In this blog, we’ll answer the following questions:

Let’s dive in!

SparkMap pulls radon data from two different sources. To learn more about Radon Zones, we have incorporated a layer from the Environmental Protection Agency into the Map Room. To learn more about Radon Levels, Buildings Tested, and High Radon Level monitoring, we have incorporated a Radon Levels indicator from the Centers for Disease Control and Prevention National Environmental Public Health Tracking Network into the Community Needs Assessment for Pro and Premium subscribers.  

What you learn from the data depends on how you access it.  

• Radon Zones in the Map Room: This free map layer shows the predicted screening levels for buildings in counties across the US. The map intends to help governments and other organizations target risk reduction activities and resources.  

• Radon Levels in the Community Needs Assessment: This indicator is available to Pro and Premium subscribers and provides three main pieces of information.  
  • Radon levels: The total number of buildings tested for an area, the rate of buildings tested, and the mean, median, and maximum radon levels found.  
  • Prevalence of elevated radon levels: The proportion of buildings tested with elevated radon levels. 
  • High radon levels: Historic radon levels to provide a timeline from 2005 – 2017.   

Although the data look at radon levels in different ways, you can interpret them similarly. When it comes to radon, the higher the number, the more elevated radon levels exist. On the map, Zone 1 is characterized by indoor radon screening levels of greater than 4 pCi/L. On the other hand, Zone 3 areas are characterized by indoor screenings of less than 2 pCi/L. Therefore, one can expect buildings in Zone 1 to have higher levels of radon exposure than those in Zone 3.  

Access radon data in the Map Room for free or in the Community Needs Assessment with a Pro or Premium subscription. 

• https://www.epa.gov/radon  
• https://www.epa.gov/radon/epa-map-radon-zones-and-supplemental-information  

1. https://www.cdc.gov/radon/about/index.html  
2. https://ephtracking.cdc.gov/  

The post Data Dive: Radon appeared first on SparkMap.

The year ahead will bring exciting changes to SparkMap! A glimpse of what you can expect includes: enhanced digital accessibility, timely blogs and webinars, and even more flexible subscription options. While SparkMap will grow and change this year, one thing remains the same: our commitment to providing timely, relevant, and usable data.

Let’s draw some inspiration for the new year by looking at all you accomplished in 2025.

50,433 Maps made in 2025!

In 2025, you made over 50,000 maps—that’s over 138 maps per day! You were most interested in learning about agricultural, population, and food access information for your communities. The National Commodity Crop Productivity Index 3.0 appeared in the most maps, breaking the three-year reign of the Vulnerable Populations Footprint as most-used layer. Other additions to the Top 5 Map layers in 2025 included 911 Service Area Boundaries, Overall Population, and Food Desert Census Tracts. These five layers accounted for 13.4% of the maps made in 2025. If you haven’t yet, be sure to check them out!

Visit the Map Room

88,706 Community Needs Assessments made in 2025!

In 2025, you broke Community Needs Assessment records at SparkMap by creating reports with a combined 3.71 million indicators. The most popular indicator categories included Demographics, Income and Economics, and Housing and Families. The top-accessed indicators included Population Under 18, Poverty – Population Below 100% FPL, Urban and Rural Population, Housing Stock – Age, and Population with Limited English Proficiency. As you learned more about the makeup of your community, you also focused on their wellbeing by looking closely at Education, through Educational Attainment indicators, and Health Outcomes, by looking closer at Heart Disease, Diabetes, and Mortality. The Community Needs Assessment now has more indicators available than ever (424!!). Be sure to explore some new data as you work in 2026. 

Visit the Community Needs Assessment

The post 2025 Year in Review appeared first on SparkMap.

