data

ExploreMoHealth: An Example of How One State is Taking Health Data to the Next Level

The traditional delivery of health care is moving beyond just treatment within the walls of hospitals and into managing the health of populations in the community. Health care providers are increasing their focus on upstream social determinants of health that often lead to poor health outcomes. This shift is being driven in part by new regulations that require hospitals to work with public health experts, such as local public health agencies, and to develop community health needs assessments and improvement plans. These factors are expediting the collaborative delivery of care across the continuum – integrating voices from public health, social service, and community action organizations. It is critical that these collaborative efforts are founded on insightful, firm data that identify areas of need within different populations and across geographic locations. That’s where ExploreMoHealth steps in.

ExploreMOHealth was created in partnership between Missouri Foundation for Health (MFH) and the MHA Health Institute, the not-for-profit corporation affiliated with the Missouri Hospital Association. By combining resources, MFH and MHA were able to create one of the most unique health-related datasets in the country. The Center for Applied Research and Engagement Systems (CARES) at the University of Missouri helped to bring the data alive and make it accessible across sectors.

Data is the key to diagnosing and addressing some of our region’s most pressing health issues, and by making robust and up-to-date information available to the public, ExploreMOHealth is helping to further the missions of MFH and MHA to improve health and the health care system while also supporting other health related initiatives across the state with data for better decision-making.

Building on the Missouri ZIP Health Rankings Project, a collaboration between researchers at the Washington University School of Medicine and the Hospital Industry Data Institute, ExploreMOHealth provides a unique approach to measuring community health at the ZIP code level, using hospital discharge and census-based data applied to the County Health Rankings model of population health.

The data provided by the project are designed to inform community health needs assessments at a granular geographic level to inform targeted resource allocations for community health improvement initiatives. Project findings have been presented at the annual meeting of the American Public Health Association and published in the Journal of Public Health Management and Practice.

The ExploreMoHealth platform was launched last week and while still in its infancy, it’s generating excitement and opportunity across the state.

They’re Here! American Community Survey & 500 Cities Data Updates

Our team has been hard at work the past couple of months integrating new data. We are excited to share the 2012-2016 data from the American Community Survey (ACS) and recently updated indicators from the 500 Cities Project.

Below are just a few of the highlights. To find the full list of updated data, simply search for “ACS” and “500 Cities” in our Map Room search window.

American Community Survey 2012-2016

Whether you’re an Xer, Boomer, or Millennial, retirement is a ubiquitous dream. This map shows average retirement income by census tract.

How much of your community’s population is Hispanic in origin? Are they mostly Puerto Rican, South American, or Dominican? The ACS data can help you find out.


500 Cities

Mental health awareness, prevention, and treatment are becoming increasingly important to the overall health of community. Use the 500 Cities data to explore prevalence in your census tract.

Obesity data are hard to come by. That’s why we are excited (not about the obesity, but about these data) to be able to display this updated indicator.

Want more? Simply search for “ACS” and “500 Cities” in our Map Room search window.

GIS Shows Where to Effectively Target Infrastructure Spending

This post was written by Terry Bills. It was originally published in the ESRI blog on January 19, 2017.

During rush hour on August 1, 2007, sections of the Interstate 35W bridge in Minneapolis, Minnesota, began to collapse and fall into the Mississippi River, killing 13 people and injuring another 145. This was only one of a series of high-profile bridge failures that have resulted in lives being lost. The cause, according to many experts, is that the United States has been systematically underinvesting in infrastructure and maintenance for some time. In fact, recent figures indicate that state and local spending on infrastructure is at a 30-year low.

Click the map to zoom to your area.

Effectively addressing America’s infrastructure needs begins with knowing where to make the most strategic investments. And that is where GIS can play an important role in understanding the condition of our infrastructure, where the largest bottlenecks occur, and where dollars should be targeted for the greatest benefit to the nation’s economy.

With the incoming presidential administration’s commitment to improving America’s infrastructure, Esri’s unique geographic information system (GIS) capabilities present an opportunity for national government figures to launch a forward-thinking program to restore the nation’s vast system of highways, bridges, airports, and ports.

Click the map to zoom to your area.

