{"id":977,"date":"2016-12-28T17:58:50","date_gmt":"2016-12-28T17:58:50","guid":{"rendered":"https:\/\/courses.lumenlearning.com\/waymakermath4libarts\/?post_type=chapter&p=977"},"modified":"2017-04-18T20:11:37","modified_gmt":"2017-04-18T20:11:37","slug":"putting-it-together-describing-data","status":"publish","type":"chapter","link":"https:\/\/courses.lumenlearning.com\/waymakermath4libarts\/chapter\/putting-it-together-describing-data\/","title":{"raw":"Putting It Together: Describing Data","rendered":"Putting It Together: Describing Data"},"content":{"raw":"
The following is excerpted from \"The Trials and Tribulations of Data Visualization for Good\" by Jake Porway<\/a>.<\/div>\r\n

The trials and tribulations of data visualization for good<\/h2>\r\n\u201cI love big data.\u00a0 It\u2019s got such potential for storytelling.\u201d\u00a0 At DataKind<\/a>, we hear some version of this narrative every week.\u00a0 As more and more social organizations dip their toes into using data, invariably the conversation about data visualization comes up. There is a growing feeling that data visualization, with its combination of \u201cengaging visuals\u201d and \u201cdata-driven interactivity\u201d, may be the magic bullet that turn opaque spreadsheets and dry statistics into funding, proof, and global action.\r\n\r\nHowever, after four years of applying data-driven techniques to social challenges at DataKind, we feel that data visualization, while it does have an important place in our work, is a mere sliver of what it takes to work with data.\u00a0 Worse, the ubiquity of data visualization tools has lead to a wasteland of confusing, ugly, and sometimes unhelpful pie charts, word clouds, and worse.\r\n\r\n \r\n\r\n[caption id=\"attachment_978\" align=\"aligncenter\" width=\"737\"]\"Two<\/a> Ugh.[\/caption]\r\n\r\nThe challenge is that data visualization is not an end-goal, it is a process.\u00a0 It is often the final step in a long manufacturing chain along which data is poked, prodded, and molded to get to that pretty graph.\u00a0 Ignoring that process is at best misinformed, and at worst destructive.\r\n\r\nLet me show you an example:\u00a0 In New York City, we had a very controversial program called Stop and Frisk<\/a> that allowed police officers to stop people on the street they felt were a potential threat in an attempt to find and reclaim illegal weapons.\r\n\r\nAfter a Freedom of Information Act (FOIA)<\/a> request by the New York Civil Liberties Union<\/a> (NYCLU) resulted in the New York Police Department<\/a> (NYPD) releasing all of their Stop and Frisk data publicly, people flocked to the data to independently pick apart how effective the program was.\r\n\r\nThe figure below comes from WNYC, a public radio station located in New York City.\u00a0 Here they\u2019ve shaded each city block brighter pink the more stops and frisks occurred there.\u00a0 The green dots on the map indicate where guns were found.\u00a0 What the figure shows is that the green dots do not appear as close to the hot pink squares as one would believe they should.\u00a0 The implication, then, is that Stop and Frisk may not actually be all that effective in getting guns off the street.\"Map<\/a>\r\n\r\nBut then a citizen journalist created this<\/em> map of the same data.\r\n\r\n\"Map<\/a>\r\n\r\nBy simply changing the shading scheme slightly he notes that this map makes the green dots look much closer to the hot pink squares.\u00a0 In fact, he goes further to remove the artificial constraints of the block-by-block analysis and smooths over the whole area in New York, resulting in a map where those green dots stare unblinkingly on top of the hot-red stop and frisk regions.\r\n\r\n\"Heat<\/a>\r\n\r\nThe argument this author makes visually is that Stop and Frisk does<\/em> in fact work.\r\n\r\nSo who\u2019s right here?\u00a0 Well both of them.\u00a0 And neither of them.\u00a0 These pictures are just that \u2013 pictures.\u00a0 Though they \u201cuse\u201d data, they are not science. They are not analyses. They are mere visuals.\r\n\r\nWhen data visualization is used simply to show alluring infographics about whether people like Coke or Pepsi better, the stakes of persuasion like this are low.\u00a0 But when they are used as arguments for or against public policy, the misuse of data visualization to persuade can have drastic consequences.\u00a0 Data visualization without rigorous analysis is at best just rhetoric and, at worse, incredibly harmful.<\/strong>\r\n\r\n\u201cData for Humans vs. Data for Machines\u201d<\/strong>\r\n\r\nThe fundamental challenge underlying this inadvertently malicious use of data comes, I believe, from a vagueness in terminology.\u00a0 When people crow about \u201cthe promise of data\u201d, they are often describing two totally different activities under the same umbrella.\u00a0 I\u2019ve dubbed these two schools of thought \u201cdata for humans\u201d vs. \u201cdata for machines\u201d.\r\n\r\nData for Humans:<\/strong>\u00a0 The most popular use of data, especially in the social sector, places all of the emphasis on the data itself as the savior.\u00a0 The idea is that, if we could just show people more data, we could prove our impact, encourage funding, and change behavior.\u00a0 Your bar charts, maps, and graphs pointing-up-and-to-the-right all fall squarely into this category.\u00a0 In fact almost all data visualization falls here, relying on the premise that showing a decisionmaker some data about the past will be all it takes to drive future change.\r\n\r\nUnfortunately, while I believe data is a necessary part of this advocacy work, it is never sufficient by itself.\u00a0 The challenge with using \u201cdata for humans\u201d is threefold:\r\n
    \r\n \t
  1. Humans don\u2019t make decisions based on data<\/strong>, at least not alone.<\/strong>\u00a0 Plato once said \u201cHuman behavior flows from three main sources: desire, emotion, and knowledge.\u201d\u00a0 I want to believe he listed those aspects in that order intentionally. Study<\/a> after study<\/a> has shown that humans rationalize beliefs with data, not vice versa.\u00a0 If behavior change were driven by data and graphs alone, we would be 50 years into a united battle against climate change.\u00a0 Conversely, we will leap to conclusions from data visualizations that \u201cfeel\u201d right, but are not rigorously tested, like the conclusions from the Stop and Frisk images above.<\/li>\r\n \t
  2. The public still treats data and data visualization as \u201cfact\u201d and \u201cscience\u201d.<\/strong>\u00a0 I believe the public has gained enough visual literacy to question photojournalists or documentary filmmakers\u2019 motives, aware that theirs is an auteur behind the final piece that intends for us to walk away with their chosen understanding.\u00a0 We have yet to bring that same skepticism to data visualization, though we need to. The result of this illiteracy is that we are less critical of graphs and charts than written arguments because the use of data gives the sense that \u201cfact\u201d or \u201cscience\u201d is at work, even if what we\u2019re doing is little more than visually bloviating.<\/li>\r\n \t
  3. The data or visualization you see at the end of the road is opaque to interrogation.<\/strong>\u00a0 It is difficult, if not impossible to know where that \u201c58%\u201d statistic or that flashy bar graph came from, grinning up at you from the page.\u00a0 Because we don\u2019t have ways to know how the data was collected, manipulated, and designed, we can\u2019t answer any of the questions we might want to raise above. If point 2 means we need to treat data visualization as photojournalism, then this point implores us to go further to requiring forensic photographers in this work.<\/li>\r\n<\/ol>\r\nData for Machines:<\/strong> For these reasons, DataKind specializes in projects focusing on what we refer to as \u201cData for Machines\u201d.\u00a0 The promise of abundant data is not that we can show people more data, but that we can take advantage of computers, algorithms, and rigorous statistical methodologies to learn from these new datasets.\u00a0 The data is not the end goal, it is the raw resource we use to fuel computer systems that can learn from this information and, in many cases, even predict what is likely to happen in the future.\r\n\r\nFor example, instead of engaging in the Stop and Frisk gallery debate above, DataKind volunteers loaded the NYPD data into computers and created statistical models to rigorously test whether or not racial discrimination was occurring disproportionately in different parts of the city.\u00a0 While the models needed further evaluation, this analysis shows how data should be used. People shouldn\u2019t try to draw conclusions from pictures of data \u2013 we\u2019re notoriously bad at that as humans \u2013 we should be building models and using scientific methods to learn from data.\r\n

