Subscriber Spotlight: IFF St. Louis Education Widget

We are always excited to shine the spotlight on our subscribers, and we can’t help but shine it brightly on one of our longest standing clients, IFF, this week. IFF, one of the nation’s leading nonprofit community development financial institutions, recently completed a study for the City of St. Louis, MO, outlining changes in access to performing public schools and public school seats in St. Louis City from 2008-2013. Along with the study, IFF also worked with us here at PolicyMap to create a widget making some of its results available to policymakers and the public in an interactive way. The study and widget have been getting press in many places, from the St. Louis Post Dispatch to St. Louis Public Radio to Fox news.
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America the Beautiful

Katharine Lee Bates, July 4, 1895

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New Data Now Available on People with Disabilities

Make sure to check out PolicyMap’s data on people with disabilities.  The 2012 American Community Survey reveals that over 37 million Americans or about 12% of the population have one or more disabilities, amounting to one of the largest minority groups in the entire nation.  You can explore several different dimensions of disability data on PolicyMap, including: Age, Employment Status, and Poverty Status.  The data is available across a wide range of geographies, from Census Tract to State.

It appears that rural counties generally suffer from higher rates of disability than counties in metropolitan regions, and the South has elevated rates of disability compared to other regions of the country.  Those with disabilities in rural regions are likely to have less accessibility to medical service providers, or lack abundant employment options that could accommodate their particular disability.  Meanwhile, in many urban areas, it appears that disability rates are highest in areas stricken by poverty, suggesting a clear relationship between the two indicators.

The map displayed below shows the Estimated Percent of People Unemployed with a disability by County in 2012 in areas of the Midwest and Upper South.  Pockets of high concentration of unemployed individuals with a disability are notable in rural regions of the Upper South, but are less pronounced in the metropolitan areas around Chicago, Atlanta, and Washington D.C.

This rich dataset could be used by policy makers in order to better target resources to those with disabilities in their local communities.  Those with disabilities are at a higher risk of being unfairly marginalized in our society and economy.  Hopefully, with a clearer understanding of the characteristics of those with disabilities, we can better address their needs.

Data on the population with disabilities can be found under Demographics > People With Disabilities. More detailed information concerning disability data methods and definitions can be founds here.



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PolicyMap Attends GovLab’s Open Data 500 Roundtable

PolicyMap was invited to the White House Conference Center on Wednesday for GovLab’s Open Data 500 Roundtable. The gathering was to discuss the myriad challenges of accessing and using Commerce data (such as Census and BEA data), meeting with members of NYU’s GovLab, the U.S. Department of Commerce, and around twenty other nonprofits and companies.  Along with our colleagues at organizations like Google Maps, MapQuest, IBM Smarter Cities, and Social Explorer, we discussed interoperability of datasets across government agencies, public/private partnerships, and feedback loops with the Department of Commerce.  The Department of Commerce wanted to hear from us about how we use datasets from various government and third party data providers to complement those we get from Commerce.  They also wanted ideas on how the Department can work with private companies and nonprofits like PolicyMap in order to provide the best possible service for the millions of people using Commerce data via our platforms and services.  And they sought to understand how Commerce can be more responsive to our needs for voicing questions and data quality concerns.

We met with government appointees within the Department of Commerce, as well as chiefs of the U.S. Census, NOAA, the BEA and the U.S. Patent and Trademark Office.  The day was filled with stimulating idea generation from brilliant minds across these agencies and companies.  We came away with a very positive impression that the Commerce Department is dedicated to improving their data dissemination techniques, to working across agencies for data interoperability, and to working with the private sector to understand our requirements and our users’ needs.  Secretary of Commerce’s Penny Pritzker’s remarks were particularly salient after a day of discussing how we can best work together with the Department of Commerce.

We look forward to continuing the discussion through an online forum that GovLab will be unveiling in the coming months.



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Viral or no, PolicyMap does Epidemiology at Johns Hopkins Summer Institute

When we hear about a new viral map making the rounds, we probably aren’t expecting to see influenza cases by state. The most-”liked” maps in this day and age are those that use readily available data to tell us something fun and innocently meaningful about ourselves and our differences as a country. Often, the maps that win the popularity contest are based on what we share on social media in the first place, such as our relative preferences for professional sports teams, or whether we’d rather talk about beer or church. In other cases, administrative datasets can be mined for viral purposes, such as relative popularity of baby names or even the ranked statistic where each state fares the worst. Ben Blatt’s recent article for Slate explains some of the issues with these maps, which are valuable for entertainment purposes but usually shouldn’t be taken too seriously. Blatt does a great job explaining what us data nerds know already – when it comes to mapping, a clever presentation can obscure important details and shortcomings in methodology. This is why we at PolicyMap prefer to serve the data to you straight, rather than like this:

Obesity Viral Map

This week I am in Baltimore, taking a course in Social Epidemiology at the Johns Hopkins Bloomberg School of Public Health. The course focuses on the causal relationships that exist among non-communicable diseases and social conditions. In social epidemiology, the focus is on mitigating or eliminating conditions in the population as a whole, such as income inequality, racism, or car-oriented urban design; these strategies will have a positive effect on health outcomes for the entire population. Data and maps are essential to this way of studying public health, especially since differences in these socioeconomic and structural conditions often manifest at the regional or neighborhood level. Variations among states can be associated with policies, differences in population characteristics, or both.