Humans have fundamentally altered the natural environment, driven by our need for food, water, shelter, and materials. In the process of meeting these needs, we have fragmented habitats, changed the makeup of natural communities, altered the composition and quantity of freshwater resources, and affected the air quality and climate of the environment in which we all live.¹ All these changes impact our health, well-being, and quality of life. Biodiversity and natural areas not only support critical ecosystem services such as clean air and water but also have an important role in the physical and psychological health of humans.²

In this blog, we explore issues of conservation for economic and community developers, farmers, and agriculturalists to consider when undertaking projects. We will also highlight data related to endangered species, water, weather, and soil.

Endangered Species

In the United States, there are over 1,300 threatened and endangered species.³ These are plants and animals at risk of going extinct. Nearly all the decline of these species is due to human activities including land use changes, habitat fragmentation, and the introduction of chemicals and other pollutants into the environment. One famous example is the use of DDT in agriculture, which led to the decline of bald eagle populations and other birds of prey.⁴

Many different types of projects need to account for potential disruption of habitat to endangered or threatened species and prepare mitigation efforts accordingly. Additionally, farmers and ranchers may need to be aware of potential habitats on their land so they can take proper precautions when expanding agricultural activities. Data from the US Fish and Wildlife Service on SparkMap identifies threatened and endangered species and highlights critical habitat for some species (Figure 1). Note, specific location data is not made publicly available by Fish and Wildlife to prevent potential poaching of species.

The Map Room also includes layers such as pesticide use, mining activity, forest cover, soil types, and more—each of which can help provide a clearer, more comprehensive view of the broader environmental context affecting biodiversity and endangered species (Figure 2).

Water Use

For many people, when they hear the word ‘conservation,’ one of the first things they think of is water. This is in no small part because of public awareness campaigns around water conservation, especially in areas of the western United States where water rights and supply can be a charged subject. If you have spent time on the west coast, you’ve also probably heard about environmental movements to protect streams from being dammed for hydro-electric power to conserve habitat for species such as salmon. Even in areas where there is an abundance of water, there is still considerable concern over the quality of that water due to agricultural runoff of soil, pesticides, and fertilizer; the dumping of pollutants into water from industry; or health inspection failures of public water sources. Water free from contamination is crucial for all living things.

Human alteration of habitat through water withdrawal, the channelization of streams for irrigation, the dumping of pollutants, or the construction of dams, all play a role in the degradation of natural ecosystems. The United States Geological Survey (USGS) has several datasets around water use with more scheduled to be released in 2025. Current usage data is available in the SparkMap Community Needs Assessment as the Public Water Supply Usage and Irrigation Water Withdrawal indicators (Figure 3).

Additional relevant map layers include water usage for thermos-electric power generation at the plant level, power plant locations and energy generation by primary fuel, water bottling locations, 303(d) impaired streams, toxic release inventory sites, and many more, all of which can be found for free in our Map Room by searching “water.”

Additional Layers

SparkMap hosts a variety of other layers that may be useful for planning around conservation objectives including land cover type from the National Land Cover Dataset, tree canopy coverage, park information including greenways and other open spaces from Trust for Public Land, drought from the US Drought Monitor, temperature and precipitation data from PRISM, heat data from Center for Disease Control, and many more! We are also committed to expanding our conservation and environmental data catalog. If you have suggestions for what kind of data would be useful for you, please reach out to the SparkMap team via our contact page.

References

1. Jonathan A. Foley et al (2005). Global Consequences of Land Use. Science, 309,570-574.
2. Sandifer, P. A., Sutton-Grier, A. E., & Ward, B. P. (2015). Exploring connections among nature, biodiversity, ecosystem services, and human health and well-being: Opportunities to enhance health and biodiversity conservation. Ecosystem services, 12, 1-15.
3. Environmental Protection Agency. Learn more about Threatened and Endangered Species. Accessed May 20, 2025. https://www.epa.gov/endangered-species/learn-more-about-threatened-and-endangered-species
4. Grier JW (1982) Ban of DDT and subsequent recovery of reproduction in bald eagles. Science 218:1232-1234.

The post Conservation and Community Planning: Using Biodiversity, Water, and Soil Data appeared first on SparkMap.

