{"id":12173,"date":"2019-09-24T12:28:13","date_gmt":"2019-09-24T16:28:13","guid":{"rendered":"https:\/\/cnr.ncsu.edu\/geospatial\/?p=12173"},"modified":"2024-05-01T18:45:53","modified_gmt":"2024-05-01T22:45:53","slug":"human-experience-map","status":"publish","type":"post","link":"https:\/\/cnr.ncsu.edu\/geospatial\/news\/2019\/09\/24\/human-experience-map\/","title":{"rendered":"Putting Human Experience on the Map"},"content":{"rendered":"\n\n\n\n\n<p>In our daily comings and goings between home, work and school, the streets and bike paths we choose to navigate are also an emotional map of our commute. Where do we consistently experience frustration with traffic? Or anxiety from a tricky intersection?<\/p>\n\n\n\n<p>To <a href=\"https:\/\/gcmillar.github.io\/\"><u>Garrett Millar<\/u><\/a>, Ph.D. candidate in NC State\u2019s <a href=\"https:\/\/cnr.ncsu.edu\/geospatial\/academics\/phd-in-geospatial-analytics\/\"><u>Geospatial Analytics doctoral program<\/u><\/a>, mapping the locations of strong emotions is a powerful way to help improve the human experience\u2013\u2013by helping decision-makers identify where environments inspire either calm or stress.<\/p>\n\n\n\n<p>Millar earned a bachelor\u2019s degree in Psychology from NC State in 2016 and immediately dove into doctoral work. At the Center for Geospatial Analytics, his research has shifted from \u201cseeing how people interact with technology,\u201d he says, to \u201cusing technology to see how people interact with the world.\u201d A large part of his dissertation has focused on creating interactive spatial tools that reveal the environmental context of human emotion.<\/p>\n\n\n\n<p>\u201cHuman emotion is a very complex phenomenon and extremely hard to pin down and measure,\u201d Millar explains. Spatial data provide a quantitative way of understanding the deeply important <em>context<\/em> of emotions, he says, and the environmental stimuli that spark them.<\/p>\n\n\n\n<p>By developing interactive geovisualizations that pair physiological data with detailed maps, Millar is \u201chelping to better understand why people are having particular experiences\u201d in specific places.<\/p>\n\n\n\n<p><strong>Understanding cyclists\u2019 stress with Stress3d<\/strong><\/p>\n\n\n\n<p>One part of Millar\u2019s doctoral research has involved creating an interactive online mapping application called <a href=\"https:\/\/gcmillar.github.io\/stress3d\"><u>Stress3d<\/u><\/a>, which will help <a href=\"http:\/\/www.nweurope.eu\/projects\/project-search\/cycle-highways-innovation-for-smarter-people-transport-and-spatial-planning\/\"><u>transportation planners in The Netherlands<\/u><\/a> design or modify bicycle highways to minimize rider stress.<\/p>\n\n\n\n<p>Millar began his work on Stress3d in Faculty Fellow <a href=\"https:\/\/cnr.ncsu.edu\/geospatial\/people\/joshua-gray\"><u>Josh Gray<\/u><\/a>\u2019s GIS 713 \u201cGeospatial Data Mining and Analysis\u201d course, using a dataset provided, at Millar\u2019s request, by the Experience Lab at the Breda University of Applied Sciences. At first, \u201cthere was a huge learning curve\u201d to working with the Experience Lab\u2019s data, Millar says, \u201cbut the most exciting learning curve there could be, because I was mapping what people felt, where they felt it, and trying to infer why.\u201d<\/p>\n\n\n\n<p>When he shared a preliminary analysis with Experience Lab researchers, \u201cthey liked what they saw,\u201d Millar says, and they invited him to spend several weeks working onsite in The Netherlands to expand the application and even create another.<\/p>\n\n\n\n<p>The Stress3d application Millar created interactively displays data for a two-hour bike ride between the cities of Tilburg and Waalwijk, with data points from twelve bicyclists equipped with wearable sensors and a GoPro camera.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large wp-image-12174\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"583\" src=\"https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/12\/2019\/09\/Stress3d_thumbnail-1500-1024x583.jpg\" alt=\"screenshot of Stress3d online mapping application\" class=\"wp-image-12174\" srcset=\"https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-1024x583.jpg 1024w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-300x171.jpg 300w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-768x437.jpg 768w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-460x262.jpg 460w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-920x524.jpg 920w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-376x214.jpg 376w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-752x428.jpg 752w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-345x196.jpg 345w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-690x393.jpg 690w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-950x541.jpg 950w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-783x446.jpg 783w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-720x410.jpg 720w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-1440x820.jpg 1440w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-600x342.jpg 600w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-848x483.jpg 848w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-555x316.jpg 