GIS can provide a common platform that would allow the US Congress, the administration, and the various state departments of transportation to share an understanding of the current condition of our nation’s assets, where the needs are greatest, and how we can identify the most strategic infrastructure investments to deliver the greatest benefit. Additionally, GIS provides an effective way to monitor the progress and performance of such investments and communicate the benefits to the public in an easy-to-understand fashion.

Maintaining our critical infrastructure over time is also less costly when best practices in asset management are followed. And a precondition to effective asset management is having solid asset inventory and condition assessments, all managed and maintained in GIS. Analysis supported by GIS can help us understand trade-offs between different investments, ensuring that we target dollars to deliver the greatest impact. As such, GIS is a platform of insight that helps agencies make smarter investments in the future of American infrastructure.

Learn more about how GIS can restore infrastructure—see the story map Strengthening America’s Infrastructure.

New Data Shows Poverty Rates Lower in 23 States

This article was written by Brian Glassman, Poverty Statistics Branch. It was originally published to the Census blog, Random Samplings, on October 13, 2016.

The official poverty rate for the United States declined in 2015 to 13.5 percent from a rate of 14.8 percent in 2014. However, this decrease in poverty was not uniform across states or Metropolitan Statistical Areas when looking at data from the American Community Survey — another key data source for examining poverty at state and local levels. In fact, poverty rates decreased in 23 states and did not increase in any state in 2015, as shown in Figure 1. However, poverty rates in 27 states and Washington, D.C., were statistically unchanged.

Many factors contribute to a change in a state’s poverty rate. The bar chart below shows several possibly related economic factors that give a broader sense of the economic changes happening within states. From 2014 to 2015, unemployment rates decreased and median household income increased in each of the 23 states where poverty decreased. For the 27 states and Washington, D.C., that had no change in poverty in 2015, unemployment decreased in 14 states and Washington, D.C., and median income increased in 16 states and Washington, D.C.

Estimates of households with income under $10,000 and households receiving food stamp/SNAP benefits are two other conditions potentially related to poverty status. In 2015, there were 12 states where both poverty rates and the percentage of households with food stamp/SNAP benefits decreased. In 11 states, the poverty rates did not decrease but the percentage of households receiving food stamp/SNAP benefits fell.

There were 16 states where poverty rates fell and the percentage of households with income less than $10,000 also fell. However, in three states and Washington, D.C., the percentage of households with income less than $10,000 fell without a drop in the poverty rate. To see changes from 2014 to 2015 in poverty rates, unemployment rates, median income, food stamp/SNAP participation and percentage of households with income below $10,000 for each state, visit <https://www.census.gov/data/tables/2016/demo/income-poverty/glassman-acs.html>.

The chart below shows that changes were not uniform across metropolitan statistical areas (MSAs), even for those MSAs in a state that experienced a decline in poverty. Between 2014 and 2015, out of a total of 380 MSAs, poverty rates decreased in 63 and increased in 14. The Washington, D.C., MSA is not included in this analysis. The chart also separates MSAs into two categories: (1) those in the 23 states that experienced a decrease in poverty rates from 2014 to 2015 and (2) those in the 27 states that experienced no change in poverty rates. If an MSA crosses state borders, it is assigned to the state where the majority of its population resides.

The key thing to note from the chart is that a decline in the state poverty rate may not be shared by all MSAs in the state. Poverty rates increased in some MSAs located in states in which poverty rates decreased (this includes Asheville, N.C.; Redding, Calif.; Hinesville, Ga.; Sebring, Fla.; Corpus Christi, Texas; Killeen-Temple, Texas; and Lubbock, Texas). Similarly, even in states that experienced no significant change in state poverty rates, some MSA poverty rates did change.

Click the map to zoom to your area.

500 Cities Project: Local Data for Better Health

The 500 Cities Project is a collaboration between the Centers for Disease Control and Prevention, the Robert Wood Johnson Foundation, and the CDC Foundation. The 500 Cities project aims to provide city- and census tract-level small area estimates for chronic disease risk factors, health outcomes, and clinical preventive service use for the largest 500 cities in the United States. Small area estimates allow cities and local health departments to better understand the burden and geographic distribution of health-related variables and to plan for public health interventions.

