DATA – Page 452 – The Living Library

The Modern Beauty of 19th-Century Data Visualizations

Curated on January 28, 2015October 30, 2018 by Stefaan Verhulst

Laura Bliss at CityLab: “The Library of Congress‘ online presence is a temple of American history, an unmatched, searchable collection of digitized photographs, maps, recordings, sheet music, and documents in the millions, dating back to the 15th century.

Sifting through these treasures isn’t so easy, though. When you do manage the clunky search interface and stumble across a gorgeous 1870s statistical atlas, it’s hard to zoom in closely on its pages and properly marvel at the antique gem.
Problem solved, thanks to the info-nerds at Vintage Visualizations, a project of the Brooklyn Brainery. They’ve reproduced a number of the LOC’s Civil War-era data visualizations in high-quality poster prints, and they are mouthwateringly cool. For example, I really wish we still ranked city populations like this chart does, which traces a century of census data in colorful Jenga towers (NYC, forever the biggest apple!):

Behold, the ratio of “church accommodation” by state, circa 1870, displayed like wallpaper swatches….(More):

Big Data Now

Curated on January 27, 2015August 15, 2018 by Stefaan Verhulst

Timothy McGovern at Radar – O’Reilly: “In the four years we’ve been producing Big Data Now, our wrap-up of important developments in the big data field, we’ve seen tools and applications mature, multiply, and coalesce into new categories. This year’s free wrap-up of Radar coverage is organized around seven themes:

Cognitive augmentation: As data processing and data analytics become more accessible, jobs that can be automated will go away. But to be clear, there are still many tasks where the combination of humans and machines produce superior results.
Intelligence matters: Artificial intelligence is now playing a bigger and bigger role in everyone’s lives, from sorting our email to rerouting our morning commutes, from detecting fraud in financial markets to predicting dangerous chemical spills. The computing power and algorithmic building blocks to put AI to work have never been more accessible.
The convergence of cheap sensors, fast networks, and distributed computation: The amount of quantified data available is increasing exponentially — and aside from tools for centrally handling huge volumes of time-series data as it arrives, devices and software are getting smarter about placing their own data accurately in context, extrapolating without needing to ‘check in’ constantly.
Reproducing, managing, and maintaining data pipelines: The coordination of processes and personnel within organizations to gather, store, analyze, and make use of data.
The evolving, maturing marketplace of big data components: Open-source components like Spark, Kafka, Cassandra, and ElasticSearch are reducing the need for companies to build in-house proprietary systems. On the other hand, vendors are developing industry-specific suites and applications optimized for the unique needs and data sources in a field.
The value of applying techniques from design and social science: While data science knows human behavior in the aggregate, design works in the particular, where A/B testing won’t apply — you only get one shot to communicate your proposal to a CEO, for example. Similarly, social science enables extrapolation from sparse data. Both sets of tools enable you to ask the right questions, and scope your problems and solutions realistically.
The importance of building a data culture: An organization that is comfortable with gathering data, curious about its significance, and willing to act on its results will perform demonstrably better than one that doesn’t. These priorities must be shared throughout the business.
The perils of big data: From poor analysis (driven by false correlation or lack of domain expertise) to intrusiveness (privacy invasion, price profiling, self-fulfilling predictions), big data has negative potential.

Download our free snapshot of big data in 2014, and follow the story this year on Radar.”

Survive and Thrive: How Big Data Is Transforming Health Care

Curated on January 27, 2015August 15, 2018 by Stefaan Verhulst

Jackie Roche at Pacific Standard: “When you step on a scale, take your temperature, or check your blood pressure, you’re using data from your body to measure your health. Advances in fitness trackers have made health quantification more accessible to casual users. But for researchers, health care providers, and people with chronic conditions, advances in tracking technology, data analysis, and automation offer significant improvements in medical treatment and quality of life.