555w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-1110x632.jpg 1110w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-360x205.jpg 360w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-220x125.jpg 220w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-440x251.jpg 440w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-825x470.jpg 825w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-659x375.jpg 659w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-1318x750.jpg 1318w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-992x565.jpg 992w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500.jpg 1500w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-1200x683.jpg 1200w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-500x285.jpg 500w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-1000x569.jpg 1000w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-410x233.jpg 410w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-820x467.jpg 820w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-285x162.jpg 285w, https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/22\/2019\/09\/Stress3d_thumbnail-1500-570x325.jpg 570w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Millar\u2019s interactive online application Stress3d allows transportation planners to follow the experience of bicyclists and visualize their perspective during times of high and low stress.<\/figcaption><\/figure>\n\n\n\n<p>\u201cThere are physiological indicators that can be used to infer whether a person is feeling an emotion and how strongly they\u2019re feeling it,\u201d Millar explains. The dozen bicyclists in Stress3d\u2019s dataset wore a device similar to a smartwatch to measure changes in a small electric current passing through the sweat on their skin. These measurements of so-called \u201cskin conductance\u201d are commonly used in psychological research to detect spikes in sweat production (and, by proxy, stress).<\/p>\n\n\n\n<p>\u201cThe tricky thing with physiological data, though, is that you could have the most perfect data\u2013\u2013highly accurate measurements of physiology and of location\u2013\u2013but the <em>context<\/em> is always a bit cloudy,\u201d Millar points out.<\/p>\n\n\n\n<p>To solve that problem, Stress3d provides street-view images of the environment around each data point along a cyclist\u2019s journey\u2013\u2013essentially allowing researchers to visualize what each cyclist was seeing when they experienced stress.<\/p>\n\n\n\n<p>The approach can help inform road redesign plans, Millar says, by quantifying both negative and positive reactions. If many cyclists experience stress at the same location, their combined experiences may indicate a particularly problematic stretch of highway. In contrast, an area that elicits consistently positive responses might have characteristics that should be replicated elsewhere. Users of Millar\u2019s application can zoom in to the cyclists\u2019 line of sight to identify any problems (or pluses) and devise strategies to fix (or learn from) them.<\/p>\n\n\n\n<p><strong>Mapping excitement for art and more<\/strong><\/p>\n\n\n\n<p>Millar\u2019s collaboration with the Experience Lab has since expanded to include additional spatial explorations of physiological data\u2013\u2013tracking the <a href=\"https:\/\/www.celth.nl\/blogs\/measuring-van-gogh-experience-nuenen\"><u>emotional responses of museum-goers<\/u><\/a> as they wander through art exhibits. To help staff at the Vincent Van Gogh Centre in Nuenen consider a museum redesign, Millar built a digital 3-D model of the building, overlaid with skin conductance data from visitors, creating another interactive online map he dubbed <a href=\"https:\/\/gcmillar.github.io\/Nuenen3d\/\"><u>Emotion Museum<\/u><\/a>. The application will help museum staff understand which exhibits visitors find most exciting, and therefore which might be best to feature, without needing to conduct a detailed survey.<\/p>\n\n\n\n<p>Millar has also started working with researchers at Harvard Medical School to help them leverage the use of wearable sensors (specifically Fitbits) to track the well-being of healthcare workers. The project is part of the extensive <a href=\"https:\/\/www.nurseshealthstudy.org\/\"><u>Nurses\u2019 Health Study<\/u><\/a>, conducted in partnership with Brigham and Women\u2019s Hospital, which has been investigating risk factors for major chronic diseases since the 1970s.<\/p>\n\n\n\n<p>\u201cPeople don\u2019t exist without their environment,\u201d Millar explains. Understanding that environment is therefore key to understanding, and optimizing, emotional and physical well-being.<\/p>\n\n\n\n<p>\u201cIf we can figure out how we respond to the environment,\u201d Millar says, \u201cwe can figure out a better way to live in it.\u201d<\/p>\n","protected":false,"raw":"<!-- wp:ncst\/dynamic-header {\"block\":\"ncst\/default-post-header\"} -->\n<!-- wp:ncst\/default-post-header {\"caption\":\"Garrett Millar earned his bachelor\u2019s degree in Psychology from NC State and is on track to graduate in May 2020 from the Geospatial Analytics Ph.D. program. As part of his doctoral research, he creates data-driven visualizations that explore how people relate to their environments.\"} \/-->\n<!-- \/wp:ncst\/dynamic-header -->\n\n<!