In this blog, you will learn all about leveraging the power of your three new time saving secret weapons: Community Demographic Mapping, LOFT, and VPF.  

Accessing the Tools

Each of these tools can be accessed in the SparkMap Map Room. Once there, navigate to the “Tools” button on the left menu. Once you’ve selected Tools, you should see the option to select Community Demographic Mapping, LOFT or VPF amongst other tools. For a complete tutorial on how to use Community Demographic Mapping, LOFT, or VPF tools, check out the SparkMap Support pages. 

Community Demographic Mapping

One of the best ways to learn about your service area is to understand the people that live and work in it. The Community Demographic Tool lets you get a quick snapshot of an area (i.e., county, city or town, census tract, or state). Once an area is selected, a report will be generated providing detailed population information.  

These reports are quick to generate and packed with important information. With a Community Demographic Profile, you will receive information on community poverty levels (Figure 1), educational attainment, population, persons per square mile, gender, age, and race/ethnicity (Figure 2). 

Location Opportunity Footprint Tool

The LOFT was created to identify areas of opportunity based on school proficiency, job gravity, and cost of living. Essentially, the foundation of the Tool rests on the idea that areas with better schools, more job opportunities, and lower housing and transportation costs provide greater opportunity.

A unique feature of the LOFT is that you can set the school proficiency, job gravity, and transportation and housing costs to the ranges you’re looking for and the map will dynamically update to show areas that meet one, two, or all three thresholds (Figure 3).

In addition to the map, you can generate a Quick Report of the LOFT data. In this report, you will receive more detailed information on the areas of your map that meet all three thresholds. This will include basic demographic and breakout data like race/ethnicity information, gender, and age (Figure 4).

Vulnerable Populations Footprint Tool

Highlighting strengths in a community is helpful, but the SparkMap team also wanted to provide you with a quick way to understand the most prominent areas of need in a community. Thus, we created the VPF. The VPF Tool highlights areas of need based on two data-backed criteria: population below poverty level and population who have completed less than high school. As with the LOFT, you can adjust the thresholds of the percentage of the population living below poverty and population with less than a high school degree to meet specific requirements you might be looking for. Once you’ve adjusted the thresholds, areas in red meet both criteria, areas in orange meet the poverty criteria, and areas in purple meet the education criteria (Figure 5).

With the VPF, you can generate a similar Quick Report which will provide additional demographic data on areas within your map view that meet both thresholds (Figure 6). These may be considered areas of highest priority.

Why use the Footprint Tools?

The Community Demographic Mapping, LOFT, and VPF are great starting places to support your work. With the click of a button, you can immediately understand who lives and works in your community and identify areas of opportunity and vulnerability. Take your work a step further by adding layers like Business Births or Job Creation alongside LOFT to focus on areas of economic growth. Or, add layers like Head Start Facilities and Childcare Costs alongside the VPF to understand more about the potential childcare burden placed on families in highly vulnerable communities.  

When you root plans in data, there is no telling how your communities can grow and flourish. Community Demographic Mapping, LOFT, and VPF tools provide three quick ways to learn about your community and make data-based decisions.  

We can’t wait to see what you do!  

The post Identify Community Strengths and Opportunities appeared first on SparkMap.

While SparkMap grew and changed this year, one thing remained the same: our commitment to providing timely, relevant, and usable data. With this data, you made more Maps and Community Needs Assessments than ever on the site. Let’s check out your work!

62,438 Maps made in 2024!

In 2024, you made over 64,000 maps—more than double the number made in 2023! This year, you were most interested in learning about the vulnerabilities, opportunities, and health-related aspects of your communities. For the third year in a row, the Vulnerable Populations Footprint was the most popular layer, appearing in over 5,000 unique maps! As a new addition to the Top 5 layers of 2024, you included the Federally Qualified Health Centers layer on 532 unique maps. With four of the above layers appearing in SparkMap’s Top 5 for the second year in a row, it’s clear these data provide valuable information on communities across the country. If you haven’t yet, check out the Top 5 Map Room layers of 2024: Vulnerable Populations Footprint, National Commodity Crop Productivity Index 3.0, Fast Food Restaurants, Location Opportunity Footprint, and Federally Qualified Health Centers.  

