How Data Is Made: School District Poverty

Otto von Bismarck said that laws are like sausages: it’s better not to see them being made. But by that logic, data is like ice cream, crayons, glass, and beer: One of the most popular factory tours around. One of the sausage-ier datasets we have on PolicyMap is our map of school-age poverty, measured by school district, which comes by way of the Census’s Small Area Income and Poverty Estimates (SAIPE). Last summer, I got to take the factory tour of SAIPE, at the National Center for Education Statistics’s STATS-DC Conference.

PolicyMap already has lots of poverty data straight from the Census and American Community Survey. That’s all sitting in plain view in the “Incomes & Spending” menu. But hidden away in the Education menu, under “Student Populations,” is “Students in Poverty.” How is this different, you might ask, from the Census’s “Percent of People in Poverty Under 18”? Continue reading

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Symptoms of an epidemic: measuring the flu season

Chances are, if you live in North America, you know someone who has been sick with the flu recently. The spread of influenza is a seasonal concern for healthcare workers in the United States. Although many people contract and spread influenza throughout the year, flu season typically begins in late November or early December and spikes early in the following year. A year ago, in January 2014, nearly ten percent of all deaths in the U.S. were flu- or pneumonia- related. Several signs indicate that this current flu season may be even more severe than the last, due in part to a less-effective vaccine. (Note that it’s still important to get vaccinated for the flu every year!)

The Centers for Disease Control leads national influenza surveillance that tracks multiple aspects of the spread of the flu each season, including the strain of the flu and how it affects the population. This surveillance helps healthcare workers track the severity of the flu and respond accordingly. The two measures we have on PolicyMap are visits to health centers for flu-like illness (flu activity) and the geographic spread of the disease – whether epidemiologists consider it to be sporadic, locally concentrated, regional, or widespread.
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PolicyMap at ALA Midwinter, Booth# 5028


Are you attending the ALA Midwinter Meeting in Chicago, IL between January 30th and February 3rd? Come visit PolicyMap in isle 50, booth 5028! Learn how PolicyMap is a multi-discipline tool that professors from many subjects can utilize.

Start a free trial for your entire university, simply by stopping by our booth.

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Mapchats – a data and mapping webinar series

At PolicyMap, we’ve long endeavored to contribute meaningfully to conversations on issues central to our mission and important to our users. From open data and mapping technology to education, food-related topics, the arts, and public health, we’re committed to building knowledge in our community. In the past, we’ve done this through data storytelling on our blog and conference participation – but starting this month, we’re launching a new community initiative.

Mapchats logoIntroducing Mapchats: a  webinar series featuring experts on a variety of topics—from public policy to education and the arts—and how their work incorporates data and maps. We invite you to join us to learn about the latest trends and technology at the intersection of people, places and information.

Register for GIS in the ClassroomMapchats is kicking off Thursday, 1/29, with GIS in the Classroom. With the advent of new and better technologies, GIS is becoming much more accessible. Mapchats will hear from professors in various fields about how they are adapting to the fast-changing world of GIS and its application across disciplines.

 Register now


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Mapping Racial and Ethnic Diversity

What makes a neighborhood? What makes a community? I’m sure we’d all have various answers – they aren’t easy questions. Maybe it’s your favorite coffee shop or the local market or the park where you walk your dog but I’m guessing that many of us would answer in some way, that people make the place. We wanted to explore how people are distributed across places, and not surprisingly, since we’re spatial thinkers over here, we analyzed some data and created a few maps.

First, we decided to look into this idea of residential diversity. Following the similar methodology as the Census, we calculated an index of diversity as the likelihood that two individuals chosen at random are of different races or ethnicities. For example, an index value of 15 would suggest that given the proportions of various racial and ethnic groups in a geography, there is a 15% chance that two individuals chosen at random in that geography would be of different races or ethnicities. In a nutshell, lower index values between 0 and 20 suggest more homogeneity and higher index values above 50 suggest more heterogeneity.

Racial and ethnic diversity can be indicative of economic and behavioral patterns. For example, racially and ethnically homogenous areas are sometimes representative of concentrated poverty or concentrated wealth. They could also be indicative of discriminatory housing policies or other related barriers.

The national map of the diversity index is pretty striking. We see that to the east and south of the Appalachian Mountains there is a belt of higher diversity values and these values decrease fairly abruptly to the west. In the Midwest and extending up through Maine, many counties have lower diversity values. Not too surprisingly, if we zoom into large cities like Los Angeles, New York or D.C., we see higher diversity values, or more heterogeneity.

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Minimum Wage: Living near the Edge (of a State)

When researchers and journalists want to study the effects of minimum wage laws on employment, often they look at state boundaries, where they can compare similar areas with similar economies, but different minimum wage laws. Classical economics says that when the minimum wage rises, total employment should fall; these studies look to see if this is occurs in reality.

The classic study along these lines was done by David Card and Alan Krueger. In 1992, New Jersey increased its minimum wage from $4.25 to $5.05 an hour. Across the Delaware River, Pennsylvania kept its base wage at the federal minimum of $4.25. Card and Krueger looked at employment in fast food restaurants in New Jersey and eastern Pennsylvania. They found that rather than there being a drop in employment, there was actually a slight increase in New Jersey after the higher minimum wage took effect. Other researchers, such as David Neumark and William Wascher have disputed these findings, saying employment did in fact fall in New Jersey, and the debate is ongoing.