Watch the “Using the 500 Cities Measures” webinar facilitated by Community Commons.

To find the 500 Cities data in Community Commons. Visit the Map Room and search for “500 Cities.”

Click the map to interact.

Purpose of the 500 Cities Project

  • The 500 Cities project aims to identify, analyze, and report city and census tract-level data for 27 chronic disease measures. Small area estimates are used to calculate the data for America’s 500 largest cities.
  • This project is a first-of-its-kind data analysis. The 500 Cities data complements existing surveillance data and is designed to help communities better understand the health issues affecting their residents.
  • These high-quality, small-area epidemiological data can be used by individual cities and groups of cities to aid in the development and implementation of effective and targeted prevention programs. They can also be used to help identify emerging health problems and monitor health-related interventions. For example, city planners and elected officials can use the 500 Cities data to target neighborhoods where health issues, like hypertension, are at their highest.
  • The project covers the largest 497 American cities and the largest cities in Vermont (Burlington), West Virginia (Charleston), and Wyoming (Cheyenne) to ensure that each state has at least one city represented.

Click the map to interact.

Fun Stats about the Cities

  • Many states only have one city represented (i.e. – Rapid City, South Dakota). California has 121 cities represented.
  • The smallest city represented is Burlington, Vermont with a population of 42,417. The largest city represented is New York City, New York with a population of 8,175,133.
  • Among the 500 cities, there are approximately 28,000 census tracts. The smallest census tract has less than 50 persons living within its boundaries. The largest census tract has 28,960 persons.
  • The smallest census tract included is less than 1 square mile. The largest is more than 642 square miles.

Learn more about the 500 Cities Project – watch these previously recorded webinars.


What’s New on Community Commons?

Our team has been hard at work the past couple of months integrating new data. We are excited to share the 2011-2015 data from the American Community Survey (ACS) and the recently released indicators from the 500 Cities Project.

Below are just a few of the highlights. To find the full list of updated data, simply search for “ACS” and “500 Cities” in our Map Room search window.

American Community Survey 2011-2015

¿Hablas español? Parlez-vous français? Sprichst du deutsch? Below is a map showing dominant language, by tract, excluding English.

dominant language

Click the map to zoom to your area.

legend 1  legend-2

How strong is the spirit of entrepreneurship in your community? Learn more about  the prevalence of self-employment income with this updated tract-level data.

Self-Employed Data

Click the map to zoom to your area.

500 Cities

Chomp! Here’s some data you can really sink your teeth into. The map below shows dental care visits and dental care access data.

dental care

Click the map to zoom to your area.

Sleepless in Seattle? More like sleepless in Tacoma! Learn more about sleep habits across the country.

sleepless in seattle

Click the map to zoom to your area.

Want more? Simply search for “ACS” and “500 Cities” in our Map Room search window.

LGBT Data: Who Is Helping to Fill the Gap?

While age, gender, race, and ethnicity have become standard when collecting demographic information, often sexual orientation and broader gender identity is not consistently or uniformly collected. This leaves gaps in understanding the growing population of Americans who identify as lesbian, gay, bisexual, or transgender (LGBT). A few organizations and data collection agencies are taking strides to include questions that help collect data on the LGBT community. Let’s explore a few of them:

Center for America Progress – “SOGI Data”

The Center for American Progress – a non-partisan research institute – published a fact sheet back in March outlining guidelines for collecting what they call “SOGI” data – sexual orientation and gender identity. The author states, “Collecting more and better data about sexual orientation and gender identity, or SOGI, is essential to meet the needs of LGBT people and their families across the United States.”

The fact sheet is a great resource if you’re going to be designing any type of survey instrument that plans to collect demographic information. With sample language, suggested terminology, and considerations for medical records, this fact sheet is an excellent resource to understand how to be more inclusive with our instruments and start closing the data gap for the LGBT population.

PROMO – Promoting Equality for All Missourians Hospital Inclusivity MapPROMO Hospital Map

PROMO crowd-sourced a data collection project helping to rate hospital inclusivity across the state. As their website states, “this LGBT inclusive map is helping LGBT Missourians locate hospitals in their region who are LGBT welcoming, by identifying core policies that allow lesbian, gay, bisexual and transgender patients the opportunity to access healthcare free from discrimination.”