87,498 Community Needs Assessments in 2024!

In 2024, we reached the millions club! You included 1,173,108 Demographics indicators in Community Needs Assessments. Some of the most popular Demographics indicators included Total Population, Population with Any Disability, Urban and Rural Population, and Population Change. As you learned more about the makeup of your community, you also focused on their wellbeing by looking closely at Health Outcomes, Income & Economics, Housing & Families, and Social & Economic factors. Explore our full indicator list to dig even deeper in 2025.

2024 was the biggest year at SparkMap thus far, and we have even more in store this year. As we look ahead to SparkMap’s 5^th anniversary in 2025, we can’t wait for you to see what’s in store. Here’s to record shattering impact in 2025!

The post 2024 Year in Review appeared first on SparkMap.

To learn more about Federally Qualified Health Centers in your community, this blog will answer the following questions: 

Let’s dive in!

What data are described here?

In this blog, we’re focusing on FQHC data provided by the US Department of Health and Human Services, Health Resources & Services Administration, 2023. Recently, the SparkMap team integrated six new indicators related to FQHCs into our Community Needs Assessment that all SparkMap Subscribers can access for free. These data include FQHC: 

• Area Served
• Maternal and Child Health
• Medical Conditions
• Patient Profile
• Patient Services Profile
• Preventative Services

What do these data tell me?

Each of these indicators provide different information relating to FQHCs in your community. Below, we provide a brief overview of what you can expect to find. 

• Area Served: Provides information on FQHC providers, number of services delivered, and counties served. 
• Maternal and Child Health: Provides an overview of the prenatal and perinatal services provided to prenatal care patients, plus variables like early entry into prenatal care and low or very low birth weight (Figure 1). 
• Medical Conditions: Provides an overview of the prevalence of medical conditions including diabetes, hypertension, asthma, and HIV in patients. 
• Patient Profile: Provides a demographic profile of patients of FQHCs in the selected community. 
• Patient Services Profile: Provides an overview of patient services (e.g., medical, dental, mental health) provided. 
• Preventative Services: Provides an overview of preventative service utilization (e.g., cancer screenings, childhood immunization status, weight screening, and tobacco use screening) among patients (Figure 2). 

How can I interpret these data?

Each of these indicators are interpreted slightly differently, as they report different information. However, in general you will find the percentage of patients or services provided for the indicator you selected. For example, if you were to look at the Patient Profile indicator, you would see the percentage of patients in different age groups, that report different ethnicities, and that have different types of insurance (Figure 3). No matter which indicators you select, you will see explainer text to aid with interpretation plus several data visuals to help illustrate the information most effectively. 

Where can I access these data?

You can access all FQHC indicators discussed in this blog with the Community Needs Assessment tool. Once you have selected your location of interest, you can find these indicators in the “Clinical Care and Prevention” section of the Community Needs Assessment indicators.  

Where can I find more information?

• https://www.hrsa.gov/  
• https://bphc.hrsa.gov/  

Reference

1. https://www.healthcare.gov/glossary/federally-qualified-health-center-fqhc/  

The post Data Dive: Federally Qualified Health Centers appeared first on SparkMap.

Here are 5 best practices we’ve learned along the way when it comes to selecting data for your CHNA:

1. Be aware of data recency and release cycles and limit the use of untimely data.

Use regularly updata and released data in your CHNA. Good examples of regularly updated data include indicators like the percent of population living in poverty, unemployment rates, and health insurance status. These data have predictable publishing cycles and do not leave long gaps between releases. Examples of data to use with consideration include transit availability, indices like the modified food retail environment, and FBI-reported crime rates. These data are less predictable and/or updated less frequently. Including them in your CHNA is still appropriate but showing impact or trends over time will be more challenging.