While you may not see any “viral maps” on PolicyMap, you can see some neat epidemiological maps, such as the rate of mortality from coronary heart disease, the leading cause of death in the United States:

Perhaps even more importantly, PolicyMap data can help us navigate the structural elements in our society that reinforce inequality in health outcomes. For instance, reliance on an automobile for transportation limits our physical activity, which is known to lead to higher obesity rates and cardiovascular disease. In most American states, over 85% of the population drives to work.

Now, that’s something to share on social media! Will you help our no-nonsense maps of the obesity epidemic go viral?



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Healthcare Data from Census’ ACS is now on PolicyMap!

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New and updated indicators from Census’ 2008-2012 American Community Survey (ACS) are now available on PolicyMap. Some of the most interesting new data coming out of the ACS gives us a picture of uninsured and insured populations across the nation, people with disabilities, veterans and educational attainment by race. You’ll find these, and all of the latest ACS data in interactive maps and tables, for free on PolicyMap! Want to do more? Learn what you can do with a subscription to PolicyMap!

Health: Healthcare Uninsured Population
  • Total: all of those uninsured in the US, down to the Census tract level

  • By Race: all detailed race categories

  • By Age: children, young adults, adults, older age

  • By Income: <$25,000/year, <$50,000, <$75,000, <$100,000

  • By Employment: unemployed without insurance and employed without insurance

Health: Healthcare Insured Population
  • Total: all of those with health insurance in the US, down to the Census tract level

  • By Race: all detailed race categories

  • By Age: children, young adults, adults, older age

  • By Employment: employed with insurance and unemployed with insurance

  • By Type: privately insured, publicly insured, those with employer-based insurance, those with direct purchase insurance, Medicare and Medicaid

Demographics: Veterans
  • Veterans by race and ethnicity

  • Veterans by service in: War in Afghanistan or Iraq War, Gulf War, Vietnam War, Korean War

Education: Educational Attainment by Race
Demographics: People with Disabilities
  • Total: all people with one or more disabilities in the US, down to the Census tract level

  • By Age: children, older age

  • By Employment: employed with a disability, unemployed with a disability

  • In poverty with a disability



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Veteran Data Now on PolicyMap!

Here at PolicyMap, there are some data topics that we get repeated requests from our users for, and we are always excited when we are able to fulfill them. Over the years, we have gotten several requests for veteran data, so we are very excited to let users know that we have added veteran population data from the 2008-2012 American Community Survey (ACS) to PolicyMap!

According to the 2008-2012 ACS, there are more than 21.8 million veterans in the United States. While many sources assert that the veteran population is in decline, particularly with many World War II vets passing away, the demographics and geographic location of the veteran population continues changing as well. Some research suggests that the overall decline in the veteran population has been accompanied by an increasing concentration of veterans in small, more rural communities, as well as an increase in the residential segregation of veterans from the non-veteran population.

The map below shows the percent of the population 18 years or older who served in the War in Afghanistan and/or the Iraq War. The data suggests that there is a trend towards high concentrations in a number of rural counties in the South.

In addition to the breakdowns by which war(s) a veteran served in, the data on PolicyMap is also provided by race and ethnicity as well. According to projections done by the Department of Veterans Affairs, minority veterans are expected to make up a growing percentage of the veteran population over the next few decades. In fact, the Department predicts that by 2040, 1 out of 3 minority citizens will be a veteran.

It is important to note that there are some limitations to this data, which the Census speaks to here.



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New Public School Data on PolicyMap

For those interested in education data, a great new dataset is now up on PolicyMap: point-level public school data from the National Center for Educational Statistics (part of the US Department of Education). We’ve always had public school points on PolicyMap, but this is much more detailed, with lots more data than there used to be.

(This does not replace our uber-detailed GreatSchools school performance data, which has standardized testing data for public schools; that data remains available and will be updated soon. The GreatSchools data is available to PolicyMap subscribers; this NCES data is available to all users.)

Some of the interesting new indicators include: school status (is it open?), total students, total teachers, student/teacher ratio, free/reduced-price lunch eligible students, Title I eligibility, charter/magnet school status, and enrollment by grade and by race. And a lot more.

Making this data uniquely useful are 26 filters, to narrow or better illustrate the data. Let’s say you want to only look at operational high schools with more than 650 students. Just check those filters, and there’s your map. Or do a color code filter. Here’s a map showing schools by student/teacher ratio:

This data is in the Education menu, and is named “Public Schools”.