One easy way to find these differences in minimum wages is through PolicyMap. (You knew that was coming.) We use data from the U.S. Department of Labor to show the minimum wage in each state, updated to January 1st this year. This map makes it obvious why everyone wants to compare New Jersey and Pennsylvania; one of the highest minimum wage states is right next to one of the lowest.

Note that this is only state minimum wage; municipalities with higher minimum wages, such as San Jose, are not shown. This data is free to the public, and is available in the “Incomes & Spending” menu.

This year, New Jersey raises its minimum wage from $8.25 to $8.38, part of an automatic cost-of-living increase passed by voters in 2013. Pennsylvania’s remains at the federal minimum of $7.25. According to the Philadelphia Inquirer, it’s difficult to attribute aggregate changes in employment to minimum wage laws, but New Jersey’s been doing fine, adding 34,250 private-sector jobs in 2014.

National Public Radio’s Planet Money podcast had a unique take on this question. They visited a mall in California that sits on the border of Santa Clara and San Jose. San Jose’s minimum wage is $10, as opposed to Santa Clara’s, which is $8. Different stores pay their employees according to where in the mall they are. Here, the area with the lower minimum wage has a harder time finding employees, because everyone wants to work in the higher wage area. A shoe store manager in the low wage area says, “We get the bottom of the barrel here. Not really focused. … One guy came in high the other day.” It’s worth a listen.

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Economic divide in Westchester public schools

Percent of Free and Reduced LunchAlexander Roberts, executive director of the fair housing group Community Innovations Inc., said he looked for years for a visual representation that showed how Westchester County exclusionary zoning affects all residents in Westchester County.

He said he found one by creating a map of county school districts with the percentage of students eligible for free or reduced-price lunches in each area.

“If you look at it, it’s such a blatant example of the social engineering going on in Westchester County,” he said. “There is a history and a philosophy in this country that public education was the great equalizer and this is certainly not the case in Westchester. If you’re poor, you’re not likely to be able to be in one of the better school districts.”

Roberts focused specifically on the Yonkers and Mount Vernon school districts, which had 72 percent and 66 percent of their respective student bodies eligible for free or reduced-price lunches. The map was created using PolicyMap, an online data mapping tool, and used numbers from 2011-12 school year recipients and the 2011-12 school year counts of students as reported in the Common Core of Data from the National Center for Education Statistics.

Read the full article by Mark Lungariello on Westchester Business Journal here.

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Congratulations to PolicyMap’s Elizabeth Nash!

Elizabeth Nash, our tireless Director of Data and Product Development, was recently elected to the Association of Public Data Users (APDU) Board of Directors.  APDU is a membership network of individuals and organizations who work with and care about public data – just like us!  APDU disseminates valuable information concerning the latest developments in government statistical data and fosters communication between data users and stakeholders regarding important issues of government information and statistical policy.  We have long enjoyed the information produced by APDU and know that Elizabeth is a great addition to their Board.  As a member of the Board she will not only assist in financial and business planning but will also help to produce webinars, increase membership and work on the annual conference planning.  Congratulations from all of us at PolicyMap!

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New Health Indicators from CDC Behavioral Risk Factor Surveillance System

Coming to PolicyMap in early 2015 are a set of indicators from CDC’s 2013 Behavioral Risk Factor Surveillance System (BRFSS). This dataset comes from an annual phone survey of over 400,000 Americans and includes detailed estimates for health status (e.g. diabetes, obesity, asthma) and health behaviors (physical activity, immunization, tobacco and alcohol consumption).

The following data will be available to subscribers for census tract, counties, county subdivisions, and states. Some indicators will be available for free.

  • Very Good/Excellent General Health
  • Fair/Poor General Health
  • Physical Health
  • Mental Health
  • Obesity
  • BMI
  • Physical Activity
  • Tobacco use
  • Alcohol consumption
  • Seatbelt Use
  • Fruit and Vegetable Consumption
  • Diabetes
  • Hypertension
  • High cholesterol
  • Stroke
  • Asthma
  • COPD
  • Depression
  • Immunization (Flu)
  • HIV test
  • Primary Care doctor
  • Routine Checkup within past year
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PolicyMap’s 2014 Data Year in Review

Here at PolicyMap, the Data Team has had another very successful year. We’ve been working on a lot of new data and we’ve been focused on how to make PolicyMap more easy-to-use than ever. Here are some of our most exciting accomplishments this year.

We completely overhauled of the look and feel of PolicyMap. We made each of our features more accessible and, frankly, more attractive to use. We reorganized our data menus, and we reconfigured our legend. And best of all, we combined all of our various types of data (points and layers) into one place, along with our blog posts. Just click on one of our data menus, and you’ll see that it’s easier than ever to find what you need.

And the data highlights:
Valassis Lists Vacancy data – blockgroup level quarterly data about the vacancy rate
Powerlytics IRS Tax Return data – data on characteristics of household tax returns like charitable contributions and EITC claimants
Department of Education’s Civil Rights Data Collection (CRDC) school data – details on public schools ranging from college prep to discipline
CDC Flu data – information about flu activity and the spread of the flu virus

We’ve also been on the road, contributing to the ongoing conversation about the evolving role of data and data visualization tools. In 2014 we were chosen to play an active role at:
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