–> Stay on top of data updates and release cycles – use the SparkMap What’s New feed to stay in-the-know.

2. Select a core set of indicators that have trends for the last 5-10 years in your CHNA.

By selecting indicators that are released regularly and frequently, you are also building a core set of indicators that have trends – after all, consistent collection and release cycles are central to producing valid and measurable trends. These trends will help to identify whether your services, interventions, and strategies are having an impact on health outcomes in your service area. For example, access to primary care is collected annually and comparable over time. Primary care doctor recruitment and retention strategies can be visualized within the data and evaluated with greater accuracy.

3. Prioritize indicators with race, ethnicity, gender, and age breakouts.

In a perfect world, all indicators would provide demographic breakouts and allow you to easily filter data by characteristics like race, ethnicity, gender, age, and socioeconomic status. Unfortunately, demographic breakouts are not ubiquitous across common CHNA indicators and addressing issues related to equity is more difficult without them. Of note, data from the US Census Bureau often includes multiple types of breakout options. If you’re looking to see if a specific indicator has breakouts, explore our filterable Indicator Breakouts list.

4. Use locally sourced data in your CHNA, if and where available.

For example, data like the number of licensed childcare facilities or prevalence rates for STI’s are indicators that are often available via state or county departments of health. Crime rates, transit availability, and point-in-time homeless counts are also examples of data that can be more representative of local conditions by being sourced locally.

–> SparkMap Premium Subscriptions provide an Export to Word option. This features makes pulling together local data and secondary data into one assessment quicker and easier.

5. Match your data geography to your service area geography, where possible.

While county level data is great, many hospitals and health service regions don’t serve an entire county, or they serve a county plus a nearby metropolitan area. You’re likely missing part of the picture by using county-level data and may not be able to suss out issues related to equity that are hidden at a larger geography. Luckily, more and more indicators are becoming available at the ZIP code, Census Tract, and city-levels. Using these smaller geographies to drill down into the data can facilitate more meaningful prioritization, more targeted strategies, and greater health impacts.

–> SparkMap Premium Annual Subscriptions give you the flexibility to create your own service area and build a custom assessment that includes both county-level and ZIP-level data.

Overall, picking the right indicators is central to any health needs assessment process. Secondary data drives prioritization, strategy development, and how you view your impact. What are your best practices for selecting health assessment indicators? Let us know on LinkedIn and Facebook!

The post Selecting Data for a CHNA: Five Best Practices appeared first on SparkMap.

Meet SparkMap’s Beta Map Room! Here, you’ll find the same timely and reliable data you love, with exciting new features. Some of our favorite additions include a larger mapping interface, expanded exporting options, more base maps, and new ways to find popular and recently added data. Watch the video for a complete demo of the Beta Map Room.

Ready to test it out for yourself? Start exploring now!

The post Introducing the Beta Map Room appeared first on SparkMap.

Commuting has important implications for our health, environment, and economy as discussed in a previous blog post. To follow up on the previous piece, this post further explores commuting patterns by taking a closer look at commuting distances. We will highlight some new analyses and modeling done on publicly available commuting data to learn how far people in census tracts are commuting and to explore tracts where commuting distance modeling shows unexpected results. We will start with an overview of our commuting distance calculation as well as a new predictive model based on demographic and workplace area characteristics. You will learn about commuting distances in your community – with results that may surprise you. 

The Data

After incorporating some data from the Census Longitudinal Employer-Household Dynamics (LEHD) into the SparkMap Map Room, we expanded our analysis to see just how far people are commuting to work. The LEHD Origin-Destination (OD) dataset provides home census blocks and work census blocks1¹ and the number of commuters that go between the two for all participating states. We calculated the straight-line distance between the center of the census block where a person lives and where they work. To calculate the mean distance commuters traveled in a census tract, we dropped any distances over 125 miles, as we deemed them unrealistic commute distances and averaged the remaining commuter distances . You can explore the resulting commuting distance map layer in SparkMap and see what commutes are like in your community.  

