPn6sLXlWodyJJPzWJNoAu0Zl72NwU6154bVR-NC2w7C2EIgOEUg01JaHFfq8wcRv4aQOE6NsDWSzl7259ApKfDTe~3ktBh5fX7HTj348VY-pRbyGjxZ1FXgqmWUZX2oOyJaj0qGnX9AguhMJOOB8FUnFvnK8kuF1BdeCnLmEzLYJn1BjC4KWH0uFf~aaMlZbsKt9LrZIzqEjn-SfsLUR0oQFPUuMthluBRuDnWG8n~h1Lub8ExzG7S5b9xjcsTS-ve2sVBukZKg2bn8ncztl1d35dekoA2A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="1603480"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/1603480/Age_populations_and_biometrics_in_eelgrass_Zostera_marina_L"><img alt="Research paper thumbnail of Age populations and biometrics in eelgrass, Zostera marina L" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/1603480/Age_populations_and_biometrics_in_eelgrass_Zostera_marina_L">Age populations and biometrics in eelgrass, Zostera marina L</a></div><div class="wp-workCard_item"><span>Age</span><span>, Jan 1, 1980</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Abstract Differences in turion and leaf size of eelgrass, Zostera marina L., were examined in 26 ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Abstract Differences in turion and leaf size of eelgrass, Zostera marina L., were examined in 26 populations in the Limfjord, Denmark. The number of flowering turions reached a peak in late June with 24.4% of turions being reproductive. The sheath constituted about 25% of ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="1603480"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="1603480"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 1603480; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=1603480]").text(description); $(".js-view-count[data-work-id=1603480]").attr('title', description).tooltip(); 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