All posts by Brendan Buff

APDU Member Post: Assessing the Use of Differential Privacy for the 2020 Census: Summary of What We Learned from the CNSTAT Workshop

By:

Joseph Hotz, Duke University

Joseph Salvo, New York City Department of City Planning

Background

The mission of the Census Bureau is to provide data that can be used to draw a picture of the nation, from the smallest towns and villages to the neighborhoods of the largest cities. Advances in computer science, better record linkage technology, and the proliferation of large public data sets have increased the risk of disclosing information about individuals in the census.

To assess these threats, the Census Bureau conducted a simulated attack, reconstructing person-level records from published 2010 Census tabulations using its previous Disclosure Avoidance System (DAS) that was based in large part on swapping data records across households and localities. When combined with information in commercial and publicly available databases, these reconstructed data suggested that 18 percent of the U.S. population could be identified with a high level of certainty. The Census Bureau concluded that, if adopted for 2020, the 2010 confidentiality measures would lead to a high risk of disclosing individual responses violating Title 13 of the U.S. Code, the law that prohibits such disclosures.

Thus, the Census Bureau was compelled to devise new methods to protect individual responses from disclosure. Nonetheless, such efforts – however well-intentioned – may pose a threat to the content, quality and usefulness of the very data that defines the Census Bureau’s mission and that demographers and statisticians rely on to draw a portrait of the nation’s communities.

The Census Bureau’s solution to protecting privacy is a new DAS based on a methodology referred to as Differential Privacy (DP). In brief, it functions by leveraging the same database reconstruction techniques that were used to diagnose the problem in the previous system: the 2020 DAS synthesizes a complete set of person- and household-level data records based on an extensive set of tabulations to which statistical noise has been added. Viewed as a continuum between total noise and total disclosure, the core of this method involves a determination regarding the amount of privacy loss or e, that can be accepted without compromising data privacy while ensuring the utility of the data. The key then becomes “where to set the dial”—set e too low and privacy is ensured at the cost of utility, but set e too high and utility is ensured but privacy in compromised. In addition to the overall level of e, its allocation over the content and detail of the census tabulations for 2020 is important. For example, specific block-level tabulations needed for redistricting may require a substantial allocation of the privacy-loss budget to achieve acceptable accuracy for this key use, but the cost is that accuracy of other important data (including for blocks, such as persons per household) will likely be compromised. Finding ways to resolve these difficult tradeoffs represents a serious challenge for the Census Bureau and users of its data.

The CNSTAT Workshop

In order to test how well this methodology worked in terms of the accuracy of noise-infused data, the Census Bureau issued special 2010 Census files subject to the 2020 DAS. The demonstration files applied the 2020 Census DAS to the 2010 Census confidential data — that is, the unprotected data from the 2010 Census that are not publicly available. The demonstration data permit scientific inquiry into the impact of DP. In addition, the Census commissioned the Committee on National Statistics (CNSTAT) of the National Academies of Sciences, Engineering and Medicine to host a 2-day Workshop on 2020 Census Data Products: Data Needs and Privacy Considerations, held in Washington, DC, on December 11-12, 2019. The two-fold purpose of the workshop was:

  • To assess the utility of the tabulations in the 2010 Demonstration Product for specific use cases/real-life data applications.
  • Generate constructive feedback for the Census Bureau that will be useful in setting the ultimate privacy loss budget and on the allocation of shares of that budget over the broad array of possible tables and geographic levels.

We both served as the co-chairs of the Committee that planned the Workshop. The Workshop brought together a diverse group of researchers who presented findings for a wide range of use cases that relied on data from past censuses.

These presentations, and the discussions surrounding them, provided a new set of evidence-based findings on the potential consequences of the Census Bureau’s new DAS. In what follows, we summarize “what we heard” or learned from the Workshop. This summary is ours alone; we do not speak for the Workshop’s Planning Committee, CNSTAT, or the Census Bureau. Nonetheless, we hope that the summary below provides the broader community of users of decennial census data with a better understanding of some of the potential consequences of the new DAS for the utility of the 2020 Census data products. Moreover we hope it fosters an on-going dialogue between the user community and the Census Bureau on ways to help ensure that data from the 2020 Census are of high quality, while still safeguarding the privacy and confidentiality of individual responses.

