Tag: Datawrapper

US Census Bureau Educational Attainment Data

Since I used U.S. Census Bureau data for part of my discussion in my last post, I decided to quickly examine their educational attainment data. For this visualization, I used a doughnut chart. I briefly considered a chart with multiple doughnut charts to include the information desegrated by males and females, but it was not very interesting because the percentages for males and female educational attainment is remarkably similar. Women are one or two percentage points higher for the attainment of college degrees.

The Census Bureau does not publish the percentages for all options for educations by race. It just publishes the percentage of the population that has obtained a high school degree or higher and a bachelor’s degree or higher. I am not sure how best to show the data yet, but it shows a real difference in the attainment of bachelor’s degrees by race.

Unlike the Maryland Longitudinal System Center data I examined in my last post, this data reflects the population living in Maryland at the time of the survey, not just public high school graduates.

Using Datawrapper

A note about datawrapper, I had to update the chart because I forgot to uncheck the box that makes the top row the label row. It was easy to fix, but it is a reminder to check your data before publishing.

Some College, No Degree

What I find most interesting is what a large percent of the population is in the "some college, no degree" category. Seven percent of the population has earned an associate's degree, while 18 percent are in the "some college, no degree" category.

No High School Degree

Another fact that I find interesting is that nearly 10 percent of the population of Maryland age 25 and over do not have a high school diploma. From this data set I do not know if the people without a high school diploma are younger, older, or evenly distributed between younger and older people.

Data Range Chart

I have been testing the features of the Datawrapper data visualization tool. I started with maps, and still have mapping features to explore, but today I decided to try making a chart. In particular a data range chart. Like my other experiences with Datawrapper the range chart was easy to make. I just put the data into four columns, changed the percent data to just the number without the percent sign, saved the Excel workbook, and uploaded it to Datawrapper. With a few clicks and a bit of typing, I made the chart below which shows college enrollment for high school graduates.

Public Policy Questions about the CHart

Statewide 78% of public high school students enrolled in college either full-time or part-time as a degree-seeking or non-degree seeking at any point after high school graduation. The statewide average hides the variability in college enrollment in the State both by county and by family income. Only 44% of low-income high school graduates from Kent County enrolled in college, while 93% of non-low-income high school graduates from Howard County enrolled in a college. That is a difference of 49 percentage points!

All counties also have gaps in college enrollment between low-income and non-low-income high school graduates. In particular, Carroll and Queen Anne's counties have the largest gaps, 31 and 29 percentage points respectively. Talbot Couty stands out as having the smallest gap in college enrollment between low- and non-low-income high school graduates, 4 percentage points.

What I don't know from this data is what is a good level of college enrollment. Many studies have found that earning a bachelor's degree pays off financially for most people, but there are other pathways to financial and life fulfillment. This data captures some "non-traditional" education pathways such as certificate programs, so some "trades" are captured, but not all. Although I do not know what the college enrollment rate should be, I think that high school graduates should have equal opportunities to enroll in college. The huge variability in college enrollment rates might point to underlying factors that prevent some students from enrolling in college.

This data also does not tell me why high school graduates choose to enroll or not enroll in college. However, given that low-income students enrolled at a lower rate for all counties, money is likely a major factor. Other factors may be the distance to an affordable college, high school preparation, or community expectations.

Adding a Legend Caption to Reflect the Statewide Average

In my past posts exploring using Datawrapper maps I looked at the percentage of public high school graduates that enrolled in college and the percentage of those students that enrolled in college that earned a college degree by age 25. In this post I examine the percentage of high school graduates that earn a college degree by age 25.

Again I am using publically posted data from the Maryland Longitudinal Data Center. In their data set they did not post the percentage of public high school students that earned a college degree by age 25, I calculated it by dividing the number of high school graduates in a county by the number of students that earned a college degree from that county by age 25. From my understanding of the data this should work, but I haven’t done a deep analysis into the potential flaws of that process.

For this Datawrapper map, I added a legend caption to reflect the Statewide average. The caption appears directly above the legend. For now I this placement makes sense for a Statewide average. Otherwise, I used the same settings I have used with the other maps I have made thus far.

Public Policy Thoughts About the Data

According to this data, 40% of Maryland public high school students who graduated in 2011-2012 earned a college degree by age 25. There is not directly comparable data nationwide because Maryland Longitudinal Data Center only collects and publishes data about Maryland. The American Community Survey Data collects data about the educational attainment of individuals based on where they live, not where they graduated high school or where they were educated. According to that data 44% of Marylanders 25-44 years old, have earned a bachelor's degree or higher. Maryland is known as a State with a highly educated workforce. Is that because a large number of the State's high school graduates graduate college by age 25 or is it because educated workers move to the State? I do not know the answer, but I interested in exploring the data more.