Visit the map here to interact.

The Williams Institute – UCLA School of Law

food-insecurity-in-the-lgbt-community-infographic-330x838

 

The Williams Institute has published a series of guides, infographics, and articles that are great examples of LGBT data collection and analysis. One popular resource from the Institute is the 2014 report titled,”Best Practices for Asking Questions to Identify Transgender and Other Gender Minority Respondents on Population-Based Surveys” that provides great instruction and a helpful glossary of terms.

The Institute also has a variety of research studies that breakdown numbers for the LGBT population. View the full list here, or check out the three most recent publications:

Food Insecurity and SNAP Participation in the LGBT Community

How Many Adults Identify as Transgender in the United States

The LGBT Divide in California: A Look at the Socioeconomic Well-being of LGBT People in California (check out the great ESRI story map companion for inspiration)

 

 LGBTData.com – Drexel University School of Public Health

Much like Community Commons, LGBTData.com is an ever-evolving and growing data warehouse. Recognizing that there are wide gaps in the availability of LGBT data, this site aims to collect, store, and update what is available as the source availability grows and shifts. Here’s a short overview of what’s available on the site:

LGBTData.com highlights data from sources like the Center for Disease Control and Prevention (CDC), Department of Justice (DOJ), and Substance Abuse and Mental Health Services Administration (SAMHSA) and provides a geographic breakdown of where the data are available.

A list of research studies and publications that are LGBT focused and/or use LGBT-centric data. It’s nicely categorized by topic.

An outline of recommended questions to use if you plan to collect sexual orientation
data. This page also includes thoughts on why it is important to collect sexual orientation data and how to make it more valid.

Is your organization or collaborative collecting data? Are your data collection practices inclusive of the LGBT community? Got a story to share about your LGBT data or a great example that we missed? Hit us up in the comments below!

Data Viz of the Week: What Are Metropolitan Statistical Areas?

Metropolitan Statistical Areas, or MSA’s, is a term often used when describing data geography. MSA’s are defined as:

Geographic entities delineated by the Office of Management and Budget (OMB) for use by Federal statistical agencies in collecting, tabulating, and publishing Federal statistics.  A MSA contains a core urban area of 50,000 or more population. Each metro area consists of one or more counties and includes the counties containing the core urban area, as well as any adjacent counties that have a high degree of social and economic integration (as measured by commuting to work) with the urban core.

How and why are MSA’s useful?

MSA Legend

The following are just a few examples of how MSA’s are used:

  • The Bureau of Economic Analysis uses MSA’s to create change-over-time economic analysis;
  • Businesses use MSA’s to inform marketing strategies and target populations;
  • Departments of Economic Development use MSA’s in site selection processes to determine locations of new industries and labor market regions (LMR);
  • The Federal Office of Rural Health Policy uses MSA’s as part of their criteria in defining rural areas;
  • Large businesses use MSA’s to help determine sales territories.

What do MSA’s look like on a map?

You can view a list of all recognized MSA’s here. You can also add MSA’s to any map you make on Community Commons by using our reference maps.

MSA Map

Click on a MSA to learn more about the area.

MSA More Info

Click on a MSA to learn more about the area.

 

Mapping Poverty in the Appalachian Region

The Appalachian region is home to hardworking individuals who value their families, community, and living in the natural beauty of one of America’s most beautiful regions. For generations, families have earned a living on jobs provided by the region’s primary industry- coal. In its heyday of the 1910s and 20s, more than 700,000 jobs were provided by the coal industry. In Appalachia, that number now hovers around 44,000 – with not much coming in to fill the void.

When the demise of the coal industry began in the 1940s, unemployment and poverty hit the region hard. Those with higher education went to other states for better jobs and higher wages- a trend we still see today, especially among young adults. More recently, the outsourcing of jobs overseas has caused soaring unemployment in a number of counties.

The outlook for coal is only expected to worsen as federal regulations, the decreasing cost of natural gas and the increasing costs of mining in the region continue. However, people in some of Appalachia’s most impoverished counties are coming up with their own ways of building a future without coal.