What We Heard

  • Population counts for some geographic units and demographic characteristics were not adversely affected by Differential Privacy (DP). Based on results presented at the Workshop, it appears that there were not, in general, differences in population counts between the 2010 demonstration file at some levels of geography. For the nation as a whole and for individual states, the Census’s algorithm, ensured that that counts were exact, i.e., counts at these levels were held invariant by design. Furthermore, the evidence presented also indicated that the counts in the demonstration products and those for actual 2010 data were not very different for geographic areas that received direct allocations of the privacy budget, including most counties, metro areas (aggregates of counties) and census tracts. Finally, for these geographic areas, the population counts by age in the demonstration products were fairly accurate when using broader age groupings (5-10 year groupings or broader ones), as well as for some demographic characteristics (e.g., for non-Hispanic whites, and sometimes for Hispanics).
  • Concerns with data for small geographic areas and units and certain population groups. At the same time, evidence presented at the Workshop indicated that most data for small geographic areas – especially census blocks – are not usable given the privacy-loss level used to produce the demonstration file. With some exceptions, applications demonstrated that the variability of small-area data (i.e., blocks, block groups, census tracts) compromised existing analyses. Many Workshop participants indicated that a larger privacy loss budget will be needed for the 2020 Census products to attain a minimum threshold of utility for small-area data. Alternatively, compromises in the content of the publicly-released products will be required to ensure greater accuracy for small areas.

The Census did not include a direct allocation of the privacy-loss budget 2010 demonstration file to all geographic areas, such as places and county subdivisions, or to detailed race groups, such as American Indians. As noted by numerous presenters, these units and groups are very important for many use cases, as they are the basis for political, legal, and administrative decision-making. Many of these cases involve small populations and local officials rely on the census as a key benchmark; in many cases, it defines who they are.

  • Problems for temporal consistency of population counts. Several presentations highlighted the problem of temporal inconsistency of counts, i.e., from one census to the next using DP. The analyses presented at the Workshop suggested that comparisons of 2010 Census data under the old DAS to 2020 Census data under DP may well show inexplicable trends, up or down, for small geographic areas and population groups. (And comparisons of 2030 data under DP with 2020 data under DP may also show inconsistencies over time). For example, when using counts as denominators to monitor disease rates or mortality at finer levels of geography by race, by old vs young, etc., the concern is that it will be difficult to determine real changes in population counts, and, thus, real trends in disease or mortality rates, versus the impact of using DP.
  • Unexpected issues with the post-processing of the proposed DAS. The Top-Down algorithm (TDA) employed by the Census Bureau in constructing the 2010 demonstration data produced histograms at different levels of geography that are, by design, unbiased —but they are not integers and include negative counts. The post-processing required to produce a microdata file capable of generating tabulations of persons and housing units with non-negative integer counts produced biases that are responsible for many anomalies observed in the tabulations. These are both systematic and problematic for many use cases. Additional complications arise from the need to hold some data cells invariant to change (e.g., total population at the state level) and from the separate processing of person and housing unit tabulations.

The application of DP to raw census data (the Census Edited File [CEF]) produces estimates that can be used to model error, but the post-processing adds a layer of complexity that may be very difficult to model, making the creation of “confidence intervals” problematic.

  • Implications for other Census Bureau data products. Important parts of the planned 2020 Census data products cannot be handled by the current 2020 DAS and TDA approach. They will be handled using different but as-yet-unspecified methods that will need to be consistent with the global privacy-loss budget for the 2020 Census. These products were not included in the demonstration files and were out of scope for the Workshop. Nonetheless, as noted by several presenters and participants in the Workshop, these decisions raise important issues for many users and use cases going forward. To what extent will content for detailed race/Hispanic/nationality groups be available, especially for American Indian and Alaska Native populations? To what degree will data on household-person combinations and within-household composition be possible under DAS?