Looking at these calculations and the resulting map, some discrepancies in some of the distance calculations were striking. For example, why did a particular census tract have an average commute of 12 miles and the one next to it averaged 18 miles? To answer this question, we decided to explore how more data might provide insight to these differences. 

Predicting Commuting Distance

We used a Machine Learning method called Random Forest to create a model to predict the average commuting distance of residents of a census tract based on various characteristics that we will detail below. This machine learning method uses subsets of the input variables to create decision trees. It then combines these trees and ‘votes’ on a prediction to increase the accuracy of the overall model. The model was trained on data for census tracts containing more than 20 residential commuters for which there was data and excluded commuting distances of over 150 miles as these were deemed as unrealistic for everyday commuting. The data also excludes the states of Alaska, Arkansas, and Mississippi which did not participate in the LEHD program. The model was then trained on 80% of the census tracts and tested on the remaining 20% to ensure that the model worked as well on data it was not trained on as the data it was trained on. 

Which Variables were used? 

One advantage of machine learning methods like Random Forest is that they perform well with many input variables.  We used many variables to fit our model, most coming from the LEHD OD and Workplace Area Characteristics (WAC) datasets (see list below). In addition, we used the metro/non-metro designation and land area of each county as variables, as we thought these could have major impacts on how far people commute. We used the following variables as input: 

• Tract is in a county considered Metro as of 2020 
• Percent of Commuters who Reside in Census Tract that work in Good Producing industry 
• Percent of Commuters who Reside in Census Tract that work in All Other Service industry 
• Percent of Jobs Available in the Census Tract in Agriculture, Forestry, Fishing, and Hunting 
• Percent of Commuters who Reside in Census Tract Aged 55+ 
• Percent of Commuters who Reside in Census Tract Earning between $1251-$3333/Month 
• Percent of Commuters who Reside in Census Tract Earning $3333+/Month 
• Percent of Commuters who Reside in Census Tract Aged Under 30 
• Percent of Commuters who Reside in Census Tract Aged 30 – 54 
• Square Miles of the County 
• Percent of Jobs Available in the County in Public Administration 
• Percent of Commuters who Reside in County that work in Trade, Transportation, and Utilities industry 
• Percent of Commuters who Reside in County Earning Under $1250/Month 
• Percent of Jobs Available in the County in Health Care and Social Assistance 
• Percent of Jobs Available in the County in Professional, Scientific, and Technical Services 
• Percent of Jobs Available in the County in Other Services (Except Public Administration) 
• Percent of Jobs Available in the County in Accommodation and Food Services 
• Percent of Jobs Available in the County in Construction 
• Percent of Jobs Available in the County in Administrative and Support and Waste Management and Remediation Services 
• Percent of Jobs Available in the County in Retail Trade 
• Percent of Jobs Available in the County in Wholesale Trade 
• Percent of Jobs Available in the County in Educational Services 
• Percent of Jobs Available in the County in Utilities 
• Percent of Jobs Available in the County in Manufacturing 
• Percent of Jobs Available in the County in Finance and Insurance 
• Percent of Jobs Available in the County in Transportation and Warehousing 
• Percent of Jobs Available in the County in Real Estate and Rental and Leasing 
• Percent of Jobs Available in the County in Arts, Entertainment and Recreation 
• Percent of Jobs Available in the County in Information 
• Percent of Jobs Available in the County in Mining, Quarrying, and Oil and Gas Extraction 
• Percent of Jobs Available in the County in Management of Companies and Enterprises 

Commuting Distance Results

After training the data, we then tested the model on the data not used for training, which had an R-squared value of 0.79, meaning about 79% of the time the model accurately predicts the mean commuting distance for a given census tract (on data it had never seen!). The Mean Absolute Error for the model was 2.2, meaning on average each tract’s predicted value was 2 miles off from the mean commuting distance calculated from the original data. With such good results from the test, we applied the model to all census tracts in the country (outside of the states mentioned above) to find a predicted average commute distance for each census tract. We then wanted to find those tracts where the actual commuting distances as calculated from the OD dataset varied from the average distances predicted by the model.

