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Trying to figure out how to align nodes. Got suggestion (from AI) to try different type of sankey. This is that attempt.
Continue reading “Both Houses-New Type of Sankey”This Sankey diagram visualizes the journey of education related (File codes F1-F5) bills that began in the Maryland House of Delegates during the 2025 legislative session. Each bill’s path is shown from its orginating chamber to its assigned primariy committee, and then to its final outcome. Doing this is more difficult then I thought it would be, although the data is readily available on the Maryland General Assembly website as a data download. Next I’m going to try to add in education related bills that originated in the Senate. This will be even more techically difficult because the Sankey doesn’t handle the oppisite house committees well. This is a work in progress.
Today I decided to take a quick look at the percentage of all students who score proficient on the Statewide science assessment.
I also did the same map for economically disadvantaged students.
After looking at the maps I put them on the same color scale.
I found a new dataset today. It shows the number and percentage of students that are promoted in high school every year.
The map shows the percentage of 2020-2021 grade 9 students that were not promoted to grade 10.
Played around with showing Non-FARMS High School graduates who earn a college degree by age 25. The Maryland Longitudinal Data System Center publishes the data as a percentage of high school graduates that enroll in college. I used their published numbers to see the total high school graduates. I was originally interested in FARMs students, but the data was repressed for most of the schools.
As always this is just me exploring the data that is available. I am trying to make sense of the data and be able to remember the information.
I am exploring using a map to display college enrollment data for Anne Arundel County. Unfortunately, I only have a shapefile that includes Crofton HS, which is a new school, so the boundaries do not reflect the boundaries at the time. The are other specialized high schools in the county that are not reflected in the data. Since I haven’t done mapping in a while I had to remember how to upload the data, but I figured it out pretty quickly.
I am a sucker for outcome data by state. I like to take the data from these reports and graph the Maryland data.
This is primarily a blog about me exploring data visualization. I am having trouble flipping the order of the categories, I would like “completed at starting institution” to be on the bottom. I think that being able to easily control the order of the categories is very important. The order shown hides the percentage of students that have graduated from any institution.
I figured it out, but I had to reenter the data. I would also like to add national data on the same chart, but that does not seem to be an option anymore.
Apparently, I can add national data if I make a stacked bar chart, but not for a stacked column chart.
This is a look at students enrolled in Maryland community colleges in fall 2019. In fall 2019, there were 113,288 students enrolled in Maryland Community College. Of those students, 35,905 (31.7%) were enrolled full-time and 77,383 (68.3%) were enrolled part-time. I decided to dig deeper into this information by combining attendance status with student age. The Maryland Higher Education Commission (MHEC) reports enrollment of students aged 25 and older by attendance status. Approximately one-third of community college students are aged 25 or older. About 85% of community college students aged 25 and older attend part-time, while approximately 60% of community college students under age 25 attend part-time.
I made a Sankey diagram to show both age and attendance status of community college students at the same time in the same diagram. This is a different use of a Sankey diagram than I have made in the past. In the past, I have used a Sankey diagram to show individuals flowing through the education system. This diagram shows two variables related to the same population of students. I am happy with the result and find it easy to understand and read; however, I don’t know if people unfamiliar with Sankey diagrams will find it easy to read and understand. My next step will be to ask my friends and family about what they think about the visualization. I also wonder if there are other data sets that I use that would be better understood using this formate. Another way to show this information would be with a single bar graph with four segments: 1) aged 25 and over and part-time; 2) aged 25 and older full-time; 3) under 25 and part-time; and 4) under 25 and full-time.
Source: Maryland Higher Education Commission, Databook 2021
The bar graph shows the same information as the Sankey diagram above, but it is another formate. The purpose of this post is to show this data in different forms on the web and be able to test them on various platforms and to remember what I learned about the data. From the bar graph visualization, it is easy to see that the largest group are students who are under 25 and attending part-time and the smallest group (by far) are students who are 25 and older who are attending full-time.
AmCharts has updated its library to version 5. AmCharts5 includes updates to their Venn diagram library. Since I love Venn diagrams I spent the morning figuring it out. I created the Venn diagram below to show HS completers. It took me a while to figure out the syntax. Overall I think I was mostly able to make it do what I wanted to do. However, I since haven’t figured out how to put a category entirely in another category. For example, I would like a big circle with all HS completers that includes HS graduates and HS certificate students.
The Venn diagram below is of Maryland public school students that completed high school. It shows the overlap of how the students that: (1) earned a completion certificate (for completing a special education program); (2) met the University System of Maryland (USM) requirements); (3) met the Career and Technical Education (CTE) requirements; and/or (4) met the regular diploma requirements. “Normal diploma” is just to indicate students that did not earn either a USM or CTE credentials in addition to their high school diploma. This is primarily for my own understanding of the data and to learn web-based data visualization techniques.
Now that I have made this visualization I am not sure if using a Venn diagram is better than a Sankey diagram for this data. I would also like to add additional information to the chart such as a title and to have the actual numbers displayed on the chart. As far as the data goes, I wish that I had information about the post-high school behavior of these different groups of students. According to this data, about 60.5% of high school completers met the USM requirements.
The colors used below are not intended to mean anything beyond looking nice. It took me a while to figure out the colors. Once I figured it out I just used a rainbow with my only intention to combine blue and yellow to make green.
Source: Maryland State Department of Education, 2019 High School Completion