For example, while the Census Bureau has stated that 2025 will be the target date for the possible application of DP to the ACS, they indicated that the population estimates program will be subject to DP immediately following 2020. These estimates would then then be used for weighting and post-stratification adjustments to the ACS.

  • Need plan to educate and provide guidance for users of the 2020 Census products. Regardless of what the Census Bureau decides with respect to ε and how it is allocated across tables, the Workshop participants made clear that a major re-education plan for data users’ needs to be put in place, with a focus on how best to describe key data and the shortcomings imposed by privacy considerations and error in general. Furthermore, as many at the Workshop voiced, such plans must be in place when the 2020 Census products are released to minimize major disruptions to and problems with the myriad uses made of these data and the decisions based on them.
  • Challenging privacy concerns and their potential consequences for the success of the 2020 Census. Finally, the Workshop included a panel of experts on privacy. These experts highlighted the disclosure risks associated with advances in linking information in public data sources, like the decennial census, with commercial data bases containing information on bankruptcies and credit card debt, driver licenses, and federal, state and local government databases on criminal offenses, public housing, and even citizenship status. While there are federal and state laws in place to protect the misuse of these governmental databases as well as the census (i.e., Title 13), their adequacy is challenged by advances in data linkage technologies and algorithms. And, as several panelists noted, these potential disclosure risks may well undercut the willingness of members of various groups – including immigrants (whether citizens or not), individuals violating public housing codes, or those at risk of domestic violence – to participate in the 2020 Census.

The Census Bureau has recently stated that it plans to have CNSTAT organize a follow-up set of expert meetings to “document improvements and overcome remaining challenges in the 2020 DAS.” In our view, such efforts, however they are organized, need to ensure meaningful involvement and feedback from the user community. Many within that community remain skeptical of the Bureau’s adoption of Differential Privacy and its consequences for their use cases. So, not only is it important that Census try to address the various problems identified by Workshop presenters and others who evaluated the 2010 demonstration products, it also is essential that follow-up activities are designed to involve a broader base of user communities in a meaningful way.

We encourage members of the census data user community to become engaged in this evaluation process, agreeing, if asked, to become involved in these follow-up efforts. Such efforts will be essential to help ensure that the Census Bureau meets its dual mandate of being the nation’s leading provider of quality information about its people and economy while safeguarding the privacy of those who provide this information.

2020 APDU Conference Call for Proposals

#Trending in 2020: Data Privacy, Accuracy, and Access

APDU is welcoming proposals on any topic related to the privacy, accuracy, and access of public data.  Proposals can be for a single presentation or panel, whether based on a particular project, data practice, or formal paper.  In keeping with the theme of the conference, our interest is in highlighting the breadth of public data to both producers and consumers of public data.  Some examples of topics might cover:

  • Privacy
    • Differential privacy and tiered data
    • State/local data privacy issues
    • Data Suppression
    • Corporate data privacy (ex. Facebook’s use of differential privacy)
  • Accuracy
    • Machine learning and the use of programming languages
    • How data accuracy will affect redistricting or federal allocations
    • Federal agencies data protection actions’ impact on other agency data
    • Synthetic or administrative data
    • Decennial Census
      • Citizenship question
      • Complete Count Committee
  •  Access
    • Future public data and policy developments
    • Current availability of public data (health, education, the economy, energy, the environment, climate, and other areas)
    • Federal statistical microdata such as ResearchDataGov
    • Federal Data Strategy updates and advocacy

Proposal Deadline: February 28, 2020.

You may submit ideas for a single presentation or a full panel (three presenters, plus a moderator). However, it is possible that we will accept portions of panel submissions to combine with other presenters. Submissions will be evaluated on the quality of work, relevance to APDU Conference attendees, uniqueness of topic and presenter, and thematic fit.

Please submit your proposal using the Survey Monkey collection window below.  Proposals will need to be submitted by members of APDU, and all presenters in a panel must register for the conference (full conference registration comes with a free APDU membership).  Proposers will be notified of our decision by March 13, 2020.

