Data-Smart City Solutions RePost — Map Monday: Beyond Floods

People tend not to think that bad things will happen to them. This psychological proclivity towards optimism—logically termed “optimism bias”—is in many ways a beneficial feature of the human psyche, as most live better lives when they’re not constantly obsessing over the possibility of some calamity befalling them.

However, the optimism bias also has its disadvantages, as it may discourage people from preparing for emergencies. This was the case during Hurricane Sandy, during which 77 percent of New Yorkers reported that inland flooding was much higher than they expected. In New York City alone, the storm damaged 90,000 buildings, created $19 billion in damage,and killed nearly 50 people.

Chris Bousquet, a Research Assistant and Writer with Harvard’s Ash Center, explores how data sharing can influence human action through the Beyond Floods CARTO platform.

Read more here.

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Evergreen Data ReBlog: Is Feminist Data Visualization Actually a Thing? (Yes, and How!)

How can data visualization be feminist? Data is data — it speaks for itself.

A charming idea, to be sure. But it just ain’t true. Feminist data visualization is (and must be) a thing because data, data analysis, and data visualization are never neutral. The premise that, if handled correctly, data can present neutral evidence, is deeply flawed. Culture is embedded into our data at every stage.

As long as humans have been thinking about data viz, we’ve been projecting our worldview onto it.

Guest poster Heather Krause with Datassist discusses the concepts underlying feminist data visualization, how different cultures interpret data, and what data scientists and researchers can do to account for these differences in world view when collecting, analyzing, and presenting information.

Read more here.

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Civic Analytics Network RePost: An open letter to the open data community

Open data is one of the most important and core missions of Chief Data Officers. In the past 5 years, the number of cities with an open data portal has grown significantly. While cities already have released terabytes of open data, CAN aims to set higher goals for open data to make it more accessible and usable. Our cities’ open data portals must continue to evolve to meet the public’s growing and changing needs.

While specific open data portal features may vary from city to city, there are universal requirements that all local governments need for an effective open data program…

The Civic Analytics Network (CAN), a consortium of chief data officers supported by the Ash Center for Democratic Governance and Innovation at Harvard Kennedy School, shares a set of guidelines for how best to advance the use of transparent government information. Among these guidelines is improving the usability and management of collected data. A guideline that, if followed, would likely prompt a spontaneous standing ovation from our otherwise calm and unflappable government auditing community.

Read more here, and go ahead and check out the Ash Center’s Data Smart City Solutions page while you’re at it.

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Stephanie Evergreen ReBlog: Qualitative Data Visualization (Gauge Diagram)

When we debuted our Qualitative Chart Chooser last November, we promised to dive into detail on specific visualizations, so let’s kick it off by discussing how and when to use one of the most derided charts of all: the gauge diagram.

I don’t know about your office, but we at OAD have a word for qualitative data: squishy. It’s squishy, and it’s smooshy. It’s sometimes indirect, and can be a real challenge to measure. Nevertheless, some of the most vital and telling information and evidence you can get might be squishy, smooshy, indirect, qualitative data. Interviews and long-winded answers to open-ended survey questions can provide context you just can’t get from pulling numbers out of a database, and can fill in those gaps of understanding left after a document review.

But how do you capture that in easily digestible picture form?

Jennifer Lyons, in a follow up guest post for Evergreen Data, breaks down one way to present qualitative data. Read more here!

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Goodwill Community Foundation Repost: Why, and how, to make a pivot table in Excel

When you have a lot of data, it can sometimes be difficult to analyze all of the information in your worksheet. Pivot tables can help make your worksheets more manageable by summarizing data and allowing you to manipulate it in different ways.

Check out the two-part video series below on the many uses and strengths of Excel pivot tables, and geek out quietly to yourself over your newfound powers of data analysis. You know we are!




Check out more here.

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Stephanie Evergreen Reblog: How dataviz can unintentionally perpetuate inequality (3 part series)

Maps seem to be especially prone to misrepresenting people in disadvantaged situations, particularly as we get ever closer to being able to pinpoint individuals’ locations geographically. Vidhya shared with me a map of concentrated poverty in Minnesota (a factor extremely comingled with being a person of color), where individual participants were marked by red dots. When presented to the actual participants living in these areas, they were not stoked. Instead, they felt like they were perceived as a threat, and the little pixels made them look almost like an infestation on an otherwise subtly colored map.

In this Stephanie Evergreen series, she explores some of the missteps that researchers and presenters of data can make when attempting to quantify the human experience. Visual presentation influences how data is perceived and understood. It is very important that we as collectors and presenters of data consider how it may (or may not) come across, particularly to communities involved and represented in the research.


An approximation of the offending map



The same information, presented in a way that “aggregates the issue rather than identifying individuals”

Part 1

Part 2

Part 3

We as auditors and eager life-long learners sometimes tackle complex issues of social significance. The last thing we want is the wrong graphic or stock photo to undermine our findings or the legitimacy of our work. This means approaching the presentation of those findings (in the report or otherwise) with the same care and deliberation with which we approach other aspects of the audit process, and creating an end product that successfully communicates our intended message.

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Stephanie Evergreen Reblog: Two alternatives to using a second Y-axis

Almost as often as I see a pie chart with a hundred tiny slivers, I see line graphs using two y-axes. And it is just as bad.

Is a chart that looks like this:

secondaryaxisoriginal…really so bad?

While “bad” is perhaps a strong word for using 2 Y-axes, Stephanie Evergreen shares some alternatives in this post that may do away with the resulting confusion. Follow the link, or check out some options below.





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