Where are the Data on America’s Physical Infrastructure?

How we move ourselves and our resources as a nation is a critical and complex system. Our economy relies upon the efficient movement of goods, information and people, which in turn rely on the infrastructure supporting this transport.

Seeing as the current condition and future viability of our nation’s infrastructure has important public safety, economic, health and social implications, it would seem that having a central database on infrastructure systems would be of great value. Moreover, in their 2013 Report Card, the American Society of Civil Engineers (ASCE) gave America’s infrastructure a fairly dismal grade of D+, or “Poor: At Risk”. This wasn’t quite the vote of confidence that most of us would feel comfortable with.

So, seeing these potential gaps and opportunities, some of us at PolicyMap looked into the availability of infrastructure data, specifically if there were spatial datasets that could allow municipalities, organizations, and residents to know more about the condition of local bridges, roads, and waterways, for example.

Unfortunately, there wasn’t a central database or map at all; instead, infrastructure data exist as a patchwork of different datasets from different sources covering different areas of the country. The good news is that depending on the type of infrastructure system you’re interested in, some national datasets are publicly-available. Here’s a quick summary of some of the more useful and easily-retrievable datasets:

• Bridges (FHWA/BTS)
• Roads (FHWA/BTS)
• Aviation Delays (BTS, DOT)
• Dams (FHWA/BTS)
• Brownfield Sites (EPA)
• Superfund Sites (EPA)
• Parks and Recreation (USGS GAP PADUS Database)
• Airports and Runway (FAA)
• Major Ports (FHWA/BTS)
• Pipeline and Hazmat Incidents (PHMSA DOT)

Some infrastructure data are easier to find at the municipal level, as some cities are leading the way in both collecting and releasing data on their infrastructure systems and capital improvement projects (CIPs). In particular, San Francisco has an interactive map of their CIPs, paving sites and green infrastructure locations. Their 10-year plan also includes a handful of informative maps on street improvement locations, water and wastewater system pumps, pipes and hydrants, as well as charts on funding allocations and needs. CALTRANS, the Department of Transportation in California, also provides geospatial data on transportation improvement projects, bridge locations and the rail network.

So what’s the take-away message here? Well, it seems that in order to accurately assess the current state and plan for future demand, having a centrally-accessible source of infrastructure projects would be extremely valuable and informative. But, it’s worth recognizing that this scale of a project would require massive organization, reporting and perhaps even an Act of Congress. We’ll wait and see.

What did we miss? How is your city or community accounting for its infrastructure?

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Credit Unions: Your Neighborhood Cooperative

You’re probably getting your hot dogs, American flags, and sparklers ready for Independence Day, but did you know that Friday, July 3rd is International Day of Cooperatives? According to the International Cooperative Alliance, cooperatives (co ops) are businesses owned and run collectively by and for their members.

One common example of a cooperative organization is a credit union. Credit unions are financial institutions where, unlike banks, all members own a share in the overall business. Credit unions do not have external stockholders, and are not accountable to creating profits– this cooperative structure allows credit unions to offer favorable interest rates on loans, and offer cash dividends to members based on the size of their investment.

PolicyMap is celebrating this Day of Cooperatives by updating our credit union locations, from the NCUA. Do successful cooperative financial institutions foster greater wealth in their communities? The map shows the nation’s largest credit unions by dollar amount of shares/deposits, in relation to per capita income.

Tomorrow, be sure to sit back with a cold lemonade and explore the many facets of credit unions while you enjoy your day off. Filter the dataset by credit union type, membership, charter (who is eligible to become a member), loans, employees, assets, and more. Credit unions can be found on PolicyMap in the Lending tab.

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We’re Hiring!

If processing data, blogging about mapping, strategizing about taxonomies and collaborating on data visualization tools sound up your alley, check out the Data Associate job opening on our new PolicyMap careers page.  We’d love to talk to you about why you love PolicyMap and want to be a part of our team!

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We’re at ALA 2015 Annual Conference! Come visit us!

The ALA 2015 Annual Conference this year is in fabulous San Francisco! Just in time for the festivities that’s happening today. Very exciting.

Come by our booth at 3807 and say hi! We’ve met lot of great librarians from across the country, learned so much more about our tool, and are excited to continue our conversations when we get back.

We’ve met Conference Elvis (Twitter handle to come…) and we were lucky enough to have the a NASA super hero come see us also.

We still have two more days so come by booth #3807, enter to win the Chromebook and learn more about PolicyMap today.