What strikes me about the map is the difference in the percentage of high school graduates who earn a college degree by age 25 in Baltimore City, 16%, and Howard County, 60%. That is a huge difference, 44 percentage points. As someone who is familiar with Maryland I am not surprised by the difference, but the difference is striking. I want to dig deeper into the data. I want to see if low-income high school graduates from Howard County earn college degrees at a similar rate to high-income graduates in the county, or is the rate more similar to that of jurisdiction that have higher levels of poverty such as Baltimore City. I also want to look at the opposite for Baltimore City.

When looking back on the map showing percent college enrollees with a college degree by age 25, there is no jurisdiction that stands out as being radically different in this map, but I will need to dig deeper into the data.

If I can understand the potential reasons behind the data better I hope I will be able to give better policy advice.

Yesterday I made my first map using Datawrapper. I noted in the write-up of my experience that I could not figure out how to display the data as a percentage; I ended up displaying it as a decimal. I figured that it was possible because I had seen Datawrapper maps with percentages, so I was pretty sure I just needed to dig through the menus and options. Unsurprisingly, I quickly found a tutorial written by Datawrapper that explained how to customize a choropleth map, which included the information I needed to figure out how to display percentages.

Since the Datawrapper tutorial does not directly address displaying map data as a percentage, I will give a quick explanation to remember how to do it next time. Basically, it is a three-step process.

  1. Upload the data striped of the percent sign as you want it displayed, not in decimile form. For example if you want a data point to be 8% upload the data as 8. This will allow your data to be displayed in the map. If you add the percent sign the data will not be displayed.
  2. On the “visulize” step choose a percent number format (there are a few choices) from the legend menu. This will display the data as a percentage in the legend.
  3. Use “Tooltips” to add the percent symbol after the variable code for the data. It is the second box.

As I suspected, it was not difficult to get the data to display as I wanted. It just took some digging into the menus and options.

Now for a look at the map.

As with the first map I made, I used data published by the Maryland Longitudinal Data Center. For their visualization of the data, you have to pull up each county individually because they have rich data on students from each county. I like seeing all of the counties at once on one map. I used the same color scheme as with my last map, with red being the lower percentages and green being the higher percentages. For this map, I allowed the lowest percentage to be the darkest red and the highest percentage to be the darkest green. I have not yet given much thought to if that is the best way to display the data.

Another piece of data that I want to explore adding to the map is the Statewide average. I know that the Statewide average is 51%, that is, Statewide, 51% of public school students who graduated in 2011-2012 earned a certificate, associate's, bachelor's, or master's degree or higher by age 25. At least among the students captured in the dataset.

It stands out that only 22% of students from Baltimore City who enroll in college earn a college degree by age 25. What the data does not tell me is why. Since I am a curious person, I plan to dig into the data more to look for why this might be. I might not find the whole story, but I hope to find some elements of the story. Dorchester and Somerset counties also have low levels of degree attainment for students who enroll in college. For future maps I want to look at total college degree attainment by high school graduates, college degree attainment by FARMS and non-FARMS students, and by FARMS percentage of the entire county. I also want to see how the college enrollment rate correlates with the college graduation rate. While examining this data, I want to see the capabilities Datawrapper has to display the data.

As a public policy professional, I consume and manipulate a ton of data. Unfortunately, as a government employee, I often do not have access to the latest data tools. There tend to be many layers of approval and cost restrictions. Therefore, I am really excited to give Datawrapper a try here on my own website. It offers an extensive free service that I want to explore with public data.

Today I was able to follow the easy instructions to install the Datawrapper Plugin on my WordPress-based website. Even without much website experience, the installation was easy. Then I decided to build a map in Datawrapper using data I have been looking at published by the Maryland Longitudinal Data Center for one of their data dashboards. I am really excited about their data, but I feel like I have to manipulate it myself to understand it. The map below is me just dipping my toe into the water to understand the data and how to use Datawrapper.

The map shows the percentage of public high school graduates from 2011-2012 that enrolled in college at any point after high school graduation. I was looking at 2011-2012 graduates because I am also interested in the number/percentage that graduated from college by age 25. I will likely examine the college graduation data in a future post.

It was really easy to upload the data into Datawrapper, I already had it in an Excel file. The map below was created in a few minutes. I did have an issue with getting the data to display using the percent symbol. I will have to see if there is a way to get the percent symbol to display using the program.

I am not sure about the choices I made regarding the scale and colors for the map. I want to make a bunch of maps and graphs to publish here on my website to explore how best to represent the data.

Overall I am really excited that I was able to quickly make a professional-looking map. I am looking forward to testing its capabilities and seeing if I can learn any insights about the data.

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