Still, poverty, unemployment, and low-paying wages persist. While communities across the US struggle with poverty, some of the most impoverished- and unnoticed- are in Appalachia.

Poverty in Appalachian Counties 

Appalachia has some of the highest poverty rates in the US. The US poverty rate is 15.6 percent, while the Appalachian region is 19.7 percent. However, what is most revealing is when you compare an Appalachian state’s poverty level to the same state’s Appalachian region’s poverty level. For example, in Virginia the poverty rate is 11.5 percent, much lower than the US rate. However, when you look at Virginia’s Appalachian region’s poverty rate, it increases considerably to 18.8 percent (Fahe.org). As depicted in the map below, the same is true for states like Kentucky and West Virginia as well. That’s the difference that frequently goes unnoticed.

CC Map Poverty3

Click the map to zoom to a community.

Unemployment in Appalachian Counties Relative to US Rate

Unemployment in the Appalachian region is 6.5 percent, and in the US it’s 6.2 percent. While this difference is not startling at first glance, when you look to specific counties, it becomes a much bigger issue. When coal companies and manufacturing plants close their doors, a jobs void affects workers in the surrounding areas. In Lewis County, KY, when Nine West shoe company closed their plant to move overseas, the county’s unemployment rate climbed to 12 percent.

CC Map Unemployment

Click the map to zoom to a community.

Appalachian Counties’ Per Capita Income

Unemployment is only one facet of the poverty in the region. As you can see from the map below, the Appalachian region has some of the lowest wages in the US. The average income in the Appalachian region is $37,260 and $46,049 in the US. What’s perhaps most telling is the labor market engagement map. It’s an index that describes the level of employment, labor force participation, and level of education in a census tract. Much of the region is plagued by poor labor market engagement, a direct reflection of poor job prospects and low wages.

CC Map Per Capita Income

Click the map to zoom to a community.

CC Map labr mrkt engagement

Click the map to zoom to a community.

Building Opportunity in Appalachia

The data and statistics look disheartening, but fortunately one of the greatest assets the region has are its people.

In an interview with The Atlantic, Peter Hillie, President of the Mountain Association for Community Economic Development (MACED) says unlike the past, Appalachia’s future prosperity will require more than one industry to come in and save the region, “There’s not a silver bullet,” he said, “There’s just a lot of little silver BBs.” One of those BBs is getting young people to stay.

In the same interview, Ada Smith from Lechter County, KY said “For people who grow up here or have roots in this place, parents and grandparents who know there’s not a lot of opportunity here encourage their loved ones to go and find jobs elsewhere.” Some counties are beginning to see the return of young people who left. One man returned from Louisville to open a tattoo parlor, others have opened restaurants, t-shirt companies and record shops. With a loan from MACED, one former coal miner even bought sheep from Vermont to start his own business- Good Shepherd Cheese.

Perhaps most inspiring is the work done by Smith and other young people from Appalachian states. Dismayed by the perception that staying in poor Appalachia counties is equated with failure, the group created Stay Together Appalachian Youth (STAY). Made up of young people from Appalachian states, the group discusses how they can better their local communities and work to encourage other young people to stay and work towards the same goal. “I feel like people are having conversations and willing to try different things that they never would have before,” Smith said. The enthusiasm for the movement is catching on, as more young people are moving to Whitesburg (where Smith lives) for the sense of community among other young adults that live there.

Building prosperity in Appalachia requires a homegrown effort, with commitments and investments from everyone. It needs people thinking about how they can work across county lines and state lines to move forward as a region –with its young people as the catalyst.

What is Farm to School?

When kids are given the opportunity to taste fresh food, meet the farmers and producers that raise it, and connect these experiences to the earth through school gardens, they are more likely to eat healthier, improve academic performance, positively change family eating habits at home and support community businesses like family farms. This is called “farm to school.”

Across the country, 42 percent of school districts are participating in farm to school activities, and as a result, 23.6 million students are developing healthy eating habits and learning where their food comes from. According to the National Farm to School Network, the ways in which schools are adopting farm to school practices varies by location but always includes one or more of the following:

  • Changes in school meals procurement to purchase and promote local foods
  • Food and agriculture-based learning opportunities to enhance the quality of the educational experience, promote public health outcomes and better understanding of our food system
  • School gardens for kids to engage in hands-on learning, school food production and become well-balanced community citizens

By adopting farm to school practices, kids, communities and farmers all win!