About APDU

The Association of Public Data Users (APDU) is a national network that links users, producers, and disseminators of government statistical data. APDU members share a vital concern about the collection, dissemination, preservation, and interpretation of public data.  The conference is in Arlington, VA on July 29-30, 2020, and brings together data users and data producers for conversations and presentations on a wide variety of data and statistical topics.

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The 2020 Census is Here and Businesses can Help

Companies make strategic decisions every day that rely on accurate data about customers, employees and markets. In the United States, the information gleaned from the decennial population census is an important ingredient in much of the data that companies use to make a range of decisions such as where to locate new stores/facilities, how to market products, and what services to offer customers. The federal government also uses census information to distribute more than $1.5 trillion for programs like roads, education and workforce development that help to strengthen the economy.

The next nationwide count starts in most of the country this March, and companies can help ensure its accuracy by encouraging employees and customers to participate.

Below are a series of resources from the US Census Bureau and ReadyNation that businesses and business membership organizations may find helpful when developing plans to support the count:

  • Newsletter language: Templates for (i) business organizations to engage their membership and (ii) companies to engage their employees.

FY20 Budget Moves from House to Senate

The House has passed appropriations bills to the Senate for FY2020, and there are important developments for statistical agencies. The Census Bureau, Bureau of Labor Statistics (BLS), and Bureau of Economic Analysis (BEA) each received modest to substantial increases in their budgets.

With massive increases in spending by the Census Bureau needed to successfully complete the Decennial Census, Congress appropriated $7.558B for the Census Bureau, with $274M for Current Surveys and Programs and $7.284B for Periodic Censuses and Programs. Importantly, this provides 6.696B for the Decennial Census, which is the minimum requested to complete the count effectively.

BEA received $107.9M, which assumes full funding for efforts to produce annual GDP for Puerto Rico. In addition, Congress apportioned $1.5M to the Outdoor Recreation Satellite Account, and $1M to develop income growth indicators.

After several years of flat funding, the BLS operational budget has been increased to $655M. This includes $587M for necessary expenses for the Bureau of Labor Statistics, including advances or reimbursements to State, Federal, and local agencies and their employees for services rendered, with no more than $68M that may be expended from the Employment Security Administration account in the Unemployment Trust Fund. This number includes $27M for the relocation of the BLS headquarters to the Suitland Federal Center and $13M for investments in BLS such as an annual supplement to the Current Population Survey on contingent work, restoration of certain Local Area Unemployment Statistics data, and development of a National Longitudinal Survey of Youth.

Webinar Q & A: How Will New Census Bureau Privacy Measures Change 2020 Decennial Census Data?

On November 18, APDU hosted a webinar on new measures taken by the Census Bureau to protect respondent privacy in the decennial census known as “differential privacy.” The webinar recording is available to APDU members in the member email and in a follow-up email to webinar registrants. Below are follow-up answers to questions from the question and answer portion of the webinar.

Has there been any discussion concerning cell specific error terms—akin to ACS MOEs seen in the summary files?

DVR answer – We do not know whether error terms will be provided with the Decennial census counts. Computing the error terms from the underlying data uses up part of the privacy loss budget. Census would have to decide whether that use of the privacy loss budget would be worth doing. If part of the privacy loss budget is used to compute error terms, the actual could will be more inaccurate. The Census Bureau recognizes the importance of such error terms – see https://arxiv.org/abs/1809.02201 for more details.

Do I understand correctly that a variable is invariant means that it will be reported as tabulated with no change?

That is correct. An “invariant” is a variable to which no noise will be added—it will be reported as enumerated (including any editing or imputation).

Is there any chance that the Bureau will realize that the cost/benefit of this is totally unacceptable? It seems like a massive over-reaction to me.

APDU has no formal position on this, but highly encourages all data users to submit their feedback to the Census Bureau’s email dcmd.2010.demonstration.data.products@census.gov

For more information about the comment process, see https://www.census.gov/programs-surveys/decennial-census/2020-census/planning-management/2020-census-data-products/2010-demonstration-data-products.html

Why is Illinois very high in the test tables? Is it because MCDs are a key part of the state’s political structure? Why wouldn’t New England states also be high in your tables, since MCDs are keys in those states?