    Celebrating Visualization<\/strong><\/h3>\r\nNo surprise, creating data visualization well simply entails designing in a way that leads people to make scientific conclusions themselves.\r\n\r\nThere are many examples of insightful<\/a>, persuasive<\/a>, and downright clever<\/a> data visualizations, but perhaps one of the best visualization practices I know of is to turn the idea of visualization on its head.\u00a0 Data\u00a0 visualization is incredibly good for allowing one to ask questions, not answer them.\u00a0 The huge amount of data that we have available to us now means that we need visual techniques just to help us make sense of what we need to try to make sense of.\r\n

    So where do we go from here?<\/strong><\/h3>\r\nFirst off, you can boycott the tyranny of pie charts and word clouds, rail against those three pitfalls, and share these last two examples far and wide. But I think we can also all go out and start thinking about how data can truly be used to its fullest advantage. Aside from just using \u201cdata for machines,\u201d the best data visualization should raise questions and inspire exploration, not just sum up information or try to tell us the answer. Today we have more information than ever before and we have a new opportunity to use it to mobilize others, provided we do so with sensitivity.\u00a0 Now, more than ever, we need to all be out there on the front lines looking beyond data visualization as merely a way to satisfy our funders\u2019 requirements and instead looking at data as a way to ask deep questions of our world and our future.","rendered":"
    The following is excerpted from “The Trials and Tribulations of Data Visualization for Good” by Jake Porway<\/a>.<\/div>\n