Community Commons has recently added data from the USDA Food Nutrition Service showing which school districts around the country are adopting or planning to adopt farm to school policies. Areas in dark blue indicate an active involvement and implementation of programs and policies in the 2013-2014 school year. Areas in light blue indicate plans for implementation in future school years. Areas in yellow indicate no activities and no current plans.

Map of Farm to School Participation

Click on this map to zoom to your own location

Click anywhere on the map to learn more about the school district and the nature of their participation.

Click to learn more about school district

This most current data from the USDA shows that 42% of all schools in the nation and nearly 24 million children are impacted by this program. If you would like to learn more about the Farm to School program contact the National Farm to School Network at info@farmtoschool.org.

Data Viz of the Week: Index Scores Summarize Community Conditions

Along with single-indicator map layers, Community Commons offers several indices that provide a community score for a particular topic. An index is based on several indicators put together, and thus allows you to summarize many factors while looking at just a single score.

Though they can often be intimidating, indices are a great way to understand how your community is doing in comparison to others.

This feature will present just three of the several indices offered in the Community Commons map room. Try adding one to your map to see how your community ranks.

Low Transportation Cost Index

This index takes into account several transportation-related indicators and scores the transportation cost for each census tract around the country. In this case, the higher the score, the lower the transportation cost. Using this index layer may come in handy when thinking about proposals to increase public transportation in your community.

Low Transportation Cost Index sized

Labor Market Engagement Index

The labor market engagement index is based on educational attainment, employment level, and labor force participation in a particular area. Using these three criteria, this index provides a score from 1-100 to summarize the intensity of the labor market and human capital in a census block group. These scores might be useful in determining where to focus on increasing employment opportunities in your neighborhood.

Labor Market Engagement Index sized

Per Capita Income Disparity Index

Indices can also be helpful in displaying disparities within and between communities. The per capita income disparity index is based on data from the American Community Survey to illustrate where income disparities exist across race/ethnicity in communities. This index provides a summary score for communities across the country, where a high score indicates high disparity.

Per Capita Income Disparity Index sized

Using an index can be helpful when you want to provide a summary of many indicators in an easy-to-understand score. Try adding one of these indices to your map today!

At Community Commons, we love to explore data and create new indices. We partner wth CARES to do these analyses, such as what we created with Environments Supporting Healthy Eating (ESHE). Look for an in depth feature on the ESHE Index later in July. If you have data you’d like to explore as an index, please contact CARES at cares.missouri.edu

Nutrition Data Represents Collaboration Between Government Agencies

Originally published on the U.S. Census Bureau Random Samplings Blog on December 9, 2015 by Lucinda Dalzell, Sara Stefanik and David Powers

In 2015, the U.S. Census Bureau released the 2014 Small Area Income and Poverty Estimates (SAIPE) for all school districts, counties, and states. These estimates are used to allocate federal funds to school districts for the next school year. Also released were counts of Supplemental Nutrition Assistance Program participants at the county and state levels for most years between 1989 and 2013. These counts are drawn from the source data of the SAIPE, and are the only source of SNAP participant total all U.S. counties.

SAIPE

Formerly known as the Food Stamp Program, SNAP is a nutrition assistance program for low-income individuals and families administered by the U.S. Department of Agriculture’s Food and Nutrition Service. The Food Stamp Act of 1977 requires that states report the number of SNAP participants by program area to USDA each year. USDA receives and makes available county-level SNAP data from roughly half of all states. In order to obtain and validate county-level data for the remaining states, each year a team of Census Bureau staff members collaborates with USDA and state government agencies.

This multi-agency collaboration allows the Census Bureau to publish a full county-level SNAP data set that otherwise would not be available. This unique SNAP data set is also an important input for the SAIPE program.

SAIPE Change

By utilizing other Census Bureau population data, data users can create SNAP statistics by metropolitan statistical area status and by census region. Table 1 presents SNAP participation rates, shares of SNAP participants, and shares of population by metropolitan statistical area status and by census region. We compute the SNAP “participation rate” as the number of SNAP participants divided by the population size.

