Archive for the 'Data & Methods' Category

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Big data and smart cities

Urban Demographics posted a presentation by Rob Kitchin based on his paper “The real-time city? Big data and smart urbanism” (gated version; working paper version).


‘Smart cities’ is a term that has gained traction in academia, business and government to describe cities that, on the one hand, are increasingly composed of and monitored by pervasive and ubiquitous computing and, on the other, whose economy and governance is being driven by innovation, creativity and entrepreneurship, enacted by smart people. This paper focuses on the former and, drawing on a number of examples, details how cities are being instrumented with digital devices and infrastructure that produce ‘big data’. Such data, smart city advocates argue enables real-time analysis of city life, new modes of urban governance, and provides the raw material for envisioning and enacting more efficient, sustainable, competitive, productive, open and transparent cities. The final section of the paper provides a critical reflection on the implications of big data and smart urbanism, examining five emerging concerns: the politics of big urban data, technocratic governance and city development, corporatisation of city governance and technological lock-ins, buggy, brittle and hackable cities, and the panoptic city.

Sage Stats

The University of Michigan Library has just acquired access to Sage Stats, a resource for local area statistics:

Sage Stats features data series on U.S. states, counties, cities, and metropolitan areas. Topics covered include the economy, education, crime, government finance, health, population, religion, social welfare, and transportation. Some series go back more than 20 years. Sage Stats makes it easy to download data, compare indicators or create simple visualizations of local area data.

Access is available to the Ann Arbor, Flint and Dearborn campuses at

Apple Research Kit: New Frontiers in Data Collection & Informed Consent

The Apple Research Kit allows researchers to develop an iPhone app, which interested respondents can download from the Apple Store. The respondent goes through an on-line consent form and then responds to questions, tasks (walking), etc. Some of the diagnostic tools are based on previously developed apps from the Apple Healthkit.

As of now, apps have been developed for collecting data for research projects on asthma, cardiovascular disease, diabetes, Parkinson’s, mind, body, and wellness after breast cancer, and for a population-based study, the LGBTQ population.

Here is a description of the informed consent process for these iPhone apps:
Participant-Centered Consent Toolkit

Listed below are a few press releases associated with the Pride Study – the population based study of the gay population. Following those posts are some more general critiques of this way of gathering data. The post from the Verge is probably the most critical raising issues of “on the internet no one knows you are a dog” and gaming the consent process (lying about eligibility for the study). On the plus side, the participant pool is going to be easier to sign up and won’t be limited to those who live close to research hospitals. Here is an excerpt from Business Insider to the reaction to the app launch for the Stanford Heart study:

It’s really incredible … in the first 24 hours of research kit we’ve had 11,000 people sign up for a study in cardiovascular disease through Stanford University’s app. And, to put that in perspective – Stanford has told us that it would have taken normally 50 medical centers an entire year to sign up that many participants. So, this is – research kit is an absolute game changer.

The participant pool is limited to iPhone users (no android version of these apps), although some will have a web interface (the Pride Study).

Launch of the Pride Study
UCSF Researchers Launch Landmark Study of LGBTQ Community Health
Jyoti Madhusoodanan | UCSF Press Release
June 25, 2015

A big LGBT health study is coming to the iPhone
Stephanie M. Lee | BuzzFeed
June 25, 2015

How The iPhone Is Powering A Massive LGBT Health Study
Kif Leswing | International Business Times
June 25, 2015

Critiques of the Apple ResearchKit
Apple’s new ResearchKit: ‘Ethics quagmire’ or medical research aid?
Arielle Duhaime-Ross | The Verge
March 10, 2015

In-Depth: Apple ResearchKit concerns, potential, analysis
March 9, 2015

What’s the Matter with Polling?

What is the Matter with Polling?
Cliff Zukin | New York Times
June 20, 2015

This article focuses on political polling – and predictions from political polls, but much of the content is relevant to other sorts of telephone-based opinion surveys, many of which are used by social scientists: Survey of Consumers, Pew, Gallup, etc.

The article focuses on (a) the move from landline to cellphones; (b) the growing non-response rate; (c) costs; (d) and sample metrics, e.g., representativeness.

The decline in landline phones makes telephone surveys more expensive since cell phones cannot be reached through automatic dialers. The landline phone vs cellphone distribution comes from the National Health Interview Survey. Here’s a recent summary of the data. The article summarizes this as “About 10 years ago. . . . about 6 percent of the public used only cellphones. The N.H.I.S. estimate for the first half of 2014 found that this had grown to 43 percent, with another 17 percent “mostly” using cellphones. In other words, a landline-only sample conducted for the 2014 elections would miss about three-fifths of the American public, almost three times as many as it would have missed in 2008.”

The other issue for polling is the growing non-response rate.

When I first started doing telephone surveys in New Jersey in the late 1970s, we considered an 80 percent response rate acceptable, and even then we worried if the 20 percent we missed were different in attitudes and behaviors than the 80 percent we got. Enter answering machines and other technologies. By 1997, Pew’s response rate was 36 percent, and the decline has accelerated. By 2014 the response rate had fallen to 8 percent.