This three-part series explores health quantification through the eyes of Rutgers University Ph.D student Maria Qadri, who has both professional and personal experience in the matter. Qadri’s research aims to help people with traumatic brain injury and Parkinson’s Disease better manage their illness, and, as a Type 1 diabetic, glucose monitoring is a major part of her own life. Below, we take a look at how number crunching and personal data factors into Qadri’s research and life….(More).”

Open Data Is Finally Making A Dent In Cities

Curated on January 23, 2015October 9, 2018 by Stefaan Verhulst

Brooks Rainwater at Co-Exist: “As with a range of leading issues, cities are at the vanguard of this shifting environment. Through increased measurement, analysis, and engagement, open data will further solidify the centrality of cities.
In the Chicago, the voice of the mayor counts for a lot. And Mayor Emmanuel has been at the forefront in supporting and encouraging open data in the city, resulting in a strong open government community. The city has more than 600 datasets online, and has seen millions of page views on its data portal. The public benefits have accrued widely with civic initiatives like Chicagolobbyists.org, as well as with a myriad of other open data led endeavors.
Transparency is one of the great promises of open data. Petitioning the government is a fundamental tenet of democracy and many government relations’ professionals perform this task brilliantly. At the same time that transparency is good for the city, it’s good for citizens and democracy. Through the advent of Chicagolobbyists.org, anyone can now see how many lobbyists are in the city, how much they are spending, who they are talking to, and when it is happening.
Throughout the country, we are seeing data driven sites and apps like this that engage citizens, enhance services, and provide a rich understanding of government operations In Austin, a grassroots movement has formed with advocacy organization Open Austin. Through hackathons and other opportunities, citizens are getting involved, services are improving, and businesses are being built.
Data can even find your dog, reducing the number of stray animals being sheltered, with StrayMapper.com. The site has a simple map-based web portal where you can type in whether you are missing a dog or cat, when you lost them, and where. That information is then plugged into the data being collected by the city on stray animals. This project, developed by a Code for America brigade team, helps the city improve its rate of returning pets to owners.
It’s not only animals that get lost or at least can’t find the best way home. I’ve found myself in that situation too. Thanks to Ridescout, incubated in Washington, D.C., at 1776, I have been able to easily find the best way home. Through the use of open data available from both cities and the Department of Transportation, Ridescout created an app that is an intuitive mobility tool. By showing me all of the available options from transit to ridesharing to my own two feet, it frequently helps me get from place to place in the city. It looks like it wasn’t just me that found this app to be handy; Daimler recently acquired Ridescout as the auto giant continues its own expansion into the data driven mobility space.”

The downside of Open Data

Curated on January 22, 2015August 15, 2018 by Stefaan Verhulst

Joshua Chambers at FutureGov: “…Inaccurate public datasets can cause big problems, because apps that feed off of them could be giving out false information. I was struck by this when we reported on an app in Australia that was issuing alerts for forest fires that didn’t exist. The data was coming from public emergency calls, but wasn’t verified before being displayed. This meant that app users would be alerted of all possible fires, but also could be caused unnecessarily panic. The government takes the view that more alerts are better than slower verified ones, but there is the potential for people to become less likely to trust all alerts on the app.
No-one wants to publish inaccurate data, but accuracy takes time and costs money. So we come to a central tension in discussions about open data: is it better to publish more data, with the risk of inaccuracy, or limit publication to datasets which are accurate?
The United Kingdom takes the view that more data is best. I interviewed the UK’s lead official on open data, Paul Maltby, a couple of years ago, and he told me that: “There’s a misnomer here that everything has to be perfect before you can put it out,” adding that “what we’re finding is that, actually, some of the datasets are a bit messy. We try to keep them as high-quality as we can; but other organisations then clean up the data and sell it on”.
Indeed, he noted that some officials use data accuracy as an excuse to not publish information that could hold their departments to account. “There’s sometimes a reluctance to get data out from the civil service; and whilst we see many examples of people understanding the reasons why data has been put to use, I’d say the general default is still not pro-release”.
Other countries take a different view, however. Singapore, for example, publishes much less data than Britain, but has more of a push on making its data accurate to assist startups and app builders….(More)”