Just in time for Halloween: Here comes the Tax Man

Goblins! Witches! Zombies! Werewolves! This time of year we hear a lot about scary things, so here at PolicyMap we thought we’d celebrate with something truly bone-chilling. Yep, you guessed it. Here comes the Tax Man.

Just in time for Halloween, we have added IRS tax data to our platform. While we all fear the reaper as tax returns come due, it turns out our tax data provides some very interesting data and maps. Now on PolicyMap you can find information on tax returns, exemptions, adjusted gross income, and tax liability in communities across the country. This dataset also includes details about tax credits and deductions. Some examples of what you can now map include: student loans, mortgage interest, sales and real estate taxes, child credits, dependent care expenses, unemployment compensation, retirement contributions, and how much people are withdrawing from retirement accounts. We’ll quickly highlight two of these indicators here, and leave it to you to login to PolicyMap to check out the rest under the Incomes & Spending data tab.

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Mapping Community Development Loans with PolicyMap

When PolicyMap was first conceived by The Reinvestment Fund (our parent organization), it was envisioned as a tool to help Community Development Finance Institutions (like TRF) do research for their lending activities. Community Development Finance Institutions (or CDFIs) are financial institutions that make loans in low-income (or otherwise underserved) markets. PolicyMap could help answer questions like: Where are underserved communities? Where can investment create successful development? What kind of investment would be most helpful?

For example, if a neighborhood lacks a supermarket, a CDFI can use PolicyMap to see where existing supermarkets are, and whether the neighborhood will be able to support a supermarket if it gets built. Or if an area lacks affordable housing, a CDFI can use PolicyMap to see if a new housing development will benefit the community.

Since PolicyMap started, our user-base has gone way past CDFIs, but our latest project harkens back to these beginnings, with the OFN Coverage Map.

The Opportunity Finance Network (OFN) is an umbrella organization of CDFIs, and they wanted to answer another set of questions: Where are CDFI investments being made? And where are they not being made? Where should existing CDFIs focus new efforts, and what areas have been successful so far? To answer these questions, they’re using PolicyMap to show where loans have been made by CDFIs across the country. Continue reading

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How do you count a No-Stat address, anyway?

PolicyMap’s postal vacancy data from Valassis Lists has three different measures of vacancy that stem from how the USPS carriers track addresses. The most common type of vacancy is non-seasonal; this is a home or business that’s expected to be occupied year-round. A property can also be seasonally vacant, if it is a vacation home or a business that only operates for part of the year, such as a ski lodge or frozen custard stand. The third category is No-stat. These addresses aren’t actually counted as “vacant,” so what are they, how did they get into the data, and why do they matter?

No-stat is an umbrella category for addresses that the postal service considers unlikely to have mail delivered for one of several reasons. The chart below shows the percent of all addresses considered No-stat in the city of Houston, two ZIP codes within the city (77006, between downtown Houston and the Texas Medical Center, and 77004, in the Third Ward neighborhood), and the county of Colorado TX, just outside the expansive Houston metropolitan area.

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Location Affordability Version 2: Better Than the Original

Brand new datasets are great. When HUD’s Location Affordability data came out last year, we couldn’t wait to add it, because of how simply it illustrated the impact of housing and transportation costs on the budgets of various household types.

What’s better than new datasets? When a new dataset is so useful, its creator decides to make it better. And that just happened with Location Affordability. Soon after the original Location Affordability was released, HUD arranged a conference call of the data’s key users, which we participated in. Based on the feedback from that call, HUD made some significant improvements.

Version 2 of Location Affordability uses new methodology to produce more accurate estimates of housing and transportation costs, so this data which was pretty neat to begin with is now even more powerful. The specifics of the update are pretty wonky, but if you’re reading this blog, you’re probably pretty wonky too, so here are the highlights of the new version, according to HUD:

  • Moving to a Simultaneous Equation Modeling (SEM) approach from Ordinary Least Squares regression: SEM better incorporates and accounts for interaction effects on the model’s dependent variables, resulting in a model that has greater econometric validity
  • Adding variables for housing stock: the model now includes variables for percent of single-family detached housing units and the number of rooms per dwelling unit
  • Adding variables for local commercial amenities
  • Splitting population data by tenure (renter vs. homeowner)

And whereas version 1 covered 94% of the country’s population, version 2 covers 100%.