Table 1 SAIPE repost

Table 1. SNAP and Population Data by Metro Area Status and by Region. Source: U.S. Census Bureau. Based on authors’ calculations, using SNAP data, population estimates, and metropolitan statistical area definitions.

The 2013 SNAP participation rate is 14.4 percent in metropolitan areas and 17.3 percent in non-metropolitan areas. At the regional level, the 2013 SNAP participation rate is 13.9 percent in the Northeast, 14.4 percent in the Midwest, 16.7 percent in the South, and 12.7 percent in the West.

The SNAP data are also available in our new interactive treemap web tool for years 2013 back through 2000.

Tree Map

Screenshot of Treemap of 2013 SNAP Participation Rates. Source: U.S. Census Bureau, Small Area Income and Poverty Estimates (SAIPE) program.

The treemap allows the data user to click on an individual box to view the specific county-level data. The larger the box for a given county, the greater the number of people or SNAP participants (depending on the selection) who reside there. Also, each box is color-shaded to indicate the SNAP participation rate range for the corresponding county.

The county and state SNAP data sets we have discussed here are available for download from the data input area of the SAIPE program website. Data users can reach our office with any questions or comments at sehsd.saipe@census.gov or at 301-763-3193.

137 Things You Can Learn About Your Community in Just 3 Clicks

Do you have a few minutes to spare? Take a little time to learn more about your community by using the Community Commons report tool. While the report tool was first designed to support those doing Community Health Needs Assessments, or CHNAs, it has evolved to be a useful tool for anyone wanting to understand their community.

Over 5,000 reports are created each month on Community Commons. Our members tell us they use these reports for assessment, case-making, advocacy, grant applications, presentations and much more. Whatever your reason, we hope you’ll find this tool helpful. We’d love to hear from you about how you use these reports. But first, let us show you how to create one handy report with 137 different indicators in just 3 clicks of the mouse.

mapsanddatamenu

The Maps & Data tab in the above black bar will bring you to the page below. Click on the Build a Report option to open up the report interface. From there, choose your state.

Click #1

You will then be prompted to choose your county or a set of counties and click View Report.

Click 2 & 3

One hundred and thirty seven indicators are now at your fingertips.

Notice the six data categories across the top with topics covering Demographics, Social & Economic Factors, Physical Environment, Clinical Care, Health Behaviors, and Health Outcomes. Within each of those data categories are over twenty different data indicators. In the example below you can see there are many different demographic indicators including population by age, language proficiency, and disability.
Data Categories

Within each of the data indicators you will find the data broken out by age, race, gender, and ethnicity wherever possible.

CHNA 1

As you can see, the data are presented several ways. You can view the information as a table, a chart, a data gauge, a map, or even download the data to view and work with in a spreadsheet such as Excel.

CHNA 2

CHNA 3

Click on any of the maps to view a larger interactive map and add additional indicators from the Community Commons database.

You can save reports you’ve created to your profile on Community Commons, or download as a PDF or Word document.

For more help navigating the report check out this guidebook for videos, printable how-to guides, and FAQs.

We’d love to hear about your experience with the report. Contact us anytime and let us know how you’ve used this tool and others on Community Commons.

Engaging Neighborhoods Through Maps and Stories

This article was originally posted on April 11, 2016 by Natasha Freidus, Co-Director of Creative Narrations, and published on their blog. The Community Commons mapping and reporting technology is embedded in the Healthier Together web initiative.

Recently, a team consisting of staff from Creative Narrations and Community Commons spent two intensive days at the Palm Healthcare Foundation for training and planning next steps for the Healthier Together initiative.  The Healthier Together website was launched a year ago with the intent of creating a mapping tool designed to track community’s progress towards improving health through qualitative and quantitative data.

Ditching the Agendas

The training team had spent a few weeks planning for this workshop. We had powerpoints, facilitator agendas, strategy exercises, and tutorials galore. We knew that there would be a mix of brand-new staff who had never used the website before as well as a few who had gone through introductory trainings with us over the past year.

