{"id":1413,"date":"2017-03-18T18:44:58","date_gmt":"2017-03-18T18:44:58","guid":{"rendered":"https:\/\/blogs.scu.edu\/dataviz\/?p=1413"},"modified":"2017-03-18T18:44:58","modified_gmt":"2017-03-18T18:44:58","slug":"new-york-times-does-a-great-job-on-data-visualizations-and-what-we-can-learn-from","status":"publish","type":"post","link":"https:\/\/blogs.scu.edu\/dataviz\/2017\/03\/18\/new-york-times-does-a-great-job-on-data-visualizations-and-what-we-can-learn-from\/","title":{"rendered":"New York Times Does a Great Job on Data Visualizations and What We Can Learn From?"},"content":{"rendered":"

One of the secrets to why New York Times keeps its success is synthesized design processes. Below are the ten characteristics that they use to make its data visualization article powerful and successful.<\/p>\n

    \n
  1. Clarity of context and purpose: New York Times establish the goal and clarify the audience\u2019s need before design a visualization. It would ask:\n
      \n
    1. Does it interact with the audience and let them feel connected?<\/li>\n
    2. Does it enhance a specific editorial perspective?<\/li>\n
    3. Which format is the best to deliver the message?<\/li>\n<\/ol>\n<\/li>\n
    4. Respect for the reader: New York Times makes its subject accessible. It not only delivers clarity but also presents simplicity. Let the readers can get quick access. Moreover, New York Times would adjust immediacy based on the level of the subject. For example, it wants its audiences to put more effort and get rewarded with the insight derived as a result. It would increase complexity.\n

      \"NYT
      NYT<\/figcaption><\/figure><\/li>\n<\/ol>\n
        \n
      1. Editorial Integration: combine the graph with the article. Let them coherent and support each other.<\/li>\n
      2. Clarity of questions: the format of visualization effectively and perfectly aligned to the questions they are answering.\n
        \"NYT\"
        NYT<\/figcaption><\/figure>\n

         <\/li>\n

      3. Data research and preparation: it makes a lot of effort on cross departments research and development of programming libraries to get rich and deep data resources, and offer multi-dimension information.<\/li>\n
      4. Visual restraint: deploy right color, catch reader\u2019s attention, and let them recognize immediately.\n
        \"NYT\"
        NYT<\/figcaption><\/figure>\n

         <\/li>\n

      5. Layout and placement: whether it is full columns, double page spreads or dramatic diagonals, the Times ensures each graphic has the perfect stage to amplify the impact of the visual\u2019s relationship with an article.<\/li>\n
      6. Diversity of techniques: its interactive graph shows immense flexibility and versatility. No repeated representation and each piece is built attentively and informatively.\n

        \"NYT\"
        NYT<\/figcaption><\/figure><\/li>\n
      7. Technical Execution: multiple formats of chart display.<\/li>\n
      8. Annotation: well use of labels, description, and text explanation to help readers understand graph.\n
        \"NYT\"
        NYT<\/figcaption><\/figure>\n

        Reference: http:\/\/www.scribblelive.com\/blog\/2012\/04\/02\/10-things-you-can-learn-from-the-new-york-times-data-visualizations\/<\/a><\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

        One of the secrets to why New York Times keeps its success is synthesized design processes. Below are the ten characteristics that they use to make its data visualization article powerful and successful. Clarity of context and purpose: New York Times establish the goal and clarify the audience\u2019s need before design a visualization. It would … Continue reading New York Times Does a Great Job on Data Visualizations and What We Can Learn From?<\/span><\/a><\/p>\n","protected":false},"author":1685,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"qubely_global_settings":"","qubely_interactions":"","kk_blocks_editor_width":"","_kiokenblocks_attr":"","_kiokenblocks_dimensions":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-1413","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"gutentor_comment":0,"qubely_featured_image_url":null,"qubely_author":{"display_name":"wsun","author_link":"https:\/\/blogs.scu.edu\/dataviz\/author\/wsun\/"},"qubely_comment":0,"qubely_category":"Uncategorized<\/a>","qubely_excerpt":"One of the secrets to why New York Times keeps its success is synthesized design processes. Below are the ten characteristics that they use to make its data visualization article powerful and successful. Clarity of context and purpose: New York Times establish the goal and clarify the audience\u2019s need before design a visualization. It would…","post_mailing_queue_ids":[],"_links":{"self":[{"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/posts\/1413","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/users\/1685"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/comments?post=1413"}],"version-history":[{"count":1,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/posts\/1413\/revisions"}],"predecessor-version":[{"id":1415,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/posts\/1413\/revisions\/1415"}],"wp:attachment":[{"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/media?parent=1413"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/categories?post=1413"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.scu.edu\/dataviz\/wp-json\/wp\/v2\/tags?post=1413"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}