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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Watch the video for Mapping the Way to Fair Housing and Environmental Justice

mapping fair housing

Thank you to our speakers, John Henneberger and Charlie Duncan from Texas Housers, for a fantastic presentation of their work. You demonstrated how collaboration with community organizers and using data spatially can make such a strong impact in community development. We had great questions and thoughtful discussion from our attendees.

Watch the webinar here:

Continue reading

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Are Students Included in Census Poverty Rates?

We love receiving questions about data here at PolicyMap! Recently, after releasing the 2009-2013 American Community Survey on PolicyMap, a user reached out with an interesting question: are college students included in Census poverty figures? This is an important consideration because, while many students earn income below the poverty level, this does not necessarily mean they are of low socioeconomic status. Thus, including them in poverty statistics can skew a look at the demographic and economic makeup of a local area.
Continue reading

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