    The trials and tribulations of data visualization for good<\/h2>\n

    \u201cI love big data.\u00a0 It\u2019s got such potential for storytelling.\u201d\u00a0 At DataKind<\/a>, we hear some version of this narrative every week.\u00a0 As more and more social organizations dip their toes into using data, invariably the conversation about data visualization comes up. There is a growing feeling that data visualization, with its combination of \u201cengaging visuals\u201d and \u201cdata-driven interactivity\u201d, may be the magic bullet that turn opaque spreadsheets and dry statistics into funding, proof, and global action.<\/p>\n

    However, after four years of applying data-driven techniques to social challenges at DataKind, we feel that data visualization, while it does have an important place in our work, is a mere sliver of what it takes to work with data.\u00a0 Worse, the ubiquity of data visualization tools has lead to a wasteland of confusing, ugly, and sometimes unhelpful pie charts, word clouds, and worse.<\/p>\n

     <\/p>\n

    \"Two<\/a><\/p>\n

    Ugh.<\/p>\n<\/div>\n

    The challenge is that data visualization is not an end-goal, it is a process.\u00a0 It is often the final step in a long manufacturing chain along which data is poked, prodded, and molded to get to that pretty graph.\u00a0 Ignoring that process is at best misinformed, and at worst destructive.<\/p>\n

    Let me show you an example:\u00a0 In New York City, we had a very controversial program called Stop and Frisk<\/a> that allowed police officers to stop people on the street they felt were a potential threat in an attempt to find and reclaim illegal weapons.<\/p>\n

    After a Freedom of Information Act (FOIA)<\/a> request by the New York Civil Liberties Union<\/a> (NYCLU) resulted in the New York Police Department<\/a> (NYPD) releasing all of their Stop and Frisk data publicly, people flocked to the data to independently pick apart how effective the program was.<\/p>\n

    The figure below comes from WNYC, a public radio station located in New York City.\u00a0 Here they\u2019ve shaded each city block brighter pink the more stops and frisks occurred there.\u00a0 The green dots on the map indicate where guns were found.\u00a0 What the figure shows is that the green dots do not appear as close to the hot pink squares as one would believe they should.\u00a0 The implication, then, is that Stop and Frisk may not actually be all that effective in getting guns off the street.\"Map<\/a><\/p>\n

    But then a citizen journalist created this<\/em> map of the same data.<\/p>\n

    \"Map<\/a><\/p>\n

    By simply changing the shading scheme slightly he notes that this map makes the green dots look much closer to the hot pink squares.\u00a0 In fact, he goes further to remove the artificial constraints of the block-by-block analysis and smooths over the whole area in New York, resulting in a map where those green dots stare unblinkingly on top of the hot-red stop and frisk regions.<\/p>\n

    \"Heat<\/a><\/p>\n

    The argument this author makes visually is that Stop and Frisk does<\/em> in fact work.<\/p>\n

    So who\u2019s right here?\u00a0 Well both of them.\u00a0 And neither of them.\u00a0 These pictures are just that \u2013 pictures.\u00a0 Though they \u201cuse\u201d data, they are not science. They are not analyses. They are mere visuals.<\/p>\n

    When data visualization is used simply to show alluring infographics about whether people like Coke or Pepsi better, the stakes of persuasion like this are low.\u00a0 But when they are used as arguments for or against public policy, the misuse of data visualization to persuade can have drastic consequences.\u00a0 Data visualization without rigorous analysis is at best just rhetoric and, at worse, incredibly harmful.<\/strong><\/p>\n

    \u201cData for Humans vs. Data for Machines\u201d<\/strong><\/p>\n

    The fundamental challenge underlying this inadvertently malicious use of data comes, I believe, from a vagueness in terminology.\u00a0 When people crow about \u201cthe promise of data\u201d, they are often describing two totally different activities under the same umbrella.\u00a0 I\u2019ve dubbed these two schools of thought \u201cdata for humans\u201d vs. \u201cdata for machines\u201d.<\/p>\n

    Data for Humans:<\/strong>\u00a0 The most popular use of data, especially in the social sector, places all of the emphasis on the data itself as the savior.\u00a0 The idea is that, if we could just show people more data, we could prove our impact, encourage funding, and change behavior.\u00a0 Your bar charts, maps, and graphs pointing-up-and-to-the-right all fall squarely into this category.\u00a0 In fact almost all data visualization falls here, relying on the premise that showing a decisionmaker some data about the past will be all it takes to drive future change.<\/p>\n