-- wp:paragraph -->\n<p>In our daily comings and goings between home, work and school, the streets and bike paths we choose to navigate are also an emotional map of our commute. Where do we consistently experience frustration with traffic? Or anxiety from a tricky intersection?<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>To <a href=\"https:\/\/gcmillar.github.io\/\"><u>Garrett Millar<\/u><\/a>, Ph.D. candidate in NC State\u2019s <a href=\"https:\/\/cnr.ncsu.edu\/geospatial\/academics\/phd-in-geospatial-analytics\/\"><u>Geospatial Analytics doctoral program<\/u><\/a>, mapping the locations of strong emotions is a powerful way to help improve the human experience\u2013\u2013by helping decision-makers identify where environments inspire either calm or stress.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Millar earned a bachelor\u2019s degree in Psychology from NC State in 2016 and immediately dove into doctoral work. At the Center for Geospatial Analytics, his research has shifted from \u201cseeing how people interact with technology,\u201d he says, to \u201cusing technology to see how people interact with the world.\u201d A large part of his dissertation has focused on creating interactive spatial tools that reveal the environmental context of human emotion.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>\u201cHuman emotion is a very complex phenomenon and extremely hard to pin down and measure,\u201d Millar explains. Spatial data provide a quantitative way of understanding the deeply important <em>context<\/em> of emotions, he says, and the environmental stimuli that spark them.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>By developing interactive geovisualizations that pair physiological data with detailed maps, Millar is \u201chelping to better understand why people are having particular experiences\u201d in specific places.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p><strong>Understanding cyclists\u2019 stress with Stress3d<\/strong><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>One part of Millar\u2019s doctoral research has involved creating an interactive online mapping application called <a href=\"https:\/\/gcmillar.github.io\/stress3d\"><u>Stress3d<\/u><\/a>, which will help <a href=\"http:\/\/www.nweurope.eu\/projects\/project-search\/cycle-highways-innovation-for-smarter-people-transport-and-spatial-planning\/\"><u>transportation planners in The Netherlands<\/u><\/a> design or modify bicycle highways to minimize rider stress.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Millar began his work on Stress3d in Faculty Fellow <a href=\"https:\/\/cnr.ncsu.edu\/geospatial\/people\/joshua-gray\"><u>Josh Gray<\/u><\/a>\u2019s GIS 713 \u201cGeospatial Data Mining and Analysis\u201d course, using a dataset provided, at Millar\u2019s request, by the Experience Lab at the Breda University of Applied Sciences. At first, \u201cthere was a huge learning curve\u201d to working with the Experience Lab\u2019s data, Millar says, \u201cbut the most exciting learning curve there could be, because I was mapping what people felt, where they felt it, and trying to infer why.\u201d<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>When he shared a preliminary analysis with Experience Lab researchers, \u201cthey liked what they saw,\u201d Millar says, and they invited him to spend several weeks working onsite in The Netherlands to expand the application and even create another.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>The Stress3d application Millar created interactively displays data for a two-hour bike ride between the cities of Tilburg and Waalwijk, with data points from twelve bicyclists equipped with wearable sensors and a GoPro camera.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:image {\"id\":12174,\"className\":\"size-large wp-image-12174\"} -->\n<figure class=\"wp-block-image size-large wp-image-12174\"><img src=\"https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/12\/2019\/09\/Stress3d_thumbnail-1500-1024x583.jpg\" alt=\"screenshot of Stress3d online mapping application\" class=\"wp-image-12174\"\/><figcaption class=\"wp-element-caption\">Millar\u2019s interactive online application Stress3d allows transportation planners to follow the experience of bicyclists and visualize their perspective during times of high and low stress.<\/figcaption><\/figure>\n<!-- \/wp:image -->\n\n<!-- wp:paragraph -->\n<p>\u201cThere are physiological indicators that can be used to infer whether a person is feeling an emotion and how strongly they\u2019re feeling it,\u201d Millar explains. The dozen bicyclists in Stress3d\u2019s dataset wore a device similar to a smartwatch to measure changes in a small electric current passing through the sweat on their skin. These measurements of so-called \u201cskin conductance\u201d are commonly used in psychological research to detect spikes in sweat production (and, by proxy, stress).<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>\u201cThe tricky thing with physiological data, though, is that you could have the most perfect data\u2013\u2013highly accurate measurements of physiology and of location\u2013\u2013but the <em>context<\/em> is always a bit cloudy,\u201d Millar points out.