Also, very importantly, the household types have changed significantly. The affordability data is calculated differently for different households according to number of people, income amount, housing needs, transportation needs, and more.

These are the new household types:

Median-Income Family 4 Median Income for Region 2
Very Low-Income Individual 1 National Poverty Line 1
Working Individual 1 50% of Median Income for Region 1
Single Professional 1 135% of Median Income for Region 1
Retired Couple 2 80% of Median Income for Region 0
Single-Parent Family 3 50% of Median Income for Region 1
Moderate Income Family 3 80% of Median Income for Region 1
Dual-Professional Family 4 150% of Median Income for Region 2


This data is free to the public, and is in the Incomes & Spending menu, under “Additional Income & Spending Data”.

Posted in Data & Features, Dataset Announcements, Incomes & Spending, Taxonomy | Tagged , | Leave a comment

New Unbanked Data on PolicyMap!

Have you been to your local bank branch lately? Perhaps withdrawn money from your checking or savings account using an ATM? Many of us who have a relationship with a traditional financial institution may take it for granted, but a lot of people are without access to these institutions. Growing attention is being paid to households who are considered “unbanked,” meaning the household lacks any kind of deposit account at an insured depository institution, or “underbanked,” meaning the household has a checking and/or savings account but has also used alternative financial services (AFS) at least once in the previous year. In light of the growing interest in this area, we are pleased to announce that we have recently added state and metropolitan area level data from the 2011 Federal Deposit Insurance Corporation (FDIC) National Survey of Unbanked and Underbanked Households to PolicyMap.
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PolicyMap Wins Gold Stevie Award for Web Programming/Design

Philadelphia, PA – 10/15/14 – PolicyMap was named the winner of a Gold Stevie® Award in the Best Web Software Programming/Design category in The 11th Annual International Business Awards today.

More than 3,500 nominations from organizations of all sizes and in virtually every industry were submitted this year for consideration in a wide range of categories, including Company of the Year, Website of the Year, Best New Product or Service of the Year, Corporate Social Responsibility Program of the Year, and Executive of the Year, among others. PolicyMap won in the Best Web Software Programming/Design category.

Its first major redesign effort since launching in 2008, PolicyMap is now faster and easier to use, making community and market data more accessible and visually compelling through maps, tables, reports and analytic tools. PolicyMap is a division of The Reinvestment Fund (TRF), a nonprofit leader in the financing of neighborhood revitalization since 1985.

“We exist on the premise that you shouldn’t need to be an expert to understand important data about your community,” says PolicyMap President, Maggie McCullough. “Our gratitude to the International Business Awards for recognizing PolicyMap’s innovation in web programming and design – and for promoting our belief in the power of information to drive change.”

Stevie Award winners were selected by more than 250 executives worldwide who participated in the judging process from May through early August.

Details about The International Business Awards and the lists of Stevie Award winners are available at www.StevieAwards.com/IBA.

For more information:
Jonah Taylor


About PolicyMap
At PolicyMap, we believe in the power of data to create change in communities and markets. To drive insight in a fast-paced world, your local data needs to be immediately available and visually compelling. PolicyMap saves you time, money and frustration by unifying the web’s largest place-based data library – including incomes, health and education data – with easy-to-use maps, tables, reports and analytic tools. PolicyMap is a division of The Reinvestment Fund, a nonprofit leader in the financing of neighborhood revitalization since 1985. Learn more at http://www.policymap.com.

 About the Stevie Awards
Stevie Awards are conferred in six programs: The International Business Awards, The American Business Awards, the Asia-Pacific Stevie Awards, the Stevie Awards for Women in Business, and the Stevie Awards for Sales & Customer Service.  The sixth program, the German Stevie Awards, opens for entries on 18 August.  Honoring organizations of all types and sizes and the people behind them, the Stevies recognize outstanding performances in the workplace worldwide.  Learn more about the Stevie Awards at www.StevieAwards.com.