    Unfortunately, while I believe data is a necessary part of this advocacy work, it is never sufficient by itself.\u00a0 The challenge with using \u201cdata for humans\u201d is threefold:<\/p>\n

      \n
    1. Humans don\u2019t make decisions based on data<\/strong>, at least not alone.<\/strong>\u00a0 Plato once said \u201cHuman behavior flows from three main sources: desire, emotion, and knowledge.\u201d\u00a0 I want to believe he listed those aspects in that order intentionally. Study<\/a> after study<\/a> has shown that humans rationalize beliefs with data, not vice versa.\u00a0 If behavior change were driven by data and graphs alone, we would be 50 years into a united battle against climate change.\u00a0 Conversely, we will leap to conclusions from data visualizations that \u201cfeel\u201d right, but are not rigorously tested, like the conclusions from the Stop and Frisk images above.<\/li>\n
    2. The public still treats data and data visualization as \u201cfact\u201d and \u201cscience\u201d.<\/strong>\u00a0 I believe the public has gained enough visual literacy to question photojournalists or documentary filmmakers\u2019 motives, aware that theirs is an auteur behind the final piece that intends for us to walk away with their chosen understanding.\u00a0 We have yet to bring that same skepticism to data visualization, though we need to. The result of this illiteracy is that we are less critical of graphs and charts than written arguments because the use of data gives the sense that \u201cfact\u201d or \u201cscience\u201d is at work, even if what we\u2019re doing is little more than visually bloviating.<\/li>\n
    3. The data or visualization you see at the end of the road is opaque to interrogation.<\/strong>\u00a0 It is difficult, if not impossible to know where that \u201c58%\u201d statistic or that flashy bar graph came from, grinning up at you from the page.\u00a0 Because we don\u2019t have ways to know how the data was collected, manipulated, and designed, we can\u2019t answer any of the questions we might want to raise above. If point 2 means we need to treat data visualization as photojournalism, then this point implores us to go further to requiring forensic photographers in this work.<\/li>\n<\/ol>\n

      Data for Machines:<\/strong> For these reasons, DataKind specializes in projects focusing on what we refer to as \u201cData for Machines\u201d.\u00a0 The promise of abundant data is not that we can show people more data, but that we can take advantage of computers, algorithms, and rigorous statistical methodologies to learn from these new datasets.\u00a0 The data is not the end goal, it is the raw resource we use to fuel computer systems that can learn from this information and, in many cases, even predict what is likely to happen in the future.<\/p>\n

      For example, instead of engaging in the Stop and Frisk gallery debate above, DataKind volunteers loaded the NYPD data into computers and created statistical models to rigorously test whether or not racial discrimination was occurring disproportionately in different parts of the city.\u00a0 While the models needed further evaluation, this analysis shows how data should be used. People shouldn\u2019t try to draw conclusions from pictures of data \u2013 we\u2019re notoriously bad at that as humans \u2013 we should be building models and using scientific methods to learn from data.<\/p>\n

      Celebrating Visualization<\/strong><\/h3>\n

      No surprise, creating data visualization well simply entails designing in a way that leads people to make scientific conclusions themselves.<\/p>\n

      There are many examples of insightful<\/a>, persuasive<\/a>, and downright clever<\/a> data visualizations, but perhaps one of the best visualization practices I know of is to turn the idea of visualization on its head.\u00a0 Data\u00a0 visualization is incredibly good for allowing one to ask questions, not answer them.\u00a0 The huge amount of data that we have available to us now means that we need visual techniques just to help us make sense of what we need to try to make sense of.<\/p>\n

      So where do we go from here?<\/strong><\/h3>\n

      First off, you can boycott the tyranny of pie charts and word clouds, rail against those three pitfalls, and share these last two examples far and wide. But I think we can also all go out and start thinking about how data can truly be used to its fullest advantage. Aside from just using \u201cdata for machines,\u201d the best data visualization should raise questions and inspire exploration, not just sum up information or try to tell us the answer. Today we have more information than ever before and we have a new opportunity to use it to mobilize others, provided we do so with sensitivity.\u00a0 Now, more than ever, we need to all be out there on the front lines looking beyond data visualization as merely a way to satisfy our funders\u2019 requirements and instead looking at data as a way to ask deep questions of our world and our future.<\/p>\n\n\t\t\t

      \n\t\t\t

      Candela Citations<\/h3>\n\t\t\t\t\t
      \n\t\t\t\t\t\t
      \n\t\t\t\t\t\t\t
      CC licensed content, Original<\/div>