Through the first morning, it became glaringly clear that we had glossed over a critical step. Our focus for the two days had been the technology, the ins and outs of using the mapping software. Making a map, however, is like telling a story.  You can have all the bells and whistles, but the special effects don’t matter if you don’t have a message.

Our map-makers hadn’t considered what story they wanted to tell.

We regrouped over lunch, and made a decision on the fly to introduce workshop participants to The Right Question Project. Both Morgan and I have worked closely with this group in the past. We’re big fans of their Question Formulation Technique that allows people to take a step back and figure out what questions they want to tackle before engaging in action.

The workshop participants divided up by geographic community, a few from each one. They began by generating questions around what they wanted to learn about improving health in their communities. They learned how to change close-ended questions to open-ended, and decided on priority questions.

From questions to data

Once the communities had decided on their questions, we asked each group to come up with what kinds of data could help them answer the question. We asked them to think broadly and then categorize their responses into two areas:

  • data that already existed
  • data they would need to collect

This was a crash course, and an area we hadn’t even anticipated tackling. Ideally, the group could have spent all day doing the above two activities. But for this time, we crammed it into about an hour and a half before turning to the mapping website. By this point, each community had an idea of what kind of questions they were looking to answer, and what kind of data could give them a clearer picture of their community.

At that point, we turned the show over to Erin Barbaro, mapmaker extraordinaire, who took us through an overview of the Community Commons platform. Community Commons is the mapping tool that operates on the backend of the Healthier Together site. Participants had the chance to play with thousands of data-sets and try their hand at uploading their own data-sets in the forms of Excel spreadsheets.

Adding the story layer

On the final day we turned from maps to stories. We presented a few different samples of how groups have used stories as a form of data – and had a chance to tell our own stories.  Our next task was to look at how to take “non-traditional forms of data”, e.g., videos, images, quotes, etc. and add them to the story layer.

Palm County

I have a little theory about technology. I find that things generally go wrong when we’re tired. I mean, our computers somehow sense that we are maxed out and should really get off of them, so they stop working. I’m not sure if that was why our server seemed to stall out, but the upshot is we had to close down the laptops, put off the story layer, and talk strategy. I’m sure those quotes will get onto the map at a later date, but in the meantime, we had a very constructive discussion on next steps for the site and how to improve the process for users.

Improving Usability

A tool is only as effective if its usable. As Healthier Together has gotten off the ground in the past year, we’ve found that the true potential of the website has been untapped. Everyone was very enthusiastic about the value of spending two days together, face to face, to really talk through what is working, get a bit of inspiration and context from other parts of the country and to increase engagement. On our end, we plan to highlight useful datasets and streamline the mapmaking process. On the users’ end they are going to look at increasing accountability and making sure that capturing data is built into their strategic plans.

Data Viz of the Week: 3 Ways to Map Summer Food Programs

When school lets out for the summer, it’s a time for celebration and warmer weather. But for some kids, it can also become a season of hunger.

According to the USDA Food and Nutrition Service, over 31.6 million children received low-cost or free lunches throughout the 2012 school year. Without school lunch programs during the summer, many families have a harder time making ends meet. To fight hunger in their local communities, many organizations offer summer food programs when school is out of session.

Let’s look at three ways to visualize summer food programs in the US:

Visualization One: Summer Programs in Your Community

The Maproom in Community Commons hosts many publicly available data sets, including the locations of summer food programs. Below we can see all the summer food sites in St Louis, Missouri.

Click on the map to zoom to your area.

Click on the map to zoom to your area.

Visualization Two: Additional Context

The location of summer food programs may differ depending on the resources and needs of the community. By mapping the percent of population below 50% poverty, we can see which summer food sites are in more vulnerable areas. For a higher resolution of the community’s needs throughout the county, we can map the percent of children who qualify for free or reduced-price lunches at each school.

Click the map to zoom to your location.

Click the map to zoom to your location.

Visualization Three: Potential Partners

The USDA Food and Nutrition Service has developed a list of tips to help include local food and farmers’ markets into summer food programs. To see which farmers’ markets are close to summer food sites, we can layer this data on the map as well.

Click the map to zoom to your area and apply this data in your community.

For more information about farmers’ markets and summer food site partnerships, check out the USDA videos below.