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See Round I Promise Zones on PolicyMap

We recently added areas designated as federal Promise Zones to PolicyMap. What is a Promise Zone? These areas are the first five of 20 total communities to be designated through 2015 by the Obama administration:

  • Choctaw Nation of Oklahoma
  • Kentucky Highlands
  • Los Angeles (Hollywood, East Hollywood, Koreatown, Pico Union and
    Westlake neighborhoods)
  • San Antonio (EastPoint neighborhood)
  • Philadelphia (Mantua neighborhood)

Designation as a Promise Zone does not entail any additional federal grants or funding; instead, HUD, USDA, HHS, DOJ, SBA, and other federal agencies will help local government representatives to coordinate and maximize the impact of existing programs. Of the five places named this year so far, all are home to existing programs or designations such as Promise Neighborhoods, Choice Neighborhoods, and/or Byrne Criminal Justice Innovation Program partnerships. The administration has also proposed a tax incentive for private businesses who locate jobs in these areas.
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PolicyMap Geocoder: Now Even More Gooder!

400 North Street, Harrisburg, PA. It’s a simple address. It’s a state office building. People work there. You can mail a letter there.

But for a while, you might have had some trouble finding it on PolicyMap. A couple years ago, we upgraded our geocoder (the process that finds an address on a map) so it was much more flexible in finding addresses typed into the location bar.

The new geocoder featured rooftop geocoding: It knew the precise locations of most addresses in the country. It also featured constant updates, spellchecking capabilities, and alternate street names.

The old geocoder used linear interpolation for geocoding: It knew the streets and addresses at each end of the street, and guessed where the building was based on the address number.

The new geocoder also has linear interpolation, if it can’t find the rooftop. But its interpolation performance left something to be desired. Namely, 400 North Street, Harrisburg, PA. What we found was, the new geocoder was great at finding residential addresses, but wasn’t consistent in finding commercial or government locations. The old geocoder was better at this, but didn’t have all the improvements the new one offered.

This put us in a bind. The new geocoder usually worked better, but the old geocoder was able to find a lot of addresses the new one couldn’t. If only we could somehow have both of them…

So that’s what we did. Now, when you type an address into the location bar (or feed addresses through the Data Loader) it first goes through the new geocoder, and if that fails, then through the old geocoder. Genius! In our tests, this improved our match rates significantly. Crucially, it found 400 North Street, Harrisburg, PA.

Do you need to do anything different? No. Everything will function exactly the same as it used to; you just won’t get to see the “Location not found” error as much.

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Map Vocabulary

PolicyMap has two basic types of maps. One has a very simple name, the other is more complicated.

This is a point map:

Pretty simple: the map shows points representing the locations of, in this case, schools.

So what’s this?

The data here represents a geographic area, not just a single point. It’s usually an aggregation of a mass within the area (number of people, percent of families, median dollar amount, etc.), where different colors represent a range of values among all areas. There is a technical term for this: choropleth map. (You’ll notice there’s only one “L” in choropleth; it’s not chloroform.) Map nerds know exactly what a choropleth map is. The problem is, just about no one else in the world has heard of the term, and frankly, it’s not very fun to say.

On PolicyMap, we call these Data Layers. The data is neatly layered underneath the map labels. The problem with this term is you can’t actually have multiple layers of Data Layers (how would you see the multiple colors at once?). Ironically, you can layer as many points as you want onto a point map. So it could be confusing.

Around the office, we call this “thematic data” (as would appear on a thematic map). Unfortunately, that’s not correct either, since a thematic map is any map that shows data, which would include maps with points. Occasionally, you’ll hear us slip this term into conversation, but we have a swear jar we have to drop a dollar in every time we use it.

Sometimes I call this “shaded data”. But we don’t want to imply there’s anything shady about our data.

So remember: Data layers = choropleth map = thematic data (when we slip up) = shaded data. Have a better name for this? Let us know!

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Print, Save, Email, and Embed your work on PolicyMap

Print2You’ve made the perfect map; data layers are customized, map is zoomed to your location, and data points have been added and filter. Now you want to share this wonderful map with others. The icons on the top left of the map will allow you to save and share your maps. All printed maps, tables, and reports will store a copy in your My PolicyMap, allowing you to log into your account at any time and download a copy or reopen a saved copy.

Email_This_Page-icon Email – this feature will give users to ability to quickly share an interactive map to any user. Selecting the icon will open a window which you can enter an email address or multiple email addresses and a quick message.


The recipient will receive an email with a unique URL. The URL will take them to the exact map that was shared; zoomed to the same location, data layers and/or points added, and any filters placed.

There are some limitations to the Email feature, mainly users cannot email maps with subscription datasets or features overlaid. Luckily, over 80% of the data on PolicyMap can be shared and only a few map features are not sharable.
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