Minor civil divisions are a fundamental part of Illinois’ political structure, and there are lots of MCDs with small populations. I bet that MCDs in New England have a larger populations, on average, than those in Illinois. Noise injection via differential privacy has a larger proportional impact on small populations. Thus, we see a larger fraction of Illinois MCDs with no vacant housing counts than we observe in New England states.

Will smaller geographies sum to larger ones, such as blocks to blockgroups?

Yes, smaller geographies will sum to larger units, such as blocks to block groups. The final output of the differential privacy algorithm is a set of microdata with block IDs on them. Tabulations derived from these microdata will sum up the geography hierarchy.

Why is a Laplace distribution used?

Technically the Census Bureau is using a geometric distribution, which allows the process to draw integer values for noise-introduction (and is similar to Laplace). Laplace is the current standard in differential privacy across the data privacy field. See the following two links for a more detailed discussion of Laplace vs. other symmetric distributions:

https://stats.stackexchange.com/questions/187410/what-is-the-purpose-of-using-a-laplacian-distribution-in-adding-noise-for-differ

https://www.johndcook.com/blog/2019/02/05/normal-approximation-to-laplace-distribution/

https://www.johndcook.com/blog/2017/09/20/adding-laplace-or-gaussian-noise-to-database/

Kathy’s slide 3 or 4 showed that one table that looks to be dropped for 2020 data products is HH by presence of nonrelatives. You also say Census may drop tables on young children at specific ages, such as 2 or 3. If these tables are dropped, research on the persistent and growing undercount of young children will be severely hampered. Households with nonrelatives are one of three types of complex households that have the highest correlation with young children who were originally missed in the 2010 Census, just some of whom were added back into the 2010 Census counts through the Census Followup Operation. The undercount of young children is a major issue that has been recognized by Congressional Committees, as well as the Census Bureau’s outside Advisory Committees, and Complete Count Committees. These are CRITICAL data for data users and policy makers. These tables are VERY much needed and we should urge the Census Bureau to provide these data!    

To be clear, there’s no definite decision on tables yet. The Census Bureau is proposing for the DHC tables to have a table on “SEX BY AGE FOR THE POPULATION UNDER 20 YEARS [43]” at the block level so there’ll be a count of children by specific ages, so I think that will meet your use case. One question is the needed geography for tables such as HOUSEHOLD TYPE BY RELATIONSHIP BY AGE FOR THE POPULATION UNDER 18 YEARS [36] (PCO9 in the new DHC) is proposed at the county level only, so may not meet the needs if people are using it at the tract level now.

Another issue is the importance of related children (which is not a category in the new tables) versus own children only. For example, related children are grouped with other non-related children in the PCO9 table. This is not my area of study but may be of concern to some people.

In any case, we encourage you to dig into the tables yourself and share your perspective with the Bureau. In addition to whether the tables are published, whether the data is appropriate for your use case will also depend on the level of accuracy of the numbers.  If you have a particular table of interest to your organization, we encourage you to take a look at the demonstration data and how it compares to SF1 values in 2010.

For information on how to submit comments to the Census Bureau, see https://www.census.gov/programs-surveys/decennial-census/2020-census/planning-management/2020-census-data-products/2010-demonstration-data-products.html

2019 APDU Candidate Statements

Candidate for Treasurer: Mauricio Ortiz, Chief, Regional Income Division, US Bureau of Economic Analysis

I have served as Treasurer of APDU for the last four years while I have continued to work for the U.S. Department of Commerce Bureau of Economic Analysis (BEA).  At BEA I am Chief of the Regional Income Division where we produce state, metro area, and county statistics of GDP, personal income, personal consumption expenditures, and regional price parities.  Every day at work and in my interactions with APDU I am reminded of the importance of the U.S. federal statistical system and the many data it produces.  Public federal data plays a vital role in helping private and government decision makers make well informed decisions.  These important decisions move the economy forward at both the local and national level.