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PolicyMap Presents at the ACS Data Users Conference

Late last week PolicyMap attended and presented at the first annual American Community Survey Data Users Conference in Washington, DC.  The conference kicked off with a fascinating welcome from Directors and Chiefs from the U.S. Census Bureau.  We got to hear about the direction that the U.S. Census is taking, with keeping an eye on improving efficiency and making efforts to ensure that the ACS and Census data work effectively with data from other sources.  We also learned about work that’s already underway on the 2020 Census.

The breakout sessions that followed included a presentation by PolicyMap on sources of ACS data through online GIS tools.  We discussed Community Analyst, Social Explorer and PolicyMap, with a lively discussion throughout the presentation with participation from all three services.  Unfortunately, PolicyMap was the only tool for which a demo was possible, due to technological constraints.  But the panel, which included John Parker from Iowa Legislative Services Agency, and Lee Hachadoorian from Dartmouth, seemed to go well and included an interesting Q&A session.  We were glad for the opportunity to be a part of the conversation about visualization tools using ACS data!

The afternoon sessions included stimulating conversations about using ACS data to understand income and poverty, employment and commuting.  And the Housing Applications Using ACS Data session included some valuable presentations by users of ACS housing data, including Jessica Deegan from Minnesota Housing Finance Agency, who featured PolicyMap in her presentation of their widget.

Friday’s welcoming remarks from James Treat, Chief of the American Community Survey Office, let all of us know that the ACS staff is thrilled with the multitude of applications and products that rely on the ACS.  It was a great reminder that the ACS wants to know about the ways in which we’re all busy at work using the ACS, be it in our community planning projects or our business revenue projections.

PolicyMap’s experience at the ACS Data Users Conference was an extremely positive one.  We’re already looking forward to the Association of Public Data Users Conference in September to continue our education and our conversation about PolicyMap’s presentation of data from public sources.



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Data Loading and Geocoding Part III: Using Coordinates

In our past two posts on geocoding in the data loader, we talked about how to correct incorrect addresses after loading points. But what do you do if you can’t get an address, or you can’t get the geocoder to find the address?

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Let’s look at Saint Mary’s Hospital in Leonardtown, Maryland (with the big red circle below). In order to find where this is, we’ll have to use another online mapping system.

I know what you’re thinking – “There are other online mapping systems besides PolicyMap?” We were surprised, too. But it turns out websites like Google and Bing offer pretty good solutions to finding specific places in the world. PolicyMap is good at telling you about a place, with data. But since we don’t have an army of servers trawling the internet for everything that’s ever existed, we’re not always the best option for finding a specific establishment. So we’re going to use Google Maps to find Saint Mary’s Hospital.

In order to upload new latitude and longitude coordinates, you first need to download your partially geocoded dataset. You’ll then enter the new coordinates into the spreadsheet that already contains the old ones, and then re-upload the whole thing. Here are the steps:

1. Download the partially geocoded spreadsheet. Click on “Download Points” on the bottom of the page:

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2. Open the downloaded spreadsheet. You’ll see the left two columns are called “X” and “Y”. These are your coordinates.

3. Go to the rows that don’t have numbers in the X and Y columns. Time to geocode.
One note about Google Maps: They recently changed their interface, and you may be using the old one or the new one. Both will work. I actually prefer the old interface, but the new works just as well.

According to Google, Saint Mary’s Hospital in Leonardtown, Maryland, is riiiiight here:

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It doesn’t give an address, but it knows where it is. In order to show it on PolicyMap, you need to get the latitude and longitude coordinates. It might sound daunting, but it’s actually really easy.

Simply right-click on the point on the map, and click on “What’s here?” in the dropdown menu.

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Once you click on it, the coordinates will appear near the search bar:

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Note that they appear with the latitude first, followed by the longitude. Now, just copy each one into the spreadsheet, in the X and Y columns. Remember: in the continental United States, Y is always positive (because we’re above the equator) and X is always negative.

4. When you’re done entering your new coordinates, scroll to the bottom of the spreadsheet and delete the row that says “This dataset was created by [your name]. TRF’s PolicyMap does not endorse the use of or verify the accuracy of the data contained within this dataset.”

5. Save your spreadsheet. Make sure you save it as a .csv file, and not as a .xls file.

6. Go back to PolicyMap, and go to the data loader. It’s as if you’re loading a new dataset. Go to “Create a New Dataset” and “Upload Spreadsheet of Points”. Name the dataset, choose who you want to share it with, then load the csv.

7. Unlike last time, you’ll see that on the top right, it recognizes columns Y and X as “Latitude” and “Longitude”. Click on “Next” and you’re on your way.

It’s a lot of steps, but it’s not very difficult, and once you’re done, you have all your points on the map. Have any geocoding tips to share? Let us know, either by leaving a comment or sending us an e-mail.



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