In total, we found 64,074 census tracts (110,387,396 commuters) had a predicted average commute distance within 10% of the actual average commute values using our model. Therefore, people were commuting about what the model predicted. 10,311 tracts (15,573,182 commuters) had a lower-than-expected average commute (i.e. the model predicted the average distance for that tract to be higher than what it actually was) while 7,822 tracts (11,483,577 commuters) had higher than expected average commutes (i.e. the model predicted the average distance to be lower than it actually was).  

You can see from the map (Figure 1), many of the tracts with higher-than-expected commutes are in the western United States. These areas are also characterized by high land area and low population density. Random Forest will provide details about which variables were the most important when it predicts the output. The most important variable in our model for the average commuting distance predictions was the percentage of jobs in Agriculture, Forestry, and Mining available in the county. Random Forest can only give you the relative variable importance, so to determine if the relationship was positive or negative, we put each variable through a simple linear regression model. If you are interested in other variables and their importance to the model, you can see the table in the appendix at the end of the blog. We also have the direction of the relationships as determined by the linear regression model.  

Conclusion

The machine learning model we used to predict the average commuting distance of each census tract worked well with some very interesting results into commute distance discrepancy between the model and our calculations from the OD dataset. Our hope is that planners and decision makers use these results as a jumping-off point for further analysis. Now that we can see where commuting distances deviate from what might be expected, we can start to address the question as to why. In addition, we wish to demonstrate ways in which SparkMap’s national secondary data can be analyzed in tandem to create even more streamlined and accurate analyses of community-level issues. We created a dashboard where users can choose their state and county and view some commuting statistics base on the OD dataset and our model results.

Ready to dive into the analysis?  See our analysis for your county and view the dashboard.

Please send any comments or questions about this analysis to Justin Krohn.

Footnote

1. The LEHD provides data only for payroll employees, so self-employed people and contractors are not included. The workplace census block may also be the HR headquarters and not the actual worksite which can skew the distance calculations.  ︎

Appendix

The post Decoding Commuting Distance Patterns appeared first on SparkMap.

Let’s dive in!

What data is described here?

In this Data Dive, we’re focusing on Diabetes Prevalence. This Centers for Disease Control and Prevention (CDC) National Center for Health Statistics (NCHS) provides the data for this layer and indicator. The NCHS creates their diabetes prevalence estimates by compiling three years of self-reported Behavioral Risk Factor Surveillance System (BRFSS) data.  

What does this data tell me about diabetes?

Diabetes Prevalence estimates the percentage (Figure 1) and total number of adults aged 20+ with diabetes by county. Estimates are available for all states except Florida. This information, along with a benchmark dial indicating how diabetes prevalence in your report area compares to state and national average, can be found in the Community Needs Assessment (Figure 2). 

How can I interpret this data?

When looking at both the percentage and total of the population with a diabetes diagnosis (i.e., diabetes prevalence), higher numbers indicate more diabetes diagnoses in the county. This is important to monitor as diabetes is associated with early death, higher medical costs, and increased risk of health complications.¹  

Where can I access this data? 

Diabetes Prevalence is accessible in both the SparkMap Map Room and Community Needs Assessment. In the Community Needs Assessment, breakout data is also available showing prevalence by gender and year of diagnosis (Figure 3). Further, the Community Needs Assessment provides benchmark data to highlight how your ZIP code, county, or custom area compares to average diabetes diagnoses of the state and country.

Where can I find more information about this data?

• Diabetes Data and Statistics
• Diabetes Surveillance Data FAQs
• United States Diabetes Surveillance System

Reference

1. https://www.cdc.gov/diabetes/library/socialmedia/infographics/diabetes.htmll  

The post Data Dive: Diabetes Prevalence appeared first on SparkMap.