As a public data producer and disseminator, I am extremely excited to continue to be on the APDU board member.  I want to continue to engage with APDU members (users of public data); hear their concerns about the data; help them identify data they are looking for; educate them about current and new data; gather feedback on what kind of data is not currently available but is in demand; and most importantly learn and connect with other providers of public data.  APDU members are BEA’s customers and I want to meet and hear from as many BEA customers as possible.   I am thankful to APDU for the opportunity to continue to serve as a board member.

Candidate for Secretary: Beth Jarosz, Senior Research Associate, Population Reference Bureau

At PRB I work on a wide range of U.S. demographic topics, with a focus on subnational analysis. Prior to joining PRB, I served as Senior Demographer at the San Diego Association of Governments and later taught sociology at Pensacola State College. My publications are cross-disciplinary and span topics from inequality to mortality, as well as forecast and estimation methods—all have relied heavily on public data. Throughout my career I have been a champion of public data.

I believe one of the challenges facing any member organization, including APDU, is engaging with members (and attracting new members). Over the past year, as a member of the APDU Board, I served on the conference planning committee, helped to brainstorm and organize webinars, and assisted in advertising APDU events through a variety of social media channels (e.g. Twitter and LinkedIn). As Secretary, I will become the recorder of APDU Board motions and outcomes, and I will also continue my work brainstorming, testing, and implementing new communication channels and strategies to add value for existing members and to attract new members.

Candidate for At-Large Director: Katherine Wallman, Chief Statistician of the United States (retired)

My relationship with APDU extends almost to its founding; during my tenures as Executive Director of COPAFS and as Chief Statistician of the United States, I was a regular speaker at the association’s annual conference.  While these gatherings remain a signature event, the weekly newsletter and the webinars – more recent offerings – are particularly worthwhile. On the APDU Board I can forge and support collaborations with other complementary organizations, including several disciplinary associations as well as the Inter-University Consortium for Political and Social Research, that share APDU’s concerns about the collection, dissemination, preservation, and interpretation of public data.

Candidate for At-Large Director: Amy O’Hara, Research Professor, Georgetown University

I would like to join the APDU board to improve data access and quality for members, researchers, and administrators. I would work towards establishing standards and norms for secure and responsible data use.  Our community needs to incorporate broader views of public data that feature state, local, and private sources; emphasize data utility when designing privacy protections; and promote the development of ethical reviews and social license with our data subjects.

Candidate for At-Large Director: Bernie Langer, Senior Data Analyst, PolicyMap

I am very excited about the possibility of continuing my involvement with APDU by joining its Board of Directors. As one of PolicyMap’s most senior data analysts, I have a deep and broad knowledge about federal statistical agencies and private data providers, as well as experience working with data and data users to solve problems. I’ve worked with data from the Census Bureau, BLS, IRS, SSA, HUD, USDA, FDIC, FBI, FCC, FEMA, DOT, NCES, EPA, SBA, and CDC, just to name a few. I’ve also led PolicyMap’s “Mapchats” webinar series, a forum for data providers and users to discuss their work.

I’ve attended many of APDU’s conferences and webinars and have found them invaluable. As a board member, I would be committed to maintaining the high quality of APDU’s services and events, finding additional ways for data providers and users to interact, and raising the profile of public data in society.

Intermediate Application of Data Sets: Introducing the Census Bureau’s Business Formation Statistics

In July 2019, the Census Bureau released the Business Formation Statistics (BFS), a new data product that tracks trends in business applications and formations at the state, regional and national levels.

The BFS consists of four business application series and eight business formation series. It’s unique because it relies on administrative data from the IRS, specifically the data on applications for an Employer Identification Number (EIN) via IRS Form SS-4, to determine the number of business applications submitted in a quarter, and how many result in businesses with employees.

The BFS also includes projections for business formations in the near future. The BFS began in 2012 as a research project in the Census Bureau’s Center for Economic Studies and was first released in beta form in February 2018. It’s the culmination of research efforts by the Census Bureau, Board of Governors of the Federal Reserve System, Federal Reserve Bank of Atlanta, University of Maryland and the University of Notre Dame. This webinar will provide an overview of BFS and demonstrate how to access BFS data available on the Census website.

