anchor.diff:  <% content_for :page_title do %>About - Building Inspector by NYPL Labs<% end %>
   <% content_for :dynamic_js do %>
     <%= stylesheet_link_tag    "home", :media => "all" %>
  +  <%= javascript_include_tag "about" %>
   <% end %>
   
   <%= render :partial => "partials/nav" %>
   
   <div id="main-container">
     <div id="intro" class="about">
  -    <h3>What's this all about?</h3>
  +    <h3 class="major">What's this all about?</h3>
   
       <p>For years The New York Public Library has collected maps documenting our ever-changing metropolis. Originally commissioned by insurance companies to assess property value and fire risk, these street atlases contain a wealth of detailed information about a city now largely lost to us: buildings long ago destroyed, streets renamed, whole neighborhoods redrawn or redefined. Making these lost places findable via contemporary digital maps allows us to drill down through the layers of urban change and study the city in profound new ways. But harvesting the data isn't easy.</p>
   
  
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                  <h3>How is my work reviewed?</h3>
  
  <p>Every time you inspect a building, you’re essentially casting a vote alongside your fellow several other Inspectors. We show the same footprint and task to several people and tally up those votes to decide whether there’s agreement among inspectors.</p>
  
  <p>While each task has a different means to come to consensus, it’s useful to see an example. For Footprint Inspection, it’s coming to consensus on whether the computer-detected footprint is a Yes, No, or a Fix. We show the same footprint to at least 3 different people, and every 10 minutes we tally up the votes. If 75% or more agree, that footprint has reached “consensus” and the system removes it from the inspection queue. If the jury's still out, we keep the footprint in circulation until consensus is reached, focusing our collective efforts on the buildings most in need.</p>
  
  <p>The main takeaway here is: relax! Your fellow Inspectors have got your back. <em>It's no big deal if make a mistake once in a while, or make your best guess on an ambiguous case.</em> The community will double, triple, and quadruple check you, and you them. Go Team!</p>
  
      <h4>More background on the project</h4>
  
      <p>Building Inspector was made by <a href="http://www.nypl.org/labs">NYPL Labs</a> in collaboration with the <a href="http://www.nypl.org/locations/schwarzman/map-division"> Lionel Pincus and Princess Firyal Map Division</a> at The New York Public Library. The app uses digitized city atlases georeferenced by librarians and public volunteers on NYPL's <a href="http://maps.nypl.org">Map Warper</a>, an online tool suite for creating interactive historical maps. These rectified maps are then passed through a new computer vision process devised by NYPL Labs called the <a href="https://github.com/NYPL/map-vectorizer">Map Vectorizer</a>, which recognizes building footprints and converts them into machine-readable shape files (think of it like <a href="http://en.wikipedia.org/wiki/Optical_character_recognition">OCR</a>, but for maps).</p>
  
      <p>This is a big leap forward in the work of digitizing old maps. Until now, feature extraction has been done manually by staff and volunteers, tracing each individual building footprint painstakingly by hand. This is a difficult task, usually requiring on-site training, and so it can take months or years to complete a single atlas. The Vectorizer, however, can crunch through an entire borough map in a matter of hours. The data's not perfect, but it's good enough to start figuring out easier, crowd-friendly tasks for validating and correcting it (like the yes/no/fix process at the center of this app). And all of that public input can then be used to refine and improve the Vectorizer process. Human-computer collaboration!</p>
  
      <p>But enough yapping. Let's inspect some buildings!</p>
  
      <h4>CREDITS</h4>
      <p>
      <strong>The Lionel Pincus and Princess Firyal Map Division, NYPL</strong><br />
      Geospatial Librarian - Matt Knutzen<br />
  
negative.code:           <li><%= link_to(image_tag("icon-color.svg", :class=>"task-icon"), color_path, :class=>"link-task") %> <strong>Classify Colors</strong> - The original mapmakers color-coded the buildings to indicate construction materials and use types (residential vs. commercial). Identifying the colors helps us index these important details.<br /></li>
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   <h3 class="major">How Is Accuracy Ensured?</h3>
  
  <p>Every time you inspect a building, you’re essentially casting a vote alongside your fellow Inspectors. We show the same footprint and task to several people and tally up those votes to decide whether they agree.</p>
  
  <p>While each task has a different means to come to consensus, it’s useful to see an example. For Footprint Inspection, it’s coming to consensus on whether the computer-detected footprint is a Yes, No, or a Fix. We show the same footprint to at least 3 different people, and every 10 minutes we tally up the votes. If 75% or more agree, that footprint has reached “consensus” and the system removes it from the inspection queue. If the jury's still out, we keep the footprint in circulation until consensus is reached, focusing our collective efforts on the buildings most in need.</p>
  
  <p>The main takeaway here is: relax! Your fellow Inspectors have got your back. <em>It's no big deal if make a mistake once in a while, or make your best guess on an ambiguous case.</em> The community will double, triple, and quadruple check you, and you them. Go Team!</p>
      <div id="home-explained">
    <h3 class="major">How Did We Get Here?</h3>
  
    <div class="header first">
      <h3>It all starts with atlases</h3>
  