Non-response makes surveys more expensive – more numbers to call to find a respondent and many of them dialed by hand if it is a cellphone universe. And, most important, is the representativeness of the sample that the survey ends up with. So far, surveys based on probability samples seem to still be representative, at least based on comparing sample characteristics to gold-standard benchmarks like the American Community Survey (ACS). Participation in the ACS is mandatory, although for the last several years, Republicans in the House have tried to remove this requirement. Canada did away with its mandatory requirements with its census, with disastrous results. The following is a compilation of posts related to the mandatory response requirement in the US and Canada: [Older Posts]

Measuring Race . . . Again

The following are collection of news stories on how the Census Bureau is planning to collect data on race. It is misleading to say that the Census Bureau will not collect data on race. Instead, of asking about Hispanic Origin and Race, the Census Bureau is likely to ask about “categories” that describe the person.

And, a new category might be “Middle Eastern or North African.”

The Census Bureau collects data on all sorts of topics, but the Office of Management and Budget (OMB) makes the final call on how the concept is measured by the Federal Statistical System. Links to the Census Bureau’s submission to OMB and a report based on internal research follow a nice summary by Pew.

Census considers new approach to asking about race – by not using the term at all
D’Vera Cohn | Pew Research Center
June 18, 2015

2010 Census Race and Hispanic Origin Alternative Questionnaire Experiment
from the 2010 Census Program for Evaluations and Experiments
Feb 28, 2013

National Content Test
Submission for OMB Review | Federal Register
May 22, 2015

Educational data vulnerable to ‘privacy’ legislation

When Guarding Student Data Endangers Valuable Research
Susan Dynarski | New York Times (Upshot Blog)
June 13, 2015

University of Michigan Public Policy professor, Susan Dynarski, warns researchers of pending legislation that would curtail sharing of educational data with researchers:

In response to such concerns, some pending legislation would scale back the authority of schools, districts and states to share student data with third parties, including researchers. Perhaps the most stringent of these proposals, sponsored by Senator David Vitter, a Louisiana Republican, would effectively end the analysis of student data by outside social scientists. This legislation would have banned recent prominent research documenting the benefits of smaller classes, the value of excellent teachers and the varied performance of charter schools.

Below is a summary of Vitter’s proposed legislation from his office:

Vitter Introduces Student Privacy Protection Act
David Vitter, R(LA) | From David’s Desk
May 14, 2015

Using Grid Maps to Visualize Data

Danny DeBelius of NPR’s Visuals Team discusses how geographic data is represented on maps and ways to make the visualization more accurate. The visualization they have landed on is the Hex-Tile map.

image of Hexagon Map

H/T Flowing Data, which shows other ways of producing this kind of map, including sheep and Darth Vaders.

How to Ask for Datasets

Christian Kreibich at provides some helpful tips for asking other researchers to share their data.

I’m a systems researcher. I work with data, plenty of it. Over the past decade I have sent lots of data inquiries, and have received dozens. Judging by the latter it’s safe to say that people often go about this poorly, so I’d like to give a bit of advice regarding how to formulate inquiries to other researchers. But before we start, a few clarifications. This article is dataset-centric, but the concerns apply similarly to resources such as algorithms, methods, or code. Also, I assume you have done your background research and already know whom to ask. This is not a guide for finding useful stuff. Finally, the following is by no means a complete guide on how to collaborate with other researchers, but it might provide some tips regarding how to start such a collaboration.

H/T Flowing Data

American Community Survey (ACS) Data Products Survey

The American Community Survey Office is conducting a survey to gather feedback on it’s products:

The ACS data products consist of tabulated products, such as aggregated estimates found in detailed tables or data profiles in the American FactFinder, and the Public Use Microdata Sample (PUMS) Files. We need your feedback in order to provide relevant and timely data products that are easy to access and use.

Please take a moment to complete this survey. Your responses will help us evaluate the ACS data products and dissemination and find ways to improve them. Please respond no later than May 29, 2015.

We estimate the survey will take 15 minutes to complete.

H/T Data Detectives

Backcasting Native Hawaiian Population

The Pew Research Center Fact Tank examines findings by David Swanson which uses 1910 and 1920 Census data to estimate the population of Hawaii in 1778, the year Capt. James Cook arrived.

In this case, Swanson took a detailed look at the 1910 and 1920 U.S. Census’s Native Hawaiian counts, tracking the survival rate of each five-year age group from one census to the next. For example, he looked at how many children who were newborns to age 4 in 1910 were counted as 10- to 14-year-olds in 1920, then did the same for each successive age group. For each group, he created a “reverse cohort change ratio,” which he used to go back in time and estimate the size of each age group for each decade until he got to 1770.

The article also reports on the growth of the Native Hawaiian population since the 1980s.