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>To solve that problem, Stress3d provides street-view images of the environment around each data point along a cyclist\u2019s journey\u2013\u2013essentially allowing researchers to visualize what each cyclist was seeing when they experienced stress.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>The approach can help inform road redesign plans, Millar says, by quantifying both negative and positive reactions. If many cyclists experience stress at the same location, their combined experiences may indicate a particularly problematic stretch of highway. In contrast, an area that elicits consistently positive responses might have characteristics that should be replicated elsewhere. Users of Millar\u2019s application can zoom in to the cyclists\u2019 line of sight to identify any problems (or pluses) and devise strategies to fix (or learn from) them.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p><strong>Mapping excitement for art and more<\/strong><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Millar\u2019s collaboration with the Experience Lab has since expanded to include additional spatial explorations of physiological data\u2013\u2013tracking the <a href=\"https:\/\/www.celth.nl\/blogs\/measuring-van-gogh-experience-nuenen\"><u>emotional responses of museum-goers<\/u><\/a> as they wander through art exhibits. To help staff at the Vincent Van Gogh Centre in Nuenen consider a museum redesign, Millar built a digital 3-D model of the building, overlaid with skin conductance data from visitors, creating another interactive online map he dubbed <a href=\"https:\/\/gcmillar.github.io\/Nuenen3d\/\"><u>Emotion Museum<\/u><\/a>. The application will help museum staff understand which exhibits visitors find most exciting, and therefore which might be best to feature, without needing to conduct a detailed survey.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>Millar has also started working with researchers at Harvard Medical School to help them leverage the use of wearable sensors (specifically Fitbits) to track the well-being of healthcare workers. The project is part of the extensive <a href=\"https:\/\/www.nurseshealthstudy.org\/\"><u>Nurses\u2019 Health Study<\/u><\/a>, conducted in partnership with Brigham and Women\u2019s Hospital, which has been investigating risk factors for major chronic diseases since the 1970s.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>\u201cPeople don\u2019t exist without their environment,\u201d Millar explains. Understanding that environment is therefore key to understanding, and optimizing, emotional and physical well-being.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph -->\n<p>\u201cIf we can figure out how we respond to the environment,\u201d Millar says, \u201cwe can figure out a better way to live in it.\u201d<\/p>\n<!-- \/wp:paragraph -->"},"excerpt":{"rendered":"<p>Garrett Millar has a bachelor\u2019s degree in Psychology from NC State and will graduate in May from the Geospatial Analytics Ph.D. program. Using data from wearable sensors, he creates interactive visualizations that explore the spatial side of stress.<\/p>\n","protected":false},"author":2,"featured_media":12179,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"source":"","ncst_custom_author":"","ncst_show_custom_author":false,"ncst_dynamicHeaderBlockName":"ncst\/default-post-header","ncst_dynamicHeaderData":"{\"showAuthor\":true,\"showDate\":true,\"showFeaturedVideo\":false,\"caption\":\"Garrett Millar earned his bachelor\u2019s degree in Psychology from NC State and is on track to graduate in May 2020 from the Geospatial Analytics Ph.D. program. As part of his doctoral research, he creates data-driven visualizations that explore how people relate to their environments.\"}","ncst_content_audit_freq":"","ncst_content_audit_date":"","ncst_content_audit_display":false,"ncst_backToTopFlag":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[48,44,6],"tags":[],"class_list":["post-12173","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-geospatial-analytics-phd","category-newswire","category-student"],"displayCategory":null,"acf":{"ncst_posts_meta_modified_date":null},"_links":{"self":[{"href":"https:\/\/cnr.ncsu.edu\/geospatial\/wp-json\/wp\/v2\/posts\/12173","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cnr.ncsu.edu\/geospatial\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cnr.ncsu.edu\/geospatial\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cnr.ncsu.edu\/geospatial\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/cnr.ncsu.edu\/geospatial\/wp-json\/wp\/v2\/comments?post=12173"}],"version-history":[{"count":9,"href":"https:\/\/cnr.ncsu.edu\/geospatial\/wp-json\/wp\/v2\/posts\/12173\/revisions"}],"predecessor-version":[{"id":21735,"href":"https:\/\/cnr.ncsu.edu\/geospatial\/wp-json\/wp\/v2\/posts\/12173\/revisions\/21735"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/cnr.ncsu.edu\/geospatial\/wp-json\/wp\/v2\/media\/12179"}],"wp:attachment":[{"href":"https:\/\/cnr.ncsu.edu\/geospatial\/wp-json\/wp\/v2\/media?parent=12173"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cnr.ncsu.edu\/geospatial\/wp-json\/wp\/v2\/categories?post=12173"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cnr.ncsu.edu\/geospatial\/wp-json\/wp\/v2\/tags?post=12173"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}