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Data Updates! Predominant Race/Ethnicity and Diversity Index

We’re excited to announce that two indicators have been updated to include the 2013 ACS 5-year estimates: Predominant Race/Ethnicity and the Diversity Index. You’ll find both of these indicators by going to the “Demographics” header above the map, and then scrolling down to the “Diversity” sub-header.

Just to review, the Diversity Index reflects the probability that if two people were chosen at random in a given area, they would be of different races and ethnicities. In calculating the index, we used 8 mutually-exclusive racial and ethnic categories reported by the American Community Survey. The index is calculated at the level of the county, tract and block group. Higher index values suggest more heterogeneity (i.e., diversity).

The Predominant Race and Ethnicity indicator conveys two pieces of information for the price of one: first, it shows which racial or ethnic group is the predominant group in terms of population and second, it shows what fraction of the population the predominant group represents.

Using these two indicators in tandem can give a more nuanced sense of the residential patterns of a neighborhood, town or city. In areas where the predominant racial or ethnic group is “predominant” by a smaller margin, you would expect to see higher diversity index values.

For example, let’s look at Queens, New York City. Queens County recently adopted the title as “The World’s Borough”, in celebration of the county’s diversity. Looking at the Diversity Index map, there are areas in Queens that would suggest that the motto is well-deserved, with higher values in Astoria, Flushing and Jamaica, for example.

The Predominant Race and Ethnicity map reveals additional insight into the composition of Queens – southern areas largely consist of African-American communities and in the north, Asian communities are interspersed with White and Hispanic enclaves.

What do you learn about your neighborhood with these two datasets? Write to us and let us know!

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Low-Mod Dataset Update!

It’s the annual Low-Mod Blog Post! Not quite as fantastic a name as perhaps, Bob Loblaw’s Law Blog, but arguably much more exciting. Recently, we released the American Community Survey’s 5-year estimates for 2009-2013 on PolicyMap. With this new release, we’ve updated the low to moderate income “Low-Mod” dataset to include 2013 values, as well.

Bob Loblaw’s Law Blog is in awe of the Low-Mod blog.
(source: GIPHY, credit to Arrested Development)

The Low-Mod dataset reflects the local median income as a share of area median income. For all tracts and block groups located within Census-defined metropolitan areas, this calculation is the local median income as a share of metro-area median income. For tracts and block groups outside of Census-defined metro areas, this is local median income as a share of the non-metro state median income. Lower income areas are typically designated as areas with less than or equal to 50% of the area median income, while moderate income areas are those with less than or equal to 80% of the area median income.

So, let’s use Atlanta, Georgia as an example. Recently, the Brookings Institute released a report analyzing income inequality in cities across America . Atlanta ranked highest for income inequality in both 2012 and 2013, when defining income inequality as the ratio of 95th percentile of income to the 20th percentile of income.


Using the median household income data on PolicyMap, we can see that this particular tract in Atlanta has a median household income of $19,808 and the Atlanta metro-area has a median household income of $56,605.


Dividing these two numbers yields the Low-Mod value, of 34.99%, or the share of the metro median income represented by the tract-wide median income. This tract would certainly fall into the “lower-income” category.


Commonly these indicators are used to meet eligibility guidelines of the HUD Community Development Block Grant (CDBG) program. Annually, the CDBG program allocates about $3 billion dollars to reinvestment and development programs through a highly competitive application process. CDBG allocatees are required to demonstrate that their project or activity will primarily benefit low- to moderate-income residents and families. For projects that benefit all residents, the guidelines require that for the given community, 51% of its residents are low- to moderate-income earners.

On PolicyMap, Low-Mod values are presented at the block group and tract level, as well as for median household and family income. Let us know how you use this dataset!

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PolicyMap at the Podium!

Working with data and maps can sometimes be solitary, and sometimes it’s nice to get out and talk with people about the work we do with data and maps. This past week PolicyMap spoke at two conferences, with two very different topics — government finance and food insecurity.

Last Tuesday, I spoke in our hometown of Philadelphia at the Government Finance Officers Associations (GFOA) annual conference in a panel about data-driven decisions. The panel discussed the concepts of “big data” and “open data” and went through strategies to use data to inform policymaking. The panelists included Jonathan Feldman, Chief Information Officer from the City of Asheville, NC and Stacey Mosley, Data Services Manager with the City of Philadelphia. I rode on their coat tails, describing the work PolicyMap has done with data and mapping for the City of Philadelphia as well as The Reinvestment Fund’s Market Value Analysis (MVA) in Baltimore as well as dozens of other cities throughout the country.