Presenters:

Jason Jindrich, Survey Statistician, US Census Bureau

Jeff McHugh, Chief, New and Emerging Indicators Programs, US Census Bureau

Rebecca Hutchinson, Big Data Lead, Economic Indicators Division, US Census Bureau

Pricing:

APDU, C2ER, and LMI Institute Members: Free

Non-Members: $50.00

2019 APDU Data Viz Award Winners Announced

APDU received many excellent submissions for the 2019 APDU Data Viz Awards, and our expert review committee has concluded their deliberations. This was a difficult process with such great options to choose from – we are very grateful for the work put into developing and submitting these visualizations.

We would like to thank our review committee for their time and effort in evaluating the submissions. This year, Mark Mather of the Population Reference Bureau and Steven Romalewski of the Center for Urban Research at The Graduate Center/CUNY served on the committee.

Without further ado, the winners this year include:

State and Local Governments

2018 Vintage Population Estimates Dashboard

  • Tim Kuhn, Tennessee State Data Center @ Boyd Center for Business and Economic Research at Univ. Tenn

Federal Government

The Population 65 Years and Older: 2016

Megan Rabe, U.S. Census Bureau

Private Firms

Ididio Career Overviews

  • Kris Heim, Ripe Data
  • Elsa Schaefer, Ripe Data

Researchers and Students

What is in my water?

  • Xindi Hu, Harvard University; Mathematica
  • Paul von Chamier, Harvard University
  • Daniel Tompkins, Harvard University

Congratulations to this year’s winners! Register for the 2019 APDU Annual Conference today to learn from awardees about how they created these excellent visualizations.

APDU Board Member: Why Do We Attend the APDU Conference? Our Business Depends on It.

By: Elizabeth Nash, APDU Board Member

Although many APDU Conference attendees show up to share information about their government agencies’ data products, private companies like PolicyMap have a compelling business reason to attend:  it’s the easiest, most reliable way to learn about what’s in store for data, and data is our lifeblood.

As a board member of APDU, each year I get to put together a session at the conference.  This year, I’m responsible for the first day’s keynote on the Census Bureau’s challenge of balancing privacy and reliability.  I had heard about the so-called “differential privacy” solution to privacy concerns in Census products, but I was eager to learn more—particularly about how privacy concerns will affect the quality of the data products we rely on so heavily at PolicyMap.  We’ll get a chance to hear about the changes in store for Census 2020, the American Community Survey, and other data products.  We’ll also get a glimpse of the implications of those decisions for Census data users down the road.

After the keynote, I’m looking forward to an animated lunchtime roundtable and conversation about the subject, hearing reactions from data users and Census employees on these changes.

Join us on July 9th and 10th in Arlington, VA at the APDU Annual Conference to hear the latest updates from public data providers and users.

 

APDU Data Viz Awards: Call for Visualizations

The Association of Public Data Users (APDU) is pleased to announce the 2019 Data Viz Awards. We are once again soliciting creative and meaningful graphic designs that use publicly-available data (for example, data from the Census Bureau or Bureau of Labor Statistics) to convey a compelling point or story.

About the Award

APDU started the Data Viz Awards in response to our members’ growing need to communicate their data and research to a variety of audiences using graphic technologies and cutting-edge techniques. APDU hopes to engage data users and help them understand and share data for analysis and decision making.
Winners will be invited to present at the 2019 APDU Annual Conference on July 10, 2019 in Arlington, VA. Winners in the “Researchers & Students” category will also receive a free APDU membership for 2019.

What We’re Looking For

APDU will select creative and compelling images in four categories:

  • State/Local government, including independent and quasi-independent agencies;
  • Federal government, including independent and quasi-independent agencies;
  • Private firms, which can include consultancies, advocacy groups, or any other private firms using public data; and
  • Researchers/Students, which can include any visuals published or formally presented by researchers or students in higher education, think tanks, research organizations, nonprofits, or similar.

Submissions must have been made publicly available between June 2018 and May 2019. We are accepting submissions that appeared in a published research paper or article either in print or on the web, in a public presentation, as a stand-alone infographic, as a website feature, and/or as another official product.

Deadline: Friday, May 17, 2019

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