On Thursday, PolicyMap member, Kristin Crandall, had the pleasure of speaking at the 2015 Marketing and Public Policy Conference, held by the American Marketing Association in Washington, D.C.  Kristin presented alongside Sonya Grier (American University) and Lauren Block (City University of New York) in a session about Food Insecurity, Waste, and Disparities.  She explained TRF’s Limited Supermarket Access (LSA) study and shared ways that PolicyMap is being used around the country to advance healthy food access work. The theme for this year’s conference was “Marketing and Public Policy as a Force for Social Change.”


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Inman News Gets Inside Scoop on Maggie McCullough

Maggie McCullough profiled by Inman News

Inman News got the inside scoop on PolicyMap Founder and President Maggie McCullough in a January interview. Inman News subscriber? Read the interview on their website. For everyone else, check out a screenshot of the full text, and read an excerpt below:

Inman News: How’d you come up with the idea for your startup?

Maggie McCullough: I was doing research for state and local governments and was desperately trying to figure out how to incorporate some data and maps as a part of my research. But the desktop mapping software was hard to learn. I even took a course and still had trouble.

It just seemed that the data and maps should be available on the Web (not locked in someone’s desktop) and that making maps should be as simple as anything else you do on the Web (like shopping!); I wanted it to be simple.

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Fun with Taxes!

What can you tell about somebody based on their tax returns? You can find out how much money they make. You can find out how they make their money – Salaries and wages? Business? Investments? Retirement income?

Are they a low-income earner, receiving the Earned Income Tax Credit (EITC)?

You can find out about their family. How many dependents do they have? Are they claiming the child tax credit? Are their children going to preschool? Are they claiming the credit for child and dependent care expenses?

Do they own a home? Are they paying a mortgage?

Are they generous? Do they make charitable contributions? Are their homes powered by renewable energy?

Would it be rude to ask for a 1040 before going on a blind date?

Of course, you can’t look up tax returns for individual people (so don’t worry, no one will find out that you’re deducting millions of dollars in local sales taxes due to your $5,000-a-day Faberge egg habit). But data from the returns are aggregated together, so you can see the collective tax returns for entire ZIP codes, counties, and states. This data comes from the IRS’s Statistics of Income (SOI) dataset, new on PolicyMap. Continue reading

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PolicyMap *Recommended* by Library Journal!

Check out the May 15th edition of Library Journal’s Reference eReviews to read Harvard librarian Cheryl LaGuardia’s review and recommendation for PolicyMap.


“Overall, this resource is excellent and can do a lot more than space permits me to describe. Recommended at the right price.”

Cheryl LaGuardia, Harvard University
Reference eReviews, Library Journal, May 2015


Interested in giving PolicyMap a whirl at your university? Contact us for a free 30-day trial.

Subscribe by June 30th to receive a 10% discount off our already low prices, and be sure to ask us about multi-year discounts. Contact us to learn more. 

We are happy to share that we have a growing list of universities now using PolicyMap! If you are a student or professor at one of the following universities, you can access PolicyMap through your university library system.

  • American University
  • Alabama A&M University
  • Bloomsburg University
  • Binghamton University – SUNY
  • Boston College
  • Brandeis University
  • California University
  • Cheyney University
  • Clarion University
  • Cornell University
  • Curry College
  • Dartmouth College
  • East Stroudsburg University
  • Edinboro University
  • Georgia State University
  • Harvard University
  • Harford Community College
  • Jefferson University
  • Indiana University
  • Kutztown University
  • Lincoln University
  • Lock Haven University
  • Mansfield University
  • Michigan State University
  • New York University
  • Oklahoma State University
  • Penn State University
  • Rowan University
  • Rutgers University
  • Shippensburg University
  • Slippery Rock University
  • St. John Fisher College
  • Syracuse University
  • Temple University
  • Tufts University
  • UCLA
  • University of Albany – SUNY
  • University of Arkansas, Fayetteville
  • University of Arizona
  • University of California – Berkeley
  • University of Delaware
  • University of New Hampshire
  • University of New Orleans
  • University of North Texas
  • University of Pennsylvania
  • University of Pittsburgh
  • University of Vermont
  • University of Virginia
  • University of Massachusetts, Boston
  • University of New Mexico
  • Washington University in St. Louis
  • West Chester University
